Online and mobile technologies for self-management in bipolar disorder: A systematic review.
Bibliographic record
Abstract
OBJECTIVE: Internet (eHealth) and smartphone-based (mHealth) approaches to self-management for bipolar disorder are increasingly common. Evidence-based self-management strategies are available for bipolar disorder and provide a useful framework for reviewing existing eHealth/mHealth programs to determine whether these strategies are supported by current technologies. This review assesses which self-management strategies are most supported by technology. METHOD: Based on 3 previous studies, 7 categories of self-management strategies related to bipolar disorder were identified, followed by a systematic literature review to identify existing eHealth and mHealth programs for this disorder. Searches were conducted by using PubMed, CINAHL, PsycINFO, EMBASE, and the Cochrane Database of Systematic Reviews for relevant peer-reviewed articles published January 2005 to May 2015. eHealth and mHealth programs were summarized and reviewed to identify which of the 7 self-management strategy categories were supported by eHealth or mHealth programs. RESULTS: From 1,654 publications, 15 papers were identified for inclusion. From these, 9 eHealth programs and 2 mHealth programs were identified. The most commonly supported self-management strategy categories were "ongoing monitoring," "maintaining hope," "education," and "planning for and taking action"; the least commonly supported categories were "relaxation" and "maintaining a healthy lifestyle." eHealth programs appear to provide more comprehensive coverage of self-management strategies compared with mHealth programs. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Both eHealth and mHealth programs present a wide range of self-management strategies for bipolar disorder, although individuals seeking comprehensive interventions might be best served by eHealth programs, while those seeking more condensed and direct interventions might prefer mHealth programs. (PsycINFO Database Record
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".