A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 64, TOP 7
Turn off MathJax
Article Contents
U. Lee, G. Jung, E.-Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–25, Jan. 2023.
Citation: U. Lee, G. Jung, E.-Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–25, Jan. 2023.

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

Funds:  This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (2020R1A4A1018774)
More Information
  • With the advent of digital therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship between DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.


  • loading
  • [1]
    O. Sverdlov, J. Van Dam, K. Hannesdottir, and T. Thornton-Wells, “Digital therapeutics: An integral component of digital innovation in drug development,” Clin. Pharmacol. Ther., vol. 104, no. 1, pp. 72–80, Jul. 2018. doi: 10.1002/cpt.1036
    Digital Therapeutics Alliance, “Digital therapeutics: Combining technology and evidence-based medicine to transform personalized patient care,” 2018. [Online]. Available: https://dtxalliance.org/wp-content/uploads/2021/01/DTA_DTx-Definition-and-Core-Principles.pdf, Accessed on: Mar. 6, 2022.
    C. C. Quinn, M. D. Shardell, M. L. Terrin, E. A. Barr, S. H. Ballew, and A. L. Gruber-Baldini, “Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control,” Diabetes Care, vol. 34, no. 9, pp. 1934–1942, Sep. 2011. doi: 10.2337/dc11-0366
    A. N. C. Campbell, E. V. Nunes, A. G. Matthews, M. Stitzer, G. M. Miele, D. Polsky, E. Turrigiano, S. Walters, E. A. McClure, T. L. Kyle, A. Wahle, V. Veldhuisen, B. Goldman, D. Babcock, Q. Stabile, T. Winhusen, and U. E. Ghitza, “Internet-delivered treatment for substance abuse: A multisite randomized controlled trial,” Am. J. Psychiatry, vol. 171, no. 6, pp. 683–690, Jun. 2014. doi: 10.1176/appi.ajp.2014.13081055
    S. C. Sepah, L. H. Jiang, R. J. Ellis, K. McDermott, and A. L. Peters, “Engagement and outcomes in a digital diabetes prevention program: 3-year update,” BMJ Open Diabetes Res. Care, vol. 5, no. 1, p. e000422, Sep. 2017. doi: 10.1136/bmjdrc-2017-000422
    S. H. Kollins, D. J. DeLoss, E. Cañadas, J. Lutz, R. L. Findling, R. S. E. Keefe, J. N. Epstein, A. J. Cutler, and S. V. Faraone, “A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): A randomised controlled trial,” Lancet Digit. Health, vol. 2, no. 4, pp. e168–e178, Apr. 2020. doi: 10.1016/S2589-7500(20)30017-0
    Grand View Research, “Digital therapeutics market size, share & trends analysis report by application (diabetes, obesity, CVD), by end user (patients, providers, payers, employers), by region, and segment forecasts, 2022 - 2030,” 2022. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/digital-therapeutics-market, Accessed on: Mar. 6, 2022.
    D. A. Greenwood, M. Gee, K. J. Fatkin, and M. Peeples, “A systematic review of reviews evaluating technology-enabled diabetes self-management education and support,” J. Diabetes Sci. Technol., vol. 11, no. 5, pp. 1015–1027, Sep. 2017. doi: 10.1177/1932296817713506
    R. J. Widmer, N. M. Collins, C. S. Collins, C. West, L. O. Lerman, and A. Lerman, “Digital health interventions for the prevention of cardiovascular disease: A systematic review and meta-analysis,” Mayo Clin. Proc., vol. 90, no. 4, pp. 469–480, Apr. 2015. doi: 10.1016/j.mayocp.2014.12.026
    C. Hollis, C. J. Falconer, J. L. Martin, C. Whittington, S. Stockton, C. Glazebrook, and E. B. Davies, “Annual research review: Digital health interventions for children and young people with mental health problems—A systematic and meta—Review,” J. Child Psychol. Psychiatry, vol. 58, no. 4, pp. 474–503, Apr. 2017. doi: 10.1111/jcpp.12663
    D. Liu, F. Yang, F. Xiong, and N. Gu, “The smart drug delivery system and its clinical potential,” Theranostics, vol. 6, no. 9, pp. 1306–1323, Jun. 2016. doi: 10.7150/thno.14858
    D. C. Mohr, S. M. Schueller, E. Montague, M. N. Burns, and Rashidi, “The behavioral intervention technology model: An integrated conceptual and technological framework for eHealth and mHealth interventions,” J. Med. Internet Res., vol. 16, no. 6, p. e146, Jun. 2014. doi: 10.2196/jmir.3077
    U. Lee, K. M. Han, H. Cho, K. Chung, H. Hong, S. J. Lee, Y. Noh, S. Park, and J. M. Carroll, “Intelligent positive computing with mobile, wearable, and IoT devices: Literature review and research directions,” Ad Hoc Netw., vol. 83, pp. 8–24, Feb. 2019. doi: 10.1016/j.adhoc.2018.08.021
    B. K. Wiederhold, “Data-driven digital therapeutics: The path forward,” Cyberpsychol. Behav. Soc. Netw., vol. 24, no. 10, pp. 631–632, Oct. 2021. doi: 10.1089/cyber.2021.29227.editorial
    W. Choi, S. Park, D. Kim, Y. K. Lim, and U. Lee, “Multi-stage receptivity model for mobile just-in-time health intervention,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 3, no. 2, p. 39, Jun. 2019.
    J. Zhou, Y. H. Zhou, B. C. Wang, and J. Y. Zang, “Human-cyber-physical systems (HCPSs) in the context of new-generation intelligent manufacturing,” Engineering, vol. 5, no. 4, pp. 624–636, Aug. 2019. doi: 10.1016/j.eng.2019.07.015
    J. C. Kvedar, A. L. Fogel, E. Elenko, and D. Zohar, “Digital medicine’s march on chronic disease,” Nat. Biotechnol., vol. 34, no. 3, pp. 239–246, Mar. 2016. doi: 10.1038/nbt.3495
    N. A. Patel and A. J. Butte, “Characteristics and challenges of the clinical pipeline of digital therapeutics,” NPJ Digit. Med., vol. 3, no. 1, p. 159, Dec. 2020. doi: 10.1038/s41746-020-00370-8
    T. Webb, J. Joseph, L. Yardley, and S. Michie, “Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy,” J. Med. Internet Res., vol. 12, no. 1, p. e4, Feb. 2010. doi: 10.2196/jmir.1376
    S. C. Sepah, L. H. Jiang, and A. L. Peters, “Long-term outcomes of a web-based diabetes prevention program: 2-year results of a single-arm longitudinal study,” J. Med. Internet Res., vol. 17, no. 4, p. e92, Apr. 2015. doi: 10.2196/jmir.4052
    E. Capobianco, “On digital therapeutics,” Front. Digit. Humanit., vol. 2, p. 6, Nov. 2015.
    C. Sepah, “The double-edged sword of digital health (the downside will surprise you),” 2017. [Online]. Available: https://medium.com/goactualize/the-double-edged-sword-of-digital-health-the-downside-will-surprise-you-8eaf5c3c84df, Accessed on: Mar. 6, 2022.
    World Health Organization, “WHO guideline: Recommendations on digital interventions for health system strengthening,” World Health Organization, Geneva, Switzerland, 2019.
    U.S. Food & Drug Administration, “What is digital health?,” 2020. [Online]. Available: https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health, Accessed on: Mar. 6, 2022.
    Digital Medicine Society, “Defining digital medicine,” 2021. [Online]. Available: https://www.dimesociety.org/about-us/defining-digital-medicine/, Accessed on: Mar. 6, 2022.
