Acceptance, Drivers, and Barriers to Use of mHealth Apps to Improve Quality of Life in Female Patients Affected by Hypothyroidism: Cross-Sectional Study
Bibliographic record
Abstract
Background: Hypothyroidism is a common chronic disease that can substantially impair physical and mental well-being and is associated with lower quality of life, a trend that interventions delivered by mobile health (mHealth) apps could ameliorate. Objective: The objective of this study was to evaluate the acceptance and its influencing predictors of mHealth interventions in female patients affected by hypothyroidism to improve their quality of life. The focus on female patients reflects the significantly higher prevalence of hypothyroidism in women and their underrepresentation in many prior studies on technology acceptance and mHealth use. Methods: A survey-based, cross-sectional study, which included 318 female patients affected by hypothyroidism (assessed via self-reported diagnosis according to International Classification of Diseases-10 criteria, aged 18 y or older), was conducted online between April 2023 and April 2024 in Germany. Participants were recruited via local and online self-help groups, social media platforms, and medical practices using flyers. Sociodemographic, health, and eHealth-related data were assessed. To determine acceptance and its drivers and barriers, an extended version of the unified theory of acceptance and use of technology (UTAUT) model was applied. Group comparisons (t tests, ANOVAs) and multiple hierarchical regression analyses were conducted. Only complete datasets were included in the analysis. Results: Acceptance of mHealth apps was high (mean 4.10, SD 0.91), with 76.1% (n=242) of the participants reporting high acceptance, 18.6% (n=59) reporting moderate acceptance, and only 5.3% (n=17) reporting low acceptance. Significant predictors of acceptance were place of residence: medium-sized city (β=0.34; P=.02) and small town or rural area (β=0.28; P=.003), fatigue (β=0.54; P<.001), internet anxiety (β=-0.20; P=.002), and the UTAUT predictors effort expectancy (β=0.37; P<.001), performance expectancy (β=0.32; P<.001), and social influence (β=0.20; P<.001). The extended model explained 56.1% of the variance in acceptance. Conclusions: The high level of acceptance of mHealth apps observed among female patients affected by hypothyroidism indicates that mHealth interventions can provide such patients with valuable support to manage the disease and improve their quality of life. Addressing drivers and barriers of acceptance will be crucial for the successful implementation of mHealth interventions in hypothyroidism management, for example, by mHealth developers, clinicians, or policy makers. These include intuitive and accessible design (effort expectancy), clear communication of app benefits (performance expectancy), and fostering health care professional support (social influence), while addressing barriers such as internet anxiety. The study also contributes to advancing gender-sensitive mHealth research by applying the UTAUT model to this patient group.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".