Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

  • Inbal Nahum-Shani
  • Shawna N. Smith
  • Bonnie J. Spring
  • Linda M. Collins
  • Katie Witkiewitz
  • Ambuj Tewari
  • Susan A. Murphy
Original Article



The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment.


Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.


Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.


As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.


Just-in-time adaptive intervention Support Mobile health (mHealth) Health behavior 



We thank Daniel Almirall, Dror Ben-Zeev, Andrew Isham, and Dave Gustafson for their helpful feedback and advice. This project was supported by awards R01 DA039901, R01 AA022113, R01 HD073975, R21 AA018336, R01 AA023187, R01 HL125440, U54 EB020404, R01 DK108678, R01 DK097364, P50 DA039838, P01 CA180945, R01 DK097364, and R01 AA022931 from the National Institutes of Health, and awards IIS-1452099 and IIS-1545751 from the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations.

Compliance with ethical standards

Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards

Authors Nahum-Shani, Smith, Spring, Collins, Witkiewitz, Tewari, and Murphy declare that they have no conflict of interest. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.


  1. 1.
    Spruijt-Metz D, Wen C, O’Reilly G, et al. Innovations in the use of interactive technology to support weight management. Curr Obes Rep. 2015; 4(4): 510-519.PubMedCrossRefGoogle Scholar
  2. 2.
    Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. Pervasive Comput IEEE. 2014; 13(3): 13-17.CrossRefGoogle Scholar
  3. 3.
    Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: The mHealth evidence workshop. Am J Prev Med. 2013; 45(2): 228-236.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Consolvo S, McDonald DW, Toscos T, et al. Activity sensing in the wild: A field trial of ubifit garden. 2008.Google Scholar
  5. 5.
    Gustafson DH, McTavish FM, Chih M-Y, et al. A smartphone application to support recovery from alcoholism: A randomized clinical trial. JAMA Psychiatry. 2014; 71(5): 566-572.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Ben-Zeev D, Kaiser SM, Brenner CJ, Begale M, Duffecy J, Mohr DC. Development and usability testing of FOCUS: A smartphone system for self-management of schizophrenia. Psychiatr Rehabil J. 2013; 36(4): 289.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Riley W, Obermayer J, Jean-Mary J. Internet and mobile phone text messaging intervention for college smokers. J Am Coll Heal. 2008; 57(2): 245-248.CrossRefGoogle Scholar
  8. 8.
    Patrick K, Raab F, Adams MA, et al. A text message-based intervention for weight loss: Randomized controlled trial. J Med Internet Res. 2009;11(1).Google Scholar
  9. 9.
    Riley WT. Theoretical models to inform technology-based health behavior interventions. Behav Health Care Technol: Using Sci-Based Innov Transform Pract. 2014;13.Google Scholar
  10. 10.
    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(1): 53-71.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Collins LM, Nahum-Shani I, Almirall D. Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART). Clinical Trials. 2014;1740774514536795.Google Scholar
  12. 12.
    King G, Currie M, Petersen P. Child and parent engagement in the mental health intervention process: A motivational framework. Child Adolesc Mental Health. 2014; 19(1): 2-8.Google Scholar
  13. 13.
    Heckman BW, Mathew AR, Carpenter MJ. Treatment burden and treatment fatigue as barriers to health. Curr Opin Psychol. 2015; 5: 31-36.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Kreyenbuhl J, Nossel IR, Dixon LB. Disengagement from mental health treatment among individuals with schizophrenia and strategies for facilitating connections to care: A review of the literature. Schizophr Bull. 2009; 35(4): 696-703.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophrenia Bulletin. 2014.Google Scholar