    Digital Therapeutics Alliance, “Digital health industry categorization,” 2019. [Online]. Available: https://dtxalliance.org/wp-content/uploads/2019/11/DTA_Digital-Industry-Categorization_Nov19.pdf, Accessed on: Mar. 6, 2022.
    T. Aungst and J. Murdock, “What's the difference between digital health, digital medicine, and digital therapeutics?,” 2021. [Online]. Available: https://www.goodrx.com/healthcare-access/telehealth/digital-therapeutics-vs-digital-medicine-vs-digital-health, Accessed on: Mar. 6, 2022.
    G. E. Iyawa, M. Herselman, and A. Botha, “Digital health innovation ecosystems: From systematic literature review to conceptual framework,” Procedia Comput. Sci., vol. 100, pp. 244–252, Oct. 2016. doi: 10.1016/j.procs.2016.09.149
    U.S. Food & Drug Administration, “Enforcement policy for non-invasive remote monitoring devices used to support patient monitoring during the coronavirus disease 2019 (COVID-19) public health emergency (revised),” 2020. [Online]. Available: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/enforcement-policy-non-invasive-remote-monitoring-devices-used-support-patient-monitoring-during, Accessed on: Mar. 6, 2022.
    Digital Therapeutics Alliance, “Digital therapeutics: Reducing rural health inequalities,” 2020. [Online]. Available: https://dtxalliance.org/wp-content/uploads/2021/01/DTA_Rural-Health_r13_110220.pdf, Accessed on: Mar. 6, 2022.
    D. Morrison, S. Wyke, K. Agur, E. J. Cameron, R. I. Docking, A. M. MacKenzie, A. McConnachie, V. Raghuvir, N. C. Thomson, and F. S. Mair, “Digital asthma self-management interventions: A systematic review,” J. Med. Internet Res., vol. 16, no. 2, p. e51, Feb. 2014. doi: 10.2196/jmir.2814
    A. Khaylis, T. Yiaslas, J. Bergstrom, and C. Gore-Felton, “A review of efficacious technology-based weight-loss interventions: Five key components,” Telemed. J. E. Health, vol. 16, no. 9, pp. 931–938, Nov. 2010. doi: 10.1089/tmj.2010.0065
    K. Ghorai, S. Akter, F. Khatun, and Ray, “mHealth for smoking cessation programs: A systematic review,” J. Pers. Med., vol. 4, no. 3, pp. 412–423, Jul. 2014. doi: 10.3390/jpm4030412
    M. Aapro, Bossi, A. Dasari, L. Fallowfield, Gascón, M. Geller, K. Jordan, J. Kim, K. Martin, and S. Porzig, “Digital health for optimal supportive care in oncology: Benefits, limits, and future perspectives,” Support. Care Cancer, vol. 28, no. 10, pp. 4589–4612, Oct. 2020. doi: 10.1007/s00520-020-05539-1
    M. J. Choi, H. Kim, H. W. Nah, and D. W. Kang, “Digital therapeutics: Emerging new therapy for neurologic deficits after stroke,” J. Stroke, vol. 21, no. 3, pp. 242–258, Sep. 2019. doi: 10.5853/jos.2019.01963
    G. Abbadessa, F. Brigo, M. Clerico, S. De Mercanti, F. Trojsi, G. Tedeschi, S. Bonavita, and L. Lavorgna, “Digital therapeutics in neurology,” J. Neurol., vol. 269, no. 3, pp. 1209–1224, May 2022. doi: 10.1007/s00415-021-10608-4
    J. M. Simoni, B. A. Kutner, and K. J. Horvath, “Opportunities and challenges of digital technology for HIV treatment and prevention,” Curr. HIV/AIDS Rep., vol. 12, no. 4, pp. 437–440, Sep. 2015. doi: 10.1007/s11904-015-0289-1
    C. E. Canan, M. E. Waselewski, A. L. D. Waldman, G. Reynolds, T. E. Flickinger, W. F. Cohn, K. Ingersoll, and R. Dillingham, “Long term impact of PositiveLinks: Clinic-deployed mobile technology to improve engagement with HIV care,” PLoS One, vol. 15, no. 1, p. e0226870, Jan. 2020. doi: 10.1371/journal.pone.0226870
    M. Aitken, B. Clancy, and D. Nass, “The growing value of digital health: Evidence and impact on human health and the healthcare system,” 2017. [Online]. Available: https://www.iqvia.com/insights/the-iqvia-institute/reports/the-growing-value-of-digital-health, Accessed on: Mar. 6, 2022.
    B. Meyer, T. Berger, F. Caspar, C. Beevers, G. Andersson, and M. Weiss, “Effectiveness of a novel integrative online treatment for depression (Deprexis): Randomized controlled trial,” J. Med. Internet Res., vol. 11, no. 2, p. e15, May 2009. doi: 10.2196/jmir.1151
    K. K. Fitzpatrick, A. Darcy, and M. Vierhile, “Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial,” JMIR Ment. Health, vol. 4, no. 2, p. e19, Jun. 2017. doi: 10.2196/mental.7785
    L. M. Ritterband, F. Thorndike, L. A. Gonder-Frederick, J. C. Magee, E. T. Bailey, D. K. Saylor, and C. M. Morin, “Efficacy of an Internet-based behavioral intervention for adults with insomnia,” Arch. Gen. Psychiatry, vol. 66, no. 7, pp. 692–698, Jul. 2009. doi: 10.1001/archgenpsychiatry.2009.66
    D. F. Tolin, B. McGrath, L. R. Hale, D. N. Weiner, and R. Gueorguieva, “A multisite benchmarking trial of capnometry guided respiratory intervention for panic disorder in naturalistic treatment settings,” Appl. Psychophysiol. Biofeedback, vol. 42, no. 1, pp. 51–58, Mar. 2017. doi: 10.1007/s10484-017-9354-4
    NightWare, “NightWare improves sleep in patients with nightmare disorder,” 2021. [Online]. Available: https://nightware.com/, Accessed on: Mar. 6, 2022.
    D. R. Christensen, R. D. Landes, L. Jackson, L. A. Marsch, M. J. Mancino, M. Chopra, and W. K. Bickel, “Adding an internet-delivered treatment to an efficacious treatment package for opioid dependence,” J. Consult. Clin. Psychol., vol. 82, no. 6, pp. 964–972, Dec. 2014. doi: 10.1037/a0037496
    R. K. Merchant, R. Inamdar, and R. C. Quade, “Effectiveness of population health management using the propeller health asthma platform: A randomized clinical trial,” J. Allergy Clin. Immunol. Pract., vol. 4, no. 3, pp. 455–463, May/Jun. 2016. doi: 10.1016/j.jaip.2015.11.022
    NuvoAir, “Respiratory care for COPD and asthma,” 2021. [Online]. Available: https://www.nuvoair.com/, Accessed on: Mar. 6, 2022.
    Oleena, “Oleena registered digital therapeutic for cancer symptom management,” 2020. [Online]. Available: https://oleena.com/, Accessed on: Mar. 6, 2022.
    Insulia, “Insulia,” 2017 [Online]. Available: https://insulia.com/, Accessed on: Mar. 6, 2022.
