Papers with keyword 'mhealth'

That is, papers related to mHealth (mobile health)

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These papers relate to mobile health (mHealth), that is, the use of mobile computing and communications technology in the delivery of healthcare or collection of health information.

Papers are listed in reverse-chronological order; click an entry to pop up the abstract. For full information and pdf, please click Details link. Follow updates with RSS.

2025:
Michael V. Heinz, George D. Price, Avijit Singh, Sukanya Bhattacharya, Ching-Hua Chen, Asma Asyyed, Monique B. Does, Saeed Hassanpour, Emily Hichborn, David Kotz, Chantal A. Lambert-Harris, Zhiguo Li, Bethany McLeman, Varun Mishra, Catherine Stanger, Geetha Subramaniam, Weiyi Wu, Cynthia I. Campbell, Lisa A Marsch, and Nicholas C. Jacobson. A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder. Journal of Substance Use and Addiction Treatment (JSAT). March 2025. [Details]

Background: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid use (NPOU) and treatment discontinuation among persons receiving medications for OUD (MOUD) can provide a proactive approach to these challenges. Our study uses ecological momentary assessment (EMA) and deep learning to predict momentary NPOU, medication nonadherence, and treatment retention in MOUD patients.

Methods: Study participants included adults receiving MOUD at a large outpatient treatment program. We predicted NPOU (EMA-based), medication nonadherence (Electronic Health Record [EHR]- and EMA-based), and treatment retention (EHR-based) using context-sensitive EMAs (e.g., stress, pain, social setting). We used recurrent deep learning models with 7-day sliding windows to predict the next-day outcomes, using Area Under the ROC Curve (AUC) for assessment. We employed SHapley additive ExPlanations (SHAP) to understand feature latency and importance.

Results: Participants comprised 62 adults with 14,322 observations. Model performance varied across EMA subtypes and outcomes with AUCs spanning 0.59-0.97. Recent substance use was the best performing predictor for EMA-based NPOU (AUC=0.97) and medication nonadherence (AUC=0.68); life-contextual factors performed best for EHR-based medication nonadherence (AUC=0.89) and retention (AUC=0.80). SHAP revealed varying latencies between predictors and outcomes.

Conclusions: Findings support the effectiveness of EMA and deep learning for forecasting actionable outcomes in persons receiving MOUD. These insights will enable the development of personalized dynamic risk profiles and just-in-time adaptive interventions (JITAIs) to mitigate high-risk OUD outcomes.


Taylor Hardin and David Kotz. Data system with information provenance. U.S. Patent 12,244,726, March 4, 2025. Priority date March 2, 2020. Application March 2, 2021. Issued March 4, 2025. [Details]

A secure, integrated data system and method users both blockchain and Trusted Execution Environment (TEE) technologies to achieve information provenance for data, particularly, mobile health device data. Using a blockchain to record and enforce data access policies removes the need to trust a single entity with gatekeeping the health data. Instead, participants form a consortium and collectively partake in verifying and enforcing access policies for data stored in private data silos. Data access and computation takes place inside of TEEs, which preserves data confidentiality and provides a verifiable attestation that can be stored on the blockchain for the purpose of information provenance.

2024:
Varun Mishra, Sarah Hong, and David Kotz. Exploring the Relationship Between Intrinsic Motivation and Receptivity to mHealth Interventions. Proceedings of UbiComp Workshop on Computing for Well-being (WellComp). October 2024. [Details]

Just-in-Time Adaptive Interventions aim to deliver the right type and amount of support at the right time. This involves determining a user's state of receptivity - the degree to which a user is willing to accept, process, and use the intervention. Although past work has found that users are more receptive to notifications they view as useful, there is no existing research on whether users' intrinsic motivation for the underlying topic of mHealth interventions affects their receptivity. To explore this, we conducted a study with 20 participants over three weeks, where participants interacted with a chatbot-based digital coach to receive interventions about mental health, COVID-19, physical activity, and diet & nutrition. We found that significant differences in mean intrinsic motivation scores across topics were not associated with differences in mean receptivity metrics across topics. However, we discovered positive relationships between intrinsic motivation measures and receptivity for interventions about a topic.

Timothy J. Pierson, Ronald Peterson, and David F. Kotz. System and method for proximity detection with single-antenna device. U.S. Patent 11,871,233; International Patent Application WO2019210201A1, January 9, 2024. Priority date 2018-04-27; Filed 2019-04-26; Published 2021-07-29, Issued 2024-01-09. [Details]

A single-antenna device includes a single antenna, at least one processor, and at least one memory. The single-antenna device is operable to receive a signal including at least one frame. Each of said frame includes a repeating portion. The single-antenna device determines a difference of phase and amplitude of the repeating portion and further determines whether the signal is transmitted from a trusted source based at least in part on the difference of phase and amplitude of the repeating portion.

2023:
Cynthia I. Campbell, Ching-Hua Chen, Sara R. Adams, Asma Asyyed, Ninad R. Athale, Monique B. Does, Saeed Hassanpour, Emily Hichborn, Melanie Jackson-Morris, Nicholas C. Jacobson, Heather K. Jones, David Kotz, Chantal A. Lambert-Harris, Zhiguo Li, Bethany McLeman, Varun Mishra, Catherine Stanger, Geetha Subramaniam, Weiyi Wu, Christopher Zegers, and Lisa A. Marsch. Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder. Journal of Medical Internet Research (JMIR). June 2023. [Details]

Background: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD.

Objective: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD.

Methods: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed.

Results: The participants’ average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes.

Conclusions: To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data.

International Registered Report Identifier (IRRID): RR2-10.3389/fpsyt.2022.871916


Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, and David Kotz. Detecting Receptivity for mHealth Interventions. GetMobile. June 2023. [Details]

Just-In-Time Adaptive Interventions (JITAI) have the potential to provide effective support for health behavior by delivering the right type and amount of intervention at the right time. The timing of interventions is crucial to ensure that users are receptive and able to use the support provided. Previous research has explored the association of context and user-specific traits on receptivity and built machine-learning models to detect receptivity after the study was completed. However, for effective intervention delivery, JITAI systems need to make in-the-moment decisions about a user's receptivity. In this study, we deployed machinelearning models in a chatbot-based digital coach to predict receptivity for physical-activity interventions. We included a static model that was built before the study and an adaptive model that continuously updated itself during the study. Compared to a control model that sent intervention messages randomly, the machine-learning models improved receptivity by up to 36%. Receptivity to messages from the adaptive model increased over time.

2022:
Kofi Odame, Maria Nyamukuru, Mohsen Shahghasemi, Shengjie Bi, and David Kotz. Analog Gated Recurrent Neural Network for Detecting Chewing Events. IEEE Transactions on Biomedical Circuits and Systems. December 2022. [Details]

We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 μm CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers’ mastoid bones. When tested on 1.6 hours of previously-unseen data, the analog neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91% and an F1-score of 94% while consuming 1.1 μW of power. A system for detecting whole eating episodes— like meals and snacks— that is based on the novel analog neural network consumes an estimated 18.8 μW of power.

Spangler, Hillary B., Driesse, Tiffany M., Lynch, David H., Liang, Xiaohui, Roth, Robert M., Kotz, David, Fortuna, Karen, and Batsis, John A. Privacy Concerns of Older Adults Using Voice Assistant Systems. Journal of the American Geriatrics Society. August 26, 2022. [Details]

Voice assistant systems (VAS) are software platforms that complete various tasks using voice commands. It is necessary to understand the juxtaposition of younger and older adults' VAS privacy concerns as younger adults may have different concerns impacting VAS acceptance. Therefore, we examined the differences in VAS related privacy concerns across the lifespan.

George Boateng, Curtis L. Petersen, David Kotz, Karen L. Fortuna, Rebecca Masutani, and John A. Batsis. A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study. JMIR Aging. August 10, 2022. [Details]

Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings.

Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time.

Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics.

Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669.

Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.


Taylor Hardin and David Kotz. Amanuensis: provenance, privacy, and permission in TEE-enabled blockchain data systems. Proceedings of the IEEE International Conference on Distributed Computing Systems. July 2022. [Details]

Blockchain technology is heralded for its ability to provide transparent and immutable audit trails for data shared among semi-trusted parties. With the addition of smart contracts, blockchains can track and verify arbitrary computations – which enables blockchain users to verify the provenance of information derived from data through the blockchain. This provenance comes at the cost of data confidentiality and user privacy, however, which is unacceptable for many sensitive applications. The need for verifiable yet confidential data sharing and computation has led some to add trusted execution environment (TEE) hardware to blockchain platforms. By moving sensitive operations (e.g., data decryption and analysis) off of the blockchain and into a TEE, they get both the confidentiality of TEEs and the transparency of blockchains without the need to completely trust any one party in the data-sharing ecosystem.In this paper, we build on our TEE-enabled blockchain data-sharing system, Amanuensis, to ensure the freshness of access-control lists shared between the blockchain and TEE, and to improve the privacy of users interacting within the system. We also detail how TEE-based remote attestation help us to achieve information provenance – specifically, how to achieve information provenance in the context of the Intel SGX trusted execution environment. Finally, we present an evaluation of our system, in which we test several real-world machine-learning applications (logistic regression, kNN, SVM) to determine the run-time overhead of information confidentiality and provenance. Each machine-learning program exhibited a slowdown between 1.1 and 2.8x when run inside of our confidential environment, and took an average of 59 milliseconds to verify the provenance of an input data set.

Shengjie Bi and David Kotz. Eating detection with a head-mounted video camera. Proceedings of the IEEE International Conference on Healthcare Informatics. June 2022. [Details]

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or mobile platforms, in terms of computation, memory, and power. We believe this paper is the first work to experiment with video-based (rather than image-based) eating detection in free-living scenarios.

Taylor Hardin. Information Provenance for Mobile Health Data. PhD thesis, May 2022. [Details]

Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment and personal wellness, as they offer the ability to continuously monitor aspects of individuals' health as they go about their everyday activities. Many believe that combining the data produced by these mHealth apps and devices may give healthcare-related service providers and researchers a more holistic view of an individual's health, increase the quality of service, and reduce operating costs. For such mHealth data to be considered useful though, data consumers need to be assured that the authenticity and the integrity of the data has remained intact — especially for data that may have been created through a series of aggregations and transformations on many input data sets. In other words, information provenance should be one of the main focuses for any system that wishes to facilitate the sharing of sensitive mHealth data. Creating such a trusted and secure data sharing ecosystem for mHealth apps and devices is difficult, however, as they are implemented with different technologies and managed by different organizations. Furthermore, many mHealth devices use ultra-low-power micro-controllers, which lack the kinds of sophisticated Memory Management Units (MMUs) required to sufficiently isolate sensitive application code and data.

In this thesis, we present an end-to-end solution for providing information provenance for mHealth data, which begins by securing mHealth data at its source: the mHealth device. To this end, we devise a memory-isolation method that combines compiler-inserted code and Memory Protection Unit (MPU) hardware to protect application code and data on ultra-low-power micro-controllers. Then we address the security of mHealth data outside of the source (e.g., data that has been uploaded to smartphone or remote-server) with our health-data system, Amanuensis, which uses Blockchain and Trusted Execution Environment (TEE) technologies to provide confidential, yet verifiable, data storage and computation for mHealth data. Finally, we look at identity privacy and data freshness issues introduced by the use of blockchain and TEEs. Namely, we present a privacy-preserving solution for blockchain transactions, and a freshness solution for data access-control lists retrieved from the blockchain.


Lisa A. Marsch, Ching-Hua Chen, Sara R. Adams, Asma Asyyed, Monique B. Does, Saeed Hassanpour, Emily Hichborn, Melanie Jackson-Morris, Nicholas C. Jacobson, Heather K. Jones, David Kotz, Chantal A. Lambert-Harris, Zhiguo Li, Bethany McLeman, Varun Mishra, Catherine Stanger, Geetha Subramaniam, Weiyi Wu, and Cynthia I. Campbell. The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations. Frontiers in Psychiatry. April 29, 2022. Section: Addictive Disorders. [Details]

Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes.

Methods: This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes.

Discussion: Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response.

Clinical Trial Registration: Identifier: NCT04535583.