  16. 16.
    Dantzig S, Geleijnse G, Halteren AT. Toward a persuasive mobile application to reduce sedentary behavior. Pers Ubiquit Comput. 2013; 17(6): 1237-1246.CrossRefGoogle Scholar
  17. 17.
    Kennedy CM, Powell J, Payne TH, Ainsworth J, Boyd A, Buchan I. Active assistance technology for health-related behavior change: An interdisciplinary review. J Med Internet Res. 2012;14(3).Google Scholar
  18. 18.
    Kelly J, Gooding P, Pratt D, Ainsworth J, Welford M, Tarrier N. Intelligent real-time therapy: Harnessing the power of machine learning to optimise the delivery of momentary cognitive–behavioural interventions. J Ment Health. 2012; 21(4): 404-414.PubMedCrossRefGoogle Scholar
  19. 19.
    Heron KE, Smyth JM. Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010; 15(1): 1-39.PubMedCrossRefGoogle Scholar
  20. 20.
    Monden Y. Toyota Production System: An Integrated Approach to Just-in-Time. Boca Raton: CRC Press; 2011.Google Scholar
  21. 21.
    Novak GM, Patterson ET, Gavrin AD, Christian W. Just-in-time teaching blending active learning with web technology. 1999.Google Scholar
  22. 22.
    Hoffmann B, Ritchie D. Using multimedia to overcome the problems with problem based learning. Instr Sci. 1997; 25(2): 97-115.CrossRefGoogle Scholar
  23. 23.
    Hulshof CD, De Jong T. Using just-in-time information to support scientific discovery learning in a computer-based simulation. Interact Learn Environ. 2006; 14(1): 79-94.CrossRefGoogle Scholar
  24. 24.
    Quintens L, Matthyssens P. Involving the process dimensions of time in case-based research. Ind Mark Manag. 2010; 39(1): 91-99.CrossRefGoogle Scholar
  25. 25.
    Ancona DG, Okhuysen GA, Perlow LA. Taking time to integrate temporal research. Acad Manag Rev. 2001; 26(4): 512-529.Google Scholar
  26. 26.
    Funnell SC, Rogers PJ. Purposeful Program Theory: Effective Use of Theories of Change and Logic Models. Vol. 31. Hoboken, NJ: Wiley; 2011.Google Scholar
  27. 27.
    Pauker SG, Zane EM, Salem DN. Creating a safer health care system: Finding the constraint. JAMA. 2005; 294(22): 2906-2908.PubMedCrossRefGoogle Scholar
  28. 28.
    Turnbull P, Oliver N, Wilkinson B. Buyer‐supplier relations in the UK‐automotive industry: Strategic implications of the Japanese manufacturing model. Strateg Manag J. 1992; 13(2): 159-168.CrossRefGoogle Scholar
  29. 29.
    McCuaig J, Cordick A. Scaffolding for experts: Trial not error. Paper presented at: Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2007.Google Scholar
  30. 30.
    Rummel N, Weinberger A, Wecker C, et al. New challenges in CSCL: Towards adaptive script support. Paper presented at: Proceedings of the 8th International Conference on International Conference for the Learning Sciences. Vol. 3. 2008.Google Scholar
  31. 31.
    Collins LM, Murphy SA, Bierman KL. A conceptual framework for adaptive preventive interventions. Prev Sci. 2004; 5(3): 185-196.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Gwaltney CJ, Shiffman S, Balabanis MH, Paty JA. Dynamic self-efficacy and outcome expectancies: Prediction of smoking lapse and relapse. J Abnorm Psychol. 2005; 114(4): 661.PubMedCrossRefGoogle Scholar
  33. 33.
    Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol. 2008; 27(3): 379.PubMedCrossRefGoogle Scholar
  34. 34.
    Webb T, Joseph J, Yardley L, Michie S. 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. 2010; 12(1): e4.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004; 31(2): 143-164.PubMedCrossRefGoogle Scholar
  36. 36.
    Campbell ANC, Muensch F, Nunes E. Technology-based behavioral interventions for alcohol and drug use problems. Behav Health Care Technol: Using Sci-Based Innov Transf Pract. 2014;40.Google Scholar
  37. 37.
    Zubin J, Spring B. Vulnerability: A new view of schizophrenia. J Abnorm Psychol. 1977; 86(2): 103.PubMedCrossRefGoogle Scholar
  38. 38.
