Understanding Appropriation of Digital Self-Monitoring Tools in Mental Health Care: Qualitative Analysis
Bibliographic record
Abstract
Background: Digital self-monitoring tools, such as the experience sampling method (ESM), enable individuals to collect detailed information about their mental health and daily life context and may help guide and support person-centered mental health care. However, similar to many digital interventions, the ESM struggles to move from research to clinical integration. To guide the implementation of self-monitoring tools in mental health care, it is important to understand why and how clinicians and clients adopted, adapted, and incorporated these tools in practice. Objective: Therefore, this study examined how clinicians and clients within a psychiatric center appropriated an ESM-based self-monitoring tool within their therapy. Methods: Twelve clinicians and 24 clients participated in the piloting of the ESM tool, IMPROVE. After utilizing the tool, 7 clinicians and 11 clients took part in semistructured interviews. A thematic framework analysis was performed focusing on participants' prior knowledge and expectations, actual use in practice, and potential future use of ESM tools. Results: Many participants experienced that the ESM tool provided useful information about clients' mental health, especially when clinicians and clients engaged in collaborative data interpretation. However, clinicians experienced several mismatches between system usability and their technical competencies, and many clients found it difficult to comply with the self-assessments. Importantly, most participants wanted to use digital self-monitoring tools in the future. Conclusions: Clinicians' and clients' choice to adopt and integrate self-monitoring tools in their practice seems to depend upon the perceived balance between the added benefits and the effort required to achieve them. Enhancing user support or redesigning ESM tools to reduce workload and data burden could help overcome implementation barriers. Future research should involve end users in the development of ESM self-monitoring tools for mental health care and further investigate the perspectives of nonadopters.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".