Role of telerehabilitation in musculoskeletal conditions during COVID-19 pandemic
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».