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

mHealth for Wellness, Behavior Change and Prevention (2713) Mobile Health (mhealth) (2727)

Published on in Vol 3, No 2 (2015): Apr-Jun

Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

Authors of this article:

Mashfiqui Rabbi1 Author Orcid Image ;   Angela Pfammatter2 Author Orcid Image ;   Mi Zhang3 Author Orcid Image ;   Bonnie Spring2 Author Orcid Image ;   Tanzeem Choudhury1 Author Orcid Image
Article Authors Cited by (160) Tweetations (27) Metrics

Journals

  1. Naughton F, Hopewell S, Lathia N, Schalbroeck R, Brown C, Mascolo C, McEwen A, Sutton S. A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study. JMIR mHealth and uHealth 2016;4(3):e106 View
  2. Zhao J, Freeman B, Li M. Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review. Journal of Medical Internet Research 2016;18(11):e287 View
  3. Huberty J, Vranceanu A, Carney C, Breus M, Gordon M, Puzia M. Characteristics and Usage Patterns Among 12,151 Paid Subscribers of the Calm Meditation App: Cross-Sectional Survey. JMIR mHealth and uHealth 2019;7(11):e15648 View
  4. Schembre S, Liao Y, Robertson M, Dunton G, Kerr J, Haffey M, Burnett T, Basen-Engquist K, Hicklen R. Just-in-Time Feedback in Diet and Physical Activity Interventions: Systematic Review and Practical Design Framework. Journal of Medical Internet Research 2018;20(3):e106 View
  5. Rabbi M, Aung M, Gay G, Reid M, Choudhury T. Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. Journal of Medical Internet Research 2018;20(10):e10147 View
  6. Chrisman M, Chow W, Daniel C, Wu X, Zhao H. Mobile Phone Use and its Association With Sitting Time and Meeting Physical Activity Recommendations in a Mexican American Cohort. JMIR mHealth and uHealth 2016;4(2):e54 View
  7. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  8. Gough A, Prior L, Kee F, Hunter R. Physical activity and behaviour change: the role of distributed motivation. Critical Public Health 2020;30(2):153 View
  9. Hosseinpour M, Terlutter R. Your Personal Motivator is with You: A Systematic Review of Mobile Phone Applications Aiming at Increasing Physical Activity. Sports Medicine 2019;49(9):1425 View
  10. Covolo L, Ceretti E, Moneda M, Castaldi S, Gelatti U. Does evidence support the use of mobile phone apps as a driver for promoting healthy lifestyles from a public health perspective? A systematic review of Randomized Control Trials. Patient Education and Counseling 2017;100(12):2231 View
  11. Lee M, Lee H, Kim Y, Kim J, Cho M, Jang J, Jang H. Mobile App-Based Health Promotion Programs: A Systematic Review of the Literature. International Journal of Environmental Research and Public Health 2018;15(12):2838 View
  12. Feldman D, Theodore Robison W, Pacor J, Caddell L, Feldman E, Deitz R, Feldman T, Martin S, Nasir K, Blaha M. Harnessing mHealth technologies to increase physical activity and prevent cardiovascular disease. Clinical Cardiology 2018;41(7):985 View
  13. Lee U, Han K, Cho H, Chung K, Hong H, Lee S, Noh Y, Park S, Carroll J. Intelligent positive computing with mobile, wearable, and IoT devices: Literature review and research directions. Ad Hoc Networks 2019;83:8 View
  14. Kim Y, Oh B, Shin H. Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study. JMIR mHealth and uHealth 2020;8(1):e13273 View
  15. Hales S, Turner-McGrievy G, Wilcox S, Fahim A, Davis R, Huhns M, Valafar H. Social networks for improving healthy weight loss behaviors for overweight and obese adults: A randomized clinical trial of the social pounds off digitally (Social POD) mobile app. International Journal of Medical Informatics 2016;94:81 View
