Related projects: [Amanuensis], [Amulet], [Auracle], [THaW], [TISH]
Related keywords: [mhealth], [sensors], [wearable]
In the SIMBA (Sensing and Intervention for Mental, Behavioral, and Affective health) project we explore opportunities to sense physiological states (such as stress) and the receptivity to behavioral interventions.
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 our work, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. Our analysis shows that using the off-the-shelf Polar H7 sensor, 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 [mishra:jcommodity]. Then, a subsequent paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection [mishra:stress-ml].
In another line of work, we explore receptivity to mHealth interventions, including Just-In-Time Adaptive Interventions (JITAI). We explored the factors affecting users' receptivity towards JITAIs by conducting a study with 189 participants, over a period of 6 weeks. We 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. We also built machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classifier [kunzler:receptivity].
We then deployed the Ally app in a field study with 83 users. The 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 [mishra:receptivity].
More recently, 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 [hong:receptivity].
We also explored the receptivity of car drivers to in-car wellness interventions. 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. [koch:car-receptivity].
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, we conducted a study with n = 274 insurees of a health insurance company in Switzerland. We found that 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 [kramer:step-goals].
Finally, we are exploring several of these ideas in the context of the D-TECT digital phenotyping project at CTBH. We are particularly interested in the utility of EMA and digital sensing in predicting OUD (opioid-use disorder) treatment retention and buprenorphine medication adherence.
Varun Mishra and David Kotz, with others: Thomas Berger, Kelly Caine, Ching-Hua Chen, Grace Chen, Elgar Fleisch, Ryan Halter, Tian Hao, Sarah Hong, Kevin Koch, Tobias Kowatsch, Jan-Niklas Kramer, Florian Künzler, Stephanie Lewia, Shu Liu, Sarah Lord, Byron Lowens, Gunnar Pope, Bastien Presset, Jeffrey Rogers, Urte Scholz, Sougata Sen, Shawna Smith, and Felix Wortmann.
SIMBA is a project affiliated with Dartmouth's Center for Technology and Behavioral Health, which is supported by the NIH National Institute of Drug Abuse under award number NIH/NIDA P30DA029926.
Many SIMBA projects are affiliated with Dartmouth's Institute for Security, Technology, and Society (ISTS), and supported by the National Science Foundation under award numbers CNS-1314281, CNS-1314342, CNS-1619970, and CNS-1619950.
Many SIMBA projects are a partnership with the Center for Digital Health Interventions at ETH Zürch. Some of those studies were funded by CSS Insurance, Switzerland.
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