    M. F. Alwashmi, G. Mugford, W. Abu-Ashour, and M. Nuccio, “A digital diabetes prevention program (transform) for adults with prediabetes: Secondary analysis,” JMIR Diabetes, vol. 4, no. 3, p. e13904, Jul. 2019. doi: 10.2196/13904
    S. J. Athinarayanan, R. N. Adams, S. J. Hallberg, A. L. McKenzie, N. H. Bhanpuri, W. W. Campbell, J. S. Volek, S. D. Phinney, and J. McCarter, “Long-term effects of a novel continuous remote care intervention including nutritional ketosis for the management of type 2 diabetes: A 2-year non-randomized clinical trial,” Front. Endocrinol., vol. 10, p. 348, Jun. 2019. doi: 10.3389/fendo.2019.00348
    DarioHealth, “DarioHealth: Digital health solutions for chronic conditions,” 2021. [Online]. Available: https://www.dariohealth.com/, Accessed on: Mar. 6, 2022.
    J. Downing, J. Bollyky, and J. Schneider, “Use of a connected glucose meter and certified diabetes educator coaching to decrease the likelihood of abnormal blood glucose excursions: The livongo for diabetes program,” J. Med. Internet Res., vol. 19, no. 7, p. e234, Jul. 2017. doi: 10.2196/jmir.6659
    Constant Therapy Health, “Constant therapy,” 2020. [Online]. Available: https://constanttherapyhealth.com/, Accessed on: Mar. 6, 2022.
    NovaVision, “NeuroEyeCoach,” 2021. [Online]. Available: https://novavision.com/neuroeyecoach/, Accessed on: Mar. 6, 2022.
    BTS Bioengineering, “Nirvana-Virtual reality applied to neuromotor rehabilitation,” 2021. [Online]. Available: https://www.btsbioengineering.com/nirvana/, Accessed on: Mar. 6, 2022.
    IMDRF SaMD Working Group, “Software as a medical device (SaMD): Key definitions,” 2013. [Online]. Available: https://www.imdrf.org/sites/default/files/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf, Accessed on: Mar. 6, 2022.
    U.S. Food & Drug Administration, “Premarket notification 510(k),” 2020. [Online]. Available: https://www.fda.gov/medical-devices/premarket-submissions/premarket-notification-510k, Accessed on: Mar. 6, 2022.
    U.S. Food & Drug Administration, “De novo classification request,” 2019. [Online]. Available: https://www.fda.gov/medical-devices/premarket-submissions/de-novo-classification-request, Accessed on: Mar. 6, 2022.
    J. L. Johnston, S. S. Dhruva, J. S. Ross, and V. K. Rathi, “Early experience with the FDA’s breakthrough devices program,” Nat. Biotechnol., vol. 38, no. 8, pp. 933–938, Jul. 2020. doi: 10.1038/s41587-020-0636-7
    U.S. Food & Drug Administration, “Digital health software precertification (pre-cert) program,” 2019. [Online]. Available: https://www.fda.gov/medical-devices/digital-health/digital-health-software-precertification-pre-cert-program, Accessed on: Mar. 6, 2022.
    U.S. Food & Drug Administration, “De novo classification request,” 2016. [Online]. Available: https://www.fda.gov/medical-devices/premarket-submissions-selecting-and-preparing-correct-submission/de-novo-classification-request, Accessed on: Mar. 6, 2022.
    M. Caffrey, “Digital health provider noom wins full CDC recognition for mobile, online applications,” 2017. [Online]. Available: https://www.ajmc.com/newsroom/digital-health-provider-noom-wins-full-cdc-recognition-for-mobile-online-applications, Accessed on: Mar. 6, 2022.
    M. Caffrey, “Omada health receives full CDC recognition for diabetes prevention,” 2018. [Online]. Available: https://www.ajmc.com/newsroom/omada-health-receives-full-cdc-recognition-for-diabetes-prevention, Accessed on: Mar. 6, 2022.
    A. Michaelides, C. Raby, M. Wood, K. Farr, and T. Toro-Ramos, “Weight loss efficacy of a novel mobile diabetes prevention program delivery platform with human coaching,” BMJ Open Diabetes Res. Care, vol. 4, no. 1, p. e000264, Sep. 2016. doi: 10.1136/bmjdrc-2016-000264
    D. H. Gustafson, F. M. McTavish, M. Y. Chih, A. K. Atwood, R. A. Johnson, M. G. Boyle, M. S. Levy, H. Driscoll, S. M. Chisholm, L. Dillenburg, A. Isham, and D. Shah, “A smartphone application to support recovery from alcoholism: A randomized clinical trial,” JAMA Psychiatry, vol. 71, no. 5, pp. 566–572, May 2014. doi: 10.1001/jamapsychiatry.2013.4642
    F. Naughton, S. Hopewell, N. Lathia, R. Schalbroeck, C. Brown, C. Mascolo, A. McEwen, and S. Sutton, “A context-sensing mobile phone app (Q sense) for smoking cessation: A mixed-methods study,” JMIR Mhealth Uhealth, vol. 4, no. 3, p. e106, Sep. 2016. doi: 10.2196/mhealth.5787
    S. Goldstein, B. C. Evans, D. Flack, A. Juarascio, S. Manasse, F. Q. Zhang, and E. M. Forman, “Return of the JITAI: Applying a just-in-time adaptive intervention framework to the development of m-health solutions for addictive behaviors,” Int. J. Behav. Med., vol. 24, no. 5, pp. 673–682, Oct. 2017. doi: 10.1007/s12529-016-9627-y
    DarioHealth, “Diabetes management with blood glucose monitoring system,” 2021. [Online]. Available: https://www.dariohealth.com/solutions/diabetes-management/, Accessed on: Mar. 6, 2022.
    D. Muoio, “Omada health expands digital platform with type 2, hypertension programs, new features,” 2018. [Online]. Available: https://www.mobihealthnews.com/content/omada-health-expands-digital-platform-type-2-hypertension-programs-new-features, Accessed on: Mar. 6, 2022.
    Livongo Public Relations, “Livongo launches hypertension,” 2018 [Online]. Available: https://www.mylivongo.com/Livongo-Launches-Hypertension/, Accessed on: Mar. 6, 2022.
    H. W. Rodbard, “Treating hypertension reduces CV risks in diabetes care,” 2019. [Online]. Available: https://www.healio.com/news/cardiology/20190807/treating-hypertension-reduces-cv-risks-in-diabetes-care, Accessed on: Mar. 6, 2022.
    B. M. Y. Cheung and C. Li, “Diabetes and hypertension: Is there a common metabolic pathway?” Curr. Atheroscler. Rep., vol. 14, no. 2, pp. 160–166, Apr. 2012. doi: 10.1007/s11883-012-0227-2
    I. Nahum-Shani, S. N. Smith, B. J. Spring, L. M. Collins, K. Witkiewitz, A. Tewari, and S. A. Murphy, “Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support,” Ann. Behav. Med., vol. 52, no. 6, pp. 446–462, May 2018. doi: 10.1007/s12160-016-9830-8
    J. G. Thomas and D. S. Bond, “Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization,” Health Psychol., vol. 34S, pp. 1261–1267, Dec. 2015.
    M. S. Businelle, M a, D. E. Kendzor, S. G. Frank, D. J. Vidrine, and D. W. Wetter, “An ecological momentary intervention for smoking cessation: Evaluation of feasibility and effectiveness,” J. Med. Internet Res., vol. 18, no. 12, p. e321, Dec. 2016. doi: 10.2196/jmir.6058
    E. T. Hébert, C. K. Ra, A. C. Alexander, A. Helt, R. Moisiuc, D. E. Kendzor, D. J. Vidrine, R. K. Funk-Lawler, and M. S. Businelle, “A mobile just-in-time adaptive intervention for smoking cessation: Pilot randomized controlled trial,” J. Med. Internet Res., vol. 22, no. 3, p. e16907, Mar. 2020. doi: 10.2196/16907
    B. Spring, “Sense2stop: Mobile sensor data to knowledge,” 2017. [Online]. Available: https://clinicaltrials.gov/ct2/show/study/NCT03184389, Accessed on: Mar. 6, 2022.
