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Citing this Article

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Published on 21.09.16 in Vol 4, No 3 (2016): Jul-Sept

This paper is in the following e-collection/theme issue:

Works citing "Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild"

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.5960):

(note that this is only a small subset of citations)

  1. Radhakrishnan K, Kim MT, Burgermaster M, Brown RA, Xie B, Bray MS, Fournier CA. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;
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  3. Kerst A, Zielasek J, Gaebel W. Smartphone applications for depression: a systematic literature review and a survey of health care professionals’ attitudes towards their use in clinical practice. European Archives of Psychiatry and Clinical Neuroscience 2020;270(2):139
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  4. Matz SC, Appel RE, Kosinski M. Privacy in the age of psychological targeting. Current Opinion in Psychology 2020;31:116
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  5. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020;
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  6. Obuchi M, Huckins JF, Wang W, daSilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1
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  7. Severe J, Greden JF, Reddy P. Consequences of Recurrence of Major Depressive Disorder: Is Stopping Effective Antidepressant Medications Ever Safe?. FOCUS 2020;18(2):120
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  8. Miralles I, Granell C, Díaz-Sanahuja L, Van Woensel W, Bretón-López J, Mira A, Castilla D, Casteleyn S. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR mHealth and uHealth 2020;8(4):e14897
    CrossRef
  9. Jacobson NC, Chung YJ. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572
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  10. Yim SJ, Lui LM, Lee Y, Rosenblat JD, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho RC, Coles AS, Majeed A, Wong ER, Phan L, Nasri F, McIntyre RS. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602
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  11. Burger F, Neerincx MA, Brinkman W. Technological State of the Art of Electronic Mental Health Interventions for Major Depressive Disorder: Systematic Literature Review. Journal of Medical Internet Research 2020;22(1):e12599
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  12. Dias LPS, Barbosa JLV, Feijó LP, Vianna HD. Development and testing of iAware model for ubiquitous care of patients with symptoms of stress, anxiety and depression. Computer Methods and Programs in Biomedicine 2020;187:105113
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  13. Cao J, Truong AL, Banu S, Shah AA, Sabharwal A, Moukaddam N. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study. JMIR Mental Health 2020;7(1):e14045
    CrossRef
  14. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454
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  15. Hwang WJ, Jo HH. Evaluation of the Effectiveness of Mobile App-Based Stress-Management Program: A Randomized Controlled Trial. International Journal of Environmental Research and Public Health 2019;16(21):4270
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  16. Acikmese Y, Alptekin SE. Prediction of stress levels with LSTM and passive mobile sensors. Procedia Computer Science 2019;159:658
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  17. Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine 2019;49(09):1426
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  18. 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
    CrossRef
  19. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine 2019;2(1)
    CrossRef
  20. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149
    CrossRef
  21. Xu X, Chikersal P, Doryab A, Villalba DK, Dutcher JM, Tumminia MJ, Althoff T, Cohen S, Creswell KG, Creswell JD, Mankoff J, Dey AK. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1
    CrossRef
  22. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 2019;21(11)
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  23. Barnett S, Huckvale K, Christensen H, Venkatesh S, Mouzakis K, Vasa R. Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications. Journal of Medical Internet Research 2019;21(11):e16399
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  24. Majumder S, Deen MJ. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164
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  25. Felix IR, Castro LA, Rodriguez L, Banos O. Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns. International Journal of Distributed Sensor Networks 2019;15(9):155014771987418
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  26. Pham Q, Graham G, Carrion C, Morita PP, Seto E, Stinson JN, Cafazzo JA. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941
    CrossRef
  27. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Areán PA. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72
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  28. Miralles I, Granell C. Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions. International Journal of Environmental Research and Public Health 2019;16(7):1197
    CrossRef
  29. Faurholt-Jepsen M, Busk J, Þórarinsdóttir H, Frost M, Bardram JE, Vinberg M, Kessing LV. Objective smartphone data as a potential diagnostic marker of bipolar disorder. Australian & New Zealand Journal of Psychiatry 2019;53(2):119
    CrossRef
