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Abstract

Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology—and the unprecedented scope and quantity of data it generates—has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.

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2023-04-03
2024-04-20
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Literature Cited

  1. 1.
    Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G et al. 2016. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ 352:i1174
    [Google Scholar]
  2. 2.
    Al Borno M, O'Day J, Ibarra V, Dunne J, Seth A et al. 2022. OpenSense: an open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations. J. Neuroeng. Rehabil. 19:2222
    [Google Scholar]
  3. 3.
    Alavi A, Bogu GK, Wang M, Rangan ES, Brooks AW et al. 2022. Real-time alerting system for COVID-19 and other stress events using wearable data. Nat. Med. 28:1175–84
    [Google Scholar]
  4. 4.
    Althoff T, Horvitz E, White RW. 2018. Psychomotor function measured via online activity predicts motor vehicle fatality risk. npj Digit. Med. 1:120173
    [Google Scholar]
  5. 5.
    Althoff T, Jindal P, Leskovec J. 2017. Online actions with offline impact: how online social networks influence online and offline user behavior. Proc. Int. Conf. Web. Search Data Min. 2017:537–46
    [Google Scholar]
  6. 6.
    Althoff T, Sosič R, Hicks JL, King AC, Delp SL, Leskovec J. 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature 547:7663336–39
    [Google Scholar]
  7. 7.
    Attig C, Franke T. 2020. Abandonment of personal quantification: a review and empirical study investigating reasons for wearable activity tracking attrition. Comput. Hum. Behav. 102:223–37
    [Google Scholar]
  8. 8.
    Bahmani A, Alavi A, Buergel T, Upadhyayula S, Wang Q et al. 2021. A scalable, secure, and interoperable platform for deep data-driven health management. Nat. Commun. 12:5757
    [Google Scholar]
  9. 9.
    Bandura A. 2001. Social cognitive theory: an agentic perspective. Annu. Rev. Psychol. 52:1–26
    [Google Scholar]
  10. 10.
    Bennett JE, Stevens GA, Mathers CD, Bonita R, Rehm J et al. 2018. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet 392:101521072–88
    [Google Scholar]
  11. 11.
    Boles DZ, DeSousa M, Turnwald BP, Horii RI, Duarte T et al. 2021. Can exercising and eating healthy be fun and indulgent instead of boring and depriving? Targeting mindsets about the process of engaging in healthy behaviors. Front. Psychol. 12:Oct.745950
    [Google Scholar]
  12. 12.
    Boswell MA, Evans KM, Zion SR, Boles DZ, Hicks JL et al. 2022. Mindset is associated with future physical activity and management strategies in individuals with knee osteoarthritis. Ann. Phys. Rehabil. Med. 65:6101634
    [Google Scholar]
  13. 13.
    Bot BM, Suver C, Neto EC, Kellen M, Klein A et al. 2016. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci. Data 3:160011
    [Google Scholar]
  14. 14.
    Boulton ER, Horne M, Todd C. 2018. Multiple influences on participating in physical activity in older age: developing a social ecological approach. Health Expect. 21:1239–48
    [Google Scholar]
  15. 15.
    Briant KJ, Halter A, Marchello N, Escareño M, Thompson B. 2016. The power of digital storytelling as a culturally relevant health promotion tool. Health Promot. Pract. 17:6793–801
    [Google Scholar]
  16. 16.
    Buckingham SA, Williams AJ, Morrissey K, Price L, Harrison J. 2019. Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review. Digit. Health 5:2055207619839883
    [Google Scholar]
  17. 17.
    Cohen J. 2001. Defining identification: a theoretical look at the identification of audiences with media characters. Mass Commun. Soc. 4:3245–64
    [Google Scholar]
  18. 18.
    Conroy DE, Yang C-H, Maher JP. 2014. Behavior change techniques in top-ranked mobile apps for physical activity. Am. J. Prev. Med. 46:6649–52
    [Google Scholar]
  19. 19.
