Wearable Sensors for Measurement of Viewing Behavior, Light Exposure, and Sleep
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ABSTRACT:
SUBMITTER: Bhandari K
PROVIDER: S-EPMC8587946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Polysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.
| S-EPMC6925191 | biostudies-literature
Project description:Ambulatory polysomnography (PSG) does not commonly include an objective measure of light to determine the time of lights off (Loff), and thus cannot be used to calculate important indices such as sleep onset latency and sleep efficiency. This study examined the technical specifications and appropriateness of a prototype light sensor (LS) for use in ambulatory Compumedics Somte PSG.Two studies were conducted. The first examined the light measurement characteristics of the LS when used with a portable PSG device, specifically recording trace range, linearity, sensitivity, and stability. This involved the LS being exposed to varying incandescent and fluorescent light levels in a light controlled room. Secondly, the LS was trialled in 24 home and 12 hospital ambulatory PSGs to investigate whether light levels in home and hospital settings were within the recording range of the LS, and to quantify the typical light intensity reduction at the time of Loff. A preliminary exploration of clinical utility was also conducted. Linearity between LS voltage and lux was demonstrated, and the LS trace was stable over 14 hours of recording. The observed maximum voltage output of the LS/PSG device was 250 mV, corresponding to a maximum recording range of 350 lux and 523 lux for incandescent and fluorescent light respectively. At the time of Loff, light levels were within the recording range of the LS, and on average dropped by 72 lux (9-245) in the home and 76 lux (4-348) in the hospital setting. Results suggest that clinical utility was greatest in hospital settings where patients are less mobile. The LS was a simple and effective objective marker of light level in portable PSG, which can be used to identify Loff in ambulatory PSG. This allows measurement of additional sleep indices and support with clinical decisions.
| S-EPMC5690677 | biostudies-literature
Project description:Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.
| S-EPMC7219665 | biostudies-literature
Project description:BackgroundFew studies have investigated the association between screen viewing (SV) and sleep duration among young children. This study aims to examine the association between total and device-specific SV and sleep duration among children aged 2 and below.MethodsWe conducted a cross-sectional study of 714 Singaporean children aged 2 years and below. Parents were recruited during routine well-child clinic visits from two national polyclinics. In Singapore, all parents visit well-child clinics with their children at regular intervals for routine check-ups and vaccinations. Socio-demographic characteristics, duration of total and device-specific SV, and sleep duration were reported by parents via interviewer-administered questionnaires. Multiple linear regression analysis was used to assess associations between various types of SV and sleep duration, adjusted for socio-demographic variables. Due to significant interaction between SV and age, stratified analyses for children aged less than 6 months and those aged 7-24 months were performed.ResultsThe prevalence of daily SV among children was 53.1%; 28.3% in children up to 6 months and 73.8% in children aged 7 to 24 months. TV viewing was reported for 44.3% of all children and mobile device SV for 30.1%. Children's average sleep duration was 13.9 (SD = 3.5) hours daily and younger children had longer sleep duration than older ones (up to 6 months: mean = 15.6 h, SD = 3.9; 7-24 months: mean = 12.4 h, SD = 2.2; P < 0.01). In the regression analysis among all children, each 1 h per day increment in total SV was significantly associated with 0.26 h shorter sleep duration with similar significant associations for TV (β = - 0.28 h, 95%CI: -0.50, - 0.06) and mobile devices (β = - 0.35 h, 95%CI: -0.61, - 0.09). Stratified analysis revealed significantly greater reductions in sleep with higher SV among children aged 6 months and below (β = - 0.73 h, 95%CI: -1.12, - 0.34), while associations were weaker in older children (β = - 0.13 h, 95% CI: -0.24, - 0.01).ConclusionsThis study provides evidence for a substantial association between longer SV and shorter sleep duration among very young children. These associations appeared stronger among children aged 6 months and below as compared with those aged 7 to 24 months. Further studies are warranted to confirm our findings.