    L. Yardley, B. J. Spring, H. Riper, L. G. Morrison, D. H. Crane, K. Curtis, G. C. Merchant, F. Naughton, and A. Blandford, “Understanding and promoting effective engagement with digital behavior change interventions,” Am. J. Prev. Med., vol. 51, no. 5, pp. 833–842, Nov. 2016. doi: 10.1016/j.amepre.2016.06.015
    N. Alshurafa, J. Jain, R. Alharbi, G. Iakovlev, B. Spring, and A. Pfammatter, “Is more always better?: Discovering incentivized mHealth intervention engagement related to health behavior trends” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 2, no. 4, p. 153, Dec. 2018.
    B. J. Fogg, “Creating persuasive technologies: An eight-step design process,” in Proc. 4th Int. Conf. Persuasive Technology, Claremont, USA, 2009, pp. 44.
    L. M. Ritterband, F. Thorndike, D. J. Cox, B. Kovatchev, and L. A. Gonder-Frederick, “A behavior change model for internet interventions,” Ann. Behav. Med., vol. 38, no. 1, pp. 18–27, Aug. 2009. doi: 10.1007/s12160-009-9133-4
    A. Schmidt, M. Beigl, and H. W. Gellersen, “There is more to context than location,” Comput. Graph., vol. 23, no. 6, pp. 893–901, Dec. 1999. doi: 10.1016/S0097-8493(99)00120-X
    B. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” in Proc. 1st Workshop on Mobile Computing Systems and Applications, Santa Cruz, USA, 1994, pp. 85–90.
    ArisGlobal, “Cloud based end-to-end drug development platform,” 2020. [Online]. Available: https://www.arisglobal.com/, Accessed on: Mar. 6, 2022.
    Clario, “Endpoint technology services for clinical trial management,” 2021. [Online]. Available: https://www.bioclinica.com/, Accessed on: Mar. 6, 2022.
    Advarra, “Advarra: Enabling safer, smarter, faster clinical research,” 2018. [Online]. Available: https://www.advarra.com/, Accessed on: Mar. 6, 2022.
    DSG, “DSG Clinical trial software and data management solutions,” 2022. [Online]. Available: https://dsg-us.com/index.html, Accessed on: Mar. 6, 2022.
    Mednet, “eClinical platform for clinical trials-mednet,” 2019. [Online]. Available: https://www.mednetsolutions.com/, Accessed on: Mar. 6, 2022.
    Medidata, “Medidata-unified life science platform,” 2021. [Online]. Available: https://www.medidata.com/, Accessed on: Mar. 6, 2022.
    Parexel Int. Corporation, “Clinical research organization (CRO) & biopharmaceutical services,” 2020. [Online]. Available: https://www.parexel.com/, Accessed on: Mar. 6, 2022.
    SimpleTrials, “SimpleTrials-clinical trial management system,” 2020. [Online]. Available: https://www.simpletrials.com/, Accessed on: Mar. 6, 2022.
    Veeva, “Veeva systems,” 2019. [Online]. Available: https://www.veeva.com/, Accessed on: Mar. 6, 2022.
    Global Information, Inc., “Clinical trial management systems (CTMS),” 2019. [Online]. Available: https://m.giikorea.co.kr/report/go240200-clinicaltrial-management-systems-ctms.html, Accessed on: Mar. 6, 2022.
    Y. R. Park, Y. J. Yoon, H. Koo, S. Yoo, C. M. Choi, S. H. Beck, and T. W. Kim, “Utilization of a clinical trial management system for the whole clinical trial process as an integrated database: System development,” J. Med. Internet Res., vol. 20, no. 4, p. e103, Apr. 2018. doi: 10.2196/jmir.9312
    IBM, “IBM Watson health-AI healthcare solutions,” 2022. [Online]. Available: https://www.ibm.com/watson-health, Accessed on: Mar. 6, 2022.
    Oracle, “Siebel clinical trial management system-Oracle,” 2020. [Online]. Available: https://www.oracle.com/industries/life-sciences/clinical-trial-management.html, Accessed on: Mar. 6, 2022.
    PRA Prism, “PRA prism,” 2022. [Online]. Available: https://www.nextrials.com/, Accessed on: Mar. 6, 2022.
    Winchester Business Systems, “Winchester business systems-moving science forward,” 2020. [Online]. Available: https://wbsnet.com/, Accessed on: Mar. 6, 2022.
    M. Rose, “Open data kit,” 2013. [Online]. Available: https://opendatakit.org/, Accessed on: Mar. 6, 2022.
    D. Ferreira, V. Kostakos, and A. K. Dey, “AWARE: Mobile context instrumentation framework,” Front. ICT, vol. 2, p. 6, Apr. 2015.
    S. M. Hossain, T. Hnat, N. Saleheen, N. J. Nasrin, J. Noor, B. J. Ho, T. Condie, M. Srivastava, and S. Kumar, “mCerebrum: A mobile sensing software platform for development and validation of digital biomarkers and interventions,” in Proc. 15th ACM Conf. Embedded Network Sensor Systems, Delft, Netherlands, 2017, pp. 7.
    M. Rabbi, M. Philyaw-Kotov, J. Lee, A. Mansour, L. Dent, X. L. Wang, R. Cunningham, E. Bonar, I. Nahum-Shani, P. Klasnja, M. Walton, and S. Murphy, “SARA: A mobile app to engage users in health data collection,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing and Proc. ACM Int. Symp. Wearable Computers, Hawaii, Maui, 2017, pp. 781–789.
    R. Eckhoff, N. Kizakevich, V. Bakalov, Y. Y. Zhang, S. Bryant, and M. A. Hobbs, “A platform to build mobile health apps: The personal health intervention toolkit (PHIT),” JMIR MHealth Uhealth, vol. 3, no. 2, p. e46, Jun. 2015. doi: 10.2196/mhealth.4202
    N. Kizakevich, R. Hubal, J. Brown, J. Lyden, J. Spira, R. Eckhoff, Y. Y. Zhang, S. Bryant, and G. Munoz, “PHIT for duty, a mobile approach for psychological health intervention,” Stud. Health Technol. Inform., vol. 181, pp. 268–272, Sep. 2012.
    N. Kizakevich, R. Eckhoff, J. Brown, S. J. Tueller, B. Weimer, S. Bell, A. Weeks, L. L. Hourani, J. L. Spira, and L. A. King, “PHIT for duty, a mobile application for stress reduction, sleep improvement, and alcohol moderation,” Mil. Med., vol. 183, no. suppl_1, pp. 353–363, Mar. 2018. doi: 10.1093/milmed/usx157
    J. Nakazawa, H. Tokuda, and T. Yonezawa, “Sensorizer: An architecture for regenerating cyber physical data streams from the web,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing and Proc. ACM Int. Symp. Wearable Computers, Osaka, Japan, 2015, pp. 1599–1606.
    J. B. Sun, S. Park, G. Jung, Y. Jeong, U. Lee, K. M. Chung, C. Lee, H. Kim, S. Ahn, A. Khandoker, and L. Hadjileontiadis, “BeActive: Encouraging physical activities with just-in-time health intervention and micro financial incentives,” in Proc. Symp. Emerging Research from Asia and on Asian Contexts and Cultures, Honolulu, USA, 2020, pp. 17–20.
