Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers.

BACKGROUND: Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke. METHODS: Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables. RESULTS: Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048). CONCLUSION: The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.

Association of activity status and patterns with salivary cortisol: the population-based CoLaus study.

PURPOSE: Physical activity (PA) has been shown to influence salivary cortisol concentrations in small studies conducted among athletes. We assessed the association of activity status and patterns with salivary cortisol in the general population. METHODS: Cross-sectional study including 1948 adults (54.9% women, 45-86 years). PA and sedentary behaviour (SB) were measured for 14 days by accelerometry. Low PA and high SB status were defined, respectively, as the lowest and highest tertile of each behaviour. 'Inactive', 'Weekend warrior', and 'Regularly active' patterns were also defined. Four salivary cortisol samples were collected over a single day and the following parameters were calculated: area under the curve to ground (AUCg), awakening response (CAR) and diurnal slope. RESULTS: After multivariable adjustment, low SB remained associated to steeper slopes relative to high SB (- 1.54 ± 0.03 vs. - 1.44 ± 0.04 nmol/l per hour). Non-significant trends were found for high PA relative to low PA with steeper slopes (- 1.54 ± 0.03 vs. - 1.45 ± 0.04) and lower AUCg (208.7 ± 2.0 vs. 215.9 ± 2.9 nmol.h/l). Relative to 'Inactives', 'Regularly actives' had lower AUCg (205.4 ± 2.4 vs. 215.5 ± 2.9) and 'Weekend warriors' had steeper slopes (- 1.61 ± 0.05 vs. - 1.44 ± 0.04). No associations were found for CAR. CONCLUSION: Low SB and high PA are related to lower cortisol secretion as measured by different parameters of salivary cortisol, but the effects were only modest.

The sleep/wake state scoring from mandible movement signal.

PURPOSE: Estimating the total sleep time in home recording devices is necessary to avoid underestimation of the indices reflecting sleep apnea and hypopnea syndrome severity, e.g., the apnea-hypopnea index (AHI). A new method to distinguish sleep from wake using jaw movement signal processing is assessed. METHODS: In this prospective study, jaw movement signal was recorded using the Somnolter (SMN) portable monitoring device synchronously with polysomnography (PSG) in consecutive patients complaining about a lack of recovery sleep. The automated sleep/wake scoring method is based on frequency and complexity analysis of the jaw movement signal. This computed scoring was compared with the PSG hypnogram, the two total sleep times (TST(PSG) and TST(SMN)) as well. RESULTS: The mean and standard deviation (in minutes) of TST(PSG) on the whole dataset (n = 124) were 407 ± 95.6, while these statistics were 394.2 ± 99.3 for TST(SMN). The Bland and Altman analysis of the difference between the two TST was 12.8 ± 57.3 min. The sensitivity and specificity (in percent) were 85.3 and 65.5 globally. The efficiency decreased slightly when AHI lies between 15 and 30, but remained similar for lower or greater AHI. In the 24 patients with insomnia/depression diagnosis, a mean difference in TST of -3.3 min, a standard deviation of 58.2 min, a sensitivity of 86.3%, and a specificity of 66.2% were found. CONCLUSIONS: Mandible movement recording and its dedicated signal processing for sleep/wake recognition improve sleep disorder index accuracy by assessing the total sleep time. Such a feature is welcome in home screening methods.

A comparison between jerk-cost derived from a jaw-tracking system with that from an accelerometer.

Jerk-cost is an inverse measure of movement smoothness and can be calculated from the first-time derivative of acceleration obtained from a tri-axial piezoelectric accelerometer (TPA), or from the third-time derivative of position obtained from a jaw-tracking device. The aims of this study were to determine, in 10 asymptomatic subjects who are chewing gum, (i) jerk-cost measures derived from displacement/time data obtained from the JAWS3D jaw-tracking device and from acceleration data obtained from a TPA used in the same jaw movement recordings, (ii) whether there was a significant relationship between jerk-cost measures derived from both devices and (iii) the degree of agreement between the two measures. Jerk-cost data were calculated in the opening phase, the closing phase, and over the full chewing cycle as the first-time derivative from acceleration obtained from the TPA, and the third-time derivative from JAWS3D for each of the X-, Y- and Z-direction series. There was a significant correlation between both measures of jerk-cost over the full chewing cycle and during jaw-opening (r = 0·65, 0·75, respectively; P < 0·001). There was no significant correlation in the closing phase (r = -0·02, P = 0·99). The Bland-Altman test showed that jerk-cost derived from the JAWS3D can differ by up to 78% below and 21% above that derived from the TPA. These results suggest that jerk-cost measures derived from a jaw-tracking system cannot substitute for jerk-cost measures derived from an accelerometer.

Sleep/Wake Detection by Behavioral Response to Haptic Stimuli.