| S-EPMC6332844 | biostudies-literature
Project description:Syncope is a common disorder with a lifetime prevalence of about 40%. Implantable cardiac electronic devices, including implantable loop recorders (ILR) and implantable cardioverter-defibrillators (ICD), are well established in syncope management. However, despite the successful use of ILR and ICD, diagnosis and therapy still remain challenging in many patients due to the complex hemodynamic interplay of cardiac and vascular adaptations during impending syncopes. Wearable sensors might overcome some limitations, including misdiagnosis and inappropriate defibrillator shocks, because a variety of physiological measures can now be easily acquired by a single non-invasive device at high signal quality. In neurally-mediated syncope (NMS), which is the most common cause of syncope, advanced signal processing methodologies paved the way to develop devices for early syncope detection. In contrast to the relatively benign NMS, in arrhythmia-related syncopes immediate therapeutical intervention, predominantly by electrical defibrillation, is often mandatory. However, in patients with a transient risk of arrhythmia-related syncope, limitations of ICD therapy might outweigh their potential therapeutic benefits. In this context the wearable cardioverter-defibrillator offers alternative therapeutical options for some high-risk patients. Herein, we review recent evidence demonstrating that wearable sensors might be useful to overcome some limitations of implantable devices in syncope management.
| S-EPMC4315629 | biostudies-literature
Project description:BACKGROUND:Gait impairments including shuffling gait and hesitation are common in people with Parkinson's disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal characteristics of gait, but less is known of the angular characteristics of the gait, which may provide helpful information pertaining to the functional status and effects of the treatment in PD. OBJECTIVE:This study aimed to quantify the angles of steps during walking, and explore if this novel step angle metric is associated with the severity of PD and the effects of the treatment including the acute levodopa challenge test (ALCT) and deep brain stimulation (DBS). METHODS:A total of 18 participants with PD completed the walking test before and after the ALCT, and 25 participants with PD completed the test with the DBS on and off. The walking test was implemented under two conditions: walking normally at a preferred speed (single task) and walking while performing a cognitive serial subtraction task (dual task). A total of 17 age-matched participants without PD also completed this walking test. The angular velocity was measured using wearable sensors on each ankle, and three gait angular metrics were obtained, that is mean step angle, initial step angle, and last step angle. The conventional gait metrics (ie, step time and step number) were also calculated. RESULTS:The results showed that compared to the control, the following three step angle metrics were significantly smaller in those with PD: mean step angle (F1,48=69.75, P<.001, partial eta-square=0.59), initial step angle (F1,48=15.56, P<.001, partial eta-square=0.25), and last step angle (F1,48=61.99, P<.001, partial eta-square=0.56). Within the PD cohort, both the ALCT and DBS induced greater mean step angles (ACLT: F1,38=5.77, P=.02, partial eta-square=0.13; DBS: F1,52=8.53, P=.005, partial eta-square=0.14) and last step angles (ACLT: F1,38=10, P=.003, partial eta-square=0.21; DBS: F1,52=4.96, P=.003, partial eta-square=0.09), but no significant changes were observed in step time and number after the treatments. Additionally, these step angles were correlated with the Unified Parkinson's Disease Rating Scale, Part III score: mean step angle (single task: r=-0.60, P<.001; dual task: r=-0.52, P<.001), initial step angle (single task: r=-0.35, P=.006; dual task: r=-0.35, P=.01), and last step angle (single task: r=-0.43, P=.001; dual task: r=-0.41, P=.002). CONCLUSIONS:This pilot study demonstrated that the gait angular characteristics, as quantified by the step angles, were sensitive to the disease severity of PD and, more importantly, can capture the effects of treatments on the gait, while the traditional metrics cannot. This indicates that these metrics may serve as novel markers to help the assessment of gait in those with PD as well as the rehabilitation of this vulnerable cohort.
| S-EPMC7125438 | biostudies-literature
Project description:Study objectivesTo improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes.MethodsSleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist), and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland-Altman analysis.ResultsChest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy.ConclusionsCombination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.
