Your browser doesn't support javascript.
loading
Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression.
Tong, Huong Ly; Quiroz, Juan C; Kocaballi, A Baki; Fat, Sandrine Chan Moi; Dao, Kim Phuong; Gehringer, Holly; Chow, Clara K; Laranjo, Liliana.
Afiliación
  • Tong HL; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia. Electronic address: hton5658@uni.sydney.edu.au.
  • Quiroz JC; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
  • Kocaballi AB; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; School of Computer Science, University of Technology Sydney, Sydney, Australia.
  • Fat SCM; Department of Biomedical Science, Macquarie University, Sydney, Australia.
  • Dao KP; Capital Health Network, Canberra, Australia.
  • Gehringer H; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
  • Chow CK; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
  • Laranjo L; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Prev Med ; 148: 106532, 2021 07.
Article en En | MEDLINE | ID: mdl-33774008
ABSTRACT
Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Aplicaciones Móviles / Estilo de Vida Tipo de estudio: Clinical_trials / Prognostic_studies / Systematic_reviews Límite: Female / Humans / Male Idioma: En Revista: Prev Med Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Aplicaciones Móviles / Estilo de Vida Tipo de estudio: Clinical_trials / Prognostic_studies / Systematic_reviews Límite: Female / Humans / Male Idioma: En Revista: Prev Med Año: 2021 Tipo del documento: Article