Objective The purpose of this article was to summarize the available evidence from systematic reviews on telerehabilitation in physical therapy. Methods Medline/Pubmed, EMBASE and Cochrane Library databases. In addition, the records in PROSPERO and Epistemonikos and PEDro were consulted. Systematic reviews of different conditions, populations and contexts, where the intervention to be evaluated is telerehabilitation by physical therapy were included. The outcomes were clinical effectiveness depending on specific condition, functionality, quality of life, satisfaction, adherence and safety. Data extraction and risk of bias assessment were carried out by a reviewer with non-independent verification by a second reviewer. The findings are reported qualitatively by tables and figures. Results Fifty-three systematic reviews were included of which 17 were assessed as having low risk of bias. Fifteen reviews were on cardiorespiratory rehabilitation, 14 on musculoskeletal conditions and 13 on neurorehabilitation. Other 11 reviews addressed other types of conditions and rehabilitation. Thirteen reviews evaluated with low risk of bias showed results in favor of telerehabilitation versus in-person rehabilitation or no-rehabilitation, while 17 reported no differences between the groups. Thirty-five reviews with unclear or high risk of bias showed mixed results. Conclusions Despite the contradictory results, telerehabilitation in physical therapy could be comparable to in-person rehabilitation or better than no-rehabilitation for conditions such as osteoarthritis, low back pain, hip and knee replacement, multiple sclerosis, and also in the context of cardiac and pulmonary rehabilitation. It is imperative to conduct better quality clinical trials and systematic reviews. Impact Providing with the best available evidence on the effectiveness of telerehabilitation to professionals, mainly physical therapists, will impact the decision-making process and therefore better clinical outcomes for patients, both in these times of covid-19 pandemic and in the future. The identification of research gaps will also contribute to the generation of relevant and novel research questions.

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International study conducted in 20 countries through an online survey. Participants: Physicians, respiratory therapists, nurses and physiotherapists that are currently working at the Intensive Care Unit (ICU). Main variables of interest: Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) with the ability of HCPs to correctly identify and manage 6 PVA. Results: A total of 431 HCPs answered a validated survey. The main factors associated with the proper recognition of PVA were: specific training program in mechanicalventilation (MV) (OR 2.27; 95% CI 1.14-4.52; p = 0.019), courses with more than 100 hours completed (OR 2.28; 95% CI 1.29-4.03; p = 0.005) and the number of intensive care unit (ICU) beds (OR 1.037; 95% CI 1.01-1.06; p = 0.005). The main factor that influenced PVA management was recognizing 6 PVA correctly (OR 118.98;; p < 0.001). Conclusion:Identifying and managing PVA using ventilator waveform analysis is influenced by many factors including specific training programs in MV, number of ICU beds and the recognized number of PVA.

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International study conducted in 20 countries through an online survey. Participants: Physicians, respiratory therapists, nurses and physiotherapists that are currently working at the Intensive Care Unit (ICU). Main variables of interest: Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) with the ability of HCPs to correctly identify and manage 6 PVA. Results: A total of 431 HCPs answered a validated survey. The main factors associated with the proper recognition of PVA were: specific training program in mechanicalventilation (MV) (OR 2.27; 95% CI 1.14-4.52; p = 0.019), courses with more than 100 hours completed (OR 2.28; 95% CI 1.29-4.03; p = 0.005) and the number of intensive care unit (ICU) beds (OR 1.037; 95% CI 1.01-1.06; p = 0.005). The main factor that influenced PVA management was recognizing 6 PVA correctly (OR 118.98;; p < 0.001). Conclusion:Identifying and managing PVA using ventilator waveform analysis is influenced by many factors including specific training programs in MV, number of ICU beds and the recognized number of PVA.

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Financiamiento: No se contó con fuentes de financiamiento externo. Conflicto de interés: Los autores declaran no tener conflictos de interés.