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10.1007/s10072-021-05351-7 http://scihub22266oqcxt.onion/10.1007/s10072-021-05351-7 34046795!8159018!34046795     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34046795&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Neurol+Sci 2021 ; 42 (8): 3089-3092 Nephropedia Template TP {{tp|p=34046795|t=2021. Remote smartphone gait monitoring and fall prediction in Parkinson s disease during the COVID-19 lockdown |pdf=|usr=}}{{34046795}} gab.com Text Remote smartphone gait monitoring and fall prediction in Parkinson s disease during the COVID-19 lockdown JO: Neurol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34046795 #FOAvip BACKGROUND: Falls could be serious events in Parkinson's disease (PD). Patient remote monitoring strategies are on the raise and may be an additional aid in identifying patients who are at risk of falling. The aim of the study was to evaluate if balance and timed-up-and-go data obtained by a smartphone application during COVID-19 lockdown were able to predict falls in PD patients. METHODS: A cohort of PD patients were monitored for 4 weeks during the COVID-19 lockdown with an application measuring static balance and timed-up-and-go test. The main outcome was the occurrence of falls (UPDRS-II item 13) during the observation period. RESULTS: Thirty-three patients completed the study, and 4 (12%) reported falls in the observation period. The rate of falls was reduced with respect to patient previous falls history (24%). The stand-up time and the mediolateral sway, acquired through the application, differed between "fallers" and "non-fallers" and related to the occurrence of new falls (OR 1.7 and 1.6 respectively, p < 0.05), together with previous falling (OR 7.5, p < 0.01). In a multivariate model, the stand-up time and the history of falling independently related to the outcome (p < 0.01). CONCLUSIONS: Our study provides new data on falls in Parkinson's disease during the lockdown. The reduction of falling events and the relationship with the stand-up time might suggest that a different quality of falls occurs when patient is forced to stay home - hence, clinicians should point their attention also on monitoring patients' sit-to-stand body transition other than more acknowledged features based on step quality. Twit Text FOAVip Remote smartphone gait monitoring and fall prediction in Parkinson s disease during the COVID-19 lockdown ????Neurol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34046795 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34046795 #FOAvip English Wikipedia <ref>{{Cite pmid|34046795}}</ref> Remote smartphone gait monitoring and fall prediction in Parkinson s disease during the COVID-19 lockdown #MMPMID34046795 Marano M; Motolese F; Rossi M; Magliozzi A; Yekutieli Z; Di Lazzaro V Neurol Sci 2021[Aug]; 42 (8): 3089-3092 PMID34046795show ga BACKGROUND: Falls could be serious events in Parkinson's disease (PD). Patient remote monitoring strategies are on the raise and may be an additional aid in identifying patients who are at risk of falling. The aim of the study was to evaluate if balance and timed-up-and-go data obtained by a smartphone application during COVID-19 lockdown were able to predict falls in PD patients. METHODS: A cohort of PD patients were monitored for 4 weeks during the COVID-19 lockdown with an application measuring static balance and timed-up-and-go test. The main outcome was the occurrence of falls (UPDRS-II item 13) during the observation period. RESULTS: Thirty-three patients completed the study, and 4 (12%) reported falls in the observation period. The rate of falls was reduced with respect to patient previous falls history (24%). The stand-up time and the mediolateral sway, acquired through the application, differed between "fallers" and "non-fallers" and related to the occurrence of new falls (OR 1.7 and 1.6 respectively, p < 0.05), together with previous falling (OR 7.5, p < 0.01). In a multivariate model, the stand-up time and the history of falling independently related to the outcome (p < 0.01). CONCLUSIONS: Our study provides new data on falls in Parkinson's disease during the lockdown. The reduction of falling events and the relationship with the stand-up time might suggest that a different quality of falls occurs when patient is forced to stay home - hence, clinicians should point their attention also on monitoring patients' sit-to-stand body transition other than more acknowledged features based on step quality. |*COVID-19[MESH] |*Parkinson Disease/complications/diagnosis[MESH] |Communicable Disease Control[MESH] |Gait[MESH] |Gait Analysis[MESH] |Humans[MESH] |Postural Balance[MESH] |SARS-CoV-2[MESH] |Smartphone[MESH]Remote smartphone gait monitoring and fall prediction in Parkinson s d(epmc).pdf DeepDyve Pubget Overpricing