Xiaohui Liang, John A. Batsis, Youxiang Zhu, Tiffany M. Driesse, Robert M. Roth, David Kotz, and Brian MacWhinney. Evaluating Voice-Assistant Commands for Dementia Detection. Computer Speech and Language. March 2022. Special Issue on Speech Based Evaluation of Neurological Diseases. [Details]

Early detection of cognitive decline involved in Alzheimer’s Disease and Related Dementias (ADRD) in older adults living alone is essential for developing, planning, and initiating interventions and support systems to improve users’ everyday function and quality of life. In this paper, we explore the voice commands using a Voice-Assistant System (VAS), i.e., Amazon Alexa, from 40 older adults who were either Healthy Control (HC) participants or Mild Cognitive Impairment (MCI) participants, age 65 or older. We evaluated the data collected from voice commands, cognitive assessments, and interviews and surveys using a structured protocol. We extracted 163 unique command-relevant features from each participant’s use of the VAS. We then built machine-learning models including 1-layer/2-layer neural networks, support vector machines, decision tree, and random forest, for classification and comparison with standard cognitive assessment scores, e.g., Montreal Cognitive Assessment (MoCA). Our classification models using fusion features achieved an accuracy of 68%, and our regression model resulted in a Root-Mean-Square Error (RMSE) score of 3.53. Our Decision Tree (DT) and Random Forest (RF) models using selected features achieved higher classification accuracy 80%–90%. Finally, we analyzed the contribution of each feature set to the model output, thus revealing the commands and features most useful in inferring the participants’ cognitive status. We found that features of overall performance, features of music-related commands, features of call-related commands, and features from Automatic Speech Recognition (ASR) were the top-four feature sets most impactful on inference accuracy. The results from this controlled study demonstrate the promise of future home-based cognitive assessments using Voice-Assistant Systems.

2021:
Shengjie Bi and David Kotz. Eating detection with a head-mounted video camera. Technical Report, December 2021. [Details]

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or mobile platforms, in terms of computation, memory, and power. We believe this paper is the first work to experiment with video-based (rather than image-based) eating detection in free-living scenarios.

Sougata Sen and David Kotz. VibeRing: Using vibrations from a smart ring as an out-of-band channel for sharing secret keys. Journal of Pervasive and Mobile Computing. December 2021. [Details]

Many Internet of Things (IoT) devices are capable of sensing their environment, communicating with other devices, and actuating on their environment. Some of these IoT devices, herein known as “smartThings”, collect meaningful information from raw data when they are in use and in physical contact with their user (e.g., a blood-glucose monitor); the smartThing’s wireless connectivity allows it to transfer that data to its user’s trusted device, such as a smartphone. However, an adversary could impersonate the user and bootstrap a communication channel with the smartThing while the smartThing is being used by an oblivious legitimate user.

To address this problem, in this paper, we investigate the use of vibration, generated by a smartRing, as an out-of-band communication channel to unobtrusively share a secret with a smartThing. This exchanged secret can be used to bootstrap a secure wireless channel over which the smartphone (or another trusted device) and the smartThing can communicate. We present the design, implementation, and evaluation of this system, which we call VibeRing. We describe the hardware and software details of the smartThing and smartRing. Through a user study we demonstrate that it is possible to share a secret with various objects quickly, accurately and securely as compared to several existing techniques. Overall, we successfully exchange a secret between a smartRing and various smartThings, at least 85.9% of the time. We show that VibeRing can perform this exchange at 12.5 bits/second at a bit error rate of less than 2.5%. We also show that VibeRing is robust to the smartThing’s constituent material as well as the holding style. Finally, we demonstrate that a nearby adversary cannot decode or modify the message exchanged between the trusted devices.


Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, and David Kotz. Detecting Receptivity for mHealth Interventions in the Natural Environment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp). June 2021. [Details]

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions.

We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach - Ally - that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.


Travis Peters, Timothy J. Pierson, Sougata Sen, José Camacho, and David Kotz. Recurring Verification of Interaction Authenticity Within Bluetooth Networks. Proceedings of the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec 2021). June 2021. [Details]

Although user authentication has been well explored, device-to-device authentication – specifically in Bluetooth networks – has not seen the same attention. We propose Verification of Interaction Authenticity (VIA) – a recurring authentication scheme based on evaluating characteristics of the communications (interactions) between devices. We adapt techniques from wireless traffic analysis and intrusion detection systems to develop behavioral models that capture typical, authentic device interactions (behavior); these models enable recurring verification of device behavior. To evaluate our approach we produced a new dataset consisting of more than 300 Bluetooth network traces collected from 20 Bluetooth-enabled smart-health and smart-home devices. In our evaluation, we found that devices can be correctly verified at a variety of granularities, achieving an F1-score of 0.86 or better in most cases.

Sarah Hong. Exploring the Relationship Between Intrinsic Motivation and Receptivity to mHealth Interventions. June 2021. Undergraduate Thesis. [Details]

Recent research in mHealth has shown the promise of Just-in-Time Adaptive Interventions (JITAIs). JITAIs aim to deliver the right type and amount of support at the right time. Choosing the right delivery time involves determining a user's state of receptivity, that is, the degree to which a user is willing to accept, process, and use the intervention provided.

Although past work on generic phone notifications has found evidence that users are more likely to respond to notifications with content they view as useful, there is no existing research on whether users' intrinsic motivation for the underlying topic of mHealth interventions affects their receptivity. In this work, we explore whether relationships exist between intrinsic motivation and receptivity across topics and within topics for mHealth interventions. To this end, we conducted a study with 20 participants over 3 weeks, where participants received interventions about mental health, COVID-19, physical activity, and diet & nutrition. The interventions were delivered by the chatbot-based iOS app called Elena+, and via the MobileCoach platform.

Our exploratory analysis found that significant differences in mean intrinsic motivation scores across topics were not associated with differences in mean receptivity metrics across topics. We also found that positive relationships exist between intrinsic motivation measures and receptivity for interventions about a topic.


Fedor Myagkov. Classifying Common Knee Rehabilitation Exercise Mistakes Using IMU Data. June 2021. Undergraduate Thesis. [Details]

Physical therapy following major surgeries is a branch of medicine that has seen its fair share of technologically inspired advances. One important facet of physical therapy, the “at-home exercises” patients are prescribed to do, is still somewhat of a “black box” to many physical therapists (PTs). PTs have no way of knowing (1) whether the patient is doing the home exercises, or (2) whether the patient is doing the exercises in the correct and healthy manner. This lack of awareness makes it difficult for the PT to guide the patient, which can often lead to prolonged rehabilitation periods or (sometimes) can create life-long health problems for patients. In this thesis, we provide a means for a PT to remotely monitor patient’s performance of at-home exercises. We combined the capabilities of wearable motion sensors with computational algorithms to provide patients feedback on the quality of their performed exercises. We evaluated this approach by asking 20 healthy volunteers to perform popular knee-rehabilitation exercises with various mistakes while wearing motion sensors. After preprocessing and extracting features from the sensor data, we trained machine-learning models on the extracted features. The models showed a high rate of accuracy during testing, which brings us a step closer to giving physical therapists and doctors a tool to automatically and objectively classify certain exercises and mistakes made during those exercises.

Shengjie Bi. Detection of health-related behaviours using head-mounted devices. PhD thesis, May 2021. PhD Dissertation. [Details]

The detection of health-related behaviors is the basis of many mobile-sensing applications for healthcare and can trigger other inquiries or interventions. Wearable sensors have been widely used for mobile sensing due to their ever-decreasing cost, ease of deployment, and ability to provide continuous monitoring. In this dissertation, we develop a generalizable approach to sensing eating-related behavior.

First, we developed Auracle, a wearable earpiece that can automatically detect eating episodes. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the head. This audio data is then processed by a custom circuit board. We collected data with 14 participants for 32 hours in free-living conditions and achieved accuracy exceeding 92.8% and F1 score exceeding77.5% for eating detection with 1-minute resolution.

Second, we adapted Auracle for measuring children’s eating behavior, and improved the accuracy and robustness of the eating-activity detection algorithms. We used this improved prototype in a laboratory study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved 95.5% accuracy and 95.7% F1 score for eating detection with 1-minute resolution.

Third, we developed a computer-vision approach for eating detection in free-living scenarios. Using a miniature head-mounted camera, we collected data with 10 participants for about 55 hours. The camera was fixed under the brim of a cap, pointing to the mouth of the wearer and continuously recording video (but not audio) throughout their normal daily activity. We evaluated performance for eating detection using four different Convolutional Neural Network (CNN) models. The best model achieved 90.9% accuracy and 78.7%F1 score for eating detection with 1-minute resolution. Finally, we validated the feasibility of deploying the 3D CNN model in wearable or mobile platforms when considering computation, memory, and power constraints.


Lillian M. Seo, Curtis L. Petersen, Ryan J. Halter, David F. Kotz, Karen L. Fortuna, and John A. Batsis. Usability Assessment of a Bluetooth-Enabled Resistance Exercise Band Among Young Adults. Health Technology. April 2021. [Details]

Background: Resistance-based exercises effectively enhance muscle strength, which is especially important in older populations as it reduces the risk of disability. Our group developed a Bluetooth-enabled handle for resistance exercise bands that wirelessly transmits relative force data through low-energy Bluetooth to a local smartphone or similar device. We present a usability assessment that evaluates an exercise system featuring a novel Bluetooth-enabled resistance exercise band, ultimately intended to expand the accessibility of resistance training through technology-enhanced home-based exercise programs for older adults. Although our target population is older adults, we assess the user experience among younger adults as a convenient and meaningful starting point in the testing and development of our device.

Methods: There were 32 young adults participating in three exercise sessions with the exercise band, after which each completed an adapted version of the Usefulness, Satisfaction, and Ease (USE) questionnaire to characterize the exercise system’s strengths and weaknesses in usability.

Results: Questionnaire data reflected a positive and consistent user experience, with all 20 items receiving mean scores greater than 5.0 on a seven-point Likert scale. There were no specific areas of significant weakness in the device’s user experience.

Conclusions: The positive reception among young adults is a promising indication that the device can be successfully incorporated into exercise interventions and that the system can be further developed and tested for the target population of older adults.


Kevin Koch, Varun Mishra, Shu Liu, Thomas Berger, Elgar Fleisch, David Kotz, and Felix Wortmann. When Do Drivers Interact with In-vehicle Well-being Interventions? An Exploratory Analysis of a Longitudinal Study on Public Roads. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). March 2021. [Details]

Recent developments of novel in-vehicle interventions show the potential to transform the otherwise routine and mundane task of commuting into opportunities to improve the drivers' health and well-being. Prior research has explored the effectiveness of various in-vehicle interventions and has identified moments in which drivers could be interruptible to interventions. All the previous studies, however, were conducted in either simulated or constrained real-world driving scenarios on a pre-determined route. In this paper, we take a step forward and evaluate when drivers interact with in-vehicle interventions in unconstrained free-living conditions.

To this end, we conducted a two-month longitudinal study with 10 participants, in which each participant was provided with a study car for their daily driving needs. We delivered two in-vehicle interventions - each aimed at improving affective well-being - and simultaneously recorded the participants' driving behavior. In our analysis, we found that several pre-trip characteristics (like trip length, traffic flow, and vehicle occupancy) and the pre-trip affective state of the participants had significant associations with whether the participants started an intervention or canceled a started intervention. Next, we found that several in-the-moment driving characteristics (like current road type, past average speed, and future brake behavior) showed significant associations with drivers' responsiveness to the intervention. Further, we identified several driving behaviors that "negated" the effectiveness of interventions and highlight the potential of using such "negative" driving characteristics to better inform intervention delivery. Finally, we compared trips with and without intervention and found that both interventions employed in our study did not have a negative effect on driving behavior. Based on our analyses, we provide solid recommendations on how to deliver interventions to maximize responsiveness and effectiveness and minimize the burden on the drivers.


Shengjie Bi, Tao Wang, Nicole Tobias, Josephine Nordrum, Robert Halvorsen, Ron Peterson, Kelly Caine, Xing-Dong Yang, Kofi Odame, Ryan Halter, Jacob Sorber, and David Kotz. System for detecting eating with sensor mounted by the ear. U.S. Patent Application PCT/US2019/044317; Worldwide Patent Application WO2020028481A9, February 1, 2021. Priority date 2018-07-31; Filed 2019-07-31; Amended 2021-02-01; Rejected 2025-07-02. Abandoned. [Details]

A wearable device for detecting eating episodes uses a contact microphone to provide audio signals through an analog front end to an analog-to-digital converter to digitize the audio and provide digitized audio to a processor; and a processor configured with firmware in a memory to extract features from the digitized audio. A classifier determines eating episodes from the extracted features. In embodiments, messages describing the detected eating episodes are transmitted to a cell phone, insulin pump, or camera configured to record video of the wearer's mouth.

