Perceived Sensitivity of Sensor-Based Digital Health Data: A Qualitative Interview Study (Preprint)
Bibliographic record
Abstract
Background: Digital health tools are increasingly used in mental health care to passively collect patient data and analyze health status outside of clinical settings. While technologies such as digital phenotyping, affective computing, and computational behavioral analysis offer new insights into symptom manifestation in daily life, they generate large volumes of potentially sensitive data that raise significant data privacy concerns, requiring high levels of patient awareness and consent. Empirical research is lacking on stakeholder understandings toward the sensitivity of these data and expectations for data stewardship, perspectives that are critical for developing robust informed consent and data protection policies for digital health data use. Objective: This study aimed to explore key stakeholder perspectives on the sensitivity of computer perception (CP) data, trust in existing data protections, willingness to share CP data externally, and desire for transparency of CP data transactions outside of the clinical space. Methods: As part of a larger, multisite study, we conducted qualitative interviews (n=40) via Zoom (Zoom Communications, Inc) with 20 adolescents (aged 12-17 years) familiar with CP tools and their caregivers (n=20). Interviews consisted of a series of open-ended questions regarding stakeholders' perspectives on privacy, data security, and the use and exchange of CP data. We developed a qualitative codebook to identify and label thematic patterns in responses to questions addressing the topics above, using thematic content analysis to identify themes inductively. Each interview was coded by merging work from at least two separate coders, and several team members contributed to qualitative analysis. Results: Most adolescents and caregivers viewed CP data as highly sensitive and expressed a reluctance to share these data beyond their clinical teams. While many participants expressed trust in existing data protections to protect CP data, they often misunderstood or overestimated the extent of protections to safeguard CP data. Conclusions: Our findings underscore the critical need for clear and effective patient communication and education about the risks, benefits, and protections associated with CP data through informed consent protocols. To promote greater transparency, understanding, and trust, we recommend 5 strategies: educating patients about data protection; studying secondary data exchange and reidentification risks; strengthening transparency regulations; improving data traceability mechanisms, such as distributed ledger technologies, to enhance data traceability and auditability; and adopting dynamic consent models.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".