    Consolvo S, Klasnja P, McDonald DW, Avrahami D, Froehlich J et al. 2008. Flowers or a robot army?: Encouraging awareness & activity with personal, mobile displays. Proc. Int. Conf. Ubiquitous Comput. 2008:54–63
    [Google Scholar]
  20. 20.
    Consolvo S, Klasnja P, McDonald DW, Landay JA. 2012. Designing for healthy lifestyles: design considerations for mobile technologies to encourage consumer health and wellness. Found. Trends Hum.-Comput. Interact. 6:3–4167–315
    [Google Scholar]
  21. 21.
    Crum AJ, Akinola M, Martin A, Fath S 2017. The role of stress mindset in shaping cognitive, emotional, and physiological responses to challenging and threatening stress. Anxiety Stress Coping 30:4379–95
    [Google Scholar]
  22. 22.
    Direito A, Carraça E, Rawstorn J, Whittaker R, Maddison R. 2017. mHealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Ann. Behav. Med. 51:2226–39
    [Google Scholar]
  23. 23.
    Dis. Control Complic. Trial Res. Group, Nathan DM, Genuth S, Lachin J, Cleary P et al. 1993. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329:14977–86
    [Google Scholar]
  24. 24.
    Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P et al. 2017. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLOS ONE 12:2e0169649
    [Google Scholar]
  25. 25.
    Domin A, Spruijt-Metz D, Theisen D, Ouzzahra Y, Vögele C. 2021. Smartphone-based interventions for physical activity promotion: scoping review of the evidence over the last 10 years. JMIR mHealth uHealth 9:7e24308
    [Google Scholar]
  26. 26.
    Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL et al. 2021. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat. Med. 27:61105–12
    [Google Scholar]
  27. 27.
    Dunseath S, Weibel N, Bloss CS, Nebeker C. 2018. NIH support of Mobile, Imaging, Pervasive Sensing, Social Media and Location Tracking (MISST) research: laying the foundation to examine research ethics in the digital age. npj Digit. Med. 1:20171
    [Google Scholar]
  28. 28.
    Dweck CS, Hong Y-Y, Chiu C-Y. 1993. Implicit theories: individual differences in the likelihood and meaning of dispositional inference. Pers. Soc. Psychol. Bull. 19:5644–56
    [Google Scholar]
  29. 29.
    Dweck CS, Yeager DS. 2019. Mindsets: a view from two eras. Perspect. Psychol. Sci. 14:3481–96
    [Google Scholar]
  30. 30.
    Eysenbach G, CONSORT-EHEALTH Group 2011. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J. Med. Internet Res. 13:4e126
    [Google Scholar]
  31. 31.
    French DP, Olander EK, Chisholm A, McSharry J. 2014. Which behaviour change techniques are most effective at increasing older adults’ self-efficacy and physical activity behaviour? A systematic review. Ann. Behav. Med. 48:2225–34
    [Google Scholar]
  32. 32.
    Gadaleta M, Radin JM, Baca-Motes K, Ramos E, Kheterpal V et al. 2021. Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms. npj Digit. Med. 4:1166
    [Google Scholar]
  33. 33.
    Ghanvatkar S, Kankanhalli A, Rajan V. 2019. User models for personalized physical activity interventions: scoping review. JMIR mHealth uHealth 7:1e11098
    [Google Scholar]
  34. 34.
    Gourlan M, Bernard P, Bortolon C, Romain AJ, Lareyre O et al. 2016. Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials. Health Psychol. Rev. 10:150–66
    [Google Scholar]
  35. 35.
    Green MC, Brock TC. 2000. The role of transportation in the persuasiveness of public narratives. J. Pers. Soc. Psychol. 79:5701–21
    [Google Scholar]
  36. 36.
    Greshake Tzovaras B, Angrist M, Arvai K, Dulaney M, Estrada-Galiñanes V et al. 2019. Open Humans: a platform for participant-centered research and personal data exploration. Gigascience 8:6giz076
    [Google Scholar]
  37. 37.
    Halilaj E, Hastie TJ, Gold GE, Delp SL. 2018. Physical activity is associated with changes in knee cartilage microstructure. Osteoarthr. Cartil. 26:6770–74
    [Google Scholar]
  38. 38.
