Incorporating Exercise into Telerehabilitation Interventions for Adults with Chronic Low Back Pain: A Narrative Review
Bibliographic record
Abstract
Background: Telerehabilitation involves the use of information and communication technologies to provide rehabilitation care from a distance. Telerehabilitation can be a necessary and valuable means of providing rehabilitation services, especially in chronic conditions. Sufficiently detailed evidence is needed regarding how exercise can be effectively incorporated into telerehabilitation interventions for adults with chronic low back pain (CLBP). This narrative review outlines details on how and to what extent exercise is incorporated into published telerehabilitation interventions for CLBP considering the Consensus on Exercise Reporting Template (CERT) criteria. Method: A search was conducted in EMBASE, MEDLINE, Cochrane CENTRAL, CINAHL, Google Scholar and the reference lists of relevant studies. Randomized controlled trials (RCTs) of telerehabilitation interventions for adults (>18 years) with CLBP (>3 months duration) were included for review. Results: Eight RCTs, utilizing various technologies, were eligible for inclusion (793 screened). Results suggest many studies lack comprehensive descriptions of the exercise components of the intervention. Conclusion: All interventions include some form of monitoring, and more than half included goal setting, highlighting the importance of these components. Clinicians should consider including these components alongside exercise for telerehabilitation interventions for CLBP.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".