1.
Look Before You Leap: Interventions Supervised via Telehealth Involving Activities in Weight-Bearing or Standing Positions for People After Stroke-A Scoping Review.
Ramage, ER, Fini, N, Lynch, EA, Marsden, DL, Patterson, AJ, Said, CM, English, C
Physical therapy. 2021;(6)
-
-
Free full text
-
Abstract
OBJECTIVE The COVID-19 pandemic has seen a rapid shift to telehealth-delivered physical therapy services. Common impairments after stroke create unique challenges when providing rehabilitation via telehealth, particularly when it involves activities undertaken in weight-bearing or standing positions, including walking training. Our scoping review maps the evidence regarding safety, efficacy, and feasibility of remotely supervised telehealth interventions involving activities undertaken in weight-bearing or standing positions for people after stroke. METHODS Searches of relevant databases for primary research studies were conducted using keywords relating to exercise and telehealth. Studies of stroke survivors undertaking interventions involving activities in weight-bearing or standing positions, supervised in real-time via telehealth were included. Two reviewers independently appraised all studies. Data were charted by one reviewer, checked by another, and results synthesized narratively. RESULTS Seven studies (2 randomized trials, 1 mixed-methods, and 4 pre-post studies) were included, involving 179 participants. Some studies included stroke survivors with cognitive impairment, and 2 (29%) studies included only participants who walked independently. Adherence (reported in 3 studies) and satisfaction (reported in 4 studies) were good, and no serious adverse events (data from 4 studies) related to interventions were reported. Strategies to overcome technological barriers were used to optimize intervention safety and feasibility, along with physiological monitoring, caregiver assistance, and in-person exercise prescription. However, there is limited high-quality evidence of efficacy. CONCLUSIONS We identified strategies used in research to date that can support current practice. However, urgent research is needed to ensure that stroke survivors are receiving evidence-based, effective services. IMPACT The COVID-19 pandemic has necessitated a rapid shift to telerehabilitation services for people with stroke, but there is little evidence to guide best practice. Our review provides practical guidance and strategies to overcome barriers and optimize safety and adherence for telehealth interventions involving activities in weight-bearing or standing positions.
2.
Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study.
Chae, SH, Kim, Y, Lee, KS, Park, HS
JMIR mHealth and uHealth. 2020;(7):e17216
Abstract
BACKGROUND Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. OBJECTIVE This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. METHODS The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. RESULTS The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). CONCLUSIONS This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. TRIAL REGISTRATION Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t.
