Giving Back: Return of Results Reports Using Digital Phenotyping Tools in Mental Health
Bibliographic record
Abstract
Abstract As digital phenotyping tools become more prevalent in mental health research and care, the question of how to meaningfully return data to participants, clinicians, and caregivers has grown increasingly important. Drawing from the broader DeeP-DD (Deep Phenotyping and Digitalization at the Douglas) project, this work presents the iterative development of individualized feedback reports focused on a psychiatric patient population. This paper also builds on findings from a narrative literature review to derive evidence-based practices around conveying data to participants. From the literature review, general themes such as patient engagement, visual design, ethical considerations and report content were extracted. Core design principles identified included visual simplicity, use of color, contextualized interpretation, and ethical considerations, which were compiled and helped guide the presentation of data gathered from participants in user-friendly formats. The reports created from these findings are based on both digital and clinical data, and their design was informed by key findings from the narrative review and developed through a participatory process involving patients, clinicians, and caregivers. Findings from early user feedback and existing literature suggest that well-designed reports can foster greater understanding, trust, and behavioral engagement in research and clinical care. The review also highlights important gaps, particularly in caregiver-focused communication, and discusses future directions including developing similar reports for both physicians and caregivers and the use of digital report platforms. By addressing both design and ethical considerations in a real-world setting, this work contributes to the field of return-of-results practices in psychiatric digital health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".