    Witkiewitz K, Marlatt GA. Relapse prevention for alcohol and drug problems: That was zen, this is tao. Am Psychol. 2004; 59(4): 224-235.PubMedCrossRefGoogle Scholar
  39. 39.
    Shiffman S. Conceptual issues in the study of relapse. In: Gossop M, ed. Relpse and Addictive Behavior. New York: Routledge; 1989: 149-179.Google Scholar
  40. 40.
    Folkman S, Moskowitz JT. Coping: Pitfalls and promise. Annu Rev Psychol. 2004; 55: 745-774.PubMedCrossRefGoogle Scholar
  41. 41.
    Shiffman S. Dynamic influences on smoking relapse process. J Pers. 2005; 73(6): 1715-1748.PubMedCrossRefGoogle Scholar
  42. 42.
    Strecher VJ, Seijts GH, Kok GJ, et al. Goal setting as a strategy for health behavior change. Health Educ Behav. 1995; 22(2): 190-200.CrossRefGoogle Scholar
  43. 43.
    Krueger KA, Dayan P. Flexible shaping: How learning in small steps helps. Cognition. 2009; 110(3): 380-394.PubMedCrossRefGoogle Scholar
  44. 44.
    Skinner BF. The Behavior of Organisms: An Experimental Analysis. New York: Appleton-Century-Crofts; 1938.Google Scholar
  45. 45.
    Lawson PJ, Flocke SA. Teachable moments for health behavior change: A concept analysis. Patient Educ Couns. 2009; 76(1): 25-30.PubMedCrossRefGoogle Scholar
  46. 46.
    McBride C, Emmons K, Lipkus I. Understanding the potential of teachable moments: The case of smoking cessation. Health Educ Res. 2003; 18(2): 156-170.PubMedCrossRefGoogle Scholar
  47. 47.
    Diez-Canseco F, Zavala-Loayza JA, Beratarrechea A, et al. Design and multi-country validation of text messages for an mHealth intervention for primary prevention of progression to hypertension in Latin America. JMIR mHealth uHealth. 2015;3(1).Google Scholar
  48. 48.
    Patrick K, Intille SS, Zabinski MF. An ecological framework for cancer communication: Implications for research. J Med Internet Res. 2005;7(3).Google Scholar
  49. 49.
    Hekler EB, Klasnja P, Froehlich JE, Buman MP. Mind the theoretical gap: Interpreting, using, and developing behavioral theory in HCI research. Paper presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2013.Google Scholar
  50. 50.
    Nahum-Shani I, Hekler E, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychol. 2015; 34(Supp): 1209-1219.PubMedCrossRefGoogle Scholar
  51. 51.
    Witkiewitz K, Desai SA, Bowen S, Leigh BC, Kirouac M, Larimer ME. Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychol Addict Behav. 2014; 28(3): 639.PubMedCrossRefGoogle Scholar
  52. 52.
    Thomas JG, Wing RR. Health-e-call, a smartphone-assisted behavioral obesity treatment: Pilot study. JMIR mHealth uHealth. 2013;1(1).Google Scholar
  53. 53.
    Mohr DC, Cuijpers P, Lehman K. Supportive accountability: A model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011;13(1).Google Scholar
  54. 54.
    Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1).Google Scholar
  55. 55.
    Fukuoka Y, Gay C, Haskell W, Arai S, Vittinghoff E. Identifying factors associated with dropout during prerandomization run-in period from an mHealth physical activity education study: The mPED trial. JMIR mHealth uHealth. 2015;3(2).Google Scholar
  56. 56.
    Owen JE, Jaworski BK, Kuhn E, Makin-Byrd KN, Ramsey KM, Hoffman JE. mHealth in the wild: Using novel data to examine the reach, use, and impact of PTSD Coach. JMIR Mental Health. 2015; 2(1): e7.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Dunbrack L. Will the convergence of mobile and consumer engagement technologies lead to better health outcomes? In: Krohn R, Metcalf D, eds. mHealth Innovation: Best Practices from the Mobile Frontier. Chicago: HIMSS; 2014.Google Scholar
  58. 58.
    Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial. Ann Intern Med. 2014; 16(10_Supplement): S5-S12.CrossRefGoogle Scholar
  59. 59.
    Ho J, Intille SS. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Portland, Oregon, USA; 2005Google Scholar
  60. 60.
    McIntosh BS, Seaton RAF, Jeffrey P. Tools to think with? Towards understanding the use of computer-based support tools in policy relevant research. Environ Model Softw. 2007; 22(5): 640-648.CrossRefGoogle Scholar