  16. Valle C, Queen T, Martin B, Ribisl K, Mayer D, Tate D. Optimizing Tailored Communications for Health Risk Assessment: A Randomized Factorial Experiment of the Effects of Expectancy Priming, Autonomy Support, and Exemplification. Journal of Medical Internet Research 2018;20(3):e63 View
  17. Kunkle S, Christie G, Yach D, El-Sayed A. The Importance of Computer Science for Public Health Training: An Opportunity and Call to Action. JMIR Public Health and Surveillance 2016;2(1):e10 View
  18. Abu-Saad K, Murad H, Barid R, Olmer L, Ziv A, Younis-Zeidan N, Kaufman-Shriqui V, Gillon-Keren M, Rigler S, Berchenko Y, Kalter-Leibovici O. Development and Efficacy of an Electronic, Culturally Adapted Lifestyle Counseling Tool for Improving Diabetes-Related Dietary Knowledge: Randomized Controlled Trial Among Ethnic Minority Adults With Type 2 Diabetes Mellitus. Journal of Medical Internet Research 2019;21(10):e13674 View
  19. Iribagiza C, Sharpe T, Wilson D, Thomas E. User-centered design of an air quality feedback technology to promote adoption of clean cookstoves. Journal of Exposure Science & Environmental Epidemiology 2020;30(6):925 View
  20. Siriwoen R, Chongsuwat R, Tansakul S, Siri S. Effectiveness of a Weight Management Program Applying Mobile Health Technology as a Supporting Tool for Overweight and Obese Working Women. Asia Pacific Journal of Public Health 2018;30(6):572 View
  21. Feter N, dos Santos T, Caputo E, da Silva M. What is the role of smartphones on physical activity promotion? A systematic review and meta-analysis. International Journal of Public Health 2019;64(5):679 View
  22. Hardeman W, Houghton J, Lane K, Jones A, Naughton F. A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity. International Journal of Behavioral Nutrition and Physical Activity 2019;16(1) View
  23. Samoggia A, Riedel B. Assessment of nutrition-focused mobile apps' influence on consumers' healthy food behaviour and nutrition knowledge. Food Research International 2020;128:108766 View
  24. Muroff J, Robinson W. Tools of Engagement: Practical Considerations for Utilizing Technology-Based Tools in CBT Practice. Cognitive and Behavioral Practice 2022;29(1):81 View
  25. Lyzwinski L, Caffery L, Bambling M, Edirippulige S. Consumer perspectives on mHealth for weight loss: a review of qualitative studies. Journal of Telemedicine and Telecare 2018;24(4):290 View
  26. Imschloss M, Lorenz J. How Mobile App Design Impacts User Responses to Mixed Self-Tracking Outcomes: Randomized Online Experiment to Explore the Role of Spatial Distance for Hedonic Editing. JMIR mHealth and uHealth 2018;6(4):e81 View
  27. Vogel J, Auinger A, Riedl R, Kindermann H, Helfert M, Ocenasek H, Tang D. Digitally enhanced recovery: Investigating the use of digital self-tracking for monitoring leisure time physical activity of cardiovascular disease (CVD) patients undergoing cardiac rehabilitation. PLOS ONE 2017;12(10):e0186261 View
  28. Miller C. Adaptive Intervention Designs to Promote Behavioral Change in Adults: What Is the Evidence?. Current Diabetes Reports 2019;19(2) View
  29. Hoffmann A, Faust-Christmann C, Zolynski G, Bleser G. Toward Gamified Pain Management Apps: Mobile Application Rating Scale–Based Quality Assessment of Pain-Mentor’s First Prototype Through an Expert Study. JMIR Formative Research 2020;4(5):e13170 View
  30. Miyamoto S, Henderson S, Young H, Pande A, Han J. Tracking Health Data Is Not Enough: A Qualitative Exploration of the Role of Healthcare Partnerships and mHealth Technology to Promote Physical Activity and to Sustain Behavior Change. JMIR mHealth and uHealth 2016;4(1):e5 View
