Role of telerehabilitation in musculoskeletal conditions during COVID-19 pandemic
Bibliographic record
Abstract
Sir, The COVID-19 pandemic has led to unprecedented challenges and dilemmas for physiotherapists in relation to physical exercise procedures, continuation of education, and research around the world. The global coronavirus is reminding us the importance of telehealth to deliver rehabilitation services. Implementing telehealth proactively rather than reactively is more likely to generate greater benefits in the long term and help with the everyday (and emergency) challenges in health care.[1] Several states in India have adopted safety and social distancing measures to prevent infection and to further minimize the risk of community transmission through manual contact. Telerehabilitation [Figure 1] is the common term utilized by the physiotherapist internationally for telehealth applications. Telephysiotherapy is the technology used for virtual visit consultations and protocols, which makes use of information and communication to facilitate rehabilitation of patients who are confined in their homes. It is an ultimate tool to provide high-quality personalized musculoskeletal physiotherapy practice in the society and an effective way to reduce crowding of patients and accompanying persons in clinics/hospitals at this crucial time of COVID pandemic.Figure 1: Telerehabilitation at workDespite the significant benefits, a number of challenges in the implementation of telephysiotherapy have been identified in developing countries like India such as underdeveloped infrastructure (poor energy and inadequate power supply, network issues, and shortage of multimedia devices), lack of comprehensive training for professionals, ethical issues (professional licensing, liability and malpractice, privacy, confidentiality, culture, environmental stigma, abuse, and quackery), and financial implications (affordability of mobile phones and reimbursement of rendered services). Complex optical image, sensor-based technologies, and virtual reality-based telerehabilitation systems have eroded these barriers in the last decade in developed countries.[2] Knee osteoarthritis causes musculoskeletal pain and disability affects up to one-third of people aged over 60 years. High-quality evidence has suggested that therapeutic exercise to strengthen muscles can significantly reduce pain and improve physical function and the quality of life (Qol). A health-related survey (36-item short questionnaire) to assess Qol recommends physical, psychological, and social domains of health. It refers to cognitive factors including coping, self-efficacy, somatization, pain catastrophizing, and helplessness and behavioral factors include kinesiophobia (pain related fear of movement) and pain-related fear avoidance.[3] Moderate quality of intervention and positive impact on health outcomes and satisfaction noted in musculoskeletal conditions in a systematic review of telehealth video conferencing physiotherapy.[4] University of Queensland (Australia) and the University of Sherbrooke (Canada) practiced physiotherapy, occupational therapy and speech therapy under the supervision of clinical educators at their state art of telerehabilitation clinial. Theory and practical aspects were introduced via teleheath online learning modules for the academic purpose to develop unique skill through hands-on practicum, in which students and clinical educators worked through clinical cases.[5] PhysioDirect, a united kingdom-based telephone-delivered physiotherapy service, has proven equally effective as usual treatment for people with musculoskeletal conditions. Telephysiotherapy is more accessible and affordable and does not require access to a computer or Internet and does not even require skills needed to operate high tech gadgets. It may therefore mitigate the clinical, economical, and social burden in the society and in some ways revolutionize the practice of physiotherapy in future.[6] Physiotherapists have proven their resilience and professional dedication to stand firm with other health-care professionals as a frontline rehabilitative team. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".