Mobile sensing and support for people with depression: A pilot trial in the wild

JMIR mHealth and uHealth

Volumn 4, Issue 3, 2016, Pages

  Wahle, Fabian ^a,b   Kowatsch, Tobias ^b   Fleisch, Elgar ^a,b   Rufer, Michael ^c   Weidt, Steffi ^c  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF ST GALLEN   (Switzerland)

^c UNIVERSITY HOSPITAL ZURICH   (Switzerland)

Author keywords

Activities of daily living; Classification; Cognitive behavioral therapy; Context awareness; Depression; MHealth

Indexed keywords

EID: 85086719384     PISSN: None     EISSN: 22915222     Source Type: Journal    
DOI: 10.2196/mhealth.5960     Document Type: Article

References (54)

1. World Health Organization. World Mental Health Day.: WHO; 2012. Depression: A Global Crisis URL: http://www.who.int/mental_health/management/depression/wfmh_paper_depression_wmhd_2012.pdf [accessed 2016-05-09] [WebCite Cache ID 6hNbFzaZZ].
2. World Health Organization. 2013. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 URL: http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf [accessed 2016-05-09] [WebCite Cache ID 6hNbOOIsi].
3. Holden C. Mental health. Global survey examines impact of depression. Science 2000;288:39-40. [Medline: 10766633].
4. Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin Psychol Rev 2006;26:17-31. [doi: 10.1016/j.cpr.2005.07.003] [Medline: 16199119].
5. Donker T, Blankers M, Hedman E, Ljótsson B, Petrie K, Christensen H. Economic evaluations of Internet interventions for mental health: a systematic review. Psychol Med 2015;45:3357-3376. [doi: 10.1017/S0033291715001427] [Medline: 26235445].
6. Rochlen AB, Zack JS, Speyer C. Online therapy: review of relevant definitions, debates, and current empirical support. J Clin Psychol 2004;60:269-283. [doi: 10.1002/jclp.10263] [Medline: 14981791].
7. Wagner B, Horn AB, Maercker A. Internet-based versus face-to-face cognitive-behavioral intervention for depression: a randomized controlled non-inferiority trial. J Affect Disord 2014;152-154:113-121. [doi: 10.1016/j.jad.2013.06.032] [Medline: 23886401].
8. Wahle F, Kowatsch T. Towards the design of evidence-based mental health information systems: a preliminary literature review. 2014 Presented at: Proceedings of the 35th International Conference on Information Systems; Dec 12-14, 2014; Auckland, NZ.
9. Smith A. U.S. Smartphone Use in 2015. 2015. URL: http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/ [accessed 2015-05-09] [WebCite Cache ID 6hNYNuygj].
10. eMarketer. 2 Billion Consumers Worldwide to Get Smart(phones) by. 2016. URL: http://www.emarketer.com/Articles/Print.aspx?R=1011694 [accessed 2016-05-09] [WebCite Cache ID 6hNYgEWyI].
11. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, et al. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res 2011;13:e55 [FREE Full text] [doi: 10.2196/jmir.1838] [Medline: 21840837].
12. Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Comput 2014;13:13-17. [doi: 10.1109/MPRV.2014.46].
13. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychol 2015;Suppl:1209-1219. [doi: 10.1037/hea0000306] [Medline: 26651462].
14. Freeman JB, Dale R, Farmer TA. Hand in motion reveals mind in motion. Front Psychol 2011;2:59 [FREE Full text] [doi: 10.3389/fpsyg.2011.00059] [Medline: 21687437].
15. De Mello MT, de Aquino Lemos D, Antunes HK, Bittencourt L, Santos-Silva R, Tufik S. Relationship between physical activity and depression and anxiety symptoms: a population study. J Affect Disord 2013;149:241-246. [doi: 10.1016/j.jad.2013.01.035] [Medline: 23489405].
16. Hansen BH, Kolle E, Dyrstad SM, Holme I, Anderssen SA. Accelerometer-determined physical activity in adults and older people. Med Sci Sports Exerc 2012;44:266-272. [doi: 10.1249/MSS.0b013e31822cb354] [Medline: 21796052].
17. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res 2015;17:e175 [FREE Full text] [doi: 10.2196/jmir.4273] [Medline: 26180009].
18. Canzian L, Mirco M. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis.: ACM; 2015 Presented at: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; September 07-11, 2015; Osaka, Japan. [doi: 10.1145/2750858.2805845].
19. George LK, Blazer DG, Hughes DC, Fowler N. Social support and the outcome of major depression. Br J Psychiatry 1989;154:478-485. [Medline: 2590779]
20. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand 2004;109:325-331. [doi: 10.1111/j.1600-0447.2004.00301.x] [Medline: 15049768]
21. Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H. Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study. J Med Internet Res 2016;18:e72 [FREE Full text] [doi: 10.2196/jmir.5505] [Medline: 27025287]
22. Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ 2012;184:E191-E196 [FREE Full text] [doi: 10.1503/cmaj.110829] [Medline: 22184363]
23. Kotsiantis SB, Zaharakis I, Pintelas P. Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval … in Artificial Intelligence and Applications). Amsterdam: IOS Press; 2007.
24. Hargood C, Pejovic V, Morrison L, Michaelides DT, Musolesi M, Yardley L, et al. The UBhave Framework: Dynamic Pervasive Applications for Behavioural Psychology. 2013. URL: http://eprints.soton.ac.uk/372016/2/poster.pdf [accessed 2016-05-09] [WebCite Cache ID 6hNeojNoY]