  31. Gordon E, Sohn M, Chang C, McNatt G, Vera K, Beauvais N, Warren E, Mannon R, Ison M. Effect of a Mobile Web App on Kidney Transplant Candidates' Knowledge About Increased Risk Donor Kidneys. Transplantation 2017;101(6):1167 View
  32. Paruthi G, Raj S, Colabianchi N, Klasnja P, Newman M. Finding the Sweet Spot(s). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
  33. Huang Y, Benford S, Blake H. Digital Interventions to Reduce Sedentary Behaviors of Office Workers: Scoping Review. Journal of Medical Internet Research 2019;21(2):e11079 View
  34. Brand L, Beltran A, Hughes S, O'Connor T, Baranowski J, Nicklas T, Chen T, Dadabhoy H, Diep C, Buday R, Baranowski T. Assessing Feedback in a Mobile Videogame. Games for Health Journal 2016;5(3):203 View
  35. Villinger K, Wahl D, Boeing H, Schupp H, Renner B. The effectiveness of app‐based mobile interventions on nutrition behaviours and nutrition‐related health outcomes: A systematic review and meta‐analysis. Obesity Reviews 2019;20(10):1465 View
  36. Conroy D, Lagoa C, Hekler E, Rivera D. Engineering Person-Specific Behavioral Interventions to Promote Physical Activity. Exercise and Sport Sciences Reviews 2020;48(4):170 View
  37. Kim B, Lee J. Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review. JMIR mHealth and uHealth 2017;5(5):e69 View
  38. Rabbi M, Li K, Yan H, Hall K, Klasnja P, Murphy S. ReVibe. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(4):1 View
  39. Wongvibulsin S, Martin S, Saria S, Zeger S, Murphy S. An Individualized, Data-Driven Digital Approach for Precision Behavior Change. American Journal of Lifestyle Medicine 2020;14(3):289 View
  40. Allen L, Christie G. The Emergence of Personalized Health Technology. Journal of Medical Internet Research 2016;18(5):e99 View
  41. Salwen-Deremer J, Khan A, Martin S, Holloway B, Coughlin J. Incorporating Health Behavior Theory into mHealth: an Examination of Weight Loss, Dietary, and Physical Activity Interventions. Journal of Technology in Behavioral Science 2020;5(1):51 View
  42. Ghanvatkar S, Kankanhalli A, Rajan V. User Models for Personalized Physical Activity Interventions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11098 View
  43. Recio-Rodriguez J, Agudo Conde C, Calvo-Aponte M, Gonzalez-Viejo N, Fernandez-Alonso C, Mendizabal-Gallastegui N, Rodriguez-Martin B, Maderuelo-Fernandez J, Rodriguez-Sanchez E, Gomez-Marcos M, Garcia-Ortiz L. The Effectiveness of a Smartphone Application on Modifying the Intakes of Macro and Micronutrients in Primary Care: A Randomized Controlled Trial. The EVIDENT II Study. Nutrients 2018;10(10):1473 View
  44. Grace-Farfaglia P. Social Cognitive Theories and Electronic Health Design: Scoping Review. JMIR Human Factors 2019;6(3):e11544 View
  45. Chib A, Lin S. Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps. Journal of Health Communication 2018;23(10-11):909 View
  46. Lentferink A, Oldenhuis H, de Groot M, Polstra L, Velthuijsen H, van Gemert-Pijnen J. Key Components in eHealth Interventions Combining Self-Tracking and Persuasive eCoaching to Promote a Healthier Lifestyle: A Scoping Review. Journal of Medical Internet Research 2017;19(8):e277 View
  47. Milne-Ives M, Lam C, De Cock C, Van Velthoven M, Meinert E. Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review. JMIR mHealth and uHealth 2020;8(3):e17046 View
  48. Dempsey W, Liao P, Klasnja P, Nahum-Shani I, Murphy S. Randomised Trials for the Fitbit Generation. Significance 2015;12(6):20 View
  49. Yamamoto K, Ebara T, Matsuda F, Matsukawa T, Yamamoto N, Ishii K, Kurihara T, Yamada S, Matsuki T, Tani N, Kamijima M. Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial. Journal of Occupational Health 2020;62(1) View
