Factors Associated With the Intention to Use mHealth Among Thai Middle-Aged Adults and Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: Mobile health care (mHealth) apps are emerging worldwide as a vital component of internet health care, but there are issues, especially among older adults. Objective: We aim to investigate the factors influencing the intention to use (ITU) mHealth apps, focusing on those with and without prior mHealth experience. Methods: A cross-sectional study conducted from August 2022 to July 2023 included Thai citizens aged 45 years or older. Self-reported questionnaires collected data on sociodemographic information, health conditions, smartphone or tablet ownership, and mHealth usage experience. The Thai mHealth Senior Technology Acceptance Model questionnaires with a 10-point Likert scale evaluated mHealth acceptance. A multivariable logistic regression analysis, adjusted for age, gender, education, income, and living area, was performed for 2 subgroups: those who used ITU mHealth apps and those who did not. Results: Of 1100 participants, 537 (48.8%) intended to use mHealth apps, while 563 (51.2%) did not. The ITU group had a younger average age, higher education levels, higher income, and fewer underlying diseases compared to those who did not intend to use mHealth apps. For those who had never used mHealth apps, having a smartphone was strongly associated with higher odds of ITU (adjusted odds ratio 2.81, 95% CI 1.6 to 4.93; P<.001), while having any underlying disease was associated with lower odds of ITU (adjusted odds ratio 0.63, 95% CI 0.42 to 0.97; P=.034). Higher acceptance levels, characterized by a positive attitude toward mHealth and lower fear of making mistakes, were also associated with higher ITU. For those with prior mHealth experience, acceptance in areas such as perceived ease of use, gerontechnology anxiety, and facilitating conditions was significantly associated with ITU. Conclusions: Among inexperienced users, a positive attitude toward mHealth significantly enhanced ITU. Conversely, having an underlying disease decreased ITU, indicating a need for tailored mHealth apps. For experienced users, acceptance levels in areas such as ease of use and gerontechnology anxiety were crucial. Future research should explore specific mHealth apps for more targeted insights.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".