  30. Lüscher J, Kowatsch T, Boateng G, Santhanam P, Bodenmann G, Scholz U. Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application. JMIR Research Protocols 2019;8(10):e13685
    CrossRef
  31. Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649
    CrossRef
  32. 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
    CrossRef
  33. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre. L'Encéphale 2018;44(2):168
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  34. Dias LPS, Barbosa JLV, Vianna HD. Gamification and serious games in depression care: A systematic mapping study. Telematics and Informatics 2018;35(1):213
    CrossRef
  35. Chung K, Jeon M, Park J, Lee S, Kim CO, Park JY, Guloksuz S. Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study. PLOS ONE 2018;13(6):e0199118
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  36. Wang R, Wang W, daSilva A, Huckins JF, Kelley WM, Heatherton TF, Campbell AT. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1
    CrossRef
  37. Renn BN, Pratap A, Atkins DC, Mooney SD, Areán PA. Smartphone-based passive assessment of mobility in depression: Challenges and opportunities. Mental Health and Physical Activity 2018;14:136
    CrossRef
  38. Guo Y, Hong YA, Qiao J, Xu Z, Zhang H, Zeng C, Cai W, Li L, Liu C, Li Y, Zhu M, Harris NA, Yang C. Run4Love, a mHealth (WeChat-based) intervention to improve mental health of people living with HIV: a randomized controlled trial protocol. BMC Public Health 2018;18(1)
    CrossRef
  39. Lind MN, Byrne ML, Wicks G, Smidt AM, Allen NB. The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health 2018;5(3):e10334
    CrossRef
  40. Boukhechba M, Chow P, Fua K, Teachman BA, Barnes LE. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101
    CrossRef
  41. Hardy J, Veinot TC, Yan X, Berrocal VJ, Clarke P, Goodspeed R, Gomez-Lopez IN, Romero D, Vydiswaran VV. User acceptance of location-tracking technologies in health research: Implications for study design and data quality. Journal of Biomedical Informatics 2018;79:7
    CrossRef
  42. Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120
    CrossRef
  43. Goodspeed R, Yan X, Hardy J, Vydiswaran VV, Berrocal VJ, Clarke P, Romero DM, Gomez-Lopez IN, Veinot T. Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study. JMIR mHealth and uHealth 2018;6(8):e168
    CrossRef
  44. Rohani DA, Tuxen N, Quemada Lopategui A, Kessing LV, Bardram JE. Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study. JMIR Mental Health 2018;5(2):e10122
    CrossRef
  45. Stieger M, Nißen M, Rüegger D, Kowatsch T, Flückiger C, Allemand M. PEACH, a smartphone- and conversational agent-based coaching intervention for intentional personality change: study protocol of a randomized, wait-list controlled trial. BMC Psychology 2018;6(1)
    CrossRef
  46. Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and Anxiety 2017;34(6):526
    CrossRef
  47. Chan S, Godwin H, Gonzalez A, Yellowlees PM, Hilty DM. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12)
    CrossRef
  48. Tsanas A, Saunders K, Bilderbeck A, Palmius N, Goodwin G, De Vos M. Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment. JMIR Mental Health 2017;4(2):e15
    CrossRef
  49. Aledavood T, Triana Hoyos AM, Alakörkkö T, Kaski K, Saramäki J, Isometsä E, Darst RK. Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype. JMIR Research Protocols 2017;6(6):e110
    CrossRef
  50. Sabharwal A, Veeraraghavan A. Bio-Behavioral Sensing. GetMobile: Mobile Computing and Communications 2017;21(3):11
    CrossRef
  51. Saeb S, Lattie EG, Kording KP, Mohr DC. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112
    CrossRef
  52. Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review. Journal of Medical Internet Research 2017;19(7):e262
    CrossRef
  53. Wahle F, Bollhalder L, Kowatsch T, Fleisch E. Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis. Journal of Medical Internet Research 2017;19(5):e191
    CrossRef
  54. Jang JS, Cho SH. Mobile Health (m-health) on Mental Health. Korean Journal of Stress Research 2016;24(4):231
    CrossRef
  55. Price J. What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?. Clinical Therapeutics 2016;38(12):2533
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.5960)

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  1. Martinez-Martin N. Ethical Dimensions of Commercial and DIY Neurotechnologies. 2020. :63
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  2. von Wangenheim F, Ventouris J. Perspektiven des Dienstleistungsmanagements. 2020. Chapter 34:677
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  3. Hegerl U, Dogan E, Oehler C, Sander C, Stöber F. Gesundheit digital. 2019. Chapter 4:47
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  4. Senders JT, Maher N, Hulsbergen AFC, Lamba N, Bredenoord AL, Broekman MLD. Ethics of Innovation in Neurosurgery. 2019. Chapter 14:129
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  5. Kowatsch T, Fischer-Taeschler D, Putzing F, Bürki P, Stettler C, Chiesa-Tanner G, Fleisch E. Digitale Transformation von Dienstleistungen im Gesundheitswesen VI. 2019. Chapter 12:205
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  6. Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. 2019. Chapter 29:583
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  7. Sengupta S, Adragna M. Psychiatric Nonadherence. 2019. Chapter 14:201
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  8. Castro LA, Rodríguez MD, Martínez F, Rodríguez L, Andrade G, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. 2018. :3
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