Preferences on Smartphone App Features to Monitor Physical Activity in Asymptomatic Brazilian Adults According to Cardiovascular Risk
DOI:
https://doi.org/10.1590/1980-0037.2025v27e103952Keywords:
Physical Activity, Mobile applications, Smartphone, m-health, Cardiovascular riskAbstract
Analyzing feature preferences of smartphone applications is imperative, especially among subjects with high cardiovascular risk (CVR) who primarily benefit from increasing regular physical activity. We aimed to identify feature preferences for smartphone applications to monitor physical activity in Brazilian asymptomatic adults from Brazil stratified according to CVR. We conducted a cross-sectional study with 238 asymptomatic adults. We assessed features preferences through a survey based on the following topics: Personal/individualized, Training, Performance, Social aspect, Feedback, Motivation, Suggestions, and Other. Participants were divided into two groups according to self-reported CVR (low CVR, ≤ 2, and moderate-to-high CVR, > 2 risk factors) and compared using the x2 test. The moderate-to-high CVR group considered “develop a training schedule/program” and “save and review training statistics” less critical than speech navigation and receiving encouragement and motivational messages. Although reported by both groups, “monitor own progression” is more important for low CVR. The “compete with friends” feature was less reported than “being part of a community/group” for both groups. Lastly, understanding the features preferences allows the improvement of smartphones applications to become more attractive to subjects with chronic conditions.
References
Moorhead SA, Hazlett DE, Harrison L, Caroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2013;15:e85. doi:10.2196/jmir.1933.
Carter DD, Robinson K, Forbes J, Hayes S. Experiences of mobile health in promoting physical activity: a qualitative systematic review and meta-ethnography. PLoS One. 2018;13:e0208759. doi:10.1371/journal.pone.0208759.
Sama PR, Eapen ZJ, Weinfurt KP, Shah BR, Schulman KA. An evaluation of mobile health application tools. JMIR Mhealth Uhealth. 2014;2:e19. doi:10.2196/mhealth.3088.
Jeffrey B, Bagala M, Creighton A, Leavey T, Nicholls S, Wood C, et al. Mobile phone applications and their use in the self-management of type 2 diabetes mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr. 2019;11:1–17. doi:10.1186/s13098-019-0480-4.
Murnane EL, Huffaker D, Kossinets G. Mobile health apps: adoption, adherence, and abandonment. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers. 2015. p. 261–4.
Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J Med Internet Res. 2017;19:e125. doi:10.2196/jmir.5604.
World Health Organization. Global Health Estimates: life expectancy and leading causes of death and disability. 2020.
Byambasuren O, Beller E, Glasziou P. Current knowledge and adoption of mobile health apps among Australian general practitioners: survey study. JMIR Mhealth Uhealth. 2019;7:e13199. doi:10.2196/13199.
Paiva JOV, Andrade RMC, Oliveira PAM, Duarte P, Santos IS, Evangelista ALP, et al. Mobile applications for elderly healthcare: a systematic mapping. PLoS One. 2020;15:e0236091. doi:10.1371/journal.pone.0236091.
Knitza J, Simon D, Lambrecht A, Raab C, Tascilar K, Hagen M, et al. Mobile health usage, preferences, barriers, and eHealth literacy in rheumatology: patient survey study. JMIR Mhealth Uhealth. 2020;8:e19661. doi:10.2196/19661.
Sun L, Wang Y, Greene B, Xiao Q, Jiao C, Ji M, et al. Facilitators and barriers to using physical activity smartphone apps among Chinese patients with chronic diseases. BMC Med Inform Decis Mak. 2017;17:1–10. doi:10.1186/s12911-017-0446-0.
Sousa TLW, Ostoli TLVDP, Sperandio EF, Arantes RL, Gagliardi ART, Romiti M, et al. Dose-response relationship between very vigorous physical activity and cardiovascular health assessed by heart rate variability in adults: cross-sectional results from the EPIMOV study. PLoS One. 2019;31:e0210216. doi:10.1371/journal.pone.0210216.