  61. 61.
    Sarker H, Sharmin M, Ali AA, et al. Assessing the availability of users to engage in just-in-time intervention in the natural environment. 2014.Google Scholar
  62. 62.
    Pejovic V, Musolesi M. InterruptMe: Designing intelligent prompting mechanisms for pervasive applications. Paper presented at: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2014.Google Scholar
  63. 63.
    Fischer JE, Yee N, Bellotti V, Good N, Benford S, Greenhalgh C. Effects of content and time of delivery on receptivity to mobile interruptions. 2010.Google Scholar
  64. 64.
    Goldstein RZ, Bechara A, Garavan H, Childress AR, Paulus MP, Volkow ND. The neurocircuitry of impaired insight in drug addiction. Trends Cogn Sci. 2009; 13(9): 372-380.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Bartholow BD, Henry EA, Lust SA, Saults JS, Wood PK. Alcohol effects on performance monitoring and adjustment: Affect modulation and impairment of evaluative cognitive control. J Abnorm Psychol. 2012; 121(1): 173.PubMedCrossRefGoogle Scholar
  66. 66.
    Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: Evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry. 2013; 35(4): 332-338.PubMedCrossRefGoogle Scholar
  67. 67.
    McTavish FM, Chih M-Y, Shah D, Gustafson DH. How patients recovering from alcoholism use a smartphone intervention. J Dual Diagn. 2012; 8(4): 294-304.PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Nahum-Shani I, Qian M, Almirall D, et al. Experimental design and primary data analysis for developing adaptive interventions. Psychol Methods. 2012; 17(4): 457-477.PubMedCrossRefGoogle Scholar
  69. 69.
    Dennis ML, Scott CK, Funk RR, Nicholson L. A pilot study to examine the feasibility and potential effectiveness of using smartphones to provide recovery support for adolescents. Subst Abus. 2014; 36(4): 486-492.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Suffoletto B, Callaway C, Kristan J, Kraemer K, Clark DB. Text‐message‐based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcohol Clin Exp Res. 2012; 36(3): 552-560.PubMedCrossRefGoogle Scholar
  71. 71.
    Dunton GF, Liao Y, Intille SS, Spruijt‐Metz D, Pentz M. Investigating children’s physical activity and sedentary behavior using ecological momentary assessment with mobile phones. Obesity. 2011; 19(6): 1205-1212.PubMedCrossRefGoogle Scholar
  72. 72.
    Holtz B, Whitten P. Managing asthma with mobile phones: A feasibility study. Telemed e-Health. 2009; 15(9): 907-909.CrossRefGoogle Scholar
  73. 73.
    King AC, Ahn DK, Oliveira BM, Atienza AA, Castro CM, Gardner CD. Promoting physical activity through hand-held computer technology. Am J Prev Med. 2008; 34(2): 138-142.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E. An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS One. 2013; 8(12): e82901.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Thomas JG, Bond DS. Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization. Health Psychol. 2015; 34(S): 1261.PubMedCrossRefGoogle Scholar
  76. 76.
    Berkman ET, Dickenson J, Falk EB, Lieberman MD. Using SMS text messaging to assess moderators of smoking reduction: Validating a new tool for ecological measurement of health behaviors. Health Psychol. 2011; 30(2): 186.PubMedPubMedCentralCrossRefGoogle Scholar
  77. 77.
    Wenze SJ, Miller IW. Use of ecological momentary assessment in mood disorders research. Clin Psychol Rev. 2010; 30(6): 794-804.PubMedCrossRefGoogle Scholar
  78. 78.
    Shiffman S, Stone AA. Ecological momentary assessment: A new tool for behavioral medicine research. Technol Methods Behav Med. 1998;117–131.Google Scholar
  79. 79.
