SIMBA: Sensing and Intervention for Mental, Behavioral, and Affective health (2017-date)

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 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: Kelly Caine, Elgar Fleisch, Ryan Halter, Sarah Hong, Tobias Kowatsch, Jan-Niklas Kramer, Florian Künzler, Stephanie Lewia, Sarah Lord, Byron Lowens, Gunnar Pope, Bastien Presset, Urte Scholz, Sougata Sen, Shawna Smith.

Funding and acknowledgements

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.

The views and conclusions contained on this site and in its documents are those of the authors and should not be interpreted as necessarily representing the official position or policies, either expressed or implied, of the sponsor(s). Any mention of specific companies or products does not imply any endorsement by the authors or by the sponsor(s).

Papers (tagged 'simba')

[Also available in BibTeX]

Papers are listed in reverse-chronological order. Follow updates with RSS.


[Kotz research]