{"title":"Behavioural intention of mobile health adoption: A study of older adults presenting to the emergency department","authors":"Mathew Aranha , Jonah Shemie , Kirstyn James , Conor Deasy , Ciara Heavin","doi":"10.1016/j.smhl.2023.100435","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>The COVID-19 pandemic highlighted the challenges of providing quality healthcare to vulnerable populations, especially older adults who are disproportionately affected by health service disruptions. Increasingly, mobile health (mHealth) is used for remote healthcare service delivery in this group; however, a variety of factors may limit its adoption.</p></div><div><h3>Aims</h3><p>To identify the prevalence of mobile device usage among older adults (65yrs+) who present to acute hospitals and explore their willingness to use mHealth.</p></div><div><h3>Methods</h3><p>A cross-sectional study was conducted using convenience sampling to recruit adults over 65 years to complete a 28 question, 5-point-Likert questionnaire developed using the Unified Theory of Acceptance and Use of Technology (UTAUT).</p></div><div><h3>Results</h3><p>This study included 119 older adults. Fifty-three participants (44.5%) did not own a smartphone, and 53 (44.5%) had never used one. Sixty-six participants (55.5%) indicated an intention to use mHealth while 53 (44.5%) were either ambivalent or had no intention to use it. Smartphone owners were significantly more likely to use mHealth (OR:3.27, CI:1.53–6.95) than non-owners. Participants showed high self-efficacy (median = 4.0) and expected mHealth to perform well (median = 3.67) with minimal effort (median = 3.33). Within this cohort, intention to use is predicted by age (β = 0.163, p = 0.03), performance expectancy (β = 0.329, p = 0.01), effort expectancy (β = 0.231, p = 0.01) and subjective health status (β = −0.171, p = 0.01).</p></div><div><h3>Conclusions</h3><p>Many older adults attending acute hospitals remain disinclined in mHealth. This is associated with minimal experience to mobile devices. Empowering older adults to benefit from the increasingly digital landscape of healthcare will require uncovering creative ways to engage them in programs that increase their use of mHealth services.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"31 ","pages":"Article 100435"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648323000636/pdfft?md5=c961e5fb99f6594c1d2110dbae135fd0&pid=1-s2.0-S2352648323000636-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352648323000636","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Health Professions","Score":null,"Total":0}
引用次数: 0
Abstract
Background
The COVID-19 pandemic highlighted the challenges of providing quality healthcare to vulnerable populations, especially older adults who are disproportionately affected by health service disruptions. Increasingly, mobile health (mHealth) is used for remote healthcare service delivery in this group; however, a variety of factors may limit its adoption.
Aims
To identify the prevalence of mobile device usage among older adults (65yrs+) who present to acute hospitals and explore their willingness to use mHealth.
Methods
A cross-sectional study was conducted using convenience sampling to recruit adults over 65 years to complete a 28 question, 5-point-Likert questionnaire developed using the Unified Theory of Acceptance and Use of Technology (UTAUT).
Results
This study included 119 older adults. Fifty-three participants (44.5%) did not own a smartphone, and 53 (44.5%) had never used one. Sixty-six participants (55.5%) indicated an intention to use mHealth while 53 (44.5%) were either ambivalent or had no intention to use it. Smartphone owners were significantly more likely to use mHealth (OR:3.27, CI:1.53–6.95) than non-owners. Participants showed high self-efficacy (median = 4.0) and expected mHealth to perform well (median = 3.67) with minimal effort (median = 3.33). Within this cohort, intention to use is predicted by age (β = 0.163, p = 0.03), performance expectancy (β = 0.329, p = 0.01), effort expectancy (β = 0.231, p = 0.01) and subjective health status (β = −0.171, p = 0.01).
Conclusions
Many older adults attending acute hospitals remain disinclined in mHealth. This is associated with minimal experience to mobile devices. Empowering older adults to benefit from the increasingly digital landscape of healthcare will require uncovering creative ways to engage them in programs that increase their use of mHealth services.