Breaking Barriers: Mobile Health Interventions for Cardiovascular Disease
Abstract
Cardiovascular disease (CVD) is a leading global cause of death and morbidity and prevention needs to be strengthened to tackle this. Mobile health (mHealth) might present a novel and effective solution in CVD prevention, and interest in mHealth has grown dramatically since the advent of the smartphone. In this review, we discuss mHealth interventions that target multiple cardiovascular risk factors simultaneously in the context of primary as well as secondary prevention. There is some evidence that mHealth interventions improve a range of individual CVD risk factors, but a relative paucity of evidence on mHealth interventions improving multiple CVD risk factors simultaneously. The existing data suggest mHealth programs improve overall CVD risk, at least in the short term. Interpretation of the evidence is difficult in the context of poor methodology and mHealth modalities often being a part of large complex interventions. In this review we identify a number of unanswered questions including: which mode of mHealth (or combination of interventions) would be most effective, what is the durability of intervention effects, and what degree of personalization and interactivity is required.
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Digital health interventions result in changes in lifestyle behaviours as an effective tool for improving adherence.
“Studies have shown that digital health interventions result in changes in lifestyle behaviours as an effective tool for improving adherence,55,58,59 which is consistent with the present review.”
From Effectiveness of digital health interventions on blood pressure control, lifestyle behaviours and adherence to medication in patients with hypertension in low-income and middle-income countries: a systematic review and meta-analysis of randomised controlled trials (2024) · cites this paper as [58]
Cites
Mobile phone-based interventions for smoking cessation
Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., & Gu, Y. (2016) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006611.pub4 · Cited by 1,079 · see citing works
Varnfield, M., Karunanithi, M., Lee, C.-K., Honeyman, E., Arnold, D., Ding, H., Smith, C., & Walters, D. L. (2014) · Heart · doi:10.1136/heartjnl-2014-305783 · Cited by 528 · see citing works
Liang, X., Wang, Q., Yang, X., Cao, J., Chen, J., Mo, X., Huang, J., Wang, L., & Gu, D. (2011) · Diabetic Medicine · doi:10.1111/j.1464-5491.2010.03180.x · Cited by 515 · see citing works
Bobrow, K., Farmer, A. J., Springer, D., Shanyinde, M., Yu, L.-M., Brennan, T., Rayner, B., Namane, M., Steyn, K., Tarassenko, L., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.017530 · Cited by 295 · see citing works
Maddison, R., Pfaeffli, L., Whittaker, R., Stewart, R., Kerr, A., Jiang, Y., Kira, G., Leung, W., Dalleck, L., Carter, K., et al. (2015) · European Journal of Preventive Cardiology · doi:10.1177/2047487314535076 · Cited by 268 · see citing works
Mobile health use in low- and high-income countries: an overview of the peer-reviewed literature
Bastawrous, A., & Armstrong, M. J. (2013) · Journal of the Royal Society of Medicine · doi:10.1177/0141076812472620 · Cited by 236 · see citing works
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Boima, V., Doku, A., Agyekum, F., Tuglo, L. S., & Agyemang, C. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102432 · Cited by 82 · see citing works
“Studies have shown that digital health interventions result in changes in lifestyle behaviours as an effective tool for improving adherence, 55,58,59 which is consistent with the present review.”
Result
- Authors
- Klimis, H., Thakkar, J., & Chow, C. K.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Klimis, Harry
- Venue
- Canadian Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 34
- Issue
- 7
- Pages
- 905-913
- ISSN
- 0828-282X (ISSN-L)
- OpenAlex
- W2808016804
- Cited by
- 40
- References
- 6
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 8 · 2022: 7 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 2
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health, cardiovascular disease prevention, cardiovascular risk factors, primary prevention, secondary prevention, smartphone-based intervention, complex interventions, intervention durability, personalization, interactivity
- MeSH terms
- Cardiovascular Diseases, Delivery of Health Care, Humans, Telemedicine, Secondary Prevention
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