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

mHealth for Wellness, Behavior Change and Prevention (2871) Mobile Health (mhealth) (2939) Natural Language Processing (692) Fitness Trackers and Smart Pedometers/Accelerometers (561)

Published on in Vol 6, No 6 (2018): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10042, first published .
Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Authors of this article:

Yoshimi Fukuoka1 Author Orcid Image ;   Teri G Lindgren2 Author Orcid Image ;   Yonatan Dov Mintz3 Author Orcid Image ;   Julie Hooper4 Author Orcid Image ;   Anil Aswani3 Author Orcid Image
Article Authors Cited by (20) Tweetations (5) Metrics

Journals

  1. Zhang J, Oh Y, Lange P, Yu Z, Fukuoka Y. Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint. Journal of Medical Internet Research 2020;22(9):e22845 View
  2. Fukuoka Y, Haskell W, Lin F, Vittinghoff E. Short- and Long-term Effects of a Mobile Phone App in Conjunction With Brief In-Person Counseling on Physical Activity Among Physically Inactive Women. JAMA Network Open 2019;2(5):e194281 View
  3. Lindgren T, Hooper J, Fukuoka Y. Perceptions and Experiences of Women Participating in a Digital Technology–Based Physical Activity Intervention (the mPED Trial): Qualitative Study. JMIR Public Health and Surveillance 2019;5(4):e13570 View
  4. Berninger N, ten Hoor G, Plasqui G, Kok G, Peters G, Ruiter R. Sedentary Work in Desk-Dominated Environments: A Data-Driven Intervention Using Intervention Mapping. JMIR Formative Research 2020;4(7):e14951 View
  5. Li C, Lin S, Chib A. The state of wearable health technologies: a transdisciplinary literature review. Mobile Media & Communication 2021;9(2):353 View
  6. 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
  7. Ho T, Shih W, Kao W, Hsu C, Wu C. Analysis of the Development Trend of Sports Research in China and Taiwan Using Natural Language Processing. Applied Sciences 2022;12(18):9006 View
  8. Fukuoka Y, Haskell W, Vittinghoff E. Mechanisms of an App-Based Physical Activity Intervention and Maintenance in Community-Dwelling Women. Journal of Cardiovascular Nursing 2023;38(2):E61 View
  9. Soulé B, Perrin C, Marchant G. La numérisation de l’activité physique et des loisirs corporels : enjeux individuels et sociaux du « sport connecté ». Loisir et Société / Society and Leisure 2022;45(3):454 View
  10. Soulé B, Marchant G, Verchère R. Sport and fitness app uses: a review of humanities and social science perspectives. European Journal for Sport and Society 2022;19(2):170 View
  11. Oh Y, Hoffmann T, Fukuoka Y. A Novel Approach to Assess Weekly Self-efficacy for Meeting Personalized Physical Activity Goals Via a Cellphone: 12-Week Longitudinal Study. JMIR Formative Research 2023;7:e38877 View
  12. Anmella G, Sanabra M, Primé-Tous M, Segú X, Cavero M, Morilla I, Grande I, Ruiz V, Mas A, Martín-Villalba I, Caballo A, Esteva J, Rodríguez-Rey A, Piazza F, Valdesoiro F, Rodriguez-Torrella C, Espinosa M, Virgili G, Sorroche C, Ruiz A, Solanes A, Radua J, Also M, Sant E, Murgui S, Sans-Corrales M, H Young A, Vicens V, Blanch J, Caballeria E, López-Pelayo H, López C, Olivé V, Pujol L, Quesada S, Solé B, Torrent C, Martínez-Aran A, Guarch J, Navinés R, Murru A, Fico G, de Prisco M, Oliva V, Amoretti S, Pio-Carrino C, Fernández-Canseco M, Villegas M, Vieta E, Hidalgo-Mazzei D. Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies. Journal of Medical Internet Research 2023;25:e43293 View
  13. Besoain F, Gallardo I. Think About It: Promoting Physical Activity With a Mobile App With a Theory-Based Approach. IEEE Access 2023;11:37021 View
  14. 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
  15. Ghantasala R, Albers N, Penfornis K, van Vliet M, Brinkman W. Feasibility of generating structured motivational messages for tailored physical activity coaching. Frontiers in Digital Health 2023;5 View
  16. Figueroa C, Aguilera A, Hoffmann T, Fukuoka Y. The Relationship Between Barriers to Physical Activity and Depressive Symptoms in Community-Dwelling Women. Women's Health Reports 2024;5(1):242 View

Books/Policy Documents

  1. Pérez Ortega J, Almanza Ortega N, Vega Villalobos A, Aguirre L. M, Zavala Díaz C, Ortiz Hernandez J, Hernández Gómez A. Handbook of Research on Natural Language Processing and Smart Service Systems. View
  2. . Bouger pour la santé !. View