Currently submitted to: JMIR Aging
Date Submitted: Sep 19, 2026
Open Peer Review Period: Sep 20, 2026 - Nov 15, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Latent Profiles of Perceived mHealth Applicability Among Older Adults With Chronic Diseases: A Mixed-Methods Study
ABSTRACT
Background:
Older adults with chronic diseases are increasingly expected to use mobile health (mHealth) for self-management, but their adoption remains uneven across subgroups. Although mHealth can support remote monitoring and chronic disease care, older adults vary widely in whether they find these tools useful, easy to use, and trustworthy. Understanding this heterogeneity is needed to move beyond one-size-fits-all interventions and support the most vulnerable users.
Objective:
This study aimed to identify latent profiles of perceived mHealth applicability among older adults with chronic diseases, examine sociodemographic and health-related correlates of profile membership, and explore the social mechanisms underlying profile formation through qualitative data.
Methods:
An explanatory sequential mixed-methods design was adopted. Quantitatively, 500 older adults with chronic diseases were recruited from a hospital in Guangzhou, China. Latent profile analysis based on a 21-item scale identified profiles; confirmatory factor analysis supported the scale’s factor structure, and multinomial logistic regression examined correlates of profile membership. Qualitatively, semi-structured interviews with 35 participants (17 urban, 18 rural) were analyzed using framework analysis to interpret the quantitative findings and trace mechanisms of profile formation.
Results:
Three latent profiles were identified: digitally constrained (28.8%), cautious observer (44.0%), and proactive adaptor (27.2%), with entropy = 0.876 and average posterior classification probabilities of 0.93–0.97, differing significantly across all six applicability dimensions (all p < .001). Older age, female sex, and lower educational attainment were independently associated with membership in the digitally constrained profile (education: OR = 0.231, 95% CI 0.162–0.330, p < .001, per level). Qualitative analysis indicated four interconnected mechanisms: urban-rural gaps in operational skills, dissociation between instrumental utility and medical trust, overlapping causes of technology abandonment, and an empowerment paradox arising from varied support.
Conclusions:
Perceived mHealth applicability among older adults with chronic diseases is heterogeneous across three profiles with distinct correlates and mechanisms. Findings support profile-tailored training and tiered support that target operational hurdles, online medical trust, and conditions for autonomous use. Clinical Trial: JNUKY-2024-0044
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