  50. Kunkle S, Christie G, Hajat C, Yach D. The Role of the Private Sector in Tilting Health Systems Toward Chronic Disease Prevention. Global Heart 2016;11(4):451 View
  51. Pollard C, Howat P, Pratt I, Boushey C, Delp E, Kerr D. Preferred Tone of Nutrition Text Messages for Young Adults: Focus Group Testing. JMIR mHealth and uHealth 2016;4(1):e1 View
  52. Kankanhalli A, Shin J, Oh H. Mobile-Based Interventions for Dietary Behavior Change and Health Outcomes: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11312 View
  53. van den Boer J, van der Lee A, Zhou L, Papapanagiotou V, Diou C, Delopoulos A, Mars M. The SPLENDID Eating Detection Sensor: Development and Feasibility Study. JMIR mHealth and uHealth 2018;6(9):e170 View
  54. Gerrard-Longworth S, Preece S, Clarke-Cornwell A, Goulermas Y. The Performance of an Algorithm for Classifying Gym-based Tasks across Individuals with Different Body Mass Index. Measurement in Physical Education and Exercise Science 2020;24(4):282 View
  55. Hollis V, Konrad A, Springer A, Antoun M, Antoun C, Martin R, Whittaker S. What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being. Human–Computer Interaction 2017;32(5-6):208 View
  56. Direito A, Tooley M, Hinbarji M, Albatal R, Jiang Y, Whittaker R, Maddison R. Tailored Daily Activity: An Adaptive Physical Activity Smartphone Intervention. Telemedicine and e-Health 2020;26(4):426 View
  57. Kovacs G, Wu Z, Bernstein M. Rotating Online Behavior Change Interventions Increases Effectiveness But Also Increases Attrition. Proceedings of the ACM on Human-Computer Interaction 2018;2(CSCW):1 View
  58. Schoeppe S, Alley S, Van Lippevelde W, Bray N, Williams S, Duncan M, Vandelanotte C. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 2016;13(1) View
  59. Silacci A, Taiar R, Caon M. Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study. Applied Sciences 2020;11(1):313 View
  60. Davis A, Sweigart R, Ellis R. A systematic review of tailored mHealth interventions for physical activity promotion among adults. Translational Behavioral Medicine 2020;10(5):1221 View
  61. Marchant G, Bonaiuto F, Bonaiuto M, Guillet Descas E. Exercise and Physical Activity eHealth in COVID-19 Pandemic: A Cross-Sectional Study of Effects on Motivations, Behavior Change Mechanisms, and Behavior. Frontiers in Psychology 2021;12 View
  62. Samoggia A, Monticone F, Bertazzoli A. Innovative Digital Technologies for Purchasing and Consumption in Urban and Regional Agro-Food Systems: A Systematic Review. Foods 2021;10(2):208 View
  63. Chew H, Ang W, Lau Y. The potential of artificial intelligence in enhancing adult weight loss: a scoping review. Public Health Nutrition 2021;24(8):1993 View
  64. Kramer L, Blok M, van Velsen L, Mulder B, de Vet E. Supporting eating behaviour of community-dwelling older adults: co-design of an embodied conversational agent. Design for Health 2021;5(1):120 View
  65. Tong H, Quiroz J, Kocaballi A, Fat S, Dao K, Gehringer H, Chow C, Laranjo L. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive Medicine 2021;148:106532 View
  66. Normand M, Dallery J, Slanzi C. Leveraging applied behavior analysis research and practice in the service of public health. Journal of Applied Behavior Analysis 2021;54(2):457 View
  67. Blanchard C, Chin M, Gilhooly C, Barger K, Matuszek G, Miki A, Côté R, Eldridge A, Green H, Mainardi F, Mehers D, Ronga F, Steullet V, Das S. Evaluation of PIQNIQ, a Novel Mobile Application for Capturing Dietary Intake. The Journal of Nutrition 2021;151(5):1347 View
