Published on in Vol 21, No 11 (2019): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16399, first published .
Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications

Journals

  1. Fuller-Tyszkiewicz M, Richardson B, Little K, Teague S, Hartley-Clark L, Capic T, Khor S, Cummins R, Olsson C, Hutchinson D. Efficacy of a Smartphone App Intervention for Reducing Caregiver Stress: Randomized Controlled Trial. JMIR Mental Health 2020;7(7):e17541 View
  2. . Digital Sensory Phenotyping for Psychiatric Disorders. Journal of Psychiatry and Brain Science 2020 View
  3. Jayakumar P, Lin E, Galea V, Mathew A, Panda N, Vetter I, Haynes A. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. Journal of Personalized Medicine 2020;10(4):282 View
  4. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  5. Flanagan O, Chan A, Roop P, Sundram F. Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review. JMIR mHealth and uHealth 2021;9(9):e24352 View
  6. Martinez-Martin N, Greely H, Cho M. Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study. JMIR mHealth and uHealth 2021;9(7):e27343 View
  7. Wen Y, Li H, Gao Y, Teekaraman Y. Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data. Contrast Media & Molecular Imaging 2021;2021:1 View
  8. Werner‐Seidler A, Maston K, Calear A, Batterham P, Larsen M, Torok M, O’Dea B, Huckvale K, Beames J, Brown L, Fujimoto H, Bartholomew A, Bal D, Schweizer S, Skinner S, Steinbeck K, Ratcliffe J, Oei J, Venkatesh S, Lingam R, Perry Y, Hudson J, Boydell K, Mackinnon A, Christensen H. The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents. International Journal of Methods in Psychiatric Research 2023;32(3) View
  9. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  10. González-Pérez A, Matey-Sanz M, Granell C, Díaz-Sanahuja L, Bretón-López J, Casteleyn S. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. Journal of Biomedical Informatics 2023;141:104359 View
  11. Dittrich F, Albrecht U, Scherer J, Becker S, Landgraeber S, Back D, Fessmann K, Haversath M, Beck S, Abbara-Czardybon M, Quitmann H, Harren A, Aitzetmüller M, Klietz M. Development of Open Backend Structures for Health Care Professionals to Improve Participation in App Developments: Pilot Usability Study of a Medical App. JMIR Formative Research 2023;7:e42224 View
  12. Kumar D. Indigenous population genome databases for India and South Asia: emerging need for health and social applications. Journal of Genetics 2023;102(2) View
  13. Németh A, Antoniades C, Dukart J, Minnerop M, Rentz C, Schuman B, van de Warrenburg B, Willemse I, Bertini E, Gupta A, de Mello Monteiro C, Almoajil H, Quinn L, Perlman S, Horak F, Ilg W, Traschütz A, Vogel A, Dawes H. Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. The Cerebellum 2023;23(3):912 View