    Hekler EB, Michie S, Pavel M, Rivera DE, Collins LM et al. 2016. Advancing models and theories for digital behavior change interventions. Am. J. Prev. Med. 51:5825–32
    [Google Scholar]
  39. 39.
    Hicks JL, Althoff T, Sosic R, Kuhar P, Bostjancic B et al. 2019. Best practices for analyzing large-scale health data from wearables and smartphone apps. npj Digit. Med. 2:45
    [Google Scholar]
  40. 40.
    Hintze D, Findling RD, Muaaz M, Scholz S, Mayrhofer R. 2014. Diversity in locked and unlocked mobile device usage. Proc. ACM Int. Jt. Conf. Pervasive Ubiquitous Comput. 2014:379–84
    [Google Scholar]
  41. 41.
    Hintze D, Hintze P, Findling RD, Mayrhofer R. 2017. A large-scale, long-term analysis of mobile device usage characteristics. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1:213
    [Google Scholar]
  42. 42.
    Hinyard LJ, Kreuter MW. 2007. Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview. Health Educ. Behav. 34:5777–92
    [Google Scholar]
  43. 43.
    Holko M, Litwin TR, Munoz F, Theisz KI, Salgin L et al. 2022. Wearable fitness tracker use in federally qualified health center patients: strategies to improve the health of all of us using digital health devices. npj Digit. Med. 5:153
    [Google Scholar]
  44. 44.
    Jiang C, Wang X, Li X, Inlora J, Wang T et al. 2018. Dynamic human environmental exposome revealed by longitudinal personal monitoring. Cell 175:1277–91.e31
    [Google Scholar]
  45. 45.
    Karkar R, Zia J, Vilardaga R, Mishra SR, Fogarty J et al. 2016. A framework for self-experimentation in personalized health. J. Am. Med. Inform. Assoc. 23:3440–48
    [Google Scholar]
  46. 46.
    Kelty C, Panofsky A. 2014. Disentangling public participation in science and biomedicine. Genome Med. 6:18
    [Google Scholar]
  47. 47.
    Kidziński Ł, Yang B, Hicks JL, Rajagopal A, Delp SL, Schwartz MH. 2020. Deep neural networks enable quantitative movement analysis using single-camera videos. Nat. Commun. 11:14054
    [Google Scholar]
  48. 48.
    King AC, Campero I, Sheats JL, Castro Sweet CM, Garcia D et al. 2017. Testing the comparative effects of physical activity advice by humans versus computers in underserved populations: the COMPASS trial design, methods, and baseline characteristics. Contemp. Clin. Trials 61:April115–25
    [Google Scholar]
  49. 49.
    King AC, Campero MI, Sheats JL, Castro Sweet CM, Hauser ME et al. 2020. Effects of counseling by peer human advisors vs. computers to increase walking in underserved populations: the COMPASS randomized clinical trial. JAMA Intern. Med. 180:111481–90
    [Google Scholar]
  50. 50.
    King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL et al. 2016. Effects of three motivationally targeted mobile device applications on initial physical activity and sedentary behavior change in midlife and older adults: a randomized trial. PLOS ONE 11:6e0156370
    [Google Scholar]
  51. 51.
    King AC, King DK, Banchoff A, Solomonov S, Ben Natan O et al. 2020. Employing participatory citizen science methods to promote age-friendly environments worldwide. Int. J. Environ. Res. Public Health 17:51541
    [Google Scholar]
  52. 52.
    King AC, Odunitan-Wayas FA, Chaudhury M, Rubio MA, Baiocchi M et al. 2021. Community-based approaches to reducing health inequities and fostering environmental justice through global youth-engaged citizen science. Int. J. Environ. Res. Public Health 18:3892
    [Google Scholar]
  53. 53.
    King AC, Whitt-Glover MC, Marquez DX, Buman MP, Napolitano MA et al. 2019. Physical activity promotion: highlights from the 2018 Physical Activity Guidelines Advisory Committee systematic review. Med. Sci. Sports Exerc. 51:61340–53
    [Google Scholar]
  54. 54.