  68. Daryabeygi-Khotbehsara R, Shariful Islam S, Dunstan D, McVicar J, Abdelrazek M, Maddison R. Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review. Journal of Medical Internet Research 2021;23(9):e26315 View
  69. Thomas Craig K, Morgan L, Chen C, Michie S, Fusco N, Snowdon J, Scheufele E, Gagliardi T, Sill S. Systematic review of context-aware digital behavior change interventions to improve health. Translational Behavioral Medicine 2021;11(5):1037 View
  70. Varela C, Oda-Montecinos C, Andrés A, Saldaña C. Effectiveness of web-based feedback interventions for people with overweight and obesity: systematic review and network meta-analysis of randomized controlled trials. Journal of Eating Disorders 2021;9(1) View
  71. Eckert T, Wunsch K, Fiedler J, Woll A. SMARTMOVE – Einbezug von Familien in die Entwicklung und Implementierung digitaler Gesundheitsangebote. Prävention und Gesundheitsförderung 2022;17(3):313 View
  72. Saliasi I, Martinon P, Darlington E, Smentek C, Tardivo D, Bourgeois D, Dussart C, Carrouel F, Fraticelli L. Promoting Health via mHealth Applications Using a French Version of the Mobile App Rating Scale: Adaptation and Validation Study. JMIR mHealth and uHealth 2021;9(8):e30480 View
  73. Domin A, Spruijt-Metz D, Theisen D, Ouzzahra Y, Vögele C. Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years. JMIR mHealth and uHealth 2021;9(7):e24308 View
  74. Sandal L, Bach K, Øverås C, Svendsen M, Dalager T, Stejnicher Drongstrup Jensen J, Kongsvold A, Nordstoga A, Bardal E, Ashikhmin I, Wood K, Rasmussen C, Stochkendahl M, Nicholl B, Wiratunga N, Cooper K, Hartvigsen J, Kjær P, Sjøgaard G, Nilsen T, Mair F, Søgaard K, Mork P. Effectiveness of App-Delivered, Tailored Self-management Support for Adults With Lower Back Pain–Related Disability. JAMA Internal Medicine 2021;181(10):1288 View
  75. Basuodan R, Bin sheeha B, Basoudan N, Abdljabbarl N, Aldhahi M. Tele-Physical Activity Promotion Program among College Students during the COVID-19 Pandemic. Medicina 2023;59(2):332 View
  76. Al-Saedi A, Boeva V, Casalicchio E, Exner P. Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview. Sensors 2022;22(15):5544 View
  77. Tonkin E, Brimblecombe J, Wycherley T. Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review. Advances in Nutrition 2017;8(2):308 View
  78. Jiang Y, Ding X, Liu D, Gui X, Zhang W, Zhang W. Designing intelligent self-checkup based technologies for everyday healthy living. International Journal of Human-Computer Studies 2022;166:102866 View
  79. König L, Van Emmenis M, Nurmi J, Kassavou A, Sutton S. Characteristics of smartphone-based dietary assessment tools: a systematic review. Health Psychology Review 2022;16(4):526 View
  80. Kawai Y, Waki K, Yamaguchi S, Shibuta T, Miyake K, Kimura S, Toyooka T, Nakajima R, Uneda K, Wakui H, Tamura K, Nangaku M, Ohe K. The Use of Information and Communication Technology–Based Self-management System DialBeticsLite in Treating Abdominal Obesity in Japanese Office Workers: Prospective Single-Arm Pilot Intervention Study. JMIR Diabetes 2022;7(4):e40366 View
  81. Domin A, Uslu A, Schulz A, Ouzzahra Y, Vögele C. A Theory-Informed, Personalized mHealth Intervention for Adolescents (Mobile App for Physical Activity): Development and Pilot Study. JMIR Formative Research 2022;6(6):e35118 View
  82. Diaz C, Caillaud C, Yacef K. Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data. Sensors 2022;22(21):8255 View
  83. Becerro de Bengoa Vallejo R, Losa Iglesias M, Robles Sanchez O. Repeatability, Reproducibility, and Concurrent Validity of a Stethoscope and Health App System for the Quantification of Breath Rate in Healthy Adults: Repeatability and Validity Study. Journal of Medical Internet Research 2023;25:e41845 View