    N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell, “A survey of mobile phone sensing,” IEEE Commun. Mag., vol. 48, no. 9, pp. 140–150, Sep. 2010. doi: 10.1109/MCOM.2010.5560598
    B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, and G. Jacucci, “The psychophysiology primer: A guide to methods and a broad review with a focus on human-computer interaction,” Found. Trends® Hum. Comput. Interact., vol. 9, no. 3–4, pp. 151–308, Nov. 2016.
    M. L. Lee and A. K. Dey, “Reflecting on pills and phone use: Supporting awareness of functional abilities for older adults,” in Proc. SIGCHI Conf. Human Factors in Computing Systems, Vancouver, Canada, 2011, pp. 2095–2104.
    J. Lee, E. Walker, W. Burleson, M. Kay, M. Buman, and E. B. Hekler, “Self-experimentation for behavior change: Design and formative evaluation of two approaches,” in Proc. CHI Conf. Human Factors in Computing Systems, Denver, USA, 2017, pp. 6837–6849.
    S. Taki, S. Lymer, C. G. Russell, K. Campbell, R. Laws, K. L. Ong, R. Elliott, and E. Denney-Wilson, “Assessing user engagement of an mHealth intervention: Development and implementation of the growing healthy app engagement index,” JMIR Mhealth Uhealth, vol. 5, no. 6, p. e89, Jun. 2017. doi: 10.2196/mhealth.7236
    J. Brooke, “SUS: A 'quick and dirty' usability scale,” in Usability Evaluation in Industry, P. W. Jordan, B. Thomas, I. L. McClelland, B. Weerdmeester, Eds. London, UK: CRC Press, 1996, pp. 189–194.
    A. M. Lund, “Measuring usability with the use questionnaire,” Usability Interface, vol. 8, no. 2, pp. 3–6, Jan. 2001.
    H. L. O'Brien, Cairns, and M. Hall, “A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form,” Int. J. Hum. Comput. Stud., vol. 112, pp. 28–39, Apr. 2018. doi: 10.1016/j.ijhcs.2018.01.004
    S. R. Stoyanov, L. Hides, D. J. Kavanagh, O. Zelenko, D. Tjondronegoro, and M. Mani, “Mobile app rating scale: A new tool for assessing the quality of health mobile apps,” JMIR Mhealth Uhealth, vol. 3, no. 1, p. e27, Mar. 2015. doi: 10.2196/mhealth.3422
    M. Brhel, H. Meth, A. Maedche, and K. Werder, “Exploring principles of user-centered agile software development: A literature review,” Inf. Softw. Technol., vol. 61, pp. 163–181, May 2015. doi: 10.1016/j.infsof.2015.01.004
    D. Ben-Zeev, E. A. Scherer, J. D. Gottlieb, A. J. Rotondi, M. F. Brunette, E. D. Achtyes, K. T. Mueser, S. Gingerich, C. J. Brenner, M. Begale, D. C Mohr, N. Schooler, Marcy, D. G. Robinson, and J. M. Kane, “mHealth for schizophrenia: Patient engagement with a mobile phone intervention following hospital discharge,” JMIR Ment. Health, vol. 3, no. 3, p. e34, Jul. 2016. doi: 10.2196/mental.6348
    R. M. Martey, K. Kenski, J. Folkestad, L. Feldman, E. Gordis, A. Shaw, J. Stromer-Galley, B. Clegg, H. Zhang, N. Kaufman, A. N. Rabkin, S. Shaikh, and T. Strzalkowski, “Measuring game engagement: Multiple methods and construct complexity,” Simul. Gaming, vol. 45, no. 4–5, pp. 528–547, Nov. 2014.
    E. Di Lascio, S. Gashi, and S. Santini, “Unobtrusive assessment of students' emotional engagement during lectures using electrodermal activity sensors,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 2, no. 3, p. 103, Sep. 2018.
    A. Mathur, N. D. Lane, and F. Kawsar, “Engagement-aware computing: Modelling user engagement from mobile contexts,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Germany, Heidelberg, 2016, pp. 622–633.
    M. Pielot, B. Cardoso, K. Katevas, J. Serrà, A. Matic, and N. Oliver, “Beyond interruptibility: Predicting opportune moments to engage mobile phone users,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 1, no. 3, p. 91, Sep. 2017.
    M. Ghahramani, M. C. Zhou, and G. Wang, “Urban sensing based on mobile phone data: Approaches, applications, and challenges,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 627–637, May 2020. doi: 10.1109/JAS.2020.1003120
    S. Imran, T. Mahmood, A. Morshed, and T. Sellis, “Big data analytics in healthcare-a systematic literature review and roadmap for practical implementation,” IEEE/CAA J. Automa. Sinica, vol. 8, no. 1, pp. 1–22, Jan. 2021. doi: 10.1109/JAS.2020.1003384
    J. Firth, J. Torous, J. Nicholas, R. Carney, A. Pratap, S. Rosenbaum, and J. Sarris, “The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials,” World Psychiatry, vol. 16, no. 3, pp. 287–298, Oct. 2017. doi: 10.1002/wps.20472
    J. Firth, J. Torous, J. Nicholas, R. Carney, S. Rosenbaum, and J. Sarris, “Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials” J. Affect. Disord., vol. 218, pp. 15–22, Aug. 2017. doi: 10.1016/j.jad.2017.04.046
    M. X. Cui, X. Y. Wu, J. F. Mao, X. Wang, and M. Nie, “T2DM self-management via smartphone applications: A systematic review and meta-analysis,” PLoS One, vol. 11, no. 11, p. e0166718, Nov. 2016. doi: 10.1371/journal.pone.0166718
    Klasnja, E. B. Hekler, S. Shiffman, A. Boruvka, D. Almirall, A. Tewari, and S. A. Murphy, “Microrandomized trials: An experimental design for developing just-in-time adaptive interventions,” Health Psychol., vol. 34S, pp. 1220–1228, Dec. 2015.
    Li ao, Klasnja, A. Tewari, and S. A. Murphy, “Sample size calculations for micro-randomized trials in mHealth,” Statist. Med., vol. 35, no. 12, pp. 1944–1971, May 2016. doi: 10.1002/sim.6847
    W. Dempsey, Li ao, Klasnja, I. Nahum-Shani, and S. A. Murphy, “Randomised trials for the fitbit generation,” Significance, vol. 12, no. 6, pp. 20–23, Dec. 2015. doi: 10.1111/j.1740-9713.2015.00863.x
    E. Littlewood, A. Duarte, C. Hewitt, et al., “A randomised controlled trial of computerised cognitive behaviour therapy for the treatment of depression in primary care: The Randomised Evaluation of the Effectiveness and Acceptability of Computerised Therapy (REEACT) trial,” Health Technol. Assess., vol. 19, no. 101, Dec. 2015.
    M. J. Park and H. S. Kim, “Evaluation of mobile phone and internet intervention on waist circumference and blood pressure in post-menopausal women with abdominal obesity,” Int. J. Med. Inform., vol. 81, no. 6, pp. 388–394, Jun. 2012. doi: 10.1016/j.ijmedinf.2011.12.011
    S. Nundy, J. J. Dick, C. H. Chou, R. S. Nocon, M. H. Chin, and M. E. Peek, “Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants,” Health Aff., vol. 33, no. 2, pp. 265–272, Feb. 2014. doi: 10.1377/hlthaff.2013.0589
    F. Tsapeli and M. Musolesi, “Investigating causality in human behavior from smartphone sensor data: A quasi-experimental approach,” EPJ Data Sci., vol. 4, no. 1, p. 24, Dec. 2015. doi: 10.1140/epjds/s13688-015-0061-1
    A. Mehrotra, F. Tsapeli, R. Hendley, and M. Musolesi, “MyTraces: Investigating correlation and causation between users' emotional states and mobile phone interaction,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 1, no. 3, p. 83, Sep. 2017.