    King AC, Winter SJ, Chrisinger BW, Hua J, Banchoff AW. 2019. Maximizing the promise of citizen science to advance health and prevent disease. Prev. Med. 119:44–47
    [Google Scholar]
  55. 55.
    King AC, Winter SJ, Sheats JL, Rosas LG, Buman MP et al. 2016. Leveraging citizen science and information technology for population physical activity promotion. Transl. J. Am. Coll. Sports Med. 1:430–44
    [Google Scholar]
  56. 56.
    Ku JP, Sim I. 2021. Mobile health: making the leap to research and clinics. npj Digit Med. 4:183
    [Google Scholar]
  57. 57.
    Kunst A. 2022. Consumer electronics ownership in the United States 2022. Statista April 21. https://www.statista.com/forecasts/997201/consumer-electronics-ownership-in-the-us
    [Google Scholar]
  58. 58.
    Laranjo L, Ding D, Heleno B, Kocaballi B, Quiroz JC et al. 2021. Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression. Br. J. Sports Med. 55:8422–32
    [Google Scholar]
  59. 59.
    Lucivero F, Jongsma KR. 2018. A mobile revolution for healthcare? Setting the agenda for bioethics. J. Med. Ethics 44:10685–89
    [Google Scholar]
  60. 60.
    Manoogian ENC, Wei-Shatzel J, Panda S. 2022. Assessing temporal eating pattern in free living humans through the myCircadianClock app. Int. J. Obes. 46:4696–706
    [Google Scholar]
  61. 61.
    Mason KE, Pearce N, Cummins S 2018. Associations between fast food and physical activity environments and adiposity in mid-life: cross-sectional, observational evidence from UK Biobank. Lancet Public Health 3:1e24–33
    [Google Scholar]
  62. 62.
    Matheson GO, Klügl M, Engebretsen L, Bendiksen F, Blair SN et al. 2013. Prevention and management of non-communicable disease: the IOC consensus statement, Lausanne 2013. Sports Med. 43:111075–88
    [Google Scholar]
  63. 63.
    McConnell MV, Shcherbina A, Pavlovic A, Homburger JR, Goldfeder RL et al. 2017. Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart Counts Cardiovascular Health study. JAMA Cardiol. 2:167–76
    [Google Scholar]
  64. 64.
    Mercer K, Li M, Giangregorio L, Burns C, Grindrod K. 2016. Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR mHealth uHealth 4:2e40
    [Google Scholar]
  65. 65.
    Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. 2011. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol. Health 26:111479–98
    [Google Scholar]
  66. 66.
    Michie S, van Stralen MM, West R. 2011. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6:4242
    [Google Scholar]
  67. 67.
    Michie S, Yardley L, West R, Patrick K, Greaves F 2017. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J. Med. Internet Res. 19:6e232
    [Google Scholar]
  68. 68.
    Moyer-Gusé E. 2008. Toward a theory of entertainment persuasion: explaining the persuasive effects of entertainment-education messages. Commun. Theory 18:3407–25
    [Google Scholar]
  69. 69.
    Mummah SA, Robinson TN, King AC, Gardner CD, Sutton S 2016. IDEAS (integrate, design, assess, and share): a framework and toolkit of strategies for the development of more effective digital interventions to change health behavior. J. Med. Internet Res. 18:12e317
    [Google Scholar]
  70. 70.
    Murnane EL, Jiang X, Kong A, Park M, Shi W et al. 2020. Designing ambient narrative-based interfaces to reflect and motivate physical activity. Proc. Conf. Hum. Factors Comput. Syst. 2020:1–14
    [Google Scholar]
  71. 71.
    Murphy ST, Frank LB, Chatterjee JS, Baezconde-Garbanati L. 2013. Narrative versus nonnarrative: the role of identification, transportation, and emotion in reducing health disparities. J. Commun. 63:1116–37
    [Google Scholar]
  72. 72.
