Assessing the Influence of Patient Empowerment Gained Through Mental Health Apps on Patient Trust in the Health Care Provider and Patient Compliance With the Recommended Treatment: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: In chronic mental illness, noncompliance with treatment significantly worsens the illness course and outcomes for patients. Considering that nearly 1 billion people worldwide experience mental health issues, including 1 of 5 Canadians in any given year, finding tools to lower noncompliance in these populations is critical for health care systems. A promising avenue is apps that make mental health services more accessible to patients. However, little is known regarding the impact of the empowerment gained from mental health apps on patient compliance with recommended treatment. OBJECTIVE: This study aimed to investigate the impact of patient empowerment gained through mental health apps on patient trust in the health care provider and patient compliance with the recommended treatment. METHODS: A cross-sectional web-based survey was conducted in Canada. Eligible participants were Canadian adults diagnosed with chronic mental health disorders who were using at least one of the following apps: Dialogue, MindBeacon, Deprexis, Ginger, Talkspace, BetterHelp, MindStrong, Mindshift, Bloom, Headspace, and Calm. A total of 347 valid questionnaires were collected and analyzed using partial least-squares structural equation modeling. Trust in the health care provider and patient compliance were measured with multiple-item scales adapted from existing scales. Patient empowerment was conceived and measured as a higher-order construct encompassing the following 2 dimensions: patient process and patient outcome. All the items contributing to the constructs in the model were measured with 7-point Likert scales. The reliability and validity of the measurement model were assessed, and the path coefficients of the structural model were estimated. RESULTS: The results clearly show that patient empowerment gained through mental health apps positively influenced patient trust in the health care provider (β=.306; P<.001). Patient trust in the health care provider had a positive effect on patient compliance (β=.725; P<.001). The direct relationship between patient empowerment and patient compliance was not significant (β=.061, P=.23). Interestingly, the data highlight that the effect of patient empowerment on patient compliance was fully mediated by trust in the health care provider (β=.222; P<.001). The results show that patient empowerment gained through the mental health app involves 2 dimensions: a process and an outcome. CONCLUSIONS: This study shows that for individuals living with mental health disorders, empowerment gained through mental health apps enhances trust in the health care provider. It reveals that patient empowerment impacts patient compliance but only through the full mediating effect of patient trust in the health care provider, indicating that patient trust is a critical variable to enhance patient compliance. Hence, our results confirm that health care systems could encourage the use of mental health apps to favor a climate that facilitates patients' trust in health care provider recommendations, possibly leading to better compliance with the recommended treatment.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".