    Lavori PW, Dawson R. Introduction to dynamic treatment strategies and sequential multiple assignment randomization. Clin Trials. 2014; 11(4): 393-399.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Thompson D, Cantu D, Bhatt R, et al. Texting to increase physical activity among teenagers (TXT Me!): Rationale, design, and methods proposal. JMIR Res protocols. 2014;3(1).Google Scholar
  81. 81.
    Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008; 40(3): 879-891.PubMedCrossRefGoogle Scholar
  82. 82.
    Jochems EC, Mulder CL, van Dam A, et al. Motivation and treatment engagement intervention trial (MotivaTe-IT): The effects of motivation feedback to clinicians on treatment engagement in patients with severe mental illness. BMC Psychiatry. 2012; 12(1): 209.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Nezlek JB. Distinguishing affective and non‐affective reactions to daily events. J Pers. 2005; 73(6): 1539-1568.PubMedCrossRefGoogle Scholar
  84. 84.
    Pace-Schott EF, Shepherd E, Spencer RMC, et al. Napping promotes inter-session habituation to emotional stimuli. Neurobiol Learn Mem. 2011; 95(1): 24-36.PubMedCrossRefGoogle Scholar
  85. 85.
    Nett UE, Goetz T, Daniels LM. What to do when feeling bored?: Students’ strategies for coping with boredom. Learn Individ Differ. 2010; 20(6): 626-638.CrossRefGoogle Scholar
  86. 86.
    Barling J, Macintyre AT. Daily work role stressors, mood and emotional exhaustion. Work Stress. 1993; 7(4): 315-325.CrossRefGoogle Scholar
  87. 87.
    Demain S, Gonçalves A-C, Areia C, et al. Living with, managing and minimising treatment burden in long term conditions: A systematic review of qualitative research. PLoS One. 2015; 10(5): e0125457.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Crawford ER, LePine JA, Rich BL. Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test. J Appl Psychol. 2010; 95(5): 834.PubMedCrossRefGoogle Scholar
  89. 89.
    Schaufeli W, Taris T. A Critical Review of the Job Demands-Resources Model: Implications for Improving Work and Health. Bridging Occupational, Organizational and Public Health. Netherlands: Springer; 2014: 43-68.CrossRefGoogle Scholar
  90. 90.
    Cole MS, Walter F, Bedeian AG, O’Boyle EH. Job burnout and employee engagement a meta-analytic examination of construct proliferation. J Manag. 2012; 38(5): 1550-1581.Google Scholar
  91. 91.
    Hockey R. The Psychology of Fatigue: Work, Effort and Control. Cambridge: Cambridge University Press; 2013.Google Scholar
  92. 92.
    Bakker AB, Demerouti E, Sanz-Vergel AI. Burnout and work engagement: The JD–R approach. Annu Rev Organ Psychol Organ Behav. 2014; 1(1): 389-411.CrossRefGoogle Scholar
  93. 93.
    Lalmas M, O’Brien H, Yom-Tov E. Measuring user engagement. Synth Lect Inf Concepts Retr Services. 2014; 6(4): 1-132.Google Scholar
  94. 94.
    Klein M, Mogles N, Van Wissen A. Why won’t you do what’s good for you? Using intelligent support for behavior change. Human Behavior Understanding. Berlin: Springer; 2011:104–115.Google Scholar
  95. 95.
    Deci EL. Intrinsic Motivation. Vol. 23. New York: Plenum Press; 1975.Google Scholar
  96. 96.
    Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: Key issues, process model, and system features. Commun Assoc Inf Syst. 2009; 24(1): 28.Google Scholar
  97. 97.
    Consolvo S, Klasnja P, McDonald DW, Landay JA. Goal-setting considerations for persuasive technologies that encourage physical activity. 2009.Google Scholar
  98. 98.
    Engeser S, Rheinberg F. Flow, performance and moderators of challenge-skill balance. Motiv Emot. 2008; 32(3): 158-172.CrossRefGoogle Scholar
  99. 99.
    Baumeister RF, Leary MR. The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychol Bull. 1995; 117(3): 497.PubMedCrossRefGoogle Scholar
  100. 100.
    Bennett K, Grasso F, Lowers V, McKay A, Milligan C. Evaluation of an app to support older adults with wounds. Paper presented at: Proceedings of the 5th International Conference on Digital Health. 2015.Google Scholar
  101. 101.
    Moller AC, Deci EL, Ryan RM. Choice and ego-depletion: The moderating role of autonomy. Personal Soc Psychol Bull. 2006; 32(8): 1024-1036.CrossRefGoogle Scholar
