Personalized Mobile Technologies for Lifestyle Behavior Change: A Systematic Review, Meta-analysis,
Thursday, April 15, 2021
Posted by: Natalia Gromov
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 Mar 24;148:106532. doi: 10.1016/j.ypmed.2021.106532. Epub ahead
of print. PMID: 33774008.
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.
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