25. Goodwin RD. Association between physical activity and mental disorders among adults in the United States. Preventive Medicine 2003;36:698-703. [doi: 10.1016/S0091-7435(03)00042-2]
26. Ströhle A. Physical activity, exercise, depression and anxiety disorders. J Neural Transm (Vienna) 2009;116:777-784. [doi: 10.1007/s00702-008-0092-x] [Medline: 18726137]
27. De Mello MT, de Aquino Lemos V, Antunes HK, Bittencourt L, Santos-Silva R, Tufik S. Relationship between physical activity and depression and anxiety symptoms: a population study. J Affect Disord 2013;149:241-246. [doi: 10.1016/j.jad.2013.01.035] [Medline: 23489405]
28. Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging 2015;35:64-70. [doi: 10.1111/cpf.12127] [Medline: 24393233]
29. Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila A, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents' physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil 2015;7:18 [FREE Full text] [doi: 10.1186/s13102-015-0010-0] [Medline: 26251724]
30. Kunze K, Lukowicz P. Sensor placement variations in wearable activity recognition. IEEE Pervasive Comput 2014;13:32-41. [doi: 10.1109/MPRV.2014.73]
31. Rekimoto J, Miyaki T, Ishizawa T. WiFi-based continuous location logging for life pattern analysis. In: Lecture Notes in Computer Science. Berlin Heidelberg: Springer; 2007 Presented at: The 3rd International symposium of Location and Context Awareness; September 20-21, 2007; Oberpfaafenhofen, Germany p. 35-49.
32. Eagle N, Pentland A. Reality mining: sensing complex social systems. Pers Ubiquit Comput 2005;10:255-268. [doi: 10.1007/s00779-005-0046-3]
33. Oulasvirta A, Rattenbury T, Ma L, Raita E. Habits make smartphone use more pervasive. Pers Ubiquit Comput 2011;16:105-114. [doi: 10.1007/s00779-011-0412-2]
34. Iacovides A, Fountoulakis KN, Kaprinis S, Kaprinis G. The relationship between job stress, burnout and clinical depression. J Affect Disord 2003;75:209-221. [Medline: 12880934]
35. Hollon SD, Stewart MO, Strunk D. Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety. Annu Rev Psychol 2006;57:285-315. [doi: 10.1146/annurev.psych.57.102904.190044] [Medline: 16318597]
36. Lovelock H, Matthews R, Murphy K. Australian Psychological Association. Evidence Based Psychological Interventions in the Treatment of Mental Disorders: A Literature Review URL: https://www.psychology.org.au/Assets/Files/Evidence-Based-Psychological-Interventions.pdf [accessed 2016-05-09] [WebCite Cache ID 6hNb4Mm4s]
37. Johansson R, Andersson G. Internet-based psychological treatments for depression. Expert Review of Neurotherapeutics 2012;12:861-870. [doi: 10.1586/ern.12.63]
38. Kraft P, Drozd F, Olsen E. ePsychology: designing theory-based health promotion interventions. Communications of the Association for Information Systems 2009;24:24.
39. Martin L, Haskard-Zolnierek K, DiMatteo M. Health Behavior Change and Treatment Adherence: Evidence-Based Guidelines for Improving Healthcare. Oxford: Oxford University Press; 2010.
40. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med 2011;1:53-71 [FREE Full text] [doi: 10.1007/s13142-011-0021-7] [Medline: 21796270]
41. Kapp K. The Gamification of Learning and Instruction: Game-based Methods and Strategies for Training and Education. New York: Pfeiffer; 2012.
42. Massey FJ. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 1951;46:68-78. [doi: 10.1080/01621459.1951.10500769]
43. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. New York, NY: Association for Computing Machinery; 1992 Presented at: The Fifth Annual ACM Workshop on Computational Learning Theory; July 27-29, 1992; Pittsburgh, Pennsylvania.
44. Breiman L. Random forests. Machine Learning 2001;45:5-32. [doi: 10.1023/A:1010933404324]
45. Statnikov A, Wang L, Aliferis CF. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008;9:319 [FREE Full text] [doi: 10.1186/1471-2105-9-319] [Medline: 18647401]
46. Hsu CW, Chang CC, Lin CJ. A Practical Guide to Support Vector Classification. 2010. URL: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf [accessed 2016-05-09] [WebCite Cache ID 6hNd3njRJ]
47. Nelder JA, Mead R. A simplex method for function minimization. The Computer Journal 1965;7:308-313. [doi: 10.1093/comjnl/7.4.308]
48. Breiman L. Classification and Regression Trees. New York: Chapman & Hall; 1984.
49. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer; 2009.
50. Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing & Management 2009;45:427-437. [doi: 10.1016/j.ipm.2009.03.002]
51. Kearns M, Ron D. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput 1999;11:1427-1453. [Medline: 10423502]
52. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on Artificial intelligence. San Francisco: Kaufmann Publishers Inc; 1995 Presented at: International joint conference on Artificial intelligence; August 20, 1995; Montreal, Cannada.
53. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med 2000;160:2101-2107. [Medline: 10904452]
54. Chen A. New Data Shows Losing 80% of Mobile Users is Normal, and Why the Best Apps Do Better. 2015. URL: http://andrewchen.co/new-data-shows-why-losing-80-of-your-mobile-users-is-normal-and-that-the-best-apps-do-much-better/[accessed 2016-05-09] [WebCite Cache ID 6hNdZ7M0w]