John A. Batsis, Curtis L. Petersen, Matthew M. Clark, Summer B. Cook, David Kotz, Tyler L. Gooding, Meredith N. Roderka, Rima I. Al-Nimr, Dawna Pidgeon, Ann Haedrich, K.C. Wright, Christina Aquila, and Todd A. Mackenzie. Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity. BMC Geriatrics. January 2021. [Details]

Background: Older adults with obesity residing in rural areas have reduced access to weight management programs. We determined the feasibility, acceptability and preliminary outcomes of an integrated technology-based health promotion intervention in rural-living, older adults using remote monitoring and synchronous video-based technology.

Methods: A 6-month, non-randomized, non-blinded, single-arm study was conducted from October 2018 to May 2020 at a community-based aging center of adults aged ≥65 years with a body mass index (BMI) ≥30 kg/m2. Weekly dietitian visits focusing on behavior therapy and caloric restriction and twice-weekly physical therapist-led group strength, flexibility and balance training classes were delivered using video-conferencing to participants in their homes. Participants used a Fitbit Alta HR for remote monitoring with data feedback provided by the interventionists. An aerobic activity prescription was provided and monitored.

Results: Mean age was 72.9±3.9 years (82% female). Baseline anthropometric measures of weight, BMI, and waist circumference were 97.8±16.3 kg, 36.5±5.2 kg/m2, and 115.5±13.0 cm, respectively. A total of 142 participants were screened (n=27 ineligible), and 53 consented. There were nine dropouts (17%). Overall satisfaction with the trial (4.7+0.6, scale: 1 (low) to 5 (high)) and with Fitbit (4.2+0.9) were high. Fitbit was worn an average of 81.7±19.3% of intervention days. In completers, mean weight loss was 4.6±3.5 kg or 4.7±3.5% (p<0.001). Physical function measures of 30-s sit-to-stand repetitions increased from 13.5±5.7 to 16.7±5.9 (p<0.001), 6-min walk improved by 42.0±77.3 m (p=0.005) but no differences were observed in gait speed or grip strength. Subjective measures of late-life function improved (3.4±4.7 points, p<0.001).

Conclusions: A technology-based obesity intervention is feasible and acceptable to older adults with obesity and may lead to weight loss and improved physical function.


John A. Batsis, Curtis L. Petersen, Matthew M. Clark, Summer B. Cook, Francisco Lopez-Jimenez, Rima I. Al-Nimr, Dawna Pidgeon, David Kotz, Todd A. Mackenzie, and Steven J. Bartels. A Weight-Loss Intervention Augmented by a Wearable Device in Rural Older Adults with Obesity: A Feasibility Study. Journals of Gerontology - Series A: Biological Sciences and Medical Sciences. January 2021. First published 8 May 2020. [Details]

Background: Older persons with obesity aged 65+ residing in rural areas have reduced access to weight management programs due to geographic isolation. The ability to integrate technology into health promotion interventions shows a potential to reach this underserved population.

Methods: A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] ≥ 30 kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on behavior therapy and caloric restriction with twice weekly physical therapist-led group strengthening training classes in a community-based aging center. All participants were provided a Fitbit Flex 2. An aerobic activity prescription outside the strength training classes was provided.

Results: Mean age was 72.9 ± 5.3 years (82% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 ± 33.0 cm. Mean weight loss (pre/post) was 4.6 ± 3.2 kg (4.9 ± 3.4%; p < .001). Of the 40 eligible participants, 33 (75%) enrolled, and the completion rate was high (84.8%). Objective measures of physical function improved at follow-up: 6-minute walk test improved: 35.7 ± 41.2 m (p < .001); gait speed improved: 0.10 ± 0.24 m/s (p = .04); and five-times sit-to-stand improved by 2.1 seconds (p < .001). Subjective measures of late-life function improved (5.2 ± 7.1 points, p = .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 ± 5.7 and 4.4 ± 5.0, both p < .001). Participants wore their Fitbit 93.9% of all intervention days, and were overall satisfied with the trial (4.5/5.0, 1–5 low–high) and with Fitbit (4.0/5.0).

Conclusions: A multicomponent obesity intervention incorporating a wearable device is feasible and acceptable to older adults with obesity, and potentially holds promise in enhancing health.


2020:
Shengjie Bi, Yiyang Lu, Nicole Tobias, Ella Ryan, Travis Masterson, Sougata Sen, Ryan Halter, Jacob Sorber, Diane Gilbert-Diamond, and David Kotz. Measuring children’s eating behavior with a wearable device. Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI). December 2020. [Details]

Poor eating habits in children and teenagers can lead to obesity, eating disorders, or life-threatening health problems. Although researchers have studied children’s eating behavior for decades, the research community has had limited technology to support the observation and measurement of fine-grained details of a child’s eating behavior. In this paper, we present the feasibility of adapting the Auracle, an existing research-grade earpiece designed to automatically and unobtrusively recognize eating behavior in adults, for measuring children’s eating behavior. We identified and addressed several challenges pertaining to monitoring eating behavior in children, paying particular attention to device fit and comfort. We also improved the accuracy and robustness of the eating-activity detection algorithms. We used this improved prototype in a lab study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved an accuracy exceeding 85.0% and an F1 score exceeding 84.2% for eating detection with a 3-second resolution, and a 95.5% accuracy and a 95.7% F1 score for eating detection with a 1-minute resolution.

Varun Mishra, Sougata Sen, Grace Chen, Tian Hao, Jeffrey Rogers, Ching-Hua Chen, and David Kotz. Evaluating the Reproducibility of Physiological Stress Detection Models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp). December 2020. [Details]

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics.

This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper's thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions.


Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, and David Kotz. Detecting Receptivity for mHealth Interventions in the Natural Environment. Technical Report, November 16, 2020. V1. [Details]

JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach -- Walkie -- that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.

Sougata Sen and David Kotz. VibeRing: Using vibrations from a smart ring as an out-of-band channel for sharing secret keys. Proceedings of the International Conference on the Internet of Things (IoT). October 2020. [Details]

With the rapid growth in the number of IoT devices that have wireless communication capabilities, and sensitive information collection capabilities, it is becoming increasingly necessary to ensure that these devices communicate securely with only authorized devices. A major requirement of this secure communication is to ensure that both the devices share a secret, which can be used for secure pairing and encrypted communication. Manually imparting this secret to these devices becomes an unnecessary overhead, especially when the device interaction is transient. In this paper, we empirically investigate the possibility of using an out-of-band communication channel -- vibration, generated by a custom smart ring, to share a secret with a smart IoT device. This exchanged secret can be used to bootstrap a secure wireless channel over which the devices can communicate. We believe that in future IoT devices can use such a technique to seamlessly connect with authorized devices with minimal user interaction overhead. In this paper, we specifically investigate (a) the feasibility of using vibration generated by a custom wearable for communication, (b) the effect of various parameters on this communication channel, and (c) the possibility of information manipulation by an adversary or information leakage to an adversary. For this investigation, we conducted a controlled study as well as a user study with 12 participants. In the controlled study, we could successfully share messages through vibrations with a bit error rate of less than 2.5%. Additionally, through the user study we demonstrate that it is possible to share messages with various types of objects accurately, quickly and securely as compared to several existing techniques. Overall, we find that in the best case we can exchange 85.9% messages successfully with a smart device.

John Batsis, Auden C. McClure, Aaron B. Weintraub, Diane Sette, Sivan Rotenberg, Courtney J. Stevens, Diane Gilbert-Diamond, David F. Kotz, Stephen J. Bartels, Summer B. Cook, and Richard I. Rothstein. Barriers and facilitators in implementing a pilot, pragmatic, telemedicine-delivered healthy lifestyle program for obesity management in a rural, academic obesity clinic. Implementation Science Communications. September 2020. [Details]

Few evidence-based strategies are specifically tailored for disparity populations such as rural adults. Two-way video-conferencing using telemedicine can potentially surmount geographic barriers that impede participation in high-intensity treatment programs offering frequent visits to clinic facilities. We aimed to understand barriers and facilitators of implementing a telemedicine-delivered tertiary-care, rural academic weight-loss program for the management of obesity.

Curtis Lee Petersen, Ryan Halter, David Kotz, Lorie Loeb, Summer Cook, Dawna Pidgeon, Brock C. Christensen, and John A. Batsis. Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study. JMIR mHealth and uHealth. August 2020. [Details]

Background: Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process.

Objective: This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis.

Methods: Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes.

Results: The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics.

Conclusions: Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.


Travis Peters. Trustworthy Wireless Personal Area Networks. PhD thesis, August 2020. Available as Dartmouth Computer Science Technical Report TR2020-878. [Details]

In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems.

First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX.

Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time.

Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions.


Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Shawna N. Smith, David Kotz, Urte Scholz, Elgar Fleisch, and Tobias Kowatsch. Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial. Annals of Behavioral Medicine. July 2020. Published 17 March 2020. [Details]

Background: The Assistant to Lift your Level of activitY (Ally) app is a smartphone application that combines financial incentives with chatbot-guided interventions to encourage users to reach personalized daily step goals.

Purpose: To evaluate the effects of incentives, weekly planning, and daily self-monitoring prompts that were used as intervention components as part of the Ally app.

Methods: We conducted an 8 week optimization trial with n = 274 insurees of a health insurance company in Switzerland. At baseline, participants were randomized to different incentive conditions (cash incentives vs. charity incentives vs. no incentives). Over the course of the study, participants were randomized weekly to different planning conditions (action planning vs. coping planning vs. no planning) and daily to receiving or not receiving a self-monitoring prompt. Primary outcome was the achievement of personalized daily step goals.

Results: Study participants were more active and healthier than the general Swiss population. Daily cash incentives increased step-goal achievement by 8.1%, 95% confidence interval (CI): [2.1, 14.1] and, only in the no-incentive control group, action planning increased step-goal achievement by 5.8%, 95% CI: [1.2, 10.4]. Charity incentives, self-monitoring prompts, and coping planning did not affect physical activity. Engagement with planning interventions and self-monitoring prompts was low and 30% of participants stopped using the app over the course of the study.

Conclusions: Daily cash incentives increased physical activity in the short term. Planning interventions and self-monitoring prompts require revision before they can be included in future versions of the app. Selection effects and engagement can be important challenges for physical-activity apps.

Clinical Trial Information: This study was registered on ClinicalTrials.gov, NCT03384550.


Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, and Tobias Kowatsch. Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study. Journal of Medical Internet Research. July 14, 2020. [Details]

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved.

Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex.

Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex.

Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%.

Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.


Vanessa K. Rauch, Meredith Roderka, Auden C. McClure, Aaron B. Weintraub, Kevin Curtis, David F. Kotz, Richard I. Rothstein, and John A. Batsis. Willingness to pay for a telemedicine-delivered healthy lifestyle programme. Journal of Telemedicine and Telecare. June 2020. [Details]

Introduction: Effective weight-management interventions require frequent interactions with specialised multidiscipli- nary teams of medical, nutritional and behavioural experts to enact behavioural change. However, barriers that exist in rural areas, such as transportation and a lack of specialised services, can prevent patients from receiving quality care.

Methods: We recruited patients from the Dartmouth-Hitchcock Weight & Wellness Center into a single-arm, non- randomised study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Every 4 weeks, partic- ipants completed surveys that included their willingness to pay for services like those experienced in the intervention. A two-item Willingness-to-Pay survey was administered to participants asking about their willingness to trade their face- to-face visits for videoconference visits based on commute and copay.

Results: Overall, those with a travel duration of 31–45 min had a greater willingness to trade in-person visits for telehealth than any other group. Participants who had a travel duration less than 15 min, 16–30 min and 46–60 min experienced a positive trend in willingness to have telehealth visits until Week 8, where there was a general negative trend in willingness to trade in-person visits for virtual. Participants believed that telemedicine was useful and helpful.

Conclusions: In rural areas where patients travel 30–45 min a telemedicine-delivered, intensive weight-loss interven- tion may be a well-received and cost-effective way for both patients and the clinical care team to connect.


Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. Continuous Detection of Physiological Stress with Commodity Hardware. ACM Transactions on Computing for Healthcare (HEALTH). April 2020. [Details]

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.

