Smart phone technologies and ecological momentary data
Bibliographic record
Abstract
PURPOSE OF REVIEW: Depression is a complex and burdensome condition; it often leads to personal, societal and economic costs. Despite advances in treatments, its management over time remains a challenge; many treated for depression do not achieve full recovery or remain well for long. Novel ways to monitor patients are warranted, as well as better understanding of contributors to relapse or sustained wellness. Mobile health technologies (m-Health) are emerging as useful tools for real-time assessments of moods, behaviours and activities in a more convenient and less burdensome manner. Yet, there are numerous questions around privacy, reliability and accuracy of data collected via mobile apps. This review provides a critical overview of advances in m-Health and evaluate the future potential of smartphone technology in the assessment and treatment of depression. RECENT FINDINGS: There is an abundance of apps in the market that claim to exert beneficial effects on the management of depression; to date, only a small fraction has been validated in clinical trials or has had the support of academic centers. SUMMARY: Although promising, the use of mobile health applications in depression warrants further investigation and incorporation into mainstream research to facilitate greater adoption and validation of its clinical utility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".