The impact of COVID-19 related lockdown restrictions on musculoskeletal health: a systematic review
Bibliographic record
Abstract
There is limited empirical evidence on whether Coronavirus disease 2019 (COVID-19) related lockdown restrictions precipitate or perpetuate musculoskeletal (MSK) disorders. This study was aimed to synthesis literature that assessed the impact of COVID-19 related lockdown restrictions on MSK health. A literature search was conducted using MEDLINE, CINAHL, PsycINFO, Allied and Complementary Medicine Database (AMED), Web of Science, and Scopus databases. Studies meeting the following criteria were included in the review: the condition being considered was MSK health, the intervention was COVID-19 related lockdown restrictions, cross sectional studies, cohort studies, case controlled, prospective studies and retrospective studies. Data were extracted by 2 independent researchers. Risk of bias was assessed by the Newcastle-Ottawa quality assessment scale. Evidence from included studies was summarised using narrative synthesis. Fourteen studies comprising 22,471 participants of the general population from Turkey (n = 5), Italy (n = 1), Poland (n = 1), Australia (n = 2), Jordan (n = 1), Bangladesh (n = 1), Estonia (n = 1), the Netherlands (n = 1) and Saudi Arabia (n = 1) have met the inclusion criteria. The sample size of populations studied ranged from 91 to 1054. The included studies used questionnaire, visual analogic scale, or growth mixture modelling. Except for one study, all the included studies reported increased prevalence and incidence of MSK disorders due to COVID-19 related lockdown restrictions. The findings suggest that COVID-19 related lockdown restriction led to increased MSK disorders. Home-based strategies such as physical activity programmes and ergonomic workspace could potentially guide public health authorities to avoid MSK health problem.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".