Lisa A. Marsch, Aimee Campbell, Cynthia Campbell, Ching-Hua Chen, Emre Ertin, Udi Ghitza, Chantal Lambert-Harris, Saeed Hassanpour, August F. Holtyn, Yih-Ing Hser, Petra Jacobs, Jeffrey D. Klausner, Shea Lemley, David Kotz, Andrea Meier, Bethany McLeman, Jennifer McNeely, Varun Mishra, Larissa Mooney, Edward Nunes, Chrysovalantis Stafylis, Catherine Stanger, Elizabeth Saunders, Geetha Subramaniam, and Sean Young. The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network. Journal of Substance Abuse Treatment. March 2020. [Details]

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN’s efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first “prescription digital therapeutic” authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare.

John Batsis, Stephen Bartels, Rachel Dokko, Alexandra Zagaria, John Naslund, Elizabeth Carpenter-Song, and David Kotz. Opportunities to Improve a Mobile Obesity Wellness Intervention for Rural Older Adults with Obesity. Journal of Community Health. February 2020. [Details]

Older adults with obesity are at a high risk of decline, particularly in rural areas. Our study objective was to gain insights into how a potential Mobile Health Obesity Wellness Intervention (MOWI) in rural older adults with obesity, consisting of nutrition and exercise sessions, could be helpful to improve physical function. A qualitative methods study was conducted in a rural community, community-based aging center. Four community leaders, 7 clinicians and 29 patient participants underwent focus groups and semi-structured interviews. All participants had a favorable view of MOWI and saw its potential to improve health and create accountability. Participants noted that MOWI could overcome geographic barriers and provided feedback about components that could improve implementation. There was expressed enthusiasm over its potential to improve health. The use of technology in older adults with obesity in rural areas has considerable promise. There is potential that this intervention could potentially extend to distant areas in rural America that can surmount accessibility barriers. If successful, this intervention could potentially alter healthcare delivery by enhancing health promotion in a remote, geographically constrained communities. MOWI has the potential to reach older adults with obesity using novel methods in geographically isolated regions.

Alan J. Budney, Lisa A. Marsch, Will M. Aklin, Jacob T. Borodovsky, Mary F. Brunette, Andrew Campbell, Jesse Dallery, David Kotz, Ashley A. Knapp, Sarah E. Lord, Edward V. Nunes, Emily A. Scherer, Catherine Stanger, and William C. Torrey. Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects. JMIR Mental Health. February 2020. [Details]

The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.

2019:
John A. Batsis, Auden C. McClure, Aaron B. Weintraub, David F. Kotz, Sivan Rotenberg, Summer B. Cook, Diane Gilbert-Diamond, Kevin Curtis, Courtney J. Stevens, Diane Sette, and Richard I. Rothstein. Feasibility and acceptability of a rural, pragmatic, telemedicine-delivered healthy lifestyle programme. Obesity Science & Practice. December 2019. [Details]

Background: The public health crisis of obesity leads to increasing morbidity that are even more profound in certain populations such as rural adults. Live, two-way video-conferencing is a modality that can potentially surmount geographic barriers and staffing shortages. Methods: Patients from the Dartmouth-Hitchcock Weight and Wellness Center were recruited into a pragmatic, single-arm, nonrandomized study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Patients were provided hardware and appropriate software allowing for remote participation in all sessions, outside of the clinic setting. Our primary outcomes were feasibility and acceptability of the telemedicine intervention, as well as potential effectiveness on anthropometric and functional measures. Results: Of 62 participants approached, we enrolled 37, of which 27 completed at least 75% of the 16-week programme sessions (27% attrition). Mean age was 46.9 +/- 11.6 years (88.9% female), with a mean body mass index of 41.3 +/- 7.1 kg/m2 and mean waist circumference of 120.7 +/- 16.8 cm. Mean patient participant satisfaction regarding the telemedicine approach was favourable (4.48 +/- 0.58 on 1-5 Likert scale -- low to high) and 67.6/75 on standardized questionnaire. Mean weight loss at 16 weeks was 2.22 +/- 3.18 kg representing a 2.1% change (P < .001), with a loss in waist circumference of 3.4% (P = .001). Fat mass and visceral fat were significantly lower at 16 weeks (2.9% and 12.5%; both P less than .05), with marginal improvement in appendicular skeletal muscle mass (1.7%). In the 30-second sit-to-stand test, a mean improvement of 2.46 stands (P = .005) was observed. Conclusion: A telemedicine-delivered, intensive weight loss intervention is feasible, acceptable, and potentially effective in rural adults seeking weight loss.

Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, and Tobias Kowatsch. Exploring the State-of-Receptivity for mHealth Interventions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp). December 2019. [Details]

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach - Ally - which was available on Android and iOS platforms.

We define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classifier.


George Boateng, Vivian Genaro Motti, Varun Mishra, John A. Batsis, Josiah Hester, and David Kotz. Experience: Design, Development and Evaluation of a Wearable Device for mHealth Applications. Proceedings of the International Conference on Mobile Computing and Networking (MobiCom). October 2019. [Details]

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet -- an open-hardware, open-software wrist-worn computing device -- and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.

Taylor Hardin and David Kotz. Blockchain in Healthcare Data Systems: a Survey. Proceedings of the International Conference on Internet of Things: Systems, Management and Security (IOTSMS). October 2019. [Details]

There has been increasing interest in connecting disjointed Electronic Medical Records, mobile health data, and related health data systems for the purpose of improving preventative and precision medicine, while also providing individuals with greater access and control to their data. Blockchains provide data transparency, immutability, and decentralized trust -- making them a promising solution to the interoperability and security issues faced by such health data systems. Several papers have proposed the use of blockchain technology in healthcare to determine its viability as a solution and to identify potential applications and challenges. We build upon their work by 1) presenting implementation details related to blockchain applications in health data systems, 2) discussing the security, privacy, and performance trade-offs of each, and 3) identifying a set of research questions regarding the use of blockchain technology in health data systems. We find that blockchain-based healthcare research should place greater emphasis on real-world deployments and testing, smart-contract security, efficient and usable audit tools, blockchain governance, and adherence to healthcare data regulations and standards.

Sougata Sen, Varun Mishra, and David Kotz. Using vibrations from a SmartRing as an out-of-band channel for sharing secret keys. Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). September 2019. [Details]

With the rapid growth in the number of Internet of Things (IoT) devices with wireless communication capabilities, and sensitive information collection capabilities, it is becoming increasingly necessary to ensure that these devices communicate securely with only authorized devices. A major requirement of this secure communication is to ensure that both the devices share a secret, which can be used for secure pairing and encrypted communication. Manually imparting this secret to these devices becomes an unnecessary overhead, especially when the device interaction is transient. In this work, we empirically investigate the possibility of using an out-of-band communication channel -- vibration, generated by a custom smartRing -- to share a secret with a compatible IoT device. Through a user study with 12 participants we show that in the best case we can exchange 85.9% messages successfully. Our technique demonstrates the possibility of sharing messages accurately, quickly and securely as compared to several existing techniques.

John A. Batsis, Alexandra B. Zagaria, Ryan J. Halter, George G. Boateng, Patrick Proctor, Stephen J. Bartels, and David Kotz. Use of Amulet in behavioral change for geriatric obesity management. Journal of Digital Health. June 2019. [Details]

Background: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one’s physical activity and strength in routine physical activity interventions. Methods and results: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. Conclusions: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults.

John A. Batsis, George G. Boateng, Lillian M. Seo, Curtis L. Petersen, Karen L. Fortuna, Emily V. Wechsler, Ronald J. Peterson, Summer B. Cook, Dawna Pidgeon, Rachel S. Dokko, Ryan J. Halter, and David F. Kotz. Development and Usability Assessment of a Connected Resistance Exercise Band Application for Strength-Monitoring. World Academy of Science, Engineering and Technology. June 2019. Presented at the International Conference on Body Area Networks (ICBAN). [Details]

Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting is fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time and conducted three usability studies in two mixed-aged groups of participants (n=6 each) and a group of older adults with obesity participating in a weight-loss intervention (n=20). The system was favorable, acceptable and provided iterative information that could assist in future deployment on ubiquitous platforms. Our formative work provides the foundation to deliver home-based monitoring interventions in a high-risk, older adult population.

John A. Batsis, John A. Naslund, Alexandra B. Zagaria, David Kotz, Rachel Dokko, Stephen J. Bartels, and Elizabeth Carpenter-Song. Technology for Behavioral Change in Rural Older Adults with Obesity. Journal of Nutrition in Gerontology and Geriatrics. April 2019. [Details]

Background: Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population.

Methods: We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics.

Results: All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure.

Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.


Emily Greene, Patrick Proctor, and David Kotz. Secure Sharing of mHealth Data Streams through Cryptographically-Enforced Access Control. Journal of Smart Health. April 2019. [Details]

Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareHealth, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design and prototype implementation of this system make three contributions: (1) they apply cryptographically-enforced access-control measures to stream-based (specifically mHealth) data, (2) they recognize the temporal nature of mHealth data streams and support revocation of access to part or all of a data stream, and (3) they depart from the vendor- and device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.

David Kotz. Amulet: an open-source wrist-worn platform for mHealth research and education. Proceedings of the Workshop on Networked Healthcare Technology (NetHealth). January 2019. [Details]

The advent of mobile and wearable computing technology has opened up tremendous opportunities for health and wellness applications. It is increasingly possible for individuals to wear devices that can sense their physiology or health-related behaviors, collecting valuable data in support of diagnosis, treatment, public health, or other applications. From a researcher’s point of view, the commercial availability of these “mHealth” devices has made it feasible to conduct scientific studies of health conditions and to explore health-related interventions. It remains difficult, however, to conduct systems work or other experimental research involving the hardware, software, security, and networking aspects of mobile and wearable technology. In this paper we describe the Amulet platform, an open-hardware, open-software wrist-worn computing device designed specifically for mHealth applications. Our position is that the Amulet is an inexpensive platform for research and education, and we encourage the mHealth community to explore its potential.

Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Bastien Presset, David Kotz, Shawna Smith, Urte Scholz, and Tobias Kowatsch. Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial. JMIR Research Protocols. January 2019. [Details]

Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring.

Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data.

Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up.

Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants.

Conclusions: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost.

Trial Registration: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d)

International Registered Report Identifier (IRRID): DERR1-10.2196/11540


2018:
John A. Batsis, Alexandra Zagaria, David F. Kotz, Stephen J. Bartels, George G. Boateng, Patrick O. Proctor, Ryan J. Halter, and Elizabeth A. Carpenter-Song. Usability evaluation for the Amulet wearable device in rural older adults with obesity. Gerontechnology. October 2018. [Details]

Mobile health (mHealth) interventions hold the promise of augmenting existing health promotion interventions. Older adults present unique challenges in advancing new models of health promotion using technology including sensory limitations and less experience with mHealth, underscoring the need for specialized usability testing. We use an open-source mHealth device as a case example for its integration in a newly designed health services intervention. We performed a convergent, parallel mixed-methods study including semi-structured interviews, focus groups, and questionnaires, using purposive sampling of 29 older adults, 4 community leaders, and 7 clinicians in a rural setting. We transcribed the data, developed codes informed by thematic analysis using inductive and deductive methods, and assessed the quantitative data using descriptive statistics. Our results suggest the importance of end-users in user-centered design of mHealth devices and that aesthetics are critically important. The prototype could potentially be feasibly integrated within health behavior interventions. Centralized dashboards were desired by all participants and ecological momentary assessment could be an important part of monitoring. Concerns of mHealth, including the prototype device, include the device’s accuracy, its intrusiveness in daily life and privacy. Formative evaluations are critically important prior to deploying large-scale interventions.

Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. The Case for a Commodity Hardware Solution for Stress Detection. Proceedings of the Workshop on Mental Health: Sensing & Intervention. October 2018. [Details]

Timely detection of an individual's stress level has the potential to expedite and improve stress management, thereby reducing the risk of adverse health consequences that may arise due to unawareness or mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical grade sensors strapped to the user. These sensors measure physiological signals of a person and are often bulky, custom-made, expensive, and/or in limited supply, hence limiting their large-scale adoption by researchers and the general public. In this paper, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, non-clinical sensors to capture physiological signals, and make inferences about the wearer's stress level based on that data. In this paper, we describe a system involving a popular off-the-shelf heart-rate monitor, the Polar H7; we evaluated our system in a lab setting with three well-validated stress-inducing stimuli with 26 participants. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1 score of 0.81, on par with clinical-grade sensors.

Shengjie Bi, Tao Wang, Nicole Tobias, Josephine Nordrum, Shang Wang, George Halvorsen, Sougata Sen, Ronald Peterson, Kofi Odame, Kelly Caine, Ryan Halter, Jacob Sorber, and David Kotz. Auracle: Detecting Eating Episodes with an Ear-Mounted Sensor. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp). September 2018. [Details]

In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimate Auracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.

