Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Novel design for a dynamic ankle foot orthosis with motion feedback used for training in patients with hemiplegic gait: a pilot study.

BACKGROUND: We designed a novel ankle foot orthosis (AFO), namely, ideal training AFO (IT-AFO), with motion feedback on the hemiparetic lower limb to improve ambulation in individuals with stroke-related hemiplegia. We, therefore sought to compare the kinematic parameters of gait between IT-AFO with and without dynamic control and conventional anterior-type AFO or no AFO. METHODS: Gait parameters were measured using the RehaWatch® system in seven individuals with hemiplegia (mean 51.14 years). The parameters were compared across four conditions: no AFO, conventional anterior AFO, IT-AFO without dynamic control, and IT-AFO with dynamic control, with three trials of a 10-m walk test for each. RESULTS: The dorsiflexion angle increased during the swing phase when the IT-AFO was worn, and it was larger with dynamic control. These data can confirm drop foot improvement; however, the difference between the parameters with- and without-AFO control conditions was not significant in the swing phase. The IT-AFO with or without dynamic control enhanced the loading response to a greater extent between the hemiparetic and unaffected lower limbs than conventional AFO or no AFO. The duration of the stance phase on the hemiparetic lower limb was also longer when using IT-AFO with and without dynamic control than that when using conventional AFO, which improved asymmetry. User comfort and satisfaction was greater with IT-AFO than with the other conditions. CONCLUSIONS: The IT-AFO with dynamic control improved gait pattern and weight shifting to the hemiparetic lower limb, reducing gait asymmetry. The difference with and without dynamic control of IT-AFO is not statistically significant, and it is limited by sample size. However, this study shows the potential of IT-AFO in applying positive motion feedback with gait training. TRIAL REGISTRATION: Taipei Medical University-Joint Institutional Review Board. N201510010 . Registered 12 February 2015. http://ohr.tmu.edu.tw/main.php .

Chicken surimi fortified by omega-3 fatty acid addition: manufacturing and quality properties.

BACKGROUND: The meat of spent hens is hard to use owing to its small amount and poor quality. A washing process to remove sarcoplasmic proteins and other impurities can prolong the shelf life of surimi-like products. Owing to the benefits of omega-3 polyunsaturated fatty acids (ω-3 PUFAs), functional foods fortified with ω-3 PUFAs are increasingly being marketed. Hence, in this study, ω-3 FA-fortified chicken surimi was manufactured, and how to ameliorate its lipid peroxidation during frozen storage was investigated. RESULTS: A 0.10% (w/v) solution of sodium chloride (NaCl) instead of distilled water in the third washing step decreased (P < 0.05) myofibrillar protein loss and moisture content of spent hen breast protein recoveries. Oil droplets in fish, flaxseed or soybean oil-added chicken surimi were well distributed. Moreover, flaxseed oil addition increased (P < 0.05) total ω-3 FAs and ω-3/ω-6 FA ratio, while only fish oil provided long-chain PUFAs. Oil addition decreased (P < 0.05) hardness and gumminess of chicken surimi, while flaxseed oil resulted in more (P < 0.05) yellow surimi than fish and soybean oil. Fish oil-added samples showed higher (P < 0.05) lipid oxidation than flaxseed or soybean oil-added samples under -15 to -10 °C storage, but α-tocopherol addition ameliorated it. CONCLUSION: A novel semi-manufactured chicken surimi product with nutritional benefits could be developed by fortification with fish or flaxseed oil.