The impact of mind-body internet and mobile-based interventions on fatigue in adults living with chronic physical conditions: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Chronic physical conditions (CPCs) are conditions that persist for long periods and may not have a cure. Fatigue is a common symptom experienced by people living with CPCs. Mind-body internet and mobile-based interventions (IMIs) offer an accessible management strategy. The objective of this review was to assess the impact of mind-body IMIs on fatigue symptoms in adults with CPCs. Six databases were searched from inception to July 2024. Inclusion required randomized controlled trials (RCTs) of mind-body IMIs in adults (≥ 18) with CPCs that assessed fatigue pre-and post-intervention using self-report questionnaires. The primary outcome was the standardized mean fatigue change scores (Hedges' g). Sub-group analyses were conducted on CPC type, mind-body technique, fatigue questionnaire, and personnel support level. Meta-regression was performed on IMI length and age. Study quality was assessed using the Cochrane Risk of Bias 2.0 tool. The search retrieved 5239 studies. Seventeen studies met inclusion criteria: 47% neurological (n = 8), 29% cancer (n = 5), and 24% autoimmune (n = 4). Seven studies (41%) included cognitive behavioural therapy (CBT), seven used CBT combined with non-CBT techniques, and three employed non-CBT techniques. Mind-body IMIs led to significant reductions in fatigue (SMD = -0.74 [-1.09, -0.39]; p < 0.0001), with a greater effect in younger participants (p = 0.005). Heterogeneity was moderate to high. In conclusion, mind-body IMIs show promise in reducing fatigue symptoms in adults with CPCs. Further high-quality RCTs, expanding beyond CBT techniques, and using at least one common fatigue scale across conditions, would be helpful in evaluating the impact of IMIs across a broader range of CPCs.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".