Comfortability with the passive collection of smartphone data for monitoring of mental health: An online survey
Bibliographic record
Abstract
For successful integration of mobile sensing solutions in existing mental health services, patients' comfortability with mobile sensing is crucial. We thus aimed to investigate people's comfortability with mobile sensing and explore personal, mobile sensing app and data privacy related variables' impact on comfortability. We conducted an online survey including 491 participants aged >18 and ran three models of linear regression with comfortability with mobile sensing as primary outcome and personal variables as predictors in the 1st model; mobile sensing app related variables as predictors in the 2nd model; and general data privacy related variables as predictors in the 3rd model. Then, we ran an aggregated model of the previous three including all significant predictors. Like of features, perceived control and trust in mobile marketers had the highest impact on comfortability with data sensing and they also predicted intentions to accept app permissions. People are more comfortable with sharing their data and more willing to take the risks of using mobile sensing apps if they find that the features provide them with valuable feedback related to their health. It is highly important for users that they can trust the people they provide access to their data and feel in control of the data they share.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".