ResearchKit provides data collection in two ways: (1) through predefined macros for detection of active tasks, where each task state is extracted from information obtained through a combination of phone sensors (please refer to table at researchkit.org/docs/docs/ActiveTasks/ActiveTasks.html); and (2) through the iOS HealthKit and CoreMotion (https://developer.apple.com/documentation/coremotion developer.apple.com/documentation/coremotion) APIs.
Prevention of Mental Health Disorders Using Internet- and Mobile-Based Interventions: A Narrative Review and Recommendations for Future Research. Ebert DD, Cuijpers P, Muñoz RF and Baumeister H (2017) Front. Psychiatry 8:116. DOI: 10.3389/fpsyt.2017.00116
Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. General Hospital Psychiatry Volume 35, Issue 4, July–August 2013, Pages 332-338. DOI: 10.1016/j.genhosppsych.2013.03.008
AWARE: mobile context instrumentation framework; Ferreira D, Kostakos V and Dey AK Front. ICT (2015); 2:6; DOI:10.3389/fict.2015.00006
New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research; Torous J, Kiang MV, Lorme J, Onnela JP; JMIR Ment Health (2016);3(2):e16 DOI:10.2196/mental.5165
CenceMe – Injecting Sensing Presence into Social Networking Applications; Miluzzo E., Lane N.D., Eisenman S.B., Campbell A.T. (2007); In: Kortuem G., Finney J., Lea R., Sundramoorthy V. (eds) Smart Sensing and Context. EuroSSC 2007; Lecture Notes in Computer Science, vol 4793. Springer, Berlin, Heidelberg DOI: 10.1007/978-3-540-75696-5_1.
Lind, M. N., Byrne, M. L., Wicks, G., Smidt, A. M., & Allen, N. B. (2018). The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health, 5(3), e10334–10. [1]
Empath: a continuous remote emotional health monitoring system for depressive illness; Robert F. Dickerson, Eugenia I. Gorlin, John A. Stankovic; WH '11 Proceedings of the 2nd Conference on Wireless Health; Article No. 5; San Diego, California — October 10–13, 2011; DOI: 10.1145/2077546.2077552
Happier People Live More Active Lives: Using Smartphones to Link Happiness and Physical Activity; Neal Lathia, Gillian M. Sandstrom, Cecilia Mascolo, Peter J. Rentfrow; (2017); PLoS ONE 12(1): e0160589; DOI: 10.1371/journal.pone.0160589
Social fMRI: Investigating and shaping social mechanisms in the real world; Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, Alex Pentland; Pervasive and Mobile Computing (2011); DOI:10.1016/j.pmcj.2011.09.004
Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders; Place S, Blanch-Hartigan D, Rubin C, Gorrostieta C, Mead C, Kane J, Marx BP, Feast J, Deckersbach T, Pentland A, Nierenberg A, Azarbayejani A; J Med Internet Res (2017);19(3):e75 DOI:10.2196/jmir.6678
An open source mobile platform for psychophysiological self tracking; Gaggioli A, Cipresso P, Serino S, Pioggia G, Tartarisco G, Baldus G, Corda D, Riva G; Stud Health Technol Inform. (2012);173:136-8 DOI:10.3233/978-1-61499-022-2-136
Purple: A Modular System for Developing and Deploying Behavioral Intervention Technologies; Schueller SM, Begale M, Penedo FJ, Mohr DC; J Med Internet Res 2014;16(7):e181; DOI:10.2196/jmir.3376
Poster: SensingKit: a multi-platform mobile sensing framework for large-scale experiments; Kleomenis Katevas, Hamed Haddadi, Laurissa Tokarchuk; Published in: Proceeding MobiCom '14 Proceedings of the 20th annual international conference on Mobile computing and networking; Pages 375-378; Maui, Hawaii, USA — September 07–11, 2014; DOI: 10.1145/2639108.2642910
dx.doi.org
Apple’s ResearchKit: smart data collection for the smartphone era?; Jennifer Jardine, Jonathan Fisher, Benjamin Carrick; Journal of the Royal Society of Medicine; Vol 108, Issue 8, pp. 294 - 296 (2015); DOI:10.1177/0141076815600673.
Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions. Boonstra TW, Nicholas J, Wong QJ, Shaw F, Townsend S, Christensen H (2018) J Med Internet Res 20(7):e10131. [2]
ResearchKit provides data collection in two ways: (1) through predefined macros for detection of active tasks, where each task state is extracted from information obtained through a combination of phone sensors (please refer to table at researchkit.org/docs/docs/ActiveTasks/ActiveTasks.html); and (2) through the iOS HealthKit and CoreMotion (https://developer.apple.com/documentation/coremotion developer.apple.com/documentation/coremotion) APIs.