| S-EPMC5656479 | biostudies-literature
Project description:BackgroundPersons with Alzheimer's disease and related dementias (ADRD) frequently experience sleep-wake (circadian) cycle disturbances that lead them to remain awake at night, causing stress and fatigue for families and caregivers. Light therapy shows promise as a nonpharmacological treatment for regulating sleep in this population.ObjectiveWe investigated the long-term impact of a circadian-effective lighting intervention on sleep, mood, and behavior problems in persons with ADRD.MethodsThis 25-week clinical trial administered an all-day lighting intervention to 47 patients with ADRD in 9 senior-care facilities, employing wrist-worn actigraphy measures and standardized measures of sleep quality, mood, and behavior.ResultsThe intervention significantly improved Pittsburgh Sleep Quality Index scores, from an estimated mean±SEM of 11.89±0.53 at baseline to 5.36±0.63 at the end of the intervention. Additional improvements were noted for sleep efficiency data from actigraph measurements. The intervention significantly reduced Cornell Scale for Depression in Dementia scores (mean±SEM of 11.36±0.74 at baseline and 4.18±0.88 at the end of the intervention) and Cohen-Mansfield Agitation Inventory scores (mean±SEM of 47.10±1.98 at baseline and 35.33±2.23 at the end of the intervention).ConclusionA regular circadian-effective daytime lighting intervention can improve sleep at night and reduce depression and agitation in patients with dementia living in controlled environments. More importantly, the positive effects of the tailored lighting intervention on these outcomes appear to be cumulative over time.
| S-EPMC7504981 | biostudies-literature
Project description:Study objectivesOur objective was to examine the ability of a consumer-grade wearable device (Basis B1) with accelerometer and heart rate technology to assess sleep patterns compared with polysomnography (PSG) and research-grade actigraphy in healthy adults.MethodsEighteen adults underwent consecutive nights of sleep monitoring using Basis B1, actigraphy, and PSG; 40 nights were used in analyses. Discrepancies in gross sleep parameters and epoch-by-epoch agreements in sleep/wake classification were assessed.ResultsBasis B1 accuracy was 54.20 ± 8.20%, sensitivity was 98.90 ± 2.70%, and specificity was 8.10 ± 15.00%. Accuracy, sensitivity, and specificity for distinguishing between the different sleep stages were 60-72%, 48-62%, and 57-86%, respectively. Pearson correlations demonstrated strong associations between Basis B1 and PSG estimates of sleep onset latency and total sleep time; moderate associations for sleep efficiency, duration of light sleep, and duration of rapid eye movement sleep; and a weak association for duration of deep sleep. Basis B1 significantly overestimates total sleep time, sleep efficiency, and duration of light sleep and significantly underestimates wake after sleep onset and duration of deep sleep.ConclusionsBasis B1 demonstrated utility for estimates of gross sleep parameters and performed similarly to actigraphy for estimates of total sleep time. Basis B1 specificity was poor, and Basis B1 is not useful for the assessment of wake. Basis B1 accuracy for sleep stages was better than chance but is not a suitable replacement for PSG assessment. Despite low cost, ease of use, and attractiveness for patients, consumer devices are not yet accurate or reliable enough to guide treatment decision making in clinical settings.
| S-EPMC7849656 | biostudies-literature
Project description:Global industrialization and urbanization have led to increased levels of air pollution. Those with respiratory diseases, such as asthma, are at the highest risk for adverse health effects and reduced quality of life. Studying the relationship between pollutants and symptoms is usually achieved with data from government air quality monitoring stations, but these fail to report the spatial and temporal resolution required to track a person's true exposure, especially when the majority of their time is spent indoors. We develop and build eight wrist-worn wearable devices, weighing only 64 g, to measure known asthma symptom triggers: ozone, total volatile organic compounds, temperature, humidity, and activity level. The devices use commercial off-the-shelf components, costing under $150 each to build. This report focuses on the design, calibration, and testing of the devices. Emphasis is placed on the calibration of a metal-oxide-semiconductor gas sensor for detecting ozone, which is a difficult task because of the large variations in ambient temperature and humidity found when using a wearable device. Examples of testing the devices in four real environments are also discussed: 11 days inside a well-ventilated laboratory, ten days outdoors during the summer, alternating the devices between indoor and outdoor environments to examine their response to quickly changing environments, and a field test where scripted activities are performed for a full day. The work demonstrates a wearable device for environmental health studies and addresses the challenges of existing sensors for real-world applications.
| S-EPMC8318339 | biostudies-literature