  84. Chong M, Sit J, Karthikesu K, Chair S. Effectiveness of technology-assisted cardiac rehabilitation: A systematic review and meta-analysis. International Journal of Nursing Studies 2021;124:104087 View
  85. Zhao Q, Li Z, Shah D, Fischer H, Solís P, Wentz E. Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health & Place 2023;79:102691 View
  86. Diaz C, Caillaud C, Yacef K. Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review. JMIR Medical Informatics 2023;11:e41153 View
  87. Niemiec S, Wagas R, Vigen C, Blanchard J, Barber S, Schoenhals A. Preliminary User Evaluation of a Physical Activity Smartphone App for Older Adults. Health Policy and Technology 2022;11(3):100639 View
  88. Chen J, Grech A, Allman-Farinelli M. Using Popular Foods Consumed to Inform Development of Digital Tools for Dietary Assessment and Monitoring. Nutrients 2022;14(22):4822 View
  89. Goh Y, Ow Yong J, Chee B, Kuek J, Ho C. Machine Learning in Health Promotion and Behavioral Change: Scoping Review. Journal of Medical Internet Research 2022;24(6):e35831 View
  90. Kwon H, Dewan S, Oh W, Kim T. Self-Regulation and External Influence: The Relative Efficacy of Mobile Apps and Offline Channels for Personal Weight Management. Information Systems Research 2023;34(1):50 View
  91. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427 View
  92. Zhou T, Wang Y, Yan L, Tan Y. Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach. Information Systems Research 2023;34(4):1493 View
  93. Ulaszewska K, Jodczyk A, Długołęcki P, Emerla S, Stańska W, Kasiak P, Gąsior J, Parol D, Mamcarz A, Śliż D. Factors Associated with Willingness to Receive a COVID-19 Vaccine in Adult Polish Population—A Cross-Sectional Survey. Vaccines 2022;10(10):1715 View
  94. Standen E, Rothman A. Capitalizing on the potential of mobile health applications as behavioral interventions: A research agenda for calorie‐tracking and activity‐tracking applications. Social and Personality Psychology Compass 2023;17(3) View
  95. Zhang H, Ibrahim A, Parsia B, Poliakoff E, Harper S. Passive social sensing with smartphones: a systematic review. Computing 2023;105(1):29 View
  96. Kornfield R, Mohr D, Ranney R, Lattie E, Meyerhoff J, Williams J, Reddy M. Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW1):1 View
  97. Alhasani M, Mulchandani D, Oyebode O, Baghaei N, Orji R. A Systematic and Comparative Review of Behavior Change Strategies in Stress Management Apps: Opportunities for Improvement. Frontiers in Public Health 2022;10 View
  98. Simmons L, Phipps J, Whipps M, Smith P, Carbajal K, Overstreet C, McLaughlin J, De Lombaert K, Noonan D. From hybrid to fully remote clinical trial amidst the COVID-19 pandemic: Strategies to promote recruitment, retention, and engagement in a randomized mHealth trial. DIGITAL HEALTH 2022;8:205520762211290 View
  99. Albers N, Neerincx M, Brinkman W, Wang F. Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active. PLOS ONE 2022;17(12):e0277295 View
  100. Ozkaynak M, Voida S, Dunn E. Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making. Applied Clinical Informatics 2022;13(01):252 View
  101. Tong H, Quiroz J, Kocaballi A, Ijaz K, Coiera E, Chow C, Laranjo L. A personalized mobile app for physical activity: An experimental mixed-methods study. DIGITAL HEALTH 2022;8:205520762211150 View
  102. Horwitz A, Hong V, Eisenberg D, Zheng K, Albucher R, Coryell W, Pistorello J, Favorite T, King C. Engagement With Personalized Feedback for Emotional Distress Among College Students at Elevated Suicide Risk. Behavior Therapy 2022;53(2):365 View