STUDY OBJECTIVES: Actigraphy, the tool of choice for assessment of sleep phase disorders, is insensitive to movement-free waking. This study aimed to determine whether the detection of waking could be performed by recording instrumental responses to haptic stimuli delivered by a low-cost device. METHODS: Twenty adults underwent 2 nights of laboratory polysomnography (PSG) while wearing a fingerless glove under which a stimulating actigraph ("Wakemeter") was apposed to the palm. The Wakemeter, controlled by a tablet computer, delivered gentle, haptic stimuli every 10 minutes during the sleep period. If a stimulus was detected, the participant squeezed the Wakemeter. Stimulus times, response times and movements were streamed to the tablet. Concurrent PSG data were scored blind to stimuli and responses. Self-reported sleep quality ratings were collected each morning. RESULTS: The Wakemeter was acceptable to 19 of 20 participants, and effects on self-reported and objective sleep were small. The probability of a response to the stimulus during a wake epoch was high regardless of movement. In contrast, actigraphy magnitude distributions were indistinguishable across epochs scored wake without movement versus sleep, confirming a known limitation of actigraphy. A simple method for calculating sleep efficiency from responses to the stimuli yielded estimates that were highly correlated with PSG-derived estimates (rho = .69, P < .001). CONCLUSIONS: Behavioral responses to haptic stimuli detected epochs of movement-free wake during the sleep period and may augment actigraphy in the low-burden estimation of sleep efficiency. Acceptability of the method over longer recording periods remains to be established.

Food Composition Impacts the Accuracy of Wearable Devices When Estimating Energy Intake from Energy-Dense Food.

The present study aimed to assess the feasibility and reliability of an a3utomatic food intake measurement device in estimating energy intake from energy-dense foods. Eighteen volunteers aged 20-36 years were recruited from the University of Padova. The device used in the present study was the Bite Counter (Bite Technologies, Pendleton, USA). The rationale of the device is that the wrist movements occurring in the act of bringing food to the mouth present unique patterns that are recognized and recorded by the Bite Counter. Subjects were asked to wear the Bite Counter on the wrist of the dominant hand, to turn the device on before the first bite and to turn it off once he or she finished his or her meal. The accuracy of caloric intake was significantly different among the methods used. In addition, the device's accuracy in estimating energy intake varied according to the type and amount of macronutrients present, and the difference was independent of the number of bites recorded. Further research is needed to overcome the current limitations of wearable devices in estimating caloric intake, which is not independent of the food being eaten.

Ultrasensitive, passive and wearable sensors for monitoring human muscle motion and physiological signals.

Flexible sensors have attracted more and more attention as a fundamental part of anthropomorphic robot research, medical diagnosis and physical health monitoring. Here, we constructed an ultrasensitive and passive flexible sensor with the advantages of low cost, lightness and wearability, electric safety and reliability. The fundamental mechanism of the sensor is based on triboelectric effect inducing electrostatic charges on the surfaces between two different materials. Just like a plate capacitor, current will be generated while the distance or size of the parallel capacitors changes caused by the small mechanical disturbance upon it and therefore the output current/voltage will be produced. Typically, the passive sensor unambiguously monitors muscle motions including hand motion from stretch-clench-stretch, mouth motion from open-bite-open, blink and respiration. Moreover, this sensor records the details of the consecutive phases in a cardiac cycle of the apex cardiogram, and identify the peaks including percussion wave, tidal wave and diastolic wave of the radial pulse wave. To record subtle human physiological signals including radial pulsilogram and apex cardiogram with excellent signal/noise ratio, stability and reproducibility, the sensor shows great potential in the applications of medical diagnosis and daily health monitoring.

Using Actiwatch to monitor circadian rhythm disturbance in Huntington' disease: A cautionary note.

Huntington's disease (HD) is an inherited neurodegenerative disorder that is well recognised as producing progressive deterioration of motor function, including dyskinetic movements, as well as deterioration of cognition and ability to carry out activities of daily living. However, individuals with HD commonly suffer from a wide range of additional symptoms, including weight loss and sleep disturbance, possibly due to disruption of circadian rhythmicity. Disrupted circadian rhythms have been reported in mice models of HD and in humans with HD. One way of assessing an individual's circadian rhythmicity in a community setting is to monitor their sleep/wake cycles, and a convenient method for recording periods of wakefulness and sleep is to use accelerometers to discriminate between varied activity levels (including sleep) during daily life. Here we used Actiwatch(®) Activity monitors alongside ambulatory EEG and sleep diaries to record wake/sleep patterns in people with HD and normal volunteers. We report that periods of wakefulness during the night, as detected by activity monitors, agreed poorly with EEG recordings in HD subjects, and unsurprisingly sleep diary findings showed poor agreement with both EEG recordings and activity monitor derived sleep periods. One explanation for this is the occurrence of 'break through' involuntary movements during sleep in the HD patients, which are incorrectly assessed as wakeful periods by the activity monitor algorithms. Thus, care needs to be taken when using activity monitors to assess circadian activity in individuals with movement disorders.

Monitoring of cigarette smoking using wearable sensors and support vector machines.

Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.

Using piezoelectric films for classification of upper arm motions: a preliminary report.

Recently, it has been reported that finger motions could be recognized from the forearm signal detected by accelerometers. However, accelerometers are sensitive to vibration or unintended motions, which could cause large noise when classifying different hand motions. This is why our research group wanted to explore the usability of other kinds of sensors for upper arm motions classification. Therefore, the objective of this study was to examine the usefulness of a piezoelectric film for hand motion classification and its robustness to unintended motions. Experiments were conducted to record signals from the piezoelectric films for different hand motions, while the subject was asked to move the ipsilateral shoulder, the contralateral hand, or the legs. The results showed that the desired hand motion could be distinguished using a piezoelectric film despite of unintended motions.

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.

This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.