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Tong HL, Quiroz JC, Kocaballi AB, et al. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Prev Med. 2021;148:106532doi: 10.1016/j.ypmed.2021.106532.
Tong, H. L., Quiroz, J. C., Kocaballi, A. B., Fat, S. C. M., Dao, K. P., Gehringer, H., Chow, C. K., & Laranjo, L. (2021). Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive medicine, 148106532. https://doi.org/10.1016/j.ypmed.2021.106532
Tong, Huong Ly, et al. "Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression." Preventive medicine vol. 148 (2021): 106532. doi: https://doi.org/10.1016/j.ypmed.2021.106532
Tong HL, Quiroz JC, Kocaballi AB, Fat SCM, Dao KP, Gehringer H, Chow CK, Laranjo L. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Prev Med. 2021 Jul;148:106532. doi: 10.1016/j.ypmed.2021.106532. Epub 2021 Mar 24. PMID: 33774008.
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Prev Med. 2021 Jul;148:106532. doi: 10.1016/j.ypmed.2021.106532. Epub 2021 Mar 24.

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

Preventive medicine

Huong Ly Tong, Juan C Quiroz, A Baki Kocaballi, Sandrine Chan Moi Fat, Kim Phuong Dao, Holly Gehringer, Clara K Chow, Liliana Laranjo

Affiliations

  1. Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia. Electronic address: [email protected].
  2. 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.
  3. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; School of Computer Science, University of Technology Sydney, Sydney, Australia.
  4. Department of Biomedical Science, Macquarie University, Sydney, Australia.
  5. Capital Health Network, Canberra, Australia.
  6. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
  7. Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
  8. 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.

PMID: 33774008 DOI: 10.1016/j.ypmed.2021.106532

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.

Copyright © 2021 Elsevier Inc. All rights reserved.

Keywords: Fitness trackers[MeSH]; Health behavior[MeSH]; Mobile applications[MeSH]; Personalization; Tailoring

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