Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

To date, neural efficiency, an ability to economically utilize mental resources, has not been investigated after cognitive training. The purpose of this study was to provide customized cognitive training and confirm its effect on neural efficiency by investigating prefrontal cortex (PFC) activity using functional near-infrared spectroscopy (fNIRS). Before training, a prediction algorithm based on the PFC activity with logistic regression was used to predict the customized difficulty level with 86% accuracy by collecting data when subjects performed four kinds of cognitive tasks. In the next step, the intervention study was designed using one pre-posttest group. Thirteen healthy adults participated in the virtual reality (VR)-based spatial cognitive training, which was conducted four times a week for 30 min for three weeks with customized difficulty levels for each session. To measure its effect, the trail-making test (TMT) and hemodynamic responses were measured for executive function and PFC activity. During the training, VR-based spatial cognitive performance was improved, and hemodynamic values were gradually increased as the training sessions progressed. In addition, after the training, the performance on the trail-making task (TMT) demonstrated a statistically significant improvement, and there was a statistically significant decrease in the PFC activity. The improved performance on the TMT coupled with the decreased PFC activity could be regarded as training-induced neural efficiency. These results suggested that personalized cognitive training could be effective in improving executive function and neural efficiency.

This study aimed to investigate the home environment conditions of the elderly living in the community and to confirm the needs of the residential environment. This study was conducted from January 15th to 25th, 2018, in 12 elderly households in Chungcheongnam-do, using a structured questionnaire and a home environment checklist. The elderly did not feel uncomfortable with their current home, but they wanted to modify their home environment to improve their quality of life. Investigation using home environment checklists showed that demands for supportive design were lacking in the overall areas. On the results of this study, it was confirmed that it is essential to reflect the residents’ occupation, physical characteristics, and needs when constructing the home environment for the healthy life of the elderly in the community.

Sang-Mi Kim, Young Kim, Seong-A Lee. PTRS 2019;8:67-73. https://doi.org/10.14474/ptrs.2019.8.2.67

Introduction: Sleep is a critical occupation that affects health and daily functioning, yet occupation‐based approaches to addressing sleep have not been clearly defined within occupational therapy. To clarify current practice, this systematic review examined existing literature and classified occupation‐based interventions (OBIs) for sleep according to the Occupational Therapy Practice Framework (OTPF). Methods: Searches were conducted in PubMed, Web of Science, and CINAHL for articles published from January 1, 2013, to December 31, 2023, with an updated search performed up to October 27, 2024. Studies were included if they examined OBIs for sleep delivered by occupational therapists, incorporated occupation‐based approaches or theoretical frameworks, and reported outcomes related to sleep quality, sleep participation, or occupational performance. Studies of any design were considered, while review articles, dissertations, conference proceedings, and non‐English or inaccessible publications were excluded. Results were synthesized by classifying OBIs for sleep according to the OTPF. Results: A total of 7510 studies were identified. After removing duplicates, 6729 studies were screened by title and abstract, and 99 articles underwent full‐text review. Ten studies met all inclusion criteria. The included studies involved diverse populations, regardless of the presence or absence of sleep‐related problems. OTPF‐based classification showed that most interventions primarily focused on sleep preparation, suggesting that current occupational therapy approaches may not fully address broader sleep‐related needs. A multidisciplinary approach may therefore be necessary to address the complexity of sleep problems. Conclusion: These findings highlight the value of OBIs as theoretically grounded and clinically relevant strategies, while underscoring the need for further development of interventions that integrate both sleep preparation and participation. However, evidence was limited by heterogeneous outcome measures, which precluded meta‐analysis, and by the exclusive focus on occupational therapy interventions, which may not fully reflect the multidisciplinary approaches to sleep.