  103. Chew H, Lim S, Kim G, Kayambu G, So B, Shabbir A, Gao Y. Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations. Translational Behavioral Medicine 2023 View
  104. Li Z, Das S, Codella J, Hao T, Lin K, Maduri C, Chen C. An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity. IEEE Journal of Biomedical and Health Informatics 2019;23(3):999 View
  105. 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
  106. Abdul Basir S, Abdul Manaf Z, Mohd. Noor N, Mat Ludin A, Shahar S, Abdul Manaf M. The Challenges and Strategies towards Healthy Eating during COVID-19 Home Confinement Period among Working Adults with BMI ≥ 25 kg/m2 Enrolled in a Weight Loss Program: Qualitative Findings. International Journal of Environmental Research and Public Health 2022;19(11):6656 View
  107. Vos A, de Bruijn G, Klein M, Lakerveld J, Boerman S, Smit E. SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study. JMIR Formative Research 2023;7:e40851 View
  108. Albers N, Neerincx M, Penfornis K, Brinkman W. Users’ needs for a digital smoking cessation application and how to address them: A mixed-methods study. PeerJ 2022;10:e13824 View
  109. Plana D, Shung D, Grimshaw A, Saraf A, Sung J, Kann B. Randomized Clinical Trials of Machine Learning Interventions in Health Care. JAMA Network Open 2022;5(9):e2233946 View
  110. Yu S, Chen Z, Wu X. The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey. International Journal of Environmental Research and Public Health 2023;20(1):887 View
  111. Oinas-Kukkonen H, Pohjolainen S, Agyei E. Mitigating Issues With/of/for True Personalization. Frontiers in Artificial Intelligence 2022;5 View
  112. Wang W, Su Y, Gao G. Social Determinants of mHealth Effectiveness: Evidence from a Large-Scale Experiment. SSRN Electronic Journal 2018 View
  113. Zhou T, Wang Y, Yan L, Tan Y. Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach with a Dynamic Discrete-Choice Scheme. SSRN Electronic Journal 2019 View
  114. Cox D, Jennings A. The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services. Behavior Analysis in Practice 2024;17(1):123 View
  115. Yfantidou S, Sermpezis P, Vakali A. 14 Years of Self-Tracking Technology for mHealth—Literature Review: Lessons Learned and the PAST SELF Framework. ACM Transactions on Computing for Healthcare 2023;4(3):1 View
  116. An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. Journal of Sport and Health Science 2024;13(3):428 View
  117. Marcuzzi A, Nordstoga A, Bach K, Aasdahl L, Nilsen T, Bardal E, Boldermo N, Falkener Bertheussen G, Marchand G, Gismervik S, Mork P. Effect of an Artificial Intelligence–Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care. JAMA Network Open 2023;6(6):e2320400 View
  118. Schmitz K, Kanski B, Gordon B, Caru M, Vasakar M, Truica C, Wang M, Doerksen S, Lorenzo A, Winkels R, Qiu L, Abdullah S. Technology-based supportive care for metastatic breast cancer patients. Supportive Care in Cancer 2023;31(7) View
  119. Sze W, Waki K, Enomoto S, Nagata Y, Nangaku M, Yamauchi T, Ohe K. StepAdd: A personalized mHealth intervention based on social cognitive theory to increase physical activity among type 2 diabetes patients. Journal of Biomedical Informatics 2023;145:104481 View
  120. Brombacher H, Houben S, Vos S. Tangible interventions for office work well-being: approaches, classification, and design considerations. Behaviour & Information Technology 2023:1 View
  121. Bulathsinghala R, Ding W, Dharmasena R. Triboelectric nanogenerators for wearable sensing applications: A system level analysis. Nano Energy 2023;116:108792 View