    Na L, Yang C, Lo C-C, Zhao F, Fukuoka Y, Aswani A. 2018. Feasibility of reidentifying individuals in large national physical activity data sets from which protected health information has been removed with use of machine learning. JAMA Netw. Open. 1:8e186040
    [Google Scholar]
  73. 73.
    Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K et al. 2018. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann. Behav. Med. 52:6446–62
    [Google Scholar]
  74. 74.
    Norman DA, Draper SW. 1986. User Centered System Design: New Perspectives on Human-Computer Interaction Mahwah, NJ: Erlbaum
  75. 75.
    Ochoa CY, Murphy ST, Frank LB, Baezconde-Garbanati LA. 2020. Using a culturally tailored narrative to increase cervical cancer detection among Spanish-speaking Mexican-American women. J. Cancer Educ. 35:4736–42
    [Google Scholar]
  76. 76.
    Pandey V, Koul T, Yang C, McDonald D, Price Ball M et al. 2021. Galileo: citizen-led experimentation using a social computing system. Proc. CHI Conf. Hum. Factors Comput. Syst. 2021:565
    [Google Scholar]
  77. 77.
    Park C, Arian M, Liu X, Sasson L, Kahn J et al. 2021. Online mobile app usage as an indicator of sleep behavior and job performance. Proc. World Wide Web Conf. 2021:2488–500
    [Google Scholar]
  78. 78.
    Parlak O, Keene ST, Marais A, Curto VF, Salleo A. 2018. Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4:7eaar2904
    [Google Scholar]
  79. 79.
    Peyser ND, Marcus GM, Beatty AL, Olgin JE, Pletcher MJ. 2022. Digital platforms for clinical trials: the Eureka experience. Contemp. Clin. Trials 115:106710
    [Google Scholar]
  80. 80.
    Prochaska JO 2020. Transtheoretical model of behavior change. Encyclopedia of Behavioral Medicine MD Gellman 2266–70. Cham, Switz: Springer Int.
    [Google Scholar]
  81. 81.
    Ranjan Y, Rashid Z, Stewart C, Conde P, Begale M et al. 2019. RADAR-base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR mHealth uHealth 7:8e11734
    [Google Scholar]
  82. 82.
    Resnick PJ, Janney AW, Buis LR, Richardson CR. 2010. Adding an online community to an Internet-mediated walking program. Part 2: strategies for encouraging community participation. J. Med. Internet Res. 12:4e72
    [Google Scholar]
  83. 83.
    Richardson CR, Buis LR, Janney AW, Goodrich DE, Sen A et al. 2010. An online community improves adherence in an internet-mediated walking program. Part 1: results of a randomized controlled trial. J. Med. Internet Res. 12:4e71
    [Google Scholar]
  84. 84.
    Rodriguez NM, Arce A, Kawaguchi A, Hua J, Broderick B et al. 2019. Enhancing safe routes to school programs through community-engaged citizen science: two pilot investigations in lower density areas of Santa Clara County, California, USA. BMC Public Health 19:1256
    [Google Scholar]
  85. 85.
    Rowbotham S, McKinnon M, Leach J, Lamberts R, Hawe P. 2019. Does citizen science have the capacity to transform population health science?. Crit. Public Health 29:1118–28
    [Google Scholar]
  86. 86.
    Sage Bionetworks 2019. Bridge platform. Sage Bionetworks https://sagebionetworks.org/tools_resources/bridge-platform/
    [Google Scholar]
  87. 87.
    Sakaniwa R, Noguchi M, Imano H, Shirai K, Tamakoshi A et al. 2022. Impact of modifiable healthy lifestyle adoption on lifetime gain from middle to older age. Age Ageing 51:5afac080
    [Google Scholar]
  88. 88.
    Savikj M, Gabriel BM, Alm PS, Smith J, Caidahl K et al. 2019. Afternoon exercise is more efficacious than morning exercise at improving blood glucose levels in individuals with type 2 diabetes: a randomised crossover trial. Diabetologia 62:2233–37
    [Google Scholar]
  89. 89.