  102. 102.
    Fukuoka Y, Lindgren T, Jong S. Qualitative exploration of the acceptability of a mobile phone and pedometer‐based physical activity program in a diverse sample of sedentary women. Public Health Nurs. 2012; 29(3): 232-240.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Fogg B. Mobile persuasion: 20 perspectives on the future of behavior change. Mobile Persuasion; 2007.Google Scholar
  104. 104.
    Kommers P, Hooreman RW. Mobile phones for real-time teacher coaching. J Res Innov Teach. 2009; 2(1): 80-90.Google Scholar
  105. 105.
    Mael F, Jex S. Workplace boredom: An integrative model of traditional and contemporary approaches. Group Organ Manag. 2015; 40(2): 131-159.CrossRefGoogle Scholar
  106. 106.
    Linn AJ, van Weert J, Smit EG, Perry K, van Dijk L. 1+ 1 = 3? The systematic development of a theoretical and evidence-based tailored multimedia intervention to improve medication adherence. Patient Educ Couns. 2013; 93(3): 381-388.PubMedCrossRefGoogle Scholar
  107. 107.
    Paterson N, Naliuka K, Jensen SK, Carrigy T, Haahr M, Conway F. Design, implementation and evaluation of audio for a location aware augmented reality game. Paper presented at: Proceedings of the 3rd International Conference on Fun and Games. 2010.Google Scholar
  108. 108.
    Hsu A, Yang J, Yilmaz YH, Haque MS, Can C, Blandford AE. Persuasive technology for overcoming food cravings and improving snack choices. Paper presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014.Google Scholar
  109. 109.
    Diener E, Smith H, Fujita F. The personality structure of affect. J Pers Soc Psychol. 1995; 69(1): 130-141.CrossRefGoogle Scholar
  110. 110.
    Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009; 21(4): 486.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Lathia N, Pejovic V, Rachuri KK, Mascolo C, Musolesi M, Rentfrow PJ. Smartphones for large-scale behavior change interventions. IEEE Pervasive Comput. 2013; 3: 66-73.CrossRefGoogle Scholar
  112. 112.
    Sun F-T, Kuo C, Cheng H-T, Buthpitiya S, Collins P, Griss M. Activity-aware mental stress detection using physiological sensors. Mobile Computing, Applications, and Services. Berlin: Springer; 2012:211–230.Google Scholar
  113. 113.
    Kumar S, Al’Absi M, Beck GJ, Emre E, Scott M. Behavioral monitoring and assessment via mobile sensing technologies. In: Marsch LA, Lord SE, Dallery J, eds. Behavioral Healthcare and Technology: Using Science-Based Innovation to Transform Practice. New York: Oxford University Press; 2015. 27039.Google Scholar
  114. 114.
    Plarre K, Raij A, Hossain SM, et al. Continuous inference of psychological stress from sensory measurements collected in the natural environment. Paper presented at: Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on 2011.Google Scholar
  115. 115.
    Rahman MM, Ali AA, Plarre K, al’Absi M, Ertin E, Kumar S. mconverse: Inferring conversation episodes from respiratory measurements collected in the field. Paper presented at: Proceedings of the 2nd Conference on Wireless Health. 2011.Google Scholar
  116. 116.
    Ali AA, Hossain SM, Hovsepian K, Rahman MM, Plarre K, Kumar S. mPuff: Automated detection of cigarette smoking puffs from respiration measurements. Paper presented at: Proceedings of the 11th International Conference on Information Processing in Sensor Networks. 2012.Google Scholar
  117. 117.
    Tapia EM, Intille SS, Larson K. Activity Recognition in the Home Using Simple and Ubiquitous Sensors. Berlin: Springer; 2004.Google Scholar
  118. 118.
    Eagle N, Pentland A. Reality mining: Sensing complex social systems. Pers Ubiquit Comput. 2006; 10(4): 255-268.CrossRefGoogle Scholar
  119. 119.
    Rahman MM, Bari R, Ali AA, et al. Are we there yet?: Feasibility of continuous stress assessment via wireless physiological sensors. Paper presented at: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 2014.Google Scholar
