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

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 2020 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 2021:1 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 2021:1 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 Behaviour and Promote Physical Activity Using Integrated Dynamic Models: A Systematic Review (Preprint). Journal of Medical Internet Research 2020 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