Curtis L. Petersen, Emily V. Wechsler, Ryan J. Halter, George G. Boateng, Patrick O. Proctor, David F. Kotz, Summer B. Cook, and John A. Batsis. Detection and Monitoring of Repetitions Using an mHealth-Enabled Resistance Band. Proceedings of the IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). September 2018. [Details]

Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak finding algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95%CI: 0.853–0.953, p<0.001) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults.

Taylor Hardin, Ryan Scott, Patrick Proctor, Josiah Hester, Jacob Sorber, and David Kotz. Application Memory Isolation on Ultra-Low-Power MCUs. Proceedings of the USENIX Annual Technical Conference (USENIX ATC). July 2018. [Details]

The proliferation of applications that handle sensitive user data on wearable platforms generates a critical need for embedded systems that offer strong security without sacrificing flexibility and long battery life. To secure sensitive information, such as health data, ultra-low-power wearables must isolate applications from each other and protect the underlying system from errant or malicious application code. These platforms typically use microcontrollers that lack sophisticated Memory Management Units (MMU). Some include a Memory Protection Unit (MPU), but current MPUs are inadequate to the task, leading platform developers to software-based memory-protection solutions. In this paper, we present our memory isolation technique, which leverages compiler inserted code and MPU-hardware support to achieve better runtime performance than software-only counterparts.

David Kotz, Sarah E. Lord, A. James O’Malley, Luke Stark, and Lisa A. Marsch. Workshop on Emerging Technology and Data Analytics for Behavioral Health. JMIR Research Protocols. June 2018. [Details]

Wearable and portable digital devices can support self-monitoring for patients with chronic medical conditions, individuals seeking to reduce stress, and people seeking to modify health-related behaviors such as substance use or overeating. The resulting data may be used directly by a consumer, or shared with a clinician for treatment, a caregiver for assistance, or a health coach for support. The data can also be used by researchers to develop and evaluate just-in-time interventions that leverage mobile technology to help individuals manage their symptoms and behavior in real time and as needed. Such wearable systems have huge potential for promoting delivery of anywhere-anytime health care, improving public health, and enhancing the quality of life for many people. The Center for Technology and Behavioral Health at Dartmouth College, a P30 “Center of Excellence” supported by the National Institute on Drug Abuse at the National Institutes of Health, conducted a workshop in February 2017 on innovations in emerging technology, user-centered design, and data analytics for behavioral health, with presentations by a diverse range of experts in the field. The workshop focused on wearable and mobile technologies being used in clinical and research contexts, with an emphasis on applications in mental health, addiction, and health behavior change. In this paper, we summarize the workshop panels on mobile sensing, user experience design, statistics and machine learning, and privacy and security, and conclude with suggested research directions for this important and emerging field of applying digital approaches to behavioral health. Workshop insights yielded four key directions for future research: (1) a need for behavioral health researchers to work iteratively with experts in emerging technology and data analytics, (2) a need for research into optimal user-interface design for behavioral health technologies, (3) a need for privacy-oriented design from the beginning of a novel technology, and (4) the need to develop new analytical methods that can scale to thousands of individuals and billions of data points.

Andrés D. Molina-Markham, Shrirang Mare, Ronald Peterson, Jr., and David Kotz. Continuous seamless mobile device authentication using a separate electronic wearable apparatus. U.S. Patent 9,961,547, May 1, 2018. Priority date 2016-09-30, Filed 2016-09-30; Issued 2018-05-01. [Details]

A technique performs a security operation. The technique includes receiving first activity data from a mobile device, the first activity data identifying activity by a user that is currently using the mobile device. The technique further includes receiving second activity data from an electronic wearable apparatus, the second activity data identifying physical activity by a wearer that is currently wearing the electronic wearable apparatus. The technique further includes, based on the first activity data received from the mobile device and the second activity data received from the electronic wearable apparatus, performing an assessment operation that provides an assessment result indicating whether the user that is currently using the mobile device and the wearer that is currently wearing the electronic wearable apparatus are the same person. With such a technique, authentication may be continuous but without burdening the user to repeatedly re-enter a password.

Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz. Investigating Contextual Cues as Indicators for EMA Delivery. Technical Report, April 2018. [Details]

In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant’s activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates.

David Kotz, Ryan Halter, Cory Cornelius, Jacob Sorber, Minho Shin, Ronald Peterson, Shrirang Mare, Aarathi Prasad, Joseph Skinner, and Andrés Molina-Markham. Wearable computing device for secure control of physiological sensors and medical devices, with secure storage of medical records, and bioimpedance biometric. U.S. Patent 9,936,877; International Patent Application WO2013096954A1, April 10, 2018. This patent adds claims to its predecessor; Priority date 2011-12-23; Filed 2017-02-07; Issued 2018-04-10. [Details]

A wearable master electronic device (Amulet) has a processor with memory, the processor coupled to a body-area network (BAN) radio and uplink radio. The device has firmware for BAN communications with wearable nodes to receive data, and in an embodiment, send configuration data. The device has firmware for using the uplink radio to download apps and configurations, and upload data to a server. An embodiment has accelerometers in Amulet and wearable node, and firmware for using accelerometer readings to determine if node and Amulet are worn by the same subject. Other embodiments use pulse sensors or microphones in the Amulet and node to both identify a subject and verify the Amulet and node are worn by the same subject. Another embodiment uses a bioimpedance sensor to identify the subject. The wearable node may be an insulin pump, chemotherapy pump, TENS unit, cardiac monitor, or other device.

George Boateng, John A. Batsis, Patrick Proctor, Ryan Halter, and David Kotz. GeriActive: Wearable App for Monitoring and Encouraging Physical Activity among Older Adults. Proceedings of the IEEE Conference on Body Sensor Networks (BSN). March 2018. [Details]

The ability to monitor a person’s level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults’ daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.

Gunnar C. Pope, Varun Mishra, Stephanie Lewia, Byron Lowens, David Kotz, Sarah Lord, and Ryan Halter. An Ultra-Low Resource Wearable EDA Sensor Using Wavelet Compression. Proceedings of the IEEE Conference on Body Sensor Networks (BSN). March 2018. [Details]

This study presents an ultra-low resource platform for physiological sensing that uses on-chip wavelet compression to enable long-term recording of electrodermal activity (EDA) within a 64kB microcontroller. The design is implemented on a wearable platform and provides improvements in size and power compared to existing wearable technologies and was used in a lab setting to monitor EDA of 27 participants throughout a stress induction protocol. We demonstrate the device’s sensitivity to stress induction by providing descriptive statistics of 8 common EDA signal features for each stressor of the experiment. To the best of our knowledge, this is the first time a generic, 16-bit microcontroller (MCU) has been used to record real-time physiological signals on a wearable platform without the use of external memory chips or wireless transmission for extended periods of time. The compression techniques described can lead to reductions in size, power, and cost of wearable biosensors with little or no modifications to existing sensor hardware and could be valuable for applications interested in monitoring long-term physiological trends at lower data rates and memory requirements.

Joseph Carrigan, David Kotz, and Aviel Rubin. STEM Outreach Activity with Fitbit Wearable Devices. Technical Report, February 2018. [Details]

This document provides a toolkit for an STEM outreach activity based on Fitbit wearable fitness devices. The activity is targeted toward high-school students. This document provides guidance preparing for and executing the activity and measuring outcomes. This document contains templates that can be used as is or altered to suit your specific needs.

2017:
Shrirang Mare, Andrés Molina-Markham, Ronald Peterson, and David Kotz. System, Method and Authorization Device for Biometric Access Control to Digital Devices. U.S. Patent 9,832,206; International Patent Application WO2014153528A2, November 28, 2017. Priority date 2013-03-21; Filed 2014-03-21; Issued 2017-11-28. [Details]

A system and method for authenticating and continuously verifying authorized users of a digital device includes an authentication device attached to an arm or wrist of authorized users. The authentication device has an accelerometer, digital radio, a processor configured to provide identity information over the radio, and to transmit motion data. The motion data is received by the digital device and the identity transmitted is verified as an identity associated with an authorized user. Input at a touchscreen, touchpad, mouse, trackball, or keyboard of the digital device is detected, and correlated with the motion data. Access to the digital device is allowed if the detected input and the detected motion data correlate, and disallowed otherwise.

Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz. Investigating Contextual Cues As Indicators for EMA Delivery. Proceedings of the International Workshop on Smart and Ambient Notification and Attention Management (UbiTtention). September 2017. [Details]

In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular EMA trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant’s activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.

Emily Greene. ShareABEL: Secure Sharing of mHealth Data through Cryptographically-Enforced Access Control. Technical Report, July 2017. [Details]

Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareABEL, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design (and prototype implementation) of this system makes three contributions: (1) it applies cryptographically-enforced access-control measures to wearable healthcare data, which pose different challenges than Electronic Medical Records (EMRs), (2) it recognizes the temporal nature of mHealth data streams and supports revocation of access to part or all of a data stream, and (3) it departs from the vendor- and device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.

Shengjie Bi, Ellen Davenport, Jun Gong, Ronald Peterson, Kevin Storer, Tao Wang, Kelly Caine, Ryan Halter, David Kotz, Kofi Odame, Jacob Sorber, and Xing-Dong Yang. Poster: Auracle --- A Wearable Device for Detecting and Monitoring Eating Behavior. Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2017. [Details]

The Auracle aims to be a wearable earpiece that detects eating behavior, to be fielded by health-science researchers in their efforts to study eating behavior and ultimately to develop interventions useful to individuals striving to address chronic disease related to eating.

Shengjie Bi, Tao Wang, Ellen Davenport, Ronald Peterson, Ryan Halter, Jacob Sorber, and David Kotz. Toward a Wearable Sensor for Eating Detection. Proceedings of the ACM Workshop on Wearable Systems and Applications (WearSys). June 2017. [Details]

Researchers strive to understand eating behavior as a means to develop diets and interventions that can help people achieve and maintain a healthy weight, recover from eating disorders, or manage their diet and nutrition for personal wellness. A major challenge for eating-behavior research is to understand when, where, what, and how people eat. In this paper, we evaluate sensors and algorithms designed to detect eating activities, more specifically, when people eat. We compare two popular methods for eating recognition (based on acoustic and electromyography (EMG) sensors) individually and combined. We built a data-acquisition system using two off-the-shelf sensors and conducted a study with 20 participants. Our preliminary results show that the system we implemented can detect eating with an accuracy exceeding 90.9% while the crunchiness level of food varies. We are developing a wearable system that can capture, process, and classify sensor data to detect eating in real-time.

George G. Boateng. ActivityAware: Wearable System for Real-Time Physical Activity Monitoring among the Elderly. Master's thesis, May 2017. Available as Dartmouth Computer Science Technical Report TR2017-824. [Details]

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person’s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that monitors the daily activity levels (low, moderate and vigorous) of older adults in real-time. The app continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day’s accumulated time spent at that activity level, displays the results on the screen and logs summary data for later analysis.

The app implements an activity-level detection model we developed using a Linear Support Vector Machine (SVM). We trained our model using data from a user study, where subjects performed common physical activities (sit, stand, lay down, walk and run). We obtained accuracies up to 99.2% and 98.5% with 10-fold cross validation and leave-one-subject-out (LOSO) cross-validation respectively. We ran a week-long field study to evaluate the utility, usability and battery life of the ActivityAware system where 5 older adults wore the Amulet as it monitored their activity level. The utility evaluation showed that the app was somewhat useful in achieving the daily physical activity goal. The usability feedback showed that the ActivityAware system has the potential to be used by people for monitoring their activity levels. Our energy-efficiency evaluation revealed a battery life of at least 1 week before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for epidemiological studies, and eventually for the development of interventions that could improve the health of older adults.


David B. Harmon. Cryptographic transfer of sensor data from the Amulet to a smartphone. Technical Report, May 2017. [Details]

The authenticity, confidentiality, and integrity of data streams from wearable healthcare devices are critical to patients, researchers, physicians, and others who depend on this data to measure the effectiveness of treatment plans and clinical trials. Many forms of mHealth data are highly sensitive; in the hands of unintended parties such data may reveal indicators of a patient’s disorder, disability, or identity. Furthermore, if a malicious party tampers with the data, it can affect the diagnosis or treatment of patients, or the results of a research study. Although existing network protocols leverage encryption for confidentiality and integrity, network-level encryption does not provide end-to-end security from the device, through the smartphone and database, to downstream data consumers. In this thesis we provide a new open protocol that provides end-to-end authentication, confidentiality, and integrity for healthcare data in such a pipeline.