  122. Coppens I, De Pessemier T, Martens L. Connecting physical activity with context and motivation: a user study to define variables to integrate into mobile health recommenders. User Modeling and User-Adapted Interaction 2024;34(1):147 View
  123. Xu Z, Smit E. Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis. DIGITAL HEALTH 2023;9 View
  124. Waki K, Tsurutani Y, Waki H, Enomoto S, Kashiwabara K, Fujiwara A, Orime K, Kinguchi S, Yamauchi T, Hirawa N, Tamura K, Terauchi Y, Nangaku M, Ohe K. Efficacy of StepAdd, a personalized mHealth intervention based on social cognitive theory to increase physical activity among type 2 diabetes patients: protocol for a randomized controlled trial (Preprint). JMIR Research Protocols 2023 View
  125. Krukowski R, Denton A, König L. Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity 2024;21(1) View
  126. Harris J, Zaki M. Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data. Journal of Healthcare Informatics Research 2024;8(2):370 View
  127. Lau Y, Wong S, Chee D, Ng B, Ang W, Han C, Cheng L. Technology‐delivered personalized nutrition intervention on dietary outcomes among adults with overweight and obesity: A systematic review, meta‐analysis, and meta‐regression. Obesity Reviews 2024;25(5) View
  128. Zhou S, Levinson A, Zhang X, Portz J, Moore S, Gore M, Ford K, Li Q, Bull S. A Pilot Study and Ecological Model of Smoking Cues to Inform Mobile Health Strategies for Quitting Among Low-Income Smokers. Health Promotion Practice 2021;22(6):850 View
  129. Chan G, Nwagu C, Odenigbo I, Alslaity A, Orji R. The Shape of Mobile Health: A Systematic Review of Health Visualization on Mobile Devices. International Journal of Human–Computer Interaction 2024:1 View
  130. Lee S, Kim S, Park S. Dietary Management of Obesity. The Korean Journal of Gastroenterology 2024;83(3):87 View
  131. Chen Y, Woodward J, Shankar M, Bista D, Ugwoaba U, Brockmann A, Ross K, Ruiz J, Anthony L. MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance. Frontiers in Digital Health 2024;6 View
  132. Barrafrem K, Tinghög G, Västfjäll D. Behavioral and contextual determinants of different stages of saving behavior. Frontiers in Behavioral Economics 2024;3 View
  133. Idrees A, Kraft R, Mutter A, Baumeister H, Reichert M, Pryss R, Kisimbi T. Persuasive technologies design for mental and behavioral health platforms: A scoping literature review. PLOS Digital Health 2024;3(5):e0000498 View
  134. Mullick T, Shaaban S, Radovic A, Doryab A. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling. JMIR AI 2024;3:e47805 View
  135. Bucher A, Blazek E, Symons C. How Are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024 View
  136. Zhu Y, Long Y, Wang H, Lee K, Zhang L, Wang S. Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review. Journal of Medical Internet Research 2024;26:e54375 View

Books/Policy Documents

  1. Cho P, Singh K, Dunn J. Artificial Intelligence in Medicine. View
  2. Wright K. The Handbook of Applied Communication Research. View
  3. McLaughlin A, Matalenas L, Coleman M. Aging, Technology and Health. View
  4. de Moraes Lopes M, Ferreira D, Ferreira A, da Silva G, Caetano A, Braz V. Artificial Intelligence in Precision Health. View
  5. Rabbi M, Hane Aung M, Choudhury T. Mobile Health. View
  6. Ofori M, El-Gayar O. Optimizing Health Monitoring Systems With Wireless Technology. View
  7. Ensari I, Elhadad N. Digital Health. View
  8. Chinnakali P, Kumar S. Principles and Application of Evidence-based Public Health Practice. View
  9. Yuasa T, Harada F, Shimakawa H. Proceedings of Eighth International Congress on Information and Communication Technology. View
  10. Werder O. Transformational Health Communication. View