    Schneble CO, Elger BS, Shaw DM. 2020. All our data will be health data one day: the need for universal data protection and comprehensive consent. J. Med. Internet Res. 22:5e16879
    [Google Scholar]
  90. 90.
    Schraefel MC, Muresan GC, Hekler E. 2021. Experiment in a box (XB): an interactive technology framework for sustainable health practices. Front. Comput. Sci. 3:661890
    [Google Scholar]
  91. 91.
    See L, Rasiah RL, Laing R, Thompson SC. 2021. Considerations in planning physical activity for older adults in hot climates: a narrative review. Int. J. Environ. Res. Public Health 18:31331
    [Google Scholar]
  92. 92.
    Shaffer VA, Focella ES, Hathaway A, Scherer LD, Zikmund-Fisher BJ. 2018. On the usefulness of narratives: an interdisciplinary review and theoretical model. Ann. Behav. Med. 52:5429–42
    [Google Scholar]
  93. 93.
    Shameli A, Althoff T, Saberi A, Leskovec J. 2017. How gamification affects physical activity: large-scale analysis of walking challenges in a mobile application. Proc. Int. World Wide Web. Conf. 2017:455–63
    [Google Scholar]
  94. 94.
    Shen F, Sheer VC, Li R. 2015. Impact of narratives on persuasion in health communication: a meta-analysis. J. Advert. 44:2105–13
    [Google Scholar]
  95. 95.
    Short CE, James EL, Plotnikoff RC, Girgis A. 2011. Efficacy of tailored-print interventions to promote physical activity: a systematic review of randomised trials. Int. J. Behav. Nutr. Phys. Act. 8:113
    [Google Scholar]
  96. 96.
    Slade P, Habib A, Hicks JL, Delp SL. 2022. An open-source and wearable system for measuring 3D human motion in real-time. IEEE Trans. Biomed. Eng. 69:2678–88
    [Google Scholar]
  97. 97.
    Slater MD, Rouner D. 2002. Entertainment? Education and elaboration likelihood: understanding the processing of narrative persuasion. Commun. Theory 12:2173–91
    [Google Scholar]
  98. 98.
    Sousa CV, Fernandez A, Hwang J, Lu AS. 2020. The effect of narrative on physical activity via immersion during active video game play in children: mediation analysis. J. Med. Internet Res. 22:3e17994
    [Google Scholar]
  99. 99.
    Spring B, Gotsis M, Paiva A, Spruijit-Metz D. 2013. Healthy apps: mobile devices for continuous monitoring and intervention. IEEE Pulse 4:2034–40
    [Google Scholar]
  100. 100.
    Stanford Byers Cent. Biodesign 2022. CardinalKit. Stanford Biodesign. http://cardinalkit.org
    [Google Scholar]
  101. 101.
    Stanford Byers Cent. Biodesign 2022. How patients inspire our innovators. Million+ Patients Helped. https://biodesign.stanford.edu/our-impact/million-plus-patients-helped.html
    [Google Scholar]
  102. 102.
    Stephenson A, McDonough SM, Murphy MH, Nugent CD, Mair JL. 2017. Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 14:1105
    [Google Scholar]
  103. 103.
    Stockwell S, Schofield P, Fisher A, Firth J, Jackson SE et al. 2019. Digital behavior change interventions to promote physical activity and/or reduce sedentary behavior in older adults: a systematic review and meta-analysis. Exp. Gerontol. 120:March68–87
    [Google Scholar]
  104. 104.
    Stolley MR, Fitzgibbon ML, Schiffer L, Sharp LK, Singh V et al. 2009. Obesity Reduction Black Intervention Trial (ORBIT): six-month results. Obesity 17:1100–6
    [Google Scholar]
  105. 105.
    Taj F, Klein MCA, Van Halteren A. 2019. Digital health behavior change technology: bibliometric and scoping review of two decades of research. JMIR mHealth uHealth 7:12e13311
    [Google Scholar]
  106. 106.
    Taylor K, Silver L. 2019. Smartphone ownership is growing rapidly around the world, but not always equally Rep. Pew Res. Cent. Washington, DC: https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/
  107. 107.
