Intention to use Medical Apps Among Older Adults in the Netherlands: Cross-Sectional Study

Abstract

Background The increasing health service demand driven by the aging of the global population calls for the development of modes of health service delivery that are less human resource–intensive. Electronic health (eHealth) and medical apps are expected to play an important role in this development. Although evidence shows mobile medical apps might be effective in improving the care, self-management, self-efficacy, health-related behavior, and medication adherence of older adults, little is known about older adults’ intention to use these technologies when needed, or the factors influencing this intention. Objective The objective of this study was to investigate the relationship of technology acceptance factors and intention to use mobile medical apps among community-dwelling older adults. Methods Data was collected using questionnaires. The factors selected from the literature have been validated using Cronbach α and tested for significance using logistic regressions. Results Almost half (49.7%) of the included older adults reported no intention to use medical apps. Adjusted logistic regression analysis per factor showed that the factors Attitude toward use (odds ratio [OR] 8.50), Perceived usefulness (OR 5.25), Perceived ease of use (OR 4.22), Service availability (OR 3.46), Sense of control (OR 3.40), Self-perceived effectiveness (OR 2.69), Facilities (OR 2.45), Personal innovativeness (OR 2.08), Social relationships (OR 1.79), Subjective norm (OR 1.48), and Feelings of anxiety (OR 0.62) significantly influenced the intention to use mobile medical apps among older adults, whereas the factor Finance (OR 0.98) did not. When considered together, a controlled multivariate logistic regression yielded high explained variances of 0.542 (Cox-Snell R2) and 0.728 (Nagelkerke R2). Conclusions The high odds ratios and explained variance indicate that the factors associated with the intention to use medical apps are largely understood and the most important factors have been identified. To advance the evidence base, experimental controlled research should investigate the causality between the factors, intention to use, and actual use. For this purpose, our evidence suggests that policies designed to improve Attitude toward use appear most effective, followed by policies addressing Perceived usefulness, Perceived ease of use, Service availability, and Sense of control.

eHealth Applications to Support Independent Living of Older Persons: Scoping Review of Costs and Benefits Identified in Economic Evaluations

“…Financial costs directed to patients or informal caregivers might be an obstacle to using the eHealth service or application or continuation of usage of these services, especially in countries where mandatory health insurance coverage is lacking. Although a recent study in the Netherlands identified finance as a factor not significantly related to intention to use medical apps among older persons [ 77 ], future studies should focus on how the costs are covered and who should pay for the direct or indirect costs of eHealth.…”

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

“…Therefore, in this study, we selected behavioral intention to use mHealth applications from TAM as an outcome of interest. Building on the work of Askari et al [ 29 ], the statements operationalizing intention to use are taken from Venkatesh and Davis [ 44 ] and translated into Dutch, adding one new statement to account for linguistic differences.…”

“…Unfortunately, however, older adults are less likely to adopt new technologies such as mHealth [24][25][26][27][28]. Before the pandemic, almost 50% of Dutch older adults had no intention to use mHealth applications [29].…”

Understanding New Zealand Adults’ Attitudes Towards Digital Interventions for Health

“…Positive attitudes are associated with an increased likelihood of behaviour and behavioural intentions (Davis, 1989;Fishbein & Ajzen, 1975;McEachan, Conner, Taylor, & Lawton, 2011;Rosenstock, Strecher, & Becker, 1988). Such as in the case of DI, people with more favourable attitudes to DI are more likely to have positive intentions to use DI, and this is supported by research (Askari, 2020;Patel et al, 2020;Wangberg, Gammon, & Spitznogle, 2007). Therefore, people's attitudes are crucial to understanding people's readiness to engage with DI.…”