Free-living cross-comparison of two wearable monitors for sleep and physical activity in healthy young adults

A Preliminary Analysis of Stress Burden and Cognitive Function and Clinically Adjudicated Cognitive Outcomes in Black American Adults

BACKGROUND

The combination of exposure to multiple stressors and psychological distress may contribute to the disproportionate burden of dementia risk among Black Americans. This study estimates the effect of an index of stress and psychological distress (ie, "stress burden") on cognitive function and clinically adjudicated cognitive outcomes among older Black American adults, and examines sleep as a mediator.

METHODS

The sample included 204 Black adults (79% female; mean age = 64 years) from Pittsburgh, PA, USA. Stress burden comprised 3 self-reported stress and distress measures assessed in 2016: discrimination, psychological distress, and posttraumatic stress. Potential mediators included actigraphy-assessed sleep duration and efficiency from 2018. Cognitive battery and clinical adjudication in 2019 assessed cognitive function and clinically adjudicated outcomes. Causal mediation analysis estimated the direct effect between stress burden and cognitive outcomes, and indirect effects through sleep, after adjusting for sociodemographics and hypertension.

RESULTS

Higher stress burden had a significant direct effect on lower executive functioning and visuospatial performance. However, there were no significant indirect effects (ie, mediation) by sleep disturbances on any domain of cognitive function assessed. Also, there were no significant direct or indirect effects on clinically adjudicated outcomes.

CONCLUSIONS

Multiple stressors often co-occur and may contribute to racial disparities in cognitive health. Findings suggest that higher stress burden had negative effects on functioning in executive and visuospatial domains in this community-based sample of older Black American adults. However, there was no evidence of mediation by sleep. Findings highlight the importance of continued work to identify modifiable pathways between stress burden and cognitive health disparities.

The impact of online classes on sleep, physical activity, and cognition functioning among physical education students

Introduction

Online education has become a crucial component of teachers' professional development, and universities incorporate innovative pedagogical approaches to enhance teachers' training. These approaches have proven invaluable, particularly during the COVID-19 pandemic. This study investigates the impact of online versus face-to-face learning environments on sleep quality, physical activity, and cognitive functioning among physical education students.

Methods

Utilizing a unique methodological approach that combines wrist actigraphy, the Pittsburgh Sleep Quality Index, and the Cambridge Neuropsychological Test Automated Battery, we provide a comprehensive assessment of these variables. Over 4 weeks, 19 male students participated in alternating online and face-to-face class formats.

Results

Our results reveal no significant differences in sleep quality or cognitive function between learning environments. However, notable findings include significant differences in Paired Associates Learning and weekday step counts in the face-to-face setting.

Discussion

These insights suggest that while online learning environments may not adversely affect sleep or cognitive functions, they could impact certain aspects of physical activity and specific cognitive tasks. These findings contribute to the nuanced understanding of online learning's implications and can inform the design of educational strategies that promote student well-being.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.

Design of the think PHRESH longitudinal cohort study: Neighborhood disadvantage, cognitive aging, and alzheimer's disease risk in disinvested, black neighborhoods

BACKGROUND

Black Americans have disproportionately higher rates and earlier onset of Alzheimer's disease and related dementias (ADRD) relative to White Americans. We currently lack a comprehensive understanding of how the lived experience and broader societal factors, including cumulative exposure to structural racism and the mechanisms underlying the risks, may contribute to elevated ADRD risk in Black Americans.

METHODS

The Think PHRESH study builds on existing, community-based research infrastructure, from the ongoing Pittsburgh Hill/Homewood Research on Neighborhood Change and Health (PHRESH) studies, to examine the contributions of dynamic neighborhood socioeconomic conditions across the lifecourse to cognitive outcomes in mid- and late-life adults living in two historically disinvested, predominantly Black communities (anticipated n = 1133). This longitudinal, mixed-methods study rests on the premise that neighborhood racial segregation and subsequent disinvestment contributes to poor cognitive outcomes via factors including (a) low access to educational opportunities and (b) high exposure to race- and socioeconomically-relevant stressors, such as discrimination, trauma, and adverse childhood events. In turn, these cumulative exposures foster psychological vigilance in residents, leading to cardiometabolic dysregulation and sleep disruption, which may mediate associations between neighborhood disadvantage and ADRD risk. This premise recognizes the importance of potential protective factors that may promote cognitive health, including neighborhood social cohesion, safety, and satisfaction. The proposed study will leverage our existing longitudinal data on risk/protective factors and biobehavioral mediators and will include: (1) up to three waves of cognitive assessments in participants ages 50 years + and one assessment in participants ages 35-49 years; clinical adjudication of ADRD will be completed in participants who are 50+, (2) extensive surveys of risk and protective factors, (3) two assessments of blood pressure and objectively measured sleep, (4) a comprehensive assessment of life and residential history; and (5) two rounds of in-depth qualitative interviews to reveal lifecourse opportunities and barriers experienced by Black Americans in achieving optimal cognitive health