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Backgrounds: Freezing of gait (FoG) is a common and debilitating symptom of Parkinson's disease (PD) that can lead to falls and reduced quality of life. Wearable sensors have been used to detect FoG, but current methods have limitations in accuracy and practicality. In this paper, we aimed to develop a deep learning model using pressure sensor data from wearable insoles to accurately detect FoG in PD patients. Methods: We recruited 14 PD patients and collected data from multiple trials of a standardized walking test using the Pedar insole system. We proposed temporal convolutional neural network (TCNN) and applied rigorous data filtering and selective participant inclusion criteria to ensure the integrity of the dataset. We mapped the sensor data to a structured matrix and normalized it for input into our TCNN. We used a train-test split to evaluate the performance of the model. Results: We found that TCNN model achieved the highest accuracy, precision, sensitivity, specificity, and F1 score for FoG detection compared to other models. The TCNN model also showed good performance in detecting FoG episodes, even in various types of sensor noise situations. Conclusions: We demonstrated the potential of using wearable pressure sensors and machine learning models for FoG detection in PD patients. The TCNN model showed promising results and could be used in future studies to develop a real-time FoG detection system to improve PD patients' safety and quality of life. Additionally, our noise impact analysis identifies critical sensor locations, suggesting potential for reducing sensor numbers.
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BACKGROUND: Sleep has been known to affect childhood development. Sleep disturbance is likely more common in children with developmental delay (DD) than in typical development. There are few studies on the correlation between sleep disturbance and developmental features in children with DD. Therefore, this study aimed to evaluate the associations between the two in children with DD. METHODS: A total of 45 children (age range 27.0 ± 11.1) with DD were recruited and evaluated using the Sleep Disturbance Scale for Children (SDSC) and Bayley Scales of Infant and Toddler Development (BSID-III). The outcomes are expressed as means and standard deviations. The correlation between SDSC and BSID-III was assessed using Spearman's rank correlation test. Multiple regression analysis was performed to investigate the relationship between BSID-III domains and SDSC questionnaire subscales. Statistical significance was set at p < 0.05. RESULTS: Based on the correlation analysis and subsequent hierarchical regression analysis, cognition and socio-emotional domains of BSID-III were significantly associated with the DOES subscale of the SDSC questionnaire. In addition, the expressive language domain of the BSID-III was found to be associated with the DA subscale of the SDSC questionnaire. It seems that excessive daytime sleepiness might negatively affect emotional and behavioral problems and cognitive function. Also, arousal disorders seem to be related to memory consolidation process, which is thought to affect language expression. CONCLUSION: This study demonstrated that DA and DOES subscales of the SDSC questionnaire were correlated with developmental aspects in preschool-aged children with DD. Sleep problems in children with DD can negatively affect their development, thereby interfering with the effectiveness of rehabilitation. Identifying and properly managing the modifiable factors of sleep problems is also crucial as a part of comprehensive rehabilitation treatment. Therefore, we should pay more attention to sleep problems, even in preschool-aged children with DD.
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Desenvolvimento Infantil , Deficiências do Desenvolvimento , Transtornos do Sono-Vigília , Humanos , Pré-Escolar , Masculino , Feminino , Deficiências do Desenvolvimento/etiologia , Transtornos do Sono-Vigília/etiologia , Cognição , LactenteRESUMO
OBJECTIVE: To investigate the feasibility and effects of a mobile app-based home cycling exercise program compared to home cycling exercise without additional monitoring system. Compared with fitness facilities or outdoor exercise, home-based exercise programs effectively improve physical performance in an indwelling community. However, a flexible, informal environment may decrease motivation and impair adherence to physical exercise. Mobile devices for aerobic exercise and mobile applications provide real-time monitoring, immediate feedback, and encouragement to increase motivation and promote physical performance. We investigated the feasibility and effects of a mobile app-based home exercise program on body composition, muscular strength, and cardiopulmonary function. METHODS: Between February and May 2023, 20 participants were randomly allocated to the intervention (mobile application with a tablet) and control groups, and they performed aerobic exercise using a stationary bicycle for ≥150 minutes per week for 6 weeks (≤30-minute exercise session, with 3-minute warm-up and 3-minute cool-down). Karvonen formula-based heartrate defined the weekly increase in exercise intensity. Outcome measures included body-composition parameters, isokinetic knee flexor and extensor strength tests, cardiopulmonary exercise test results, and rate of target heart rate (HR) achievement. Participants were assessed at baseline and after the intervention. RESULTS: Unrelated personal events led two participants to drop out. The intervention and control groups had similar baseline characteristics. Compared with the control group, in the post-intervention isokinetic strength test, bilateral knee flexor and extensor power, and time to target HR achievement significantly increased each week in the intervention group. CONCLUSION: Home-based exercise to achieve long-term cardiovascular fitness with portable electronic/mobile devices facilitates individualized exercise using real-time feedback to improve motivation and adherence.
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BACKGROUND: Osteosarcopenia is geriatric syndrome defined as the concomitant occurrence of osteopenia/osteoporosis, and sarcopenia. Osteosarcopenia is a relatively new concept in geriatric medicine; however, it may increase the risk of fragility fractures, several morbidities and mortalities, and socioeconomic costs. Although resistance exercises and nutritional support-including protein, calcium, and vitamin D-are potential non-pharmacological management procedures, evidence is still lacking. The objective of this study was therefore to evaluate the effect of combined resistance exercise and nutritional support on the quality and quantity of bone and muscle in postmenopausal females with osteosarcopenia. METHODS: This research proposal presents the protocol for a prospective, single-center, single-blinded, two-armed randomized controlled trial. Thirty-four participants with osteosarcopenia will be recruited and randomly divided into intervention and control groups; both groups will receive nutritional supplements (protein, 40 g; vitamin D, 1600 IU; calcium, 600 mg) daily. The intervention group will undergo 24 weeks of resistance exercise of increasing intensity, achieved through a three-phase step-up process. The primary outcomes will be the changes in skeletal muscle index and bone marrow density of the lumbar spine and femoral neck between the baseline and end of intervention (24 weeks). The secondary outcomes will be the body composition, whole body phase angle, physical function assessment, quality of life, psychological assessment, and bone turnover markers of participants, surveyed at multiple time points. DISCUSSION: This randomized controlled trial may reveal the effect of resistance exercise and nutritional support on older postmenopausal women with osteosarcopenia. The results will provide evidence for developing proper non-pharmacological management guidelines for postmenopausal women. TRIAL REGISTRATION: Clinical Research Information Service of Republic of Korea, KCT0008291, Registered on 16 March 2023, https://cris.nih.go.kr/cris/search/detailSearch.do/25262 .