We present and evaluate a prototype implementation to demonstrate this protocol’s feasibility on low-power wearable devices, and present a case for the system’s ability to meet critical security properties under a specific adversary model and trust assumptions.


George Boateng, John A. Batsis, Ryan Halter, and David Kotz. ActivityAware: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device. Proceedings of the IEEE PerCom Workshop on Pervasive Health Technologies (PerHealth). March 2017. [Details]

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person’s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day’s accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals.

David Kotz, Ryan Halter, Cory Cornelius, Jacob Sorber, Minho Shin, Ronald Peterson, Shrirang Mare, Aarathi Prasad, Joseph Skinner, and Andrés Molina-Markham. Wearable computing device for secure control of physiological sensors and medical devices, with secure storage of medical records, and bioimpedance biometric. U.S. Patent 9,595,187; International Patent Application WO2013096954A1, March 14, 2017. Priority date 2011-12-23; Filed 2012-12-24; Issued 2017-03-14. [Details]

A wearable master electronic device (Amulet) has a processor with memory, the processor coupled to a body-area network (BAN) radio and uplink radio. The device has firmware for BAN communications with wearable nodes to receive data, and in an embodiment, send configuration data. The device has firmware for using the uplink radio to download apps and configurations, and upload data to a server. An embodiment has accelerometers in Amulet and wearable node, and firmware for using accelerometer readings to determine if node and Amulet are worn by the same subject. Other embodiments use pulse sensors or microphones in the Amulet and node to both identify a subject and verify the Amulet and node are worn by the same subject. Another embodiment uses a bioimpedance sensor to identify the subject. The wearable node may be an insulin pump, chemotherapy pump, TENS unit, cardiac monitor, or other device.

George Boateng and David Kotz. StressAware: An App for Real-Time Stress Monitoring on the Amulet Wearable Platform. Proceedings of the IEEE MIT Undergraduate Research Technology Conference (URTC). January 2017. [Details]

Stress is the root cause of many diseases and unhealthy behaviors. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this work, we present StressAware, an application on the Amulet wearable platform that classifies the stress level (low, medium, high) of individuals continuously and in real time using heart rate (HR) and heart-rate variability (HRV) data from a commercial heart-rate monitor. We developed our stress-detection model using a Support Vector Machine (SVM). We trained and tested our model using data from three sources and had the following preliminary results: PhysioNet, a public physiological database (94.5% accurate with 10-fold cross validation), a field study (100% accurate with 10-fold cross validation) and a lab study (64.3% accurate with leave-one-out cross-validation). Testing the StressAware app revealed a projected battery life of up to 12 days. Also, the usability feedback from subjects showed that the Amulet has a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.

2016:
Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, Kevin Storer, Steven Hearndon, Sarah Lord, Ryan Halter, David Kotz, and Jacob Sorber. The Amulet Wearable Platform: Demo Abstract. Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys). November 2016. [Details]

In this demonstration we present the Amulet Platform; a hardware and software platform for developing energy- and resource-efficient applications on multi-application wearable devices. This platform, which includes the Amulet Firmware Toolchain, the Amulet Runtime, the ARP-View graphical tool, and open reference hardware, efficiently protects applications from each other without MMU support, allows developers to interactively explore how their implementation decisions impact battery life without the need for hardware modeling and additional software development, and represents a new approach to developing long-lived wearable applications. We envision the Amulet Platform enabling long-duration experiments on human subjects in a wide variety of studies.

Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, Kevin Storer, Steven Hearndon, Kevin Freeman, Sarah Lord, Ryan Halter, David Kotz, and Jacob Sorber. Amulet: An Energy-Efficient, Multi-Application Wearable Platform. Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys). November 2016. [Details]

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage.

We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.


David Kotz, Carl A. Gunter, Santosh Kumar, and Jonathan P. Weiner. Privacy and Security in Mobile Health – A Research Agenda. IEEE Computer. June 2016. [Details]

Mobile health technology has great potential to increase healthcare quality, expand access to services, reduce costs, and improve personal wellness and public health. However, mHealth also raises significant privacy and security challenges.

George G. Boateng. StressAware: App for Continuously Measuring and Monitoring Stress Levels in Real Time on the Amulet Wearable Device. Technical Report, May 2016. [Details]

Stress is the root cause of many diseases. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this thesis, I present StressAware, an application on the Amulet wearable platform to measure the stress levels of individuals continuously and in real time. The app implements a stress detection model, continuously streams heart rate data from a commercial heart-rate monitor such as a Zephyr and Polar H7, classifies the stress level of an individual, logs the stress level and then displays it as a graph on the screen. I developed a stress detection model using a Linear Support Vector Machine. I trained my classifiers using data from 3 sources: PhysioNet, a public database with various physiological data, a field study, where subjects went about their normal daily activities and a lab study in a controlled environment, where subjects were exposed to various stressors. I used 73 data segments of stress data obtained from PhysioNet, 120 data segments from the field study, and 14 data segments from the lab study. I extracted 14 heart rate and heart rate variability features. With 10-fold cross validation for Radial Basis Function (RBF) SVM, I obtained an accuracy of 94.5% for the PhysioNet dataset and 100% for the field study dataset. And for the lab study, I obtained an accuracy of 64.29% with leave-one-out cross-validation. Testing the StressAware app revealed a projected battery life of up to 12 days before needing to recharge. Also, the usability feedback from subjects showed that the Amulet and Zephyr have a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.

Anna J. Knowles. Integrating Bluetooth Low Energy Peripherals with the Amulet. Technical Report, May 2016. [Details]

The Amulet is a health monitor, similar in size and shape to a smartwatch but specifically designed to have a longer battery life and handle data securely. It is equipped with a Bluetooth Low Energy (BLE) radio in order to receive data from BLE-enabled sensors and transmit data to smartphones, but the full implementation of BLE communication on the Amulet is still a work in progress. This thesis describes architectural changes that improve the Amulet’s ability to receive data from a variety of BLE-enabled sensors and make it easier for developers to integrate new BLE-enabled sensors with the Amulet by introducing support for connecting to multiple sensors at the same time, rewriting the radio code to be more generic, and exposing BLE functionality to the AmuletOS. We discuss the relevant parts of the AmuletOS and the BLE protocol as background, describe the current structure of BLE communications on the Amulet, and document the proposed changes to create a system for easily integrating new BLE-enabled sensors and handling connections to multiple sensors simultaneously.

Aarathi Prasad. Privacy-preserving controls for sharing mHealth data. PhD thesis, May 2016. Available as Dartmouth Computer Science Technical Report TR2016-794. [Details]

Mobile devices allow people to collect and share health and health-related information with recipients such as health providers, family and friends, employers and insurance companies, to obtain health, emotional or financial benefits. People may consider certain health information sensitive and prefer to disclose only what is necessary. In this dissertation, we present our findings about factors that affect people’s sharing behavior, describe scenarios in which people may wish to collect and share their personal health-related information with others, but may be hesitant to disclose the information if necessary controls are not available to protect their privacy, and propose frameworks to provide the desired privacy controls. We introduce the concept of close encounters that allow users to share data with other people who may have been in spatio-temporal proximity. We developed two smartphone-based systems that leverage stationary sensors and beacons to determine whether users are in spatio-temporal proximity. The first system, ENACT, allows patients diagnosed with a contagious airborne disease to alert others retrospectively about their possible exposure to airborne virus. The second system, SPICE, allows users to collect sensor information, retrospectively, from others with whom they shared a close encounter. We present design and implementation of the two systems, analyse their security and privacy guarantees, and evaluate the systems on various performance metrics. Finally, we evaluate how Bluetooth beacons and Wi-Fi access points can be used in support of these systems for close encounters, and present our experiences and findings from a deployment study on Dartmouth campus.

2015:
David Kotz, Kevin Fu, Carl Gunter, and Avi Rubin. Security for Mobile and Cloud Frontiers in Healthcare. Communications of the ACM. August 2015. [Details]

Designers and developers of healthcare information technologies must address preexisting security vulnerabilities and undiagnosed future threats.

2014:
Andrés Molina-Markham, Ronald Peterson, Joseph Skinner, Tianlong Yun, Bhargav Golla, Kevin Freeman, Travis Peters, Jacob Sorber, Ryan Halter, and David Kotz. Amulet: A secure architecture for mHealth applications for low-power wearable devices. Proceedings of the Workshop on Mobile Medical Applications-- Design and Development (WMMADD). November 2014. [Details]

Interest in using mobile technologies for health-related applications (mHealth) has increased. However, none of the available mobile platforms provide the essential properties that are needed by these applications. An mHealth platform must be (i) secure; (ii) provide high availability; and (iii) allow for the deployment of multiple third-party mHealth applications that share access to an individual’s devices and data. Smartphones may not be able to provide property (ii) because there are activities and situations in which an individual may not be able to carry them (e.g., while in a contact sport). A low-power wearable device can provide higher availability, remaining attached to the user during most activities. Furthermore, some mHealth applications require integrating multiple on-body or near-body devices, some owned by a single individual, but others shared with multiple individuals. In this paper, we propose a secure system architecture for a low-power bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The wearer can install a personalized mix of third-party applications to support the monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Our preliminary implementation and evaluation supports the hypothesis that our approach allows for the implementation of a resource monitor on far less power than would be consumed by a mobile device running Linux or Android. Our preliminary experiments demonstrate that our secure architecture would enable applications to run for several weeks on a small wearable device without recharging.

Aarathi Prasad, Jacob Sorber, Timothy Stablein, Denise Anthony, and David Kotz. Understanding User Privacy Preferences for mHealth Data Sharing. MHealth: Multidisciplinary Verticals. November 2014. [Details]
Xiaohui Liang and David Kotz. Securely Connecting Wearable Health Devices to External Displays. Proceedings of the USENIX Summit on Health Information Technologies. August 2014. No paper -- workshop presentation only. [Details]

Wearable health technology is becoming a hot commodity as it has the potential to help both patients and clinicians continuously monitor vital signs and symptoms. One popular type of wearable devices are worn on human wrist and are equipped with sensors to passively perform sensing tasks. Their constrained user interface, however, is ineffective to display the sensory data for users. We envision connecting a wrist-worn device to a display device, such as a television, so the user is able to view the sensory data. Such connections must be secure to prevent the sensory data from being eavesdropped by other devices, must be made only when the user intends, and must be easy even when a new display is encountered (such as in a medical clinic, or a hotel room). In this presentation, we will discuss the secure wearable/display connection problem by revisiting existing methods and hardware designs of wrist-worn devices and display devices. We then present possible solutions that leverage the built-in hardware components of wrist-worn devices to implement, secure, intentional, easy connections to ambient display devices.

Rima Narayana Murthy. mCollector: Sensor-enabled health-data collection system for rural areas in the developing world. Master's thesis, August 2014. Available as Dartmouth Technical Report TR2015-788. [Details]

Health data collection poses unique challenges in rural areas of the developing world. mHealth systems that are used by health workers to collect data in remote rural regions should also record contextual information to increase confidence in the fidelity of the collected data.

We built a user-friendly, mobile health-data collection system using wireless medical sensors that interface with an Android application. The data-collection system was designed to support minimally trained, non-clinical health workers to gather data about blood pressure and body weight using off-the-shelf medical sensors. This system comprises a blood-pressure cuff, a weighing scale and a portable point-of-sales printer. With this system, we introduced a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. In a preliminary user study, we found that a binary support vector machine classifier could be used to distinguish lower-arm movements from stationary arms with 90% accuracy. Predetermined thresholds for the accelerometer readings suffice to determine whether the tablet, and therefore the arm that rested on it, remained supported. Together, these two methods can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly.

Usability is a particularly important design and deployment challenge in remote, rural areas, given the limited resources for technology training and support. We conducted a field study to assess our system’s usability in Kolar town, India, where we logged health worker interactions with the app’s interface using an existing usability toolkit. Researchers analyzed logs from this toolkit to evaluate the app’s user experience and quantify specific usability challenges in the app. We have recorded experiential notes from the field study in this document.


Cory Cornelius, Ronald Peterson, Joseph Skinner, Ryan Halter, and David Kotz. A wearable system that knows who wears it. Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2014. [Details]

Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions.

Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b) the device is physically on the wearer’s body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day’s worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus S smart phone and custom-designed module for the Shimmer sensing platform.


Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, and David Kotz. Hide-n-Sense: preserving privacy efficiently in wireless mHealth. Mobile Networks and Applications (MONET). June 2014. Special issue on Wireless Technology for Pervasive Healthcare. [Details]

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Furthermore, individuals can become better engaged in monitoring and managing their own health. Although some work on mHealth sensing has addressed security, achieving strong privacy for low-power sensors remains a challenge. We make three contributions. First, we propose an mHealth sensing protocol that provides strong security and privacy properties at the link layer, with low energy overhead, suitable for low-power sensors. The protocol uses three novel techniques: adaptive security, to dynamically modify transmission overhead; MAC striping, to make forgery difficult even for small-sized Message Authentication Codes; and asymmetric resource requirements, in recognition of the limited resources in tiny mHealth sensors. Second, we demonstrate its feasibility by implementing a prototype on a Chronos wrist device, and evaluating it experimentally. Third, we provide a security, privacy, and energy analysis of our system.

Andrés Molina-Markham, Ronald A. Peterson, Joseph Skinner, Ryan J. Halter, Jacob Sorber, and David Kotz. Poster: Enabling Computational Jewelry for mHealth Applications. Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2014. [Details]

We are developing wearable devices as the foundation for a consistently present and highly available body-area mHealth network. Our vision is that a small device, such as a bracelet or pendant, will provide the availability and reliability properties essential for successful body-area mHealth networks. We call this class of device computational jewelry, and expect it will be the next frontier of mobile systems. We prototyped our first piece of computational jewelry, which we call Amulet, to enable our previously proposed vision. It runs applications that may collect sensor data from built-in sensors or from other devices, analyze and log the data, queue information for later upload, and interact with the wearer. Independent developers can develop applications that can be vetted and installed on an Amulet.

Aarathi Prasad, Xiaohui Liang, and David Kotz. Poster: Balancing Disclosure and Utility of Personal Information. Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2014. [Details]

The ubiquity of smartphones and mobile and wearable devices allow people to collect information about their health, wellness and lifestyle and share with others. If it is not clear what they need to share to receive benefits, subjects (people whose information is collected) might share too much, thus disclosing unnecessary private information. On the other hand, concerned about disclosing personal information, subjects might share less than what the recipient needs and lose the opportunity to enjoy the benefits. This balance of disclosure and utility is important when the subject wants to receive some benefits, but is concerned about disclosing private information.

We address this problem of balancing disclosure and utility of personal information collected by mobile technologies. We believe subjects can decide how best to share their information if they are aware of the benefits and risks of sharing. We developed ShareBuddy, a privacy-aware architecture that allows recipients to request information and specify the benefits the subjects will receive for sharing each piece of requested information; the architecture displays these benefits and warns subjects about the risks of sharing. We describe the ShareBuddy architecture in this poster.


Rima Murthy and David Kotz. Assessing blood-pressure measurement in tablet-based mHealth apps. Proceedings of the Workshop on Networked Healthcare Technology (NetHealth). January 2014. [Details]

We propose a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. We found that a binary support vector machine classifier could be used to distinguish different types of lower-arm movements from stationary arms with 90% accuracy overall. Predetermined thresholds for the accelerometer readings suffice to determine whether the tablet, and therefore the arm that rested on it, remained supported. Together, these two methods can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly.

2013:
Denise Anthony, Andrew Campbell, Thomas Candon, Andrew Gettinger, Carl A. Gunter, M. Eric Johnson, David Kotz, Lisa Marsch, Andrés Molina-Markham, Karen Page, and Sean Smith. Securing Information Technology in Healthcare. IEEE Security & Privacy. November 2013. Invited paper. [Details]

Information technology (IT) has great potential to improve healthcare quality while also improving efficiency, and thus has been a major focus of recent healthcare reform efforts. However, developing, deploying and using IT that is both secure and genuinely effective in the complex clinical, organizational and economic environment of healthcare is a significant challenge. Further, it is imperative that we better understand the privacy concerns of patients and providers, as well as the ability of current technologies, policies, and laws to adequately protect privacy. The Securing Information Technology in Healthcare (SITH) workshops were created to provide a forum to discuss security and privacy for experts from a broad range of perspectives, from officers at large healthcare companies, startups and nonprofits, to physicians, researchers and policy makers.

Cory T. Cornelius. Usable Security for Wireless Body-Area Networks. PhD thesis, September 2013. Available as Dartmouth Computer Science Technical Report TR2013-741. [Details]

We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems.

In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work.

These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors.


Shloka R. Kini. Please Take My Survey: Compliance with smartphone-based EMA/ESM studies. Technical Report, May 2013. [Details]

This thesis analyzes the factors that affect compliance in Ecological Momentary Assessment (EMA) survey systems using smartphones. Current EMA systems have simple parameters in their triggering mechanisms, which results in missed or ignored surveys, creating a loss of subject data. Over the course of three user studies, with slight variations, we analyze the factors that influence the willingness of a survey participant to answer surveys on an Android phone. An understanding of these factors would be valuable for mobile developers in developing advanced EMA trigger systems. After having experienced various unforeseen challenges in the process, we describe the parameters and difficulties in administering a study of this nature, making recommendations for future EMA applications and user studies. We also compare and analyze the pros and cons involved in developing various EMA systems. Psychologists and sociologists who use EMA systems to gather behavioral data might benefit from the experiential and behavioral data collected as part of our user studies.

Aarathi Prasad, Ronald Peterson, Shrirang Mare, Jacob Sorber, Kolin Paul, and David Kotz. Provenance framework for mHealth. Proceedings of the Workshop on Networked Healthcare Technology (NetHealth). January 2013. [Details]

Mobile health technologies allow patients to collect their health information outside the hospital and share this information with others. But how can data consumers know whether to trust the sensor-collected and human-entered data they receive? Data consumers might be able to verify the accuracy and authenticity of the data if they have information about its origin and about changes made to it, i.e., the provenance of the data. We propose a provenance framework for mHealth devices, to collect and share provenance metadata and help the data consumer verify whether certain provenance properties are satisfied by the data they receive. This paper describes the programming model for this framework, which describes the rules to be implemented for providing provenance-collecting capabilities to an mHealth application.

2012:
Cory Cornelius and David Kotz. Recognizing whether sensors are on the same body. Journal of Pervasive and Mobile Computing. December 2012. [Details]

In an open mobile health (mHealth) sensing system, users will be able to seamlessly pair sensors with their cellphone and expect the system to just work. This ubiquity of sensors, however, creates the potential for users to accidentally wear sensors that are not paired with their own cellphone. Our method probabilistically detects this situation by finding correlations between embedded accelerometers in the cellphone and sensor. We evaluate our method over a dataset of seven individuals with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracy of 85%.

Sasikanth Avancha, Amit Baxi, and David Kotz. Privacy in mobile technology for personal healthcare. ACM Computing Surveys. November 2012. [Details]

Information technology can improve the quality, efficiency, and cost of healthcare. In this survey, we examine the privacy requirements of mobile computing technologies that have the potential to transform healthcare. Such mHealth technology enables physicians to remotely monitor patients’ health, and enables individuals to manage their own health more easily. Despite these advantages, privacy is essential for any personal monitoring technology. Through an extensive survey of the literature, we develop a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discuss the technologies that could support privacy-sensitive mHealth systems. We end with a list of open research questions.

Aarathi Prasad, Ronald Peterson, Jacob Sorber, and David Kotz. A Provenance Framework for mHealth. Proceedings of the Workshop for Mobile Systems, Applications, and Services for Healthcare (mHealthSys) Poster Track. November 2012. [Details]

How can data consumers know whether to trust the sensor-collected and human-entered data they receive from mHealth devices? What confidence do they have that it is accurate and authentic? Data recipients might be able to verify the accuracy and authenticity of the data if they have information about its origin and about changes made to it, i.e., the provenance of the data.We define provenance in mHealth as contextual information that can attest to the authenticity and accuracy of the data and can help the recipient in interpreting the data. To realize this vision, we propose a provenance framework for mHealth. The primary function of the framework is to collect and share provenance metadata and help the data consumer verify whether certain provenance properties are satisfied by the data they receive.

Aarathi Prasad, Jacob Sorber, Timothy Stablein, Denise Anthony, and David Kotz. Understanding Sharing Preferences and Behavior for mHealth Devices. Proceedings of the Workshop on Privacy in the Electronic Society (WPES). October 2012. [Details]

mHealth devices offer many potential benefits to patients, health providers and others involved in the patients’ healthcare. If patients are not in control of the collection and sharing of their personal health information, they will have privacy concerns even while enjoying the benefits of the devices. We investigated patients’ willingness to share their personal health information, collected using mHealth devices, with their family, friends, third parties and the public. Our findings are based on a user study conducted with 41 participants. The best way to understand people’s privacy concerns is to give them the opportunity to use the device and actually share the information, and to the best of our knowledge, ours is the first study that does so. We discovered that patients want to share, selectively, their health information with people other than their doctors. We also show that privacy concerns are not static; patients may change their sharing decisions over time. Based on our findings, we suggest that privacy controls for mHealth systems should be flexible to allow patients to choose different settings for different recipients, and to change their sharing settings at any time.

Jacob Sorber, Minho Shin, Ron Peterson, and David Kotz. Plug-n-Trust: Practical trusted sensing for mHealth. Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2012. [Details]

Mobile computing and sensing technologies present exciting opportunities for healthcare. Prescription wireless sensors worn by patients can automatically deliver medical data to care providers, dramatically improving their ability to diagnose, monitor, and manage a range of medical conditions. Using the mobile phones that patients already carry to provide connectivity between sensors and providers is essential to keeping costs low and deployments simple. Unfortunately, software-based attacks against phones are also on the rise, and successful attacks on privacy-sensitive and safety-critical applications can have significant consequences for patients.

In this paper, we describe Plug-n-Trust (PnT), a novel approach to protecting both the confidentiality and integrity of safety-critical medical sensing and data processing on vulnerable mobile phones. With PnT, a plug-in smart card provides a trusted computing environment, keeping data safe even on a compromised mobile phone. By design, PnT is simple to use and deploy, while providing a flexible programming interface amenable to a wide range of applications. We describe our implementation, designed for Java-based smart cards and Android phones, in which we use a split-computation model with a novel path hashing technique to verify proper behavior without exposing confidential data. Our experimental evaluation demonstrates that PnT achieves its security goals while incurring acceptable overhead.


Emma N. Smithayer. Sensor-based system for verifying blood-pressure measurement position. Technical Report, June 2012. [Details]

Mobile maternal-health programs send workers door to door to visit pregnant women in rural India and collect data such as blood pressure or weight, then send that data to doctors for review. Since the doctors do not see the data collection, ensuring correct collection methods is crucial to allow them to make good treatment decisions. However, blood-pressure measurements are sometimes taken with the patient’s arm in the wrong position, which can cause inaccurate readings. This paper describes a system consisting of an automatic blood pressure cuff with an accelerometer and force sensors attached to determine whether the arm is at the correct angle, held still, and properly supported. A user study indicated that the prototype was effective in helping untrained users take a measurement in the correct position.

Jacob Sorber, Minho Shin, Ronald Peterson, Cory Cornelius, Shrirang Mare, Aarathi Prasad, Zachary Marois, Emma Smithayer, and David Kotz. An Amulet for trustworthy wearable mHealth. Proceedings of the Workshop on Mobile Computing Systems and Applications (HotMobile). February 2012. [Details]

Mobile technology has significant potential to help revolutionize personal wellness and the delivery of healthcare. Mobile phones, wearable sensors, and home-based tele-medicine devices can help caregivers and individuals themselves better monitor and manage their health. While the potential benefits of this “mHealth” technology include better health, more effective healthcare, and reduced cost, this technology also poses significant security and privacy challenges. In this paper we propose Amulet, an mHealth architecture that provides strong security and privacy guarantees while remaining easy to use, and outline the research and engineering challenges required to realize the Amulet vision.

Aarathi Prasad. Exposing Privacy Concerns in mHealth Data Sharing. Master's thesis, February 2012. Available as Technical Report TR2012-711. [Details]

Mobile health (mHealth) has become important in the field of healthcare information technology, as patients begin to use mobile devices to record their daily activities and vital signs. These devices can record personal health information even outside the hospital setting, while the patients are at home or at their workplace. However, the devices might record sensitive information that might not be relevant for medical purposes and in some cases may be misused. Patients need expressive privacy controls so that they can trade potential health benefits of the technology with the privacy risks. To provide such privacy controls, it is important to understand what patients feel are the benefits and risks associated with the technology and what controls they want over the information.