    Tehrani F, Teymourian H, Wuerstle B, Kavner J, Patel R et al. 2022. An integrated wearable microneedle array for the continuous monitoring of multiple biomarkers in interstitial fluid. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00887-1
    [Google Scholar]
  108. 108.
    Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai AC. 2013. Scaling up mHealth: Where is the evidence?. PLOS Med. 10:2e1001382
    [Google Scholar]
  109. 109.
    Tong HL, Laranjo L. 2018. The use of social features in mobile health interventions to promote physical activity: a systematic review. npj Digit. Med. 1:43
    [Google Scholar]
  110. 110.
    Tong HL, Quiroz JC, Kocaballi AB, Fat SCM, Dao KP et al. 2021. Personalized mobile technologies for lifestyle behavior change: a systematic review, meta-analysis, and meta-regression. Prev. Med. 148:106532
    [Google Scholar]
  111. 111.
    Torous J, Kiang MV, Lorme J, Onnela J-P. 2016. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Health 3:2e16
    [Google Scholar]
  112. 112.
    Tovino SA. 2020. Privacy and security issues with mobile health research applications. J. Law Med. Ethics 48:154–58
    [Google Scholar]
  113. 113.
    Uhlrich SD, Silder A, Beaupre GS, Shull PB, Delp SL. 2018. Subject-specific toe-in or toe-out gait modifications reduce the larger knee adduction moment peak more than a non-personalized approach. J. Biomech. 66:103–10
    [Google Scholar]
  114. 114.
    van Asbroeck S, Matthys C. 2020. Use of different food image recognition platforms in dietary assessment: comparison study. JMIR Form. Res. 4:12e15602
    [Google Scholar]
  115. 115.
    Voorheis P, Zhao A, Kuluski K, Pham Q, Scott T et al. 2022. Integrating behavioral science and design thinking to develop mobile health interventions: systematic scoping review. JMIR mHealth uHealth 10:3e35799
    [Google Scholar]
  116. 116.
    Wall J, Hellman E, Denend L, Rait D, Venook R et al. 2017. The impact of postgraduate health technology innovation training: outcomes of the Stanford Biodesign Fellowship. Ann. Biomed. Eng. 45:51163–71
    [Google Scholar]
  117. 117.
    Webb TL, Joseph J, Yardley L, Michie S 2010. 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. 12:1e4
    [Google Scholar]
  118. 118.
    Xu L, Shi H, Shen M, Ni Y, Zhang X et al. 2022. The effects of mHealth-based gamification interventions on participation in physical activity: systematic review. JMIR mHealth uHealth 10:2e27794
    [Google Scholar]
  119. 119.
    Yang Q, Van Stee SK. 2019. The comparative effectiveness of mobile phone interventions in improving health outcomes: meta-analytic review. JMIR mHealth uHealth 7:4e11244
    [Google Scholar]
  120. 120.
    Zahrt OH, Crum AJ. 2017. Perceived physical activity and mortality: evidence from three nationally representative U.S. samples. Health Psychol. 36:111017–25
    [Google Scholar]
  121. 121.
    Zahrt OH, Crum AJ. 2020. Effects of physical activity recommendations on mindset, behavior and perceived health. Prev. Med. Rep. 17:101027
    [Google Scholar]
  122. 122.
    Zhang T, Dong H. 2009. Human-centered design: an emergent conceptual model Presented at Include2019 Royal College of Art London: April 8–10
  123. 123.
    Zhou C, Occa A, Kim S, Morgan S 2020. A meta-analysis of narrative game-based interventions for promoting healthy behaviors. J. Health Commun. 25:154–65
    [Google Scholar]
  124. 124.
    Zion SR, Louis K, Horii R, Leibowitz K, Heathcote LC, Crum AJ. 2022. Making sense of a pandemic: Mindsets influence emotions, behaviors, health, and wellbeing during the COVID-19 pandemic. Soc. Sci. Med. 301:114889
    [Google Scholar]
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