Published on in Vol 6, No 1 (2018): January

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Journals

  1. Liu Y, Avello M. Status of the research in fitness apps: A bibliometric analysis. Telematics and Informatics 2021;57:101506 View
  2. Ceasar J, Claudel S, Andrews M, Tamura K, Mitchell V, Brooks A, Dodge T, El-Toukhy S, Farmer N, Middleton K, Sabado-Liwag M, Troncoso M, Wallen G, Powell-Wiley T. Community Engagement in the Development of an mHealth-Enabled Physical Activity and Cardiovascular Health Intervention (Step It Up): Pilot Focus Group Study. JMIR Formative Research 2019;3(1):e10944 View
  3. 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
  4. Gasparetti F, Aiello L, Quercia D. Personalized weight loss strategies by mining activity tracker data. User Modeling and User-Adapted Interaction 2020;30(3):447 View
  5. Forman E, Kerrigan S, Butryn M, Juarascio A, Manasse S, Ontañón S, Dallal D, Crochiere R, Moskow D. Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?. Journal of Behavioral Medicine 2019;42(2):276 View
  6. Stuber J, Mackenbach J, de Boer F, de Bruijn G, Gillebaart M, Harbers M, Hoenink J, Klein M, Middel C, van der Schouw Y, Schuitmaker-Warnaar T, Velema E, Vos A, Waterlander W, Lakerveld J, Beulens J. Reducing cardiometabolic risk in adults with a low socioeconomic position: protocol of the Supreme Nudge parallel cluster-randomised controlled supermarket trial. Nutrition Journal 2020;19(1) View
  7. Hagen L, Jiang Y, Knäuper B, Uetake K, Yang N. Mobile Health Behavior Tracking: Health Effects of Tracking Consistency and Its Prediction. SSRN Electronic Journal 2020 View
  8. Aguilera A, Figueroa C, Hernandez-Ramos R, Sarkar U, Cemballi A, Gomez-Pathak L, Miramontes J, Yom-Tov E, Chakraborty B, Yan X, Xu J, Modiri A, Aggarwal J, Jay Williams J, Lyles C. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open 2020;10(8):e034723 View
  9. Zhou M, Fukuoka Y, Goldberg K, Vittinghoff E, Aswani A. Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making 2019;19(1) View
  10. Ghelani D, Moran L, Johnson C, Mousa A, Naderpoor N. Mobile Apps for Weight Management: A Review of the Latest Evidence to Inform Practice. Frontiers in Endocrinology 2020;11 View
  11. Aswani A, Kaminsky P, Mintz Y, Flowers E, Fukuoka Y. Behavioral modeling in weight loss interventions. European Journal of Operational Research 2019;272(3):1058 View
  12. Sporrel K, Nibbeling N, Wang S, Ettema D, Simons M. Unraveling Mobile Health Exercise Interventions for Adults: Scoping Review on the Implementations and Designs of Persuasive Strategies. JMIR mHealth and uHealth 2021;9(1):e16282 View
  13. Hunter R, Gough A, Murray J, Tang J, Brennan S, Chrzanowski-Smith O, Carlin A, Patterson C, Longo A, Hutchinson G, Prior L, Tully M, French D, Adams J, McIntosh E, Xin Y, Kee F. A loyalty scheme to encourage physical activity in office workers: a cluster RCT. Public Health Research 2019;7(15):1 View
  14. Cox D. The Many Functions of Quantitative Modeling. Computational Brain & Behavior 2019;2(3-4):166 View
  15. Sporrel K, De Boer R, Wang S, Nibbeling N, Simons M, Deutekom M, Ettema D, Castro P, Dourado V, Kröse B. The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining. Frontiers in Public Health 2021;8 View
  16. 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
  17. Wiemeyer J. Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework. International Journal of Computer Science in Sport 2019;18(3):12 View
  18. Wilson‐Barnes S, Gymnopoulos L, Dimitropoulos K, Solachidis V, Rouskas K, Russell D, Oikonomidis Y, Hadjidimitriou S, María Botana J, Brkic B, Mantovani E, Gravina S, Telo G, Lalama E, Buys R, Hassapidou M, Balula Dias S, Batista A, Perone L, Bryant S, Maas S, Cobello S, Bacelar P, Lanham‐New S, Hart K. PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project. Nutrition Bulletin 2021;46(1):77 View
  19. Figueroa C, Vittinghoff E, Aguilera A, Fukuoka Y. Differences in objectively measured daily physical activity patterns related to depressive symptoms in community dwelling women – mPED trial. Preventive Medicine Reports 2021;22:101325 View
  20. 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
  21. 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
  22. 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
  23. Walsh J, Richmond J, McSharry J, Groarke A, Glynn L, Kelly M, Harney O, Groarke J. Examining the Impact of a Mobile Health Behavior Change Intervention with a brief in-person component for Cancer Survivors with Overweight/Obesity: Randomized Controlled Trial (Preprint). JMIR mHealth and uHealth 2020 View

Books/Policy Documents

  1. John M, Kleppisch M. Prävention und Gesundheitsförderung. View
  2. John M, Kleppisch M. Prävention und Gesundheitsförderung. View