The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review
Open accessAbstract
BACKGROUND: Globally, mobile phones have achieved wide reach at an unprecedented rate, and mobile phone apps have become increasingly prevalent among users. The number of health-related apps that were published on the two leading platforms (iOS and Android) reached more than 100,000 in 2014. However, there is a lack of synthesized evidence regarding the effectiveness of mobile phone apps in changing people's health-related behaviors. OBJECTIVE: The aim was to examine the effectiveness of mobile phone apps in achieving health-related behavior change in a broader range of interventions and the quality of the reported studies. METHODS: We conducted a comprehensive bibliographic search of articles on health behavior change using mobile phone apps in peer-reviewed journals published between January 1, 2010 and June 1, 2015. Databases searched included Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Education Resource Information Center (ERIC), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Articles published in the Journal of Medical Internet Research during that same period were hand-searched on the journal's website. Behavior change mechanisms were coded and analyzed. The quality of each included study was assessed by the Cochrane Risk of Bias Assessment Tool. RESULTS: A total of 23 articles met the inclusion criteria, arranged under 11 themes according to their target behaviors. All studies were conducted in high-income countries. Of these, 17 studies reported statistically significant effects in the direction of targeted behavior change; 19 studies included in this analysis had a 65% or greater retention rate in the intervention group (range 60%-100%); 6 studies reported using behavior change theories with the theory of planned behavior being the most commonly used (in 3 studies). Self-monitoring was the most common behavior change technique applied (in 12 studies). The studies suggest that some features improve the effectiveness of apps, such as less time consumption, user-friendly design, real-time feedback, individualized elements, detailed information, and health professional involvement. All studies were assessed as having some risk of bias. CONCLUSIONS: Our results provide a snapshot of the current evidence of effectiveness for a range of health-related apps. Large sample, high-quality, adequately powered, randomized controlled trials are required. In light of the bias evident in the included studies, better reporting of health-related app interventions is also required. The widespread adoption of mobile phones highlights a significant opportunity to impact health behaviors globally, particularly in low- and middle-income countries.
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“Systematic reviews that focused on one specific behavior or a certain type of health or well-being app suggest that the effectiveness of evidence-based smartphone apps can be improved by targeting the design and engagement features, such as user-friendly design, individualized and culturally tailored content, or health professional support [17-19].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In line with the findings of previous reviews, help with initial engagement could be achieved by improving the users’ app literacy skills and by providing knowledge [14,17].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Behavior change techniques, widely reported by others previously [14,17-19], were also identified as important factors to sustain engagement, including self-monitoring, feedback, goal setting, reminders, rewards, and social support.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Complementary delivery routes that include mobile health (mHealth) may help satisfy this need as they can continue to be used autonomously by participants after an intervention period [84–86].”
From Barriers and facilitators to diet, physical activity and lifestyle behavior intervention adherence: a qualitative systematic review of the literature (2023) · cites this paper as [85]
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“In line with the emerging evidence from the human-computer interaction (HCI) literature, we found that an app coupled with human support [14,17] was likely to be more effective by increasing the intervention effectiveness and engagement [78,79].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
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Cites
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048 · cited 9×
“All 23 studies had some kind of risk of bias according to the Cochrane Risk of Bias Assessment Tool.”
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“Additionally, the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement could assist to improve the reporting quality of nonrandomized evaluations of public health interventions [56].”
BackgroundA taxonomy of behavior change techniques used in interventions
Abraham, C., & Michie, S. (2008) · Health Psychology · doi:10.1037/0278-6133.27.3.379 · Cited by 2,525 · cited 1×
“Behavior change techniques used in the interventions were coded according to Abraham and Michie’s taxonomy of behavior change techniques (BCTs) [25].”
MethodWebb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1376 · Cited by 2,517 · cited 9×
“With respect to the mechanisms of behavior change, it is important to use theory to inform intervention design as well as specifying BCTs [48,49].”
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“It might be possible to blind outcome assessors, those doing data analysis, or those administering co-interventions, which is one of the 22 essential items recommended in the CONSORT checklist [54].”
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Cited by
Sorted by citations
Schoeppe, S., Alley, S., Van Lippevelde, W., Bray, N. A., Williams, S. L., Duncan, M. J., & Vandelanotte, C. (2016) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-016-0454-y · Cited by 949
Mobile phone text messaging and app-based interventions for smoking cessation
Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., Gu, Y., & Dobson, R. (2019) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006611.pub5 · Cited by 452
“Zhao 2016 Zhao J, Freeman B, Li M.”
ResultMilne-Ives, M., Lam, C., De Cock, C., Van Velthoven, M. H., & Meinert, E. (2020) · JMIR mhealth and uhealth · doi:10.2196/17046 · Cited by 393
“In addition, data for these systemic reviews were collected in 2014 [15], 2015 [13], and 2017 [12].”
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“Systematic reviews that focused on one specific behavior or a certain type of health or well-being app suggest that the effectiveness of evidence-based smartphone apps can be improved by targeting the design and engagement features, such as user-friendly design, individualized and culturally tailored content, or healt…”
BackgroundResultInfluentialFleming, G. A., Petrie, J. R., Bergenstal, R. M., Holl, R. W., Peters, A. L., & Heinemann, L. (2020) · Diabetes Care · doi:10.2337/dci19-0062 · Cited by 306
Metadata
- Authors
- Zhao, J., Freeman, B., & Li, M.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Zhao, Jing
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-11-02
- Volume
- 18
- Issue
- 11
- Pages
- e287
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5295827
- OpenAlex
- W2546565370
- Cited by
- 618
- References
- 60
- Citations by year
- 2026: 24 · 2025: 52 · 2024: 31 · 2023: 51 · 2022: 74 · 2021: 104 · 2020: 111 · 2019: 71 · 2018: 68 · 2017: 28 · 2016: 2
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Keywords
- mobile health apps, health behavior change, mobile health intervention, behaviour change techniques, self-monitoring, theory of planned behavior, app retention, real-time feedback, app design features, health professional involvement, risk of bias, application effectiveness
- MeSH terms
- Behavior Therapy, Humans, Health Behavior, Cell Phone, Mobile Applications
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