Frequency of use and Perceived helpfulness of Wellness Strategies for Bipolar and Unipolar Depression
Bibliographic record
Abstract
BACKGROUND: The majority of research in mood disorders has focused on pharmacologic, psychotherapeutic, and brain stimulation interventions. Conversely, the utility of less structured interventions, such as lifestyle modifications or wellness strategies, has remained understudied. The objective of the current study is to evaluate the frequency of use and perceived helpfulness of wellness strategies for bipolar and unipolar depression. METHODS: The Depression and Bipolar Support Alliance (DBSA) conducted an online survey asking participants about the use and helpfulness of wellness strategies. RESULTS: In total, 896 participants completed the survey (unipolar depression [n = 447] and bipolar depression [n = 449]). Wellness strategies were used by 62% and 59% of individuals with bipolar and unipolar depression, respectively. Listening to music, socializing, and adequate sleep were commonly reported wellness strategies. The majority of participants reported wellness strategies to be helpful. Use of wellness strategies was associated with greater overall perceived treatment effectiveness (P < .0001) and greater subjective helpfulness of medications (P = .039), psychotherapy (P < .0001), and peer support groups (P < .0001). CONCLUSIONS: Wellness strategies were commonly used by the majority of respondents. These strategies were subjectively helpful for most respondents and were associated with greater overall treatment effectiveness and increased helpfulness of medications, psychotherapy, and peer support groups. As such, wellness strategies should be considered while developing a holistic treatment plan for depression. Further research is needed to evaluate the antidepressant effects of specific wellness strategies to better understand the role of these interventions in the management of depression.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".