Using a mobile health device to monitor physiological stress for serious mental illness: A qualitative analysis of patient and clinician-related acceptability.
Bibliographic record
Abstract
OBJECTIVE: There is growing interest in using mobile health (mHealth) devices to monitor physiological stress associated with mental deterioration. Research is currently examining whether physiological information returned to individuals with serious mental illness (SMI) and their clinicians enhances early intervention. The aim of this study was to explore patient and clinician-related acceptability of an mHealth device to monitor stress for SMI. METHOD: = 22). Content was transcribed and analyzed using an inductive thematic analysis focusing on perceptions of potential benefit, barriers and facilitators of uptake. RESULTS: Six themes were identified. Individuals with SMI and clinicians identified two themes related to benefits of the mHealth device: (a) self-monitoring improves symptom insight and (b) clinician monitoring as a benefit to treatment. They identified one barrier theme: (c) privacy and data misuse concerns. They also identified three facilitators of uptake: (d) ease of use, (e) engaging design and (f) procedural guidelines. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: The perceived benefits of passive physiological monitoring afforded by an mHealth device come with concerns regarding its privacy and the potential for ambiguity in the patient-clinician relationship. Results suggest the importance of codesign to ensure that it is secure, easy to use and engaging. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".