Determinants of Telehealth Adoption Among Older Adults: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The aging population and the accompanying rise in chronic diseases have intensified the need to study the adoption of telehealth services. However, the success of telehealth services depends not only on their ease and usefulness but also on addressing broader concerns. Despite being a substantial user group in traditional health services, older adults may encounter barriers to adopting telehealth services. Increasing the adoption of telehealth among the older adult population is crucial for enhancing their access to care and managing the challenges of aging effectively. OBJECTIVE: We aimed to explore factors influencing the adoption of telehealth services among older adults in Malaysia, going beyond the conventional framework by incorporating transition cost and subjective well-being as additional constructs. METHODS: A cross-sectional survey was conducted among 119 adults aged ≥60 years in Malaysia, using 39 survey items adapted from existing studies. Data analysis was performed using partial least squares structural equation modeling, with both the measurement model and structural model being evaluated. To determine the predictive relevance of the model, PLSpredict was applied. In addition, importance-performance map analysis was conducted to further expand on the structural model results by assessing the performance of each variable. RESULTS: Of the 119 participants, 52 (43.7%) were women and 67 (56.3%) were men. The study found that subjective well-being (β=0.448; P<.001) was the most significant factor, followed by attitude (β=0.242; P<.001), transition cost (β=-0.163; P<.001), and perceived usefulness (β=0.100, P=.02) in influencing telehealth service intention. Furthermore, perceived ease of use (β=0.271; P<.001), availability (β=0.323; P<.001), subjective well-being (β=0.261; P<.001), and trust (β=0.156, P=.004) positively influenced perceived usefulness, while inertia (β=0.024, P=.22) did not. In addition, availability (β=0.420; P<.001) and subjective well-being (β=0.260; P<.001) were positively related to perceived ease of use, with inertia (β=-0.246; P<.001) having a negative impact. The importance-performance map analysis results showed that subjective well-being (importance=0.532) was the most crucial factor for older adult users, while availability (importance=70.735) had the highest performance in telehealth services. CONCLUSIONS: This research underscores the importance of catering to the subjective well-being of older adults and optimizing the availability of telehealth services to encourage adoption, ultimately advancing health care accessibility and quality for this vulnerable demographic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".