  120. 120.
    Segerstrom SC, O’Connor DB. Stress, health and illness: Four challenges for the future. Psychol Health. 2012; 27(2): 128-140.PubMedCrossRefGoogle Scholar
  121. 121.
    Sonnentag S, Pundt A, Albrecht A-G. Temporal perspectives on job stress. Time Work: How Time Impacts Individ. 2014; 1: 111-140.Google Scholar
  122. 122.
    Martin CA, Rivera DE, Riley WT, et al. A dynamical systems model of social cognitive theory. Paper presented at: American Control Conference (ACC). 2014.Google Scholar
  123. 123.
    Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010; 31: 399-418.PubMedCrossRefGoogle Scholar
  124. 124.
    Taylor N, Conner M, Lawton R. The impact of theory on the effectiveness of worksite physical activity interventions: A meta-analysis and meta-regression. Health Psychol Rev. 2012; 6(1): 33-73.CrossRefGoogle Scholar
  125. 125.
    Riley WT, Martin CA, Rivera DE. The importance of behavior theory in control system modeling of physical activity sensor data. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. 2014.Google Scholar
  126. 126.
    Timms KP, Rivera DE, Collins LM, Piper ME. Control systems engineering for understanding and optimizing smoking cessation interventions. Paper presented at: American Control Conference (ACC), 2013. 2013.Google Scholar
  127. 127.
    Spruijt-Metz D, Hekler E, Saranummi N, et al. Building new computational models to support health behavior change and maintenance: New opportunities in behavioral research. Transl Behav Med. 2015; 5(3): 335-346.PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Trail JB, Collins LM, Rivera DE, Li R, Piper ME, Baker TB. Functional data analysis for dynamical system identification of behavioral processes. Psychol Methods. 2014; 19(2): 175.PubMedCrossRefGoogle Scholar
  129. 129.
    Lanza ST, Piper ME, Shiffman S. New methods for advancing research on tobacco dependence using ecological momentary assessments. Nicotine Tob Res. 2014; 16(Suppl 2): S71-S72.PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Smith DM, Walls TA. mHealth analytics. Behavioral health care and technology: Using science-based innovations to transform practice. 2014:153.Google Scholar
  131. 131.
    Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro‐randomized trials in mHealth. Stat Med. 2015.Google Scholar
  132. 132.
    Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: A randomized controlled trial on adults. JMIR mHealth uHealth. 2015; 3(2): e42.PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    Kaplan RM, Stone AA. Bringing the laboratory and clinic to the community: Mobile technologies for health promotion and disease prevention. Annu Rev Psychol. 2013; 64: 471-498.PubMedCrossRefGoogle Scholar
  134. 134.
    Schueller SM, Tomasino KN, Mohr DC. Integrating human support into behavioral intervention technologies: The efficiency model of support. Clinical Psychology: Science and Practice. In Press; 2016.Google Scholar

Copyright information

© The Society of Behavioral Medicine 2016

Authors and Affiliations

  • Inbal Nahum-Shani
    • 1
  • Shawna N. Smith
    • 2
  • Bonnie J. Spring
    • 3
  • Linda M. Collins
    • 4
  • Katie Witkiewitz
    • 5
  • Ambuj Tewari
    • 6
  • Susan A. Murphy
    • 7
  1. 1.Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  2. 2.Division of General Medicine, Department of Internal Medicine and Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  3. 3.Feinberg School of MedicineNorthwestern UniversityEvanstonUSA
  4. 4.The Methodology Center and Department of Human Development & Family StudiesPenn StateState CollegeUSA
  5. 5.Department of PsychologyUniversity of New MexicoAlbuquerqueUSA
  6. 6.Department of Statistics and Department of EECSUniversity of MichiganAnn ArborUSA
  7. 7.Department of Statistics, and Institute for Social ResearchUniversity of MichiganAnn ArborUSA

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