We conducted focus groups to understand the privacy concerns that patients have when they use mHealth devices. We conducted a user study to understand how willing patients are to share their personal health information that was collected using an mHealth device. To the best of our knowledge, ours is the first study that explores users’ privacy concerns by giving them the opportunity to actually share the information collected about them using mHealth devices. We found that patients tend to share more information with third parties than the public and prefer to keep certain information from their family and friends. Finally, based on these discoveries, we propose some guidelines to developing defaults for sharing settings in mHealth systems.


2011:
Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, and David Kotz. Adapt-lite: Privacy-aware, secure, and efficient mHealth sensing. Proceedings of the Workshop on Privacy in the Electronic Society (WPES). October 2011. [Details]

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Although some work on mHealth sensing has addressed security, achieving strong security and privacy for low-power sensors remains a challenge.

We make three contributions. First, we propose Adapt-lite, a set of two techniques that can be applied to existing wireless protocols to make them energy efficient without compromising their security or privacy properties. The techniques are: adaptive security, which dynamically modifies packet overhead; and MAC striping, which makes forgery difficult even for small-sized MACs. Second, we apply these techniques to an existing wireless protocol, and demonstrate a prototype on a Chronos wrist device. Third, we provide security, privacy, and energy analysis of our techniques.


Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, and David Kotz. Hide-n-Sense: Privacy-aware secure mHealth sensing. Technical Report, September 2011. [Details]

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Furthermore, individuals can become better engaged in monitoring and managing their own health. Although some work on mHealth sensing has addressed security, achieving strong privacy for low-power sensors remains a challenge.

We make three contributions. First, we propose an mHealth sensing protocol that provides strong security and privacy properties with low energy overhead, suitable for low-power sensors. The protocol uses three novel techniques: adaptive security, to dynamically modify transmission overhead; MAC striping, to make forgery difficult even for small-sized MACs; and an asymmetric resource requirement. Second, we demonstrate a prototype on a Chronos wrist device, and evaluate it experimentally. Third, we provide a security, privacy, and energy analysis of our system.


Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, and David Kotz. Adaptive security and privacy for mHealth sensing. Proceedings of the USENIX Workshop on Health Security (HealthSec). August 2011. Short paper. [Details]

As healthcare in many countries faces an aging population and rising costs, mobile Health (mHealth) sensing technologies promise a new opportunity. However, the privacy concerns associated with mHealth sensing are a limiting factor for their widespread adoption. The use of wireless body area networks pose a particular challenge. Although there exist protocols that provide a secure and private communication channel between two devices, the large transmission overhead associated with these protocols limit their application to low-power mHealth sensing devices. We propose an adaptive security model that enables use of privacy-preserving protocols in low-power mHealth sensing by reducing the network overhead in the transmissions, while maintaining the security and privacy properties provided by the protocols.

Aarathi Prasad, Jacob Sorber, Timothy Stablein, Denise Anthony, and David Kotz. Exposing privacy concerns in mHealth. Proceedings of the USENIX Workshop on Health Security (HealthSec). August 2011. Position paper. [Details]

We conducted several exploratory focus groups to understand what privacy concerns Patients might have with the collection, storage and sharing of their personal health information, when using mHealth devices. We found that Patients want control over their health information, and we noticed privacy trends that were particular to Patients in the same age group and with similar health experiences.

Cory Cornelius and David Kotz. Recognizing whether sensors are on the same body. Proceedings of the International Conference on Pervasive Computing (Pervasive). June 2011. [Details]

As personal health sensors become ubiquitous, we also expect them to become interoperable. That is, instead of closed, end-to-end personal health sensing systems, we envision standardized sensors wirelessly communicating their data to a device many people already carry today, the cellphone. In an open personal health sensing system, users will be able to seamlessly pair off-the-shelf sensors with their cellphone and expect the system to just work. However, this ubiquity of sensors creates the potential for users to accidentally wear sensors that are not necessarily paired with their own cellphone. A husband, for example, might mistakenly wear a heart-rate sensor that is actually paired with his wife’s cellphone. As long as the heart-rate sensor is within communication range, the wife’s cellphone will be receiving heart-rate data about her husband, data that is incorrectly entered into her own health record.

We provide a method to probabilistically detect this situation. Because accelerometers are relatively cheap and require little power, we imagine that the cellphone and each sensor will have a companion accelerometer embedded with the sensor itself. We extract standard features from these companion accelerometers, and use a pair-wise statistic -- coherence, a measurement of how well two signals are related in the frequency domain -- to determine how well features correlate for different locations on the body. We then use these feature coherences to train a classifier to recognize whether a pair of sensors -- or a sensor and a cellphone -- are on the same body. We evaluate our method over a dataset of several individuals walking around with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracies over 80%.


Jacob Sorber, Minho Shin, Ron Peterson, and David Kotz. Poster: Practical Trusted Computing for mHealth Sensing. Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys). June 2011. [Details]

Mobile sensing technologies present exciting opportunities for healthcare. Wireless sensors can automatically provide sensor data to care providers, dramatically improving their ability to diagnose, monitor, and manage a wide range of medical conditions. Using mobile phones to provide connectivity between sensors and providers is essential to keeping costs low and deployments simple. Unfortunately, software-based attacks against phones, which can have significant consequences for patients, are also on the rise.

This poster describes a simple, flexible, and novel approach to protecting both the confidentiality and integrity medical sensing and data processing on vulnerable mobile phones, using plug-in smart cards---even a phone compromised by malware. We describe our design, implementation, and initial experimental results using real smart cards and Android smartphones.


David Kotz. A threat taxonomy for mHealth privacy. Proceedings of the Workshop on Networked Healthcare Technology (NetHealth). January 2011. [Details]

Networked mobile devices have great potential to enable individuals (and their physicians) to better monitor their health and to manage medical conditions. In this paper, we examine the privacy-related threats to these so-called mHealth technologies. We develop a taxonomy of the privacy-related threats, and discuss some of the technologies that could support privacy-sensitive mHealth systems. We conclude with a brief summary of research challenges.

2010:
Cory Cornelius and David Kotz. On Usable Authentication for Wireless Body Area Networks. Proceedings of the USENIX Workshop on Health Security (HealthSec). August 2010. Position paper. [Details]

We examine a specific security problem in wireless body area networks (WBANs), what we call the one body authentication problem. That is, how can we ensure that the wireless sensors in a WBAN are collecting data about one individual and not several individuals. We explore existing solutions to this problem and provide some analysis why these solutions are inadequate. Finally, we provide some direction towards a promising solution to the problem and how it can be used to create a usably secure WBAN.

Shrirang Mare and David Kotz. Is Bluetooth the right technology for mHealth? Proceedings of the USENIX Workshop on Health Security (HealthSec). August 2010. Position paper. [Details]

Many people believe mobile healthcare (mHealth) would help alleviate the rising cost of healthcare and improve the quality of service. Bluetooth, which is the most popular wireless technology for personal medical devices, is used for most of the mHealth sensing applications. In this paper we raise the question -- Is Bluetooth the right technology for mHealth? To instigate the discussion we discuss some shortcomings of Bluetooth and also point out an alternative solution.

Aarathi Prasad and David Kotz. Can I access your Data? Privacy Management in mHealth. Proceedings of the USENIX Workshop on Health Security (HealthSec). August 2010. Position paper. [Details]

Mobile health (mHealth) has become important in the field of healthcare information technology, as patients begin to use mobile medical sensors to record their daily activities and vital signs. Since their medical data is collected by their sensors, the patients may wish to control data collection and distribution, so as to protect their data and share it only when the need arises. It must be possible for patients to grant or deny access to the data on the storage unit (mobile phones or personal health records (PHR)). Thus, an efficient framework is required for managing patient consent electronically, i.e.to allow patients to express their desires about what data to collect, what to store, and how to share. We describe several challenges posed by privacy management in mobile health.

2009:
David Kotz, Sasikanth Avancha, and Amit Baxi. A privacy framework for mobile health and home-care systems. Proceedings of the Workshop on Security and Privacy in Medical and Home-Care Systems (SPIMACS). November 2009. [Details]

In this paper, we consider the challenge of preserving patient privacy in the context of mobile healthcare and home-care systems, that is, the use of mobile computing and communications technologies in the delivery of healthcare or the provision of at-home medical care and assisted living. This paper makes three primary contributions. First, we compare existing privacy frameworks, identifying key differences and shortcomings. Second, we identify a privacy framework for mobile healthcare and home-care systems. Third, we extract a set of privacy properties intended for use by those who design systems and applications for mobile healthcare and home-care systems, linking them back to the privacy principles. Finally, we list several important research questions that the community should address. We hope that the privacy framework in this paper can help to guide the researchers and developers in this community, and that the privacy properties provide a concrete foundation for privacy-sensitive systems and applications for mobile healthcare and home-care systems.

Janani Sriram, Minho Shin, Tanzeem Choudhury, and David Kotz. Activity-aware ECG-based patient authentication for remote health monitoring. Proceedings of the International Conference on Multimodal Interfaces and Workshop on Machine Learning for Multi-modal Interaction (ICMI-MLMI). November 2009. [Details]

Mobile medical sensors promise to provide an efficient, accurate, and economic way to monitor patients’ health outside the hospital. Patient authentication is a necessary security requirement in remote health monitoring scenarios. The monitoring system needs to make sure that the data is coming from the right person before any medical or financial decisions are made based on the data. Credential-based authentication methods (e.g., passwords, certificates) are not well-suited for remote healthcare as patients could hand over credentials to someone else. Furthermore, one-time authentication using credentials or trait-based biometrics (e.g., face, fingerprints, iris) do not cover the entire monitoring period and may lead to unauthorized post-authentication use. Recent studies have shown that the human electrocardiogram (ECG) exhibits unique patterns that can be used to discriminate individuals. However, perturbation of the ECG signal due to physical activity is a major obstacle in applying the technology in real-world situations. In this paper, we present a novel ECG and accelerometer-based system that can authenticate individuals in an ongoing manner under various activity conditions. We describe the probabilistic authentication system we have developed and present experimental results from 17 individuals.

Guanling Chen, Bo Yan, Minho Shin, David Kotz, and Ethan Berke. MPCS: Mobile-based Patient Compliance System for Chronic Illness Care. Proceedings of the International Workshop on Ubiquitous Mobile Healthcare Applications (MobiCare). July 2009. [Details]

More than 100 million Americans are currently living with at least one chronic health condition and expenditures on chronic diseases account for more than 75 percent of the $2.3 trillion cost of our healthcare system. To improve chronic illness care, patients must be empowered and engaged in health self-management. However, only half of all patients with chronic illness comply with treatment regimen. The self-regulation model, while seemingly valuable, needs practical tools to help patients adopt this self-centered approach for long-term care.

In this position paper, we propose Mobile-phone based Patient Compliance System (MPCS) that can reduce the time-consuming and error-prone processes of existing self-regulation practice to facilitate self-reporting, non-compliance detection, and compliance reminders. The novelty of this work is to apply social-behavior theories to engineer the MPCS to positively influence patients’ compliance behaviors, including mobile-delivered contextual reminders based on association theory; mobile-triggered questionnaires based on self-perception theory; and mobile-enabled social interactions based on social-construction theory. We discuss the architecture and the research challenges to realize the proposed MPCS.


Janani Sriram, Minho Shin, David Kotz, Anand Rajan, Manoj Sastry, and Mark Yarvis. Challenges in Data Quality Assurance in Pervasive Health Monitoring Systems. Future of Trust in Computing. July 2009. [Details]

Wearable, portable, and implantable medical sensors have ushered in a new paradigm for healthcare in which patients can take greater responsibility and caregivers can make well-informed, timely decisions. Health-monitoring systems built on such sensors have huge potential benefit to the quality of healthcare and quality of life for many people, such as patients with chronic medical conditions (such as blood-sugar sensors for diabetics), people seeking to change unhealthy behavior (such as losing weight or quitting smoking), or athletes wishing to monitor their condition and performance. To be effective, however, these systems must provide assurances about the quality of the sensor data. The sensors must be applied to the patient by a human, and the sensor data may be transported across multiple networks and devices before it is presented to the medical team. While no system can guarantee data quality, we anticipate that it will help for the system to annotate data with some measure of confidence. In this paper, we take a deeper look at potential health-monitoring usage scenarios and highlight research challenges required to ensure and assess quality of sensor data in health-monitoring systems.


[Kotz research]