    J. Dallery, R. N. Cassidy, and B. R. Raiff, “Single-case experimental designs to evaluate novel technology-based health interventions,” J. Med. Internet Res., vol. 15, no. 2, p. e22, Feb. 2013. doi: 10.2196/jmir.2227
    S. McDonald, F. Quinn, R. Vieira, N. O'Brien, M. White, D. W. Johnston, and F. F. Sniehotta, “The state of the art and future opportunities for using longitudinal n-of-1 methods in health behaviour research: A systematic literature overview,” Health Psychol. Rev., vol. 11, no. 4, pp. 307–323, Dec. 2017. doi: 10.1080/17437199.2017.1316672
    S. H. Jain, B. W. Powers, J. B. Hawkins, and J. S. Brownstein, “The digital phenotype,” Nat. Biotechnol., vol. 33, no. 5, pp. 462–463, May 2015. doi: 10.1038/nbt.3223
    G. M. Harari, N. D. Lane, R. Wang, B. S. Crosier, A. T. Campbell, and S. D. Gosling, “Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges,” Perspect. Psychol. Sci., vol. 11, no. 6, pp. 838–854, Nov. 2016. doi: 10.1177/1745691616650285
    T. R. Insel, “Digital phenotyping: Technology for a new science of behavior,” JAMA, vol. 318, no. 13, pp. 1215–1216, 2017. doi: 10.1001/jama.2017.11295
    A. K. Dey, “Understanding and using context,” Pers. Ubiquitous Comput., vol. 5, no. 1, pp. 4–7, Feb. 2001. doi: 10.1007/s007790170019
    A. Schmidt, M. Beigl, and H. W. Gellersen, “There is more to context than location,” Comput. Graph., vol. 23, no. 6, pp. 893–901, Dec. 1999.
    D. C. Mohr, M. Zhang, and S. M. Schueller, “Personal sensing: Understanding mental health using ubiquitous sensors and machine learning,” Ann. Rev. Clin. Psychol., vol. 13, pp. 23–47, May 2017. doi: 10.1146/annurev-clinpsy-032816-044949
    S. van Dantzig, G. Geleijnse, and A. T. van Halteren, “Toward a persuasive mobile application to reduce sedentary behavior,” Pers. Ubiquit. Comput., vol. 17, no. 6, pp. 1237–1246, Jul. 2013. doi: 10.1007/s00779-012-0588-0
    S. Abdullah, M. Matthews, E. L. Murnane, G. Gay, and T. Choudhury, “Towards circadian computing: “early to bed and early to rise” makes some of us unhealthy and sleep deprived,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Washington, Seattle, 2014, pp. 673–684.
    E. Thomaz, I. Essa, and G. D. Abowd, “A practical approach for recognizing eating moments with wrist-mounted inertial sensing,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Osaka, Japan, 2015, pp. 1029–1040.
    R. Alam, J. Q. Gong, M. Hanson, A. Bankole, M. Anderson, T. Smith-Jackson, and J. Lach, “Motion biomarkers for early detection of dementia-related agitation,” in Proc. 1st Workshop on Digital Biomarkers, Niagara Falls, USA, 2017, pp. 15–20.
    L. Canzian and M. Musolesi, “Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Osaka, Japan, 2015, pp. 1293–1304.
    Y. Huang, J. Q. Gong, M. Rucker, P. Chow, K. Fua, M. S. Gerber, B. Teachman, and L. E. Barnes, “Discovery of behavioral markers of social anxiety from smartphone sensor data,” in Proc. 1st Workshop on Digital Biomarkers, Niagara Falls, USA, 2017, pp. 9–14.
    G. Mark, V. M. Gonzalez, and J. Harris, “No task left behind?: Examining the nature of fragmented work,” in Proc. SIGCHI Conf. Human Factors in Computing Systems, Portland, USA, 2005, pp. 321–330.
    P. D. Adamczyk and B. P. Bailey, “If not now, when?: The effects of interruption at different moments within task execution,” in Proc. SIGCHI Conf. Human Factors in Computing Systems, Vienna, Austria, 2004, pp. 271–278.
    J. Ho and S. S. Intille, “Using context-aware computing to reduce the perceived burden of interruptions from mobile devices,” in Proc. SIGCHI Conf. Human Factors in Computing Systems, Bremen, Germany, 2005, pp. 909–918.
    W. Holland, “Statistics and causal inference,” J. Am. Stat. Assoc., vol. 81, no. 396, pp. 945–960, Dec. 1986. doi: 10.1080/01621459.1986.10478354
    D. B. Rubin, “Causal inference using potential outcomes: Design, modeling, decisions,” J. Am. Stat. Assoc., vol. 100, no. 469, pp. 322–331, Feb. 2005. doi: 10.1198/016214504000001880
    A. Boruvka, D. Almirall, K. Witkiewitz, and S. A. Murphy, “Assessing time-varying causal effect moderation in mobile health,” J. Am. Stat. Assoc., vol. 113, no. 523, pp. 1112–1121, Oct. 2018. doi: 10.1080/01621459.2017.1305274
    T. C. Qian, Klasnja, and S. A. Murphy, “Linear mixed models with endogenous covariates: Modeling sequential treatment effects with application to a mobile health study,” Stat. Sci., vol. 35, no. 3, pp. 375–390, Aug. 2020.
    T. C. Qian, H. Yoo, Klasnja, D. Almirall, and S. A. Murphy, “Estimating time-varying causal excursion effects in mobile health with binary outcomes,” Biometrika, vol. 108, no. 3, pp. 507–527, Sep. 2021. doi: 10.1093/biomet/asaa070
    W. Van den Noortgate and Onghena, “The aggregation of single-case results using hierarchical linear models,” Behav. Anal. Today, vol. 8, no. 2, pp. 196–209, Jan. 2007. doi: 10.1037/h0100613
    E. J. Daza, “Causal analysis of self-tracked time series data using a counterfactual framework for n-of-1 trials,” Methods Inf. Med., vol. 57, no. 1, pp. e10–e21, Feb. 2018.
    M. A. Hernàn and J. M. Robins, Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, 2020.
    R. Rosenbaum and D. B. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika, vol. 70, no. 1, pp. 41–55, Apr. 1983. doi: 10.1093/biomet/70.1.41
    S. Sizemore and R. Alkurdi, “Matching methods for causal inference: A machine learning update,” 2019. [Online]. Available: https://humboldt-wi.github.io/blog/research/applied_predictive_modeling_19/matching_methods/, Accessed on: Mar. 6, 2022.
    J. M. Robins and M. A. Hernan, “Estimation of the causal effects of time-varying exposures,” in Longitudinal Data Analysis, G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs, Eds. New York, USA: Chapman & Hall/CRC, 2008, pp. 553–597.