Gonze BB, Padovani RC, Simões MS, Lauria V, Proença NL, Sperandio EF, et al. Use of a smartphone app to increase physical activity levels in insufficiently active adults: feasibility sequential multiple assignment randomized trial (SMART). JMIR Res Protoc. 2020;9:e14322. doi:10.2196/14322.
Dallinga JM, Mennes M, Alpay L, Bijwaard H, Faille-Deutekom MB. App use, physical activity and healthy lifestyle: a cross sectional study. BMC Public Health. 2015;15:833. doi:10.1186/s12889-015-2165-8.
Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde (PNS) 2019: atenção primária à saúde e informações antropométricas. Rio de Janeiro; 2020.
Brasil. Ministério da Saúde. Vigitel Brasil 2023: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Brasília: Ministério da Saúde; 2023.
IBGE. Pesquisa Nacional por Amostra de Domicílios Contínua – PNAD Contínua. Acesso à internet e à televisão e posse de telefone móvel celular para uso pessoal. 2019.
Osei E, Mashamba-Thompson TP. Mobile health applications for disease screening and treatment support in low- and middle-income countries: a narrative review. Heliyon. 2021;7:e06639. doi:10.1016/j.heliyon.2021.e06639.
Curry WB, Thompson JL. Comparability of accelerometer- and IPAQ-derived physical activity and sedentary time in South Asian women: a cross-sectional study. Eur J Sport Sci. 2015;15:655–62. doi:10.1080/17461391.2014.957728.
Robbins R, Krebs P, Jagannathan R, Jean-Louis G, Duncan DT. Health app use among US mobile phone users: analysis of trends by chronic disease status. JMIR Mhealth Uhealth. 2017;5:e197. doi:10.2196/mhealth.7832.
Akbar S, Coiera E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. J Am Med Inform Assoc. 2020;27:330–40. doi:10.1093/jamia/ocz175.
Middelweerd A, van der Laan DM, van Stralen MM, Mollee JS, Stuij M, te Velde SJ, et al. What features do Dutch university students prefer in a smartphone application for promotion of physical activity? A qualitative approach. Int J Behav Nutr Phys Act. 2015;12:1–11. doi:10.1186/s12966-015-0189-1.
Lindqvist AK, Rutberg S, Soderstrom E, Ek A, Alexandrou C, Maddison R, et al. User perception of a smartphone app to promote physical activity through active transportation: inductive qualitative content analysis within the smart city active mobile phone intervention (SCAMPI) study. JMIR Mhealth Uhealth. 2020;8:8. doi:10.2196/19380.
Knight E, Petrella RJ. Prescribing physical activity for healthy aging: longitudinal follow-up and mixed method analysis of a primary care intervention. Phys Sportsmed. 2014;42:30–8. doi:10.3810/psm.2014.11.2089.
Fukuoka Y, Lindgren T, Jong S. Qualitative exploration of the acceptability of a mobile phone and pedometer-based physical activity program in a diverse sample of sedentary women. Public Health Nurs. 2012;29:232–40. doi:10.1111/j.1525-1446.2011.00997.x.
Hall AK, Cole-Lewis H, Bernhardt JM. Mobile text messaging for health: a systematic review of reviews. Annu Rev Public Health. 2015;36:393–415. doi:10.1146/annurev-publhealth-031914-122855.
Ribeiro ALP, Duncan BB, Brant LCC, Lotufo PA, Mill JG, Barreto SM. Cardiovascular health in Brazil: trends and perspectives. Circulation. 2016;133:422–33. doi:10.1161/CIRCULATIONAHA.114.008727.
Kim H, Xie B. Health literacy and internet- and mobile app-based health services: a systematic review of the literature. Proc Assoc Inf Sci Technol. 2015;52:1–4. doi:10.1002/pra2.2015.145052010075.
Sardi L, Idri A, Fernández-Alemán JL. A systematic review of gamification in e-health. J Biomed Inform. 2017;71:31–48. doi:10.1016/j.jbi.2017.05.011.
