Mobile Health Use by Older Individuals at Risk of Cardiovascular Disease and Type 2 Diabetes Mellitus in an Australian Cohort: Cross-sectional Survey Study
- PMID: 36069764
- PMCID: PMC9494219
- DOI: 10.2196/37343
Mobile Health Use by Older Individuals at Risk of Cardiovascular Disease and Type 2 Diabetes Mellitus in an Australian Cohort: Cross-sectional Survey Study
Abstract
Background: The digital transformation has the potential to change health care toward more consumers' involvement, for example, in the form of health-related apps which are already widely available through app stores. These could be useful in helping people understand their risk of chronic conditions and helping them to live more healthily.
Objective: With this study, we assessed mobile health app use among older Australians in general and among those who were at risk of cardiovascular disease or type 2 diabetes mellitus.
Methods: In this cross-sectional analysis, we used data from the second follow-up wave of the 45 and Up Study. It is a cohort study from New South Wales, Australia, with 267,153 participants aged 45 years and older that is based on a random sample from the Services Australia (formerly the Australian Government Department of Human Services) Medicare enrollment database. The 2019 follow-up questionnaire contained questions about technology and mobile health use. We further used data on prescribed drugs and hospitalizations to identify participants who already had cardiovascular disease or diabetes or who were at risk of these conditions. Our primary outcome measure was mobile health use, defined as having used a mobile health app before. We used descriptive statistics and multivariate logistic regression to answer the research questions.
Results: Overall, 31,946 individuals with a median age of 69 (IQR 63-76) years had completed the follow-up questionnaire in 2019. We classified half (16,422/31,946, 51.41%) of these as being at risk of cardiovascular disease or type 2 diabetes mellitus and 38.04% (12,152/31,946) as having cardiovascular disease or type 1 or type 2 diabetes mellitus. The proportion of mobile health app users among the at-risk group was 31.46% (5166/16,422) compared to 29.16% (9314/31,946) in the total sample. Those who used mobile health apps were more likely to be female, younger, without physical disability, and with a higher income. People at risk of cardiovascular disease or type 2 diabetes mellitus were not statistically significantly more likely to use mobile health than were people without risk (odds ratio 1.06, 95% CI 0.97-1.16; P=.18; adjusted for age, sex, income, and physical disability).
Conclusions: People at risk of cardiovascular disease or type 2 diabetes mellitus were not more likely to use mobile health apps than were people without risk. Those who used mobile health apps were less likely to be male, older, with a physical disability, and with a lower income. From the results, we concluded that aspects of equity must be considered when implementing a mobile health intervention to reach all those that can potentially benefit from it.
Keywords: aging; cardiovascular; cardiovascular diseases; cohort studies; diabetes; diabetes mellitus type 2; digital health; mHealth; mobile app; mobile applications; mobile health; telemedicine.
©Vera Helen Buss, Marlien Varnfield, Mark Harris, Margo Barr. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 07.09.2022.
Conflict of interest statement
Conflicts of Interest: None declared.
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References
-
- Bertram MY, Sweeny K, Lauer JA, Chisholm D, Sheehan P, Rasmussen B, Upreti SR, Dixit LP, George K, Deane S. Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services. Lancet. 2018 Dec 19;391(10134):2071–2078. doi: 10.1016/S0140-6736(18)30665-2.S0140-6736(18)30665-2 - DOI - PubMed
-
- GBD 2019 Risk Factors Collaborators Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020 Oct 17;396(10258):1223–1249. doi: 10.1016/S0140-6736(20)30752-2. https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(20)30752-2 S0140-6736(20)30752-2 - DOI - PMC - PubMed
-
- Digital health trends 2021: Innovation, evidence, regulation, and adoption. IQVIA Institute for Human Data Science. 2021. [2022-02-07]. https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/digital-healt... .
-
- Jensen M, Treskes R, Caiani E, Casado-Arroyo R, Cowie M, Dilaveris P, European Association of Preventive Cardiology. European Heart Rhythm Association. Association of Cardiovascular Nursing and Allied Professionals ESC working group on e-cardiology position paper: Use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention, in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee. Eur Heart J Digit Health. 2021;2(1):49–59. doi: 10.1093/ehjdh/ztab011. - DOI - PMC - PubMed
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