    J. Pearl, Causality. 2nd ed. Cambridge, USA: Cambridge University Press, 2009.
    C. W. J. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica, vol. 37, no. 3, pp. 424–438, Aug. 1969. doi: 10.2307/1912791
    G. Sugihara, R. May, H. Ye, C. H. Hsieh, E. Deyle, M. Fogarty, and S. Munch, “Detecting causality in complex ecosystems,” Science, vol. 338, no. 6106, pp. 496–500, Sep. 2012. doi: 10.1126/science.1227079
    J. M. Twenge, G. N. Martin, and W. K. Campbell, “Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology,” Emotion, vol. 18, no. 6, pp. 765–780, Sep. 2018. doi: 10.1037/emo0000403
    Z. Sarsenbayeva, G. Marini, N. van Berkel, C. Luo, W. W. Jiang, K. N. Yang, G. Wadley, T. Dingler, V. Kostakos, and J. Goncalves, “Does smartphone use drive our emotions or vice versa? A causal analysis,” in Proc. CHI Conf. Human Factors in Computing Systems, Honolulu, USA, 2020, pp. 1–15.
    A. E. Yuan and W. Shou, “Data-driven causal analysis of observational time series in ecology,” bioRxiv, 2021.
    T. Munzner, Visualization Analysis and Design. New York, USA: CRC Press, 2014.
    K. A. Cook and J. J. Thomas, “Illuminating the path: The research and development agenda for visual analytics,” 2005. [Online]. Available: https://www.osti.gov/biblio/912515, Accessed on: Mar. 6, 2022.
    W. Aigner, S. Miksch, W. Muller, H. Schumann, and C. Tominski, “Visualizing time-oriented data—A systematic view,” Comput. Graph., vol. 31, no. 3, pp. 401–409, Jun. 2007. doi: 10.1016/j.cag.2007.01.030
    M. Brehmer, B. Lee, B. Bach, N. H. Riche, and T. Munzner, “Timelines revisited: A design space and considerations for expressive storytelling,” IEEE Trans. Vis. Comput. Graph., vol. 23, no. 9, pp. 2151–2164, Sep. 2017. doi: 10.1109/TVCG.2016.2614803
    J. Walker, R. Borgo, and M. W. Jones, “TimeNotes: A study on effective chart visualization and interaction techniques for time-series data,” IEEE Trans. Vis. Comput. Graph., vol. 22, no. 1, pp. 549–558, Jan. 2016. doi: 10.1109/TVCG.2015.2467751
    R. Chen, F. Jankovic, N. Marinsek, L. Foschini, L. Kourtis, A. Signorini, M. Pugh, J. Shen, R. Yaari, V. Maljkovic, M. Sunga, H. H. Song, H. J. Jung, B. Tseng, and A. Trister, “Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams,” in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Anchorage, USA, 2019, pp. 2145–2155.
    Y. Kim, E. Heo, H. Lee, S. Ji, J. Choi, J. W. Kim, J. Lee, and S. Yoo, “Prescribing 10, 000 steps like aspirin: Designing a novel interface for data-driven medical consultations,” in Proc. CHI Conf. Human Factors in Computing Systems, Denver, USA, 2017, pp. 5787–5799.
    M. Sharmin, A. Raij, D. Epstien, I. Nahum-Shani, J. G. Beck, S. Vhaduri, K. Preston, and S. Kumar, “Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Osaka, Japan, 2015, pp. 505–516.
    J. Polack Jr., S. T. Chen, M. Kahng, K. De Barbaro, R. Basole, M. Sharmin, and D. H. Chau, “Chronodes: Interactive multifocus exploration of event sequences,” ACM Trans. Interact. Intell. Syst., vol. 8, no. 1, p. 2, Mar. 2018.
    F. Birnbaum, D. Lewis, R. K. Rosen, and M. L. Ranney, “Patient engagement and the design of digital health,” Acad. Emerg. Med., vol. 22, no. 6, pp. 754–756, Jun. 2015. doi: 10.1111/acem.12692
    K. Skivington, L. Matthews, S. A. Simpson, Craig, J. Baird, J. M. Blazeby, K. A. Boyd, N. Craig, D. French, E. McIntosh, M. Petticrew, J. Rycroft-Malone, M. White, and L. Moore, “Framework for the development and evaluation of complex interventions: Gap analysis, workshop and consultation-informed update,” Health Technol. Assess., vol. 25, no. 57, pp. 1–132, Sep. 2021. doi: 10.3310/hta25570
    S. Michie, M. Richardson, M. Johnston, C. Abraham, J. Francis, W. Hardeman, M. Eccles, J. Cane, and C. E. Wood, “The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions,” Ann. Behav. Med., vol. 46, no. 1, pp. 81–95, Aug. 2013. doi: 10.1007/s12160-013-9486-6
    D. E. Conroy, C. H. Yang, and J. Maher, “Behavior change techniques in top-ranked mobile apps for physical activity,” Am. J. Prev. Med., vol. 46, no. 6, pp. 649–652, Jun. 2014. doi: 10.1016/j.amepre.2014.01.010
    D. X. Chen, Z. J. Ding, C. G. Yan, and M. M. Wang, “A behavioral authentication method for mobile based on browsing behaviors,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1528–1541, Nov. 2020. doi: 10.1109/JAS.2019.1911648
    R. Orji, R. L. Mandryk, and J. Vassileva, “Towards a data-driven approach to intervention design: A predictive path model of healthy eating determinants,” in Proc. 7th Int. Conf. Persuasive Technology: Design for Health and Safety, Linköping, Sweden, 2012, pp. 203–214.
    N. Alkış and T. T. Temizel, “The impact of individual differences on influence strategies,” Pers. Individ. Differ., vol. 87, pp. 147–152, Dec. 2015. doi: 10.1016/j.paid.2015.07.037
    M. Kaptein, Markopoulos, B. De Ruyter, and E. Aarts, “Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles,” Int. J. Hum. Comput. Stud., vol. 77, pp. 38–51, May 2015. doi: 10.1016/j.ijhcs.2015.01.004
    W. B. Yue, Z. D. Wang, J. Y. Zhang, and X. H. Liu, “An overview of recommendation techniques and their applications in healthcare,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 701–717, Apr. 2021. doi: 10.1109/JAS.2021.1003919
    S. E. Boslaugh, M. W. Kreuter, R. A. Nicholson, and K. Naleid, “Comparing demographic, health status and psychosocial strategies of audience segmentation to promote physical activity,” Health Educ. Res., vol. 20, no. 4, pp. 430–438, Aug. 2005. doi: 10.1093/her/cyg138
    N. Askham, D. Cook, M. Doyle, H. Fereday, M. Gibson, U. Landbeck, R. Lee, C. Maynard, G. Palmer, and J. Schwarzenbach, “The six primary dimensions for data quality assessment,” DAMA UK Working Group, UK, 2013.
    M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander, “LOF: Identifying density-based local outliers,” in Proc. ACM SIGMOD Int. Conf. Management of Data, Dallas, USA, 2000, pp. 93–104.
    P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long short term memory networks for anomaly detection in time series,” in Proc. 23rd European Symp. Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2015, pp. 89–94.
    A. Mehrotra, R. Hendley, and M. Musolesi, “NotifyMeHere: Intelligent notification delivery in multi-device environments,” in Proc. Conf. Human Information Interaction and Retrieval, Glasgow, UK, 2019, pp. 103–111.
    D. Weber, A. Voit, P. Kratzer, and N. Henze, “In-situ investigation of notifications in multi-device environments,” in Proc. ACM Int. Joint Conf. Pervasive and Ubiquitous Computing, Germany, Heidelberg, 2016, pp. 1259–1264.
    J. L. Hill, “Bayesian nonparametric modeling for causal inference,” J. Comput. Graph. Stat., vol. 20, no. 1, pp. 217–240, Mar. 2011. doi: 10.1198/jcgs.2010.08162
    U. Shalit, F. D. Johansson, and D. A. Sontag, “Estimating individual treatment effect: Generalization bounds and algorithms,” in Proc. 34th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 3076–3085.
    A. M. Alaa, M. Weisz, and M. van der Schaar, “Deep counterfactual networks with propensity-dropout,” in Proc. 34th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 1–5.
    S. Wager and S. Athey, “Estimation and inference of heterogeneous treatment effects using random forests,” J. Am. Stat. Assoc., vol. 113, no. 523, pp. 1228–1242, Jun. 2018. doi: 10.1080/01621459.2017.1319839
    J. Yoon, J. Jordon, and M. van der Schaar, “GANITE: Estimation of individualized treatment effects using generative adversarial nets,” in Proc. 6th Int. Conf. Learning Representations, Vancouver, Canada, 2018, pp. 1–22.
    C. Shi, D. M. Blei, and V. Veitch, “Adapting neural networks for the estimation of treatment effects,” in Proc. 33rd Int. Conf. Neural Information Processing Systems, Vancouver, Canada, 2019, pp. 225.
    B. Lim, A. Alaa, and M. van der Schaar, “Forecasting treatment responses over time using recurrent marginal structural networks,” in Proc. 32nd Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2018, pp. 7494–7504.
    I. Bica, A. M. Alaa, J. Jordon, and M. van der Schaar, “Estimating counterfactual treatment outcomes over time through adversarially balanced representations,” in Proc. 8th Int. Conf. Learning Representations, Addis Ababa, Ethiopia, 2020, pp. 1–28.
    R. Q. Liu, C. C. Yin, and P. Zhang, “Estimating individual treatment effects with time-varying confounders,” in Proc. IEEE Int. Conf. Data Mining, Sorrento, Italy, 2020, pp. 382–391.
    D. Brodbeck, R. Gasser, M. Degen, S. Reichlin, and J. Luthiger, “Enabling large-scale telemedical disease management through interactive visualization,” European Notes in Medical Informatics, vol. 1, no. 1, pp. 1172–1177, Aug. 2005.
    A. Perer and D. Gotz, “Data-driven exploration of care plans for patients,” in Proc. CHI’13 Extended Abstracts on Human Factors in Computing Systems, Paris, France, 2013, pp. 439–444.
    D. A. Rohani, M. Faurholt-Jepsen, L. V. Kessing, and J. E. Bardram, “Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: Systematic review,” JMIR Mhealth Uhealth, vol. 6, no. 8, p. e165, Aug. 2018. doi: 10.2196/mhealth.9691
    D. Pach, A. A. Rogge, J. N. Wang, and C. M. Witt, “Five lessons learned from randomized controlled trials on mobile health interventions: Consensus procedure on practical recommendations for sustainable research,” JMIR Mhealth Uhealth, vol. 9, no. 2, p. e20630, Feb. 2021. doi: 10.2196/20630
    S. E. Lord, A. N. C. Campbell, M. F. Brunette, L. Cubillos, S. M. Bartels, W. C. Torrey, A. L. Olson, S. H. Chapman, J. A. Batsis, D. Polsky, E. V. Nunes, K. M. Seavey, and L. A. Marsch, “Workshop on implementation science and digital therapeutics for behavioral health,” JMIR Ment. Health, vol. 8, no. 1, p. e17662, Jan. 2021. doi: 10.2196/17662
    M. Gonzàlez, J. Lorés, and A. Granollers, “Enhancing usability testing through datamining techniques: A novel approach to detecting usability problem patterns for a context of use,” Inf. Softw. Technol., vol. 50, no. 6, pp. 547–568, May 2008. doi: 10.1016/j.infsof.2007.06.001
    J. I. Gold, M. SooHoo, A. M. Laikin, A. S. Lane, and M. J. Klein, “Effect of an immersive virtual reality intervention on pain and anxiety associated with peripheral intravenous catheter placement in the pediatric setting: A randomized clinical trial,” JAMA Netw. Open, vol. 4, no. 8, p. e2122569, Aug. 2021. doi: 10.1001/jamanetworkopen.2021.22569
    L. T. Car, D. A. Dhinagaran, B. M. Kyaw, T. Kowatsch, S. Joty, Y. L. Theng, and R. Atun, “Conversational agents in health care: Scoping review and conceptual analysis,” J. Med. Internet Res., vol. 22, no. 8, p. e17158, Aug. 2020. doi: 10.2196/17158
    Z. H. Shen, A. Elibol, and N. Y. Chong, “Understanding nonverbal communication cues of human personality traits in human-robot interaction,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1465–1477, Nov. 2020. doi: 10.1109/JAS.2020.1003201
    Z. T. Liu, M. Wu, W. H. Cao, L. F. Chen, J. Xu, R. Zhang, M. T. Zhou, and J. W. Mao, “A facial expression emotion recognition based human-robot interaction system,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 668–676, Sep. 2017. doi: 10.1109/JAS.2017.7510622
    J. Rooksby, A. Morrison, and D. Murray-Rust, “Student perspectives on digital phenotyping: The acceptability of using smartphone data to assess mental health,” in Proc. CHI Conf. Human Factors in Computing Systems, Glasgow, UK, 2019, pp. 425.
    H. Lee, S. Kang, and U. Lee, “Understanding privacy risks and perceived benefits in open dataset collection for mobile affective computing,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 6, no. 2, p. 61, Jul. 2022.
    J. Huh-Yoo, R. Kadri, and L. R. Buis, “Pervasive healthcare IRBs and ethics reviews in research: Going beyond the paperwork,” IEEE Pervas. Comput., vol. 20, no. 1, pp. 40–44, Jan./Mar. 2021. doi: 10.1109/MPRV.2020.3044099
    R. Chen, F. Jankovic, N. Marinsek, L. Foschini, L. Kourtis, A. Signorini, M. Pugh, J. Shen, R. Yaari, V. Maljkovic, M. Sunga, H. H. Song, H. J. Jung, B. Tseng, and A. Trister, “Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams,” in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Anchorage, USA, 2019, pp. 2145–2155.
    A. Alorwu, S. Kheirinejad, N. van Berkel, M. Kinnula, D. Ferreira, A. Visuri, and S. Hosio, “Assessing MyData scenarios: Ethics, concerns, and the promise,” in Proc. CHI Conf. Human Factors in Computing Systems, Yokohama, Japan, 2021, pp. 209.
    L. F. Cranor, Guduru, and M. Arjula, “User interfaces for privacy agents,” ACM Trans. Comput. Hum. Interact., vol. 13, no. 2, pp. 135–178, Jun. 2006. doi: 10.1145/1165734.1165735
    H. Lee and U. Lee, “Dynamic consent for sensor-driven research,” in Proc. 13th Int. Conf. Mobile Computing and Ubiquitous Network, Tokyo, Japan, 2021, pp. 1–6.
    F. Schaub, R. Balebako, A. L. Durity, and L. F. Cranor, “A design space for effective privacy notices,” in Proc. 11th USENIX Conf. Usable Privacy and Security, Ottawa, Canada, 2015, pp. 1–17.
    Y. Y. Feng, Y. X. Yao, and N. Sadeh, “A design space for privacy choices: Towards meaningful privacy control in the internet of things,” in Proc. CHI Conf. Human Factors in Computing Systems, Yokohama, Japan, 2021, pp. 64.


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(3)

    Article Metrics

    Article views (47) PDF downloads(8) Cited by()


    DownLoad:  Full-Size Img  PowerPoint