Continued Intention of mHealth Care Applications among the Elderly: An Enabler and Inhibitor Perspective
Bibliographic record
Abstract
Optimal healthcare provision for the elderly is increasingly possible via real-time health indicators’ data generated by mHealth care applications. Yet, these apps require continuous utilization, which remains problematic. This research examines gamification, usability, as well as empathetic cooperation and social interaction (ESCI) as enablers whereas inertia, sunk cost, transition cost, perceived risk, and technological anxiety are validated as inhibitors of mHealth care applications continued usage intention. Drawing on self-determination theory (SDT) and the Health IT Usability Evaluation Model (Health-ITUEM), the study also validates engagement as an influencer of continued intention. The sample comprised 643 older adults using mHealth care applications and residing in North Indian states. Structural Equation Modelling (SEM) was applied to assess and validate the hypothesized relationships. The results confirmed that usability strongly impacted engagement, followed by gamification and ESCI. Conversely, perceived risk emerged as the strongest inhibitor, followed by sunk cost, technological anxiety, and transition cost. Interestingly, Inertia had a positive and significant impact on engagement. This research is an initial endeavor to understand enablers and inhibitors of mHealth care applications (mHealth care apps) concerning older adults. The model that emerged from this study would provide valuable insights by validating various significant issues to generate engagement of the elderly towards mHealth care apps.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".