Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression

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 life...

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Bibliographic Details
Published in:Preventive medicine 2021-07, Vol.148, p.106532-106532, Article 106532
Main Authors: Tong, Huong Ly, Quiroz, Juan C., Kocaballi, A. Baki, Fat, Sandrine Chan Moi, Dao, Kim Phuong, Gehringer, Holly, Chow, Clara K., Laranjo, Liliana
Format: Article
Language:English
Subjects:
Fitness trackers[MeSH]
Health behavior[MeSH]
Mobile applications[MeSH]
Personalization
Tailoring
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Summary: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. •Personalized mobile technologies have potential to promote healthy behaviors.•Automatic data collection might help increase intervention effectiveness.•High-quality trials are needed to fully explore the potential of personalization.
ISSN:0091-7435
1096-0260
DOI:10.1016/j.ypmed.2021.106532