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

Background: Few studies have described the characteristics of a concealed intratendinous subscapularis tear (CIST), and there is a lack of research on the preoperative predictability of such lesions. Purpose: To describe the characteristics of a CIST as seen on magnetic resonance imaging (MRI) and intraoperatively and to develop a scoring system for predicting such lesions. Study Design: Case series; Level of evidence, 4. Methods: Retrospectively, we identified 43 patients with CISTs among 442 consecutive patients who had undergone rotator cuff repair from July 2014 to June 2016. Range of motion, visual analog scale results for pain and function, and patient-reported outcome scores were evaluated preoperatively and at 1 and 2 years postoperatively. CISTs were classified arthroscopically as small (<5 mm), medium (5-10 mm), and large (>10 mm). We performed repair (≥50%) or debridement (<50%) depending on the total subscapularis tendon tear size including the CIST. Preoperative MRI findings were analyzed by 2 observers and were correlated with the arthroscopic findings. A 10-point scoring system was developed based on characteristics during the physical examination (anterior tenderness, bear hug sign), MRI (biceps tendon displacement and subluxation, subscapularis signal change just lateral to the lesser tuberosity), and arthroscopic surgery (medial biceps tendon lesion, combined subscapularis tendon tear), with a cutoff value of ≥7 predicting a CIST. After the retrospective study, we prospectively enrolled 95 patients to validate the 10-point CIST scoring system. Results: All 43 patients diagnosed with a CIST during the retrospective study improved both range of motion and functional scores at 1 year postoperatively. The interrater agreement of the 2 observers was substantial for the evaluation of all parameters except for subscapularis tear classification, which was moderate. On arthroscopic surgery, 11 small, 19 medium, and 13 large CISTs were detected. The preliminary prospective study showed a sensitivity of 61.9%, specificity of 94.3%, positive predictive value of 89.0%, negative predictive value of 75.7%, and accuracy of 80.0% when the cutoff value was set at ≥7 on the CIST scoring system. Conclusion: A CIST can be suspected using a combination of preoperative MRI and intra-articular diagnostic arthroscopic findings, but a definitive diagnosis requires an arthroscopic view. On the 10-point CIST scoring system, a score of ≥5 can be suggestive of a CIST, and a score of ≥7 is most likely to predict a CIST.

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