Immuno-modulation with lifestyle behaviour change to reduce SARS-CoV-2 susceptibility and COVID-19 severity: goals consistent with contemporary physiotherapy practice
Bibliographic record
Abstract
Lifestyle-related non-communicable diseases (NCDs) and their risk factors are unequivocally associated with SARS-CoV-2 susceptibility and COVID-19 severity. NCD manifestations and their lifestyle risks are associated with chronic low-grade systemic inflammation (CLGSI). This review supports that immuno-modulation with positive lifestyle change aimed at reducing SARS-CoV-2 susceptibility and COVID-19 severity, is a goal consistent with contemporary physiotherapy practice. Physiotherapists have a long tradition of managing a , thus, managing CLGSI is a logical extension. Improving patients' lifestyle practices also reduces their NCD risks and increases activity/exercise capacity, health and wellbeing - all principal goals of contemporary physiotherapy. The COVID-19 pandemic lends further support for prioritising health and lifestyle competencies including smoking cessation; whole food plant-based nutrition; healthy weight; healthy sleep practices; and stress management; in conjunction with reducing sedentariness and increasing physical activity/exercise, to augment immunity as well as function and overall health and wellbeing. To support patients' lifestyle change efforts, physiotherapists may refer patients to other health professionals. The authors conclude that immuno-modulation with lifestyle behaviour change to reduce susceptibility to viruses including SARS-CoV-2, is consistent with contemporary physiotherapy practice. Immuno-modulation needs to be reflected in health competencies taught in physiotherapy professional education curricula and taught at standards comparable to other established interventions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".