Published on in Vol 20, No 7 (2018): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10131, first published .
Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Journals

  1. Narziev N, Goh H, Toshnazarov K, Lee S, Chung K, Noh Y. STDD: Short-Term Depression Detection with Passive Sensing. Sensors 2020;20(5):1396 View
  2. Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing L, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow P. Smartphones in mental health: a critical review of background issues, current status and future concerns. International Journal of Bipolar Disorders 2020;8(1) View
  3. Drissi N, Ouhbi S, Janati Idrissi M, Fernandez-Luque L, Ghogho M. Connected Mental Health: Systematic Mapping Study. Journal of Medical Internet Research 2020;22(8):e19950 View
  4. Helbich M. Dynamic UrbanEnvironmentalExposures onDepression andSuicide (NEEDS) in the Netherlands: a protocol for a cross-sectional smartphone tracking study and a longitudinal population register study. BMJ Open 2019;9(8):e030075 View
  5. Nicholas J, Shilton K, Schueller S, Gray E, Kwasny M, Mohr D. The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2019;7(4):e12578 View
  6. Day J, Freiberg K, Hayes A, Homel R. Towards Scalable, Integrative Assessment of Children’s Self-Regulatory Capabilities: New Applications of Digital Technology. Clinical Child and Family Psychology Review 2019;22(1):90 View
  7. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  8. Basco M, Kyrarini M, Makedon F. Personal Devices and Smartphone Applications for Detection of Depression. Psychiatric Annals 2020;50(6):255 View
  9. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  10. Fraccaro P, Beukenhorst A, Sperrin M, Harper S, Palmier-Claus J, Lewis S, Van der Veer S, Peek N. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. Journal of the American Medical Informatics Association 2019;26(11):1412 View
  11. Pryss R, Schlee W, Hoppenstedt B, Reichert M, Spiliopoulou M, Langguth B, Breitmayer M, Probst T. Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(6):e15547 View
  12. Keatley D, O’Donnell C, Joyce T. Perceptions of drink driving legal limits in England: a qualitative investigation. Psychology, Crime & Law 2020;26(8):733 View
  13. Schreiber M, Jenny G. Development and validation of the ‘Lebender emoticon PANAVA’ scale (LE-PANAVA) for digitally measuring positive and negative activation, and valence via emoticons. Personality and Individual Differences 2020;160:109923 View
  14. Odendaal W, Anstey Watkins J, Leon N, Goudge J, Griffiths F, Tomlinson M, Daniels K. Health workers’ perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. Cochrane Database of Systematic Reviews 2020 View
  15. Fillekes M, Kim E, Trumpf R, Zijlstra W, Giannouli E, Weibel R. Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators. Sensors 2019;19(20):4551 View
  16. Sarda A, Munuswamy S, Sarda S, Subramanian V. Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study. JMIR mHealth and uHealth 2019;7(1):e11041 View
  17. Ryding F, Kuss D. Passive objective measures in the assessment of problematic smartphone use: A systematic review. Addictive Behaviors Reports 2020;11:100257 View
  18. Greer B, Newbery K, Cella M, Wykes T. Predicting Inpatient Aggression in Forensic Services Using Remote Monitoring Technology: Qualitative Study of Staff Perspectives. Journal of Medical Internet Research 2019;21(9):e15620 View
  19. Rigabert A, Motrico E, Moreno-Peral P, Resurrección D, Conejo-Cerón S, Cuijpers P, Martín-Gómez C, López-Del-Hoyo Y, Bellón J. Effectiveness of online psychological and psychoeducational interventions to prevent depression: Systematic review and meta-analysis of randomized controlled trials. Clinical Psychology Review 2020;82:101931 View
  20. Abdi S, Witte L, Hawley M. Exploring the Potential of Emerging Technologies to Meet the Care and Support Needs of Older People: A Delphi Survey. Geriatrics 2021;6(1):19 View
  21. Kim M, Kim I, Lee U. Beneficial Neglect. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
  22. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  23. Maharjan S, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt B, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1) View
  24. Tonti S, Marzolini B, Bulgheroni M. Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study. JMIR Biomedical Engineering 2021;6(2):e15417 View
  25. Roberts H, Helbich M. Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study. Environment International 2021;156:106635 View
  26. 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
  27. Wies B, Landers C, Ienca M. Digital Mental Health for Young People: A Scoping Review of Ethical Promises and Challenges. Frontiers in Digital Health 2021;3 View
  28. Gurkan H, de Véricourt F. Contracting, Pricing, and Data Collection Under the AI Flywheel Effect. Management Science 2022;68(12):8791 View
  29. Rauch M, Bundscherer-Meierhofer K, Loew T, Leinberger u. Konzeption einer App mit der Technik des „Entschleunigten Atmens“ zur Selbstregulation für Jugendliche während der Corona-Pandemie. Kindheit und Entwicklung 2022;31(4):229 View
  30. Hauser T, Skvortsova V, De Choudhury M, Koutsouleris N. The promise of a model-based psychiatry: building computational models of mental ill health. The Lancet Digital Health 2022;4(11):e816 View
  31. Valentine L, D’Alfonso S, Lederman R. Recommender systems for mental health apps: advantages and ethical challenges. AI & SOCIETY 2023;38(4):1627 View
  32. Braund T, Zin M, Boonstra T, Wong Q, Larsen M, Christensen H, Tillman G, O’Dea B. Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study. JMIR Mental Health 2022;9(5):e35549 View
  33. Beukenhorst A, Sergeant J, Schultz D, McBeth J, Yimer B, Dixon W. Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study. JMIR mHealth and uHealth 2021;9(11):e28857 View
  34. González-Pérez A, Matey-Sanz M, Granell C, Casteleyn S. Using mobile devices as scientific measurement instruments: Reliable android task scheduling. Pervasive and Mobile Computing 2022;81:101550 View
  35. Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
  36. Hagaman A, Lopez Mercado D, Poudyal A, Bemme D, Boone C, van Heerden A, Byanjankar P, Man Maharjan S, Thapa A, Kohrt B, Wasti S. “Now, I have my baby so I don’t go anywhere”: A mixed method approach to the ‘everyday’ and young motherhood integrating qualitative interviews and passive digital data from mobile devices. PLOS ONE 2022;17(7):e0269443 View
  37. Moukaddam N, Sano A, Salas R, Hammal Z, Sabharwal A. Turning data into better mental health: Past, present, and future. Frontiers in Digital Health 2022;4 View
  38. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  39. Faida E, Supriyanto S, Haksama S, Markam H, Ali A. The Acceptance and Use of Electronic Medical Records in Developing Countries within the Unified Theory of Acceptance and Use of Technology Framework. Open Access Macedonian Journal of Medical Sciences 2022;10(E):326 View
  40. Addotey-Delove M, Scott R, Mars M. A healthcare workers’ mHealth adoption instrument for the developing world. BMC Health Services Research 2022;22(1) View
  41. Newn J, Kelly R, D'Alfonso S, Lederman R. Examining and Promoting Explainable Recommendations for Personal Sensing Technology Acceptance. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(3):1 View
  42. Wei S, McConnell E, Granger B, Corazzini K. Care Coordination Processes in Transitional Care for Patients With Heart Failure. Journal of Cardiovascular Nursing 2022;37(6):546 View
  43. Wade N, Ortigara J, Sullivan R, Tomko R, Breslin F, Baker F, Fuemmeler B, Delrahim Howlett K, Lisdahl K, Marshall A, Mason M, Neale M, Squeglia L, Wolff-Hughes D, Tapert S, Bagot K. Passive Sensing of Preteens’ Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Mental Health 2021;8(10):e29426 View
  44. Zhang D, Lim J, Zhou L, Dahl A. Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study. JMIR Mental Health 2021;8(12):e31633 View
  45. Van Emmenis M, Jamison J, Kassavou A, Hardeman W, Naughton F, A'Court C, Sutton S, Eborall H. Patient and practitioner views on a combined face-to-face and digital intervention to support medication adherence in hypertension: a qualitative study within primary care. BMJ Open 2022;12(2):e053183 View
  46. Baumgartner S, Sumter S, Petkevič V, Wiradhany W. A Novel iOS Data Donation Approach: Automatic Processing, Compliance, and Reactivity in a Longitudinal Study. Social Science Computer Review 2023;41(4):1456 View
  47. Schick A, Rauschenberg C, Ader L, Daemen M, Wieland L, Paetzold I, Postma M, Schulte-Strathaus J, Reininghaus U. Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field. Psychological Medicine 2023;53(1):55 View
  48. Addotey-Delove M, Scott R, Mars M. The development of an instrument to predict patients’ adoption of mHealth in the developing world. Informatics in Medicine Unlocked 2022;29:100898 View
  49. Harari G, Gosling S. Understanding behaviours in context using mobile sensing. Nature Reviews Psychology 2023;2(12):767 View
  50. Sieberts S, Burn A, Carey E, Carlson S, Fernandes B, Kalha J, Lindani S, Marten C, Neelakantan L, Ranganathan S, Sams N, Scanlan E, Shah H, Sumant S, Suver C, Tummalacherla M, Velloza J, Areán P, Collins P, Fazel M, Ford T, Freeman M, Pathare S, Zingela Z, Doerr M. Targeted recruitment and the role of choice in the engagement of youth in a randomised smartphone-based mental health study in India, South Africa, and the UK: results from the MindKind Study. Wellcome Open Research 2023;8:334 View
  51. Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips K, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Research Protocols 2024;13:e46493 View
  52. Pinto da Costa M, Virdi K, Kouroupa A, Khavandi S. A Phone Pal to overcome social isolation in patients with psychosis—Findings from a feasibility trial. PLOS Digital Health 2024;3(1):e0000410 View
  53. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024 View
  54. Nepal S, Liu W, Pillai A, Wang W, Vojdanovski V, Huckins J, Rogers C, Meyer M, Campbell A. Capturing the College Experience. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1 View
  55. Poulose S, Bhattacharjee B, Chakravorty A. Determinants and drivers of change for digital transformation and digitalization in human resource management: a systematic literature review and conceptual framework building. Management Review Quarterly 2024 View

Books/Policy Documents

  1. Rocha R, Carneiro D, Novais P. Progress in Artificial Intelligence. View
  2. Zeng Y, Fraccaro P, Peek N. Artificial Intelligence in Medicine. View
  3. Yang M, Tang J, Tang L, Hu B. Intelligence Science and Big Data Engineering. Big Data and Machine Learning. View
  4. Ning X. Theory and Practice of Business Intelligence in Healthcare. View
  5. Ghosh A, Dey S. Efficient Data Handling for Massive Internet of Medical Things. View
  6. Kolenik T. Integrating Artificial Intelligence and IoT for Advanced Health Informatics. View
  7. Glare P, Laranjo da Silva L, Kirisci L, Ashton-James C. Person Centered Medicine. View
  8. Ceja J, Arenas A, Romero C, Rodríguez S, Luna G. Information Technology and Systems. View