An umbrella review of the literature on the effectiveness of goal setting interventions in improving health outcomes in chronic conditions
Bibliographic record
Abstract
To identify the contexts in which goal setting has been used in chronic disease management interventions and to estimate the magnitude of its effect on improvement of health outcomes. The strength of evidence and extent of potential bias in the published systematic reviews of goal setting interventions in chronic conditions were summarized using AMSTAR2 quality appraisal tool, number of participants, 95% prediction intervals, and between-study heterogeneity. Components of goal setting interventions were also extracted. Nine publications and 35 meta-analysis models were identified, investigating 25 health outcomes. Of the 35 meta-analyses, none found strong evidence and three provided some suggestive evidence on symptom reduction and perceived well-being. There was weak evidence for effects on eight health outcomes (HbA1c, self-efficacy, depression, anxiety, distress, medication adherence, health-related quality of life and physical activity), with the rest classified as non-significant. Half of the meta-analyses had high level of heterogeneity. Goal setting by itself affects outcomes of chronic diseases only to a small degree. This is not unexpected finding as changing outcomes in chronic diseases requires a complex and individualized approach. Implementing goal setting in a standardized way in the management of chronic conditions would seem to be a way forward.IMPLICATIONS FOR REHABILITATIONThe link between goal setting and health outcomes seems to be weak.Some levels of positive behavioural change could be of benefits to patients as seen by improved self-efficacy, patients’ satisfaction and overall quality of life.Systematic and consistent application of personalized goal-oriented interventions considering patient’s readiness to change could better predict improved outcomes.Incorporation of various goal setting components while actively engaging patient and/or their care givers in the process could appraise how goal setting could help with challenges in faced by people living with chronic conditions in different areas. The link between goal setting and health outcomes seems to be weak. Some levels of positive behavioural change could be of benefits to patients as seen by improved self-efficacy, patients’ satisfaction and overall quality of life. Systematic and consistent application of personalized goal-oriented interventions considering patient’s readiness to change could better predict improved outcomes. Incorporation of various goal setting components while actively engaging patient and/or their care givers in the process could appraise how goal setting could help with challenges in faced by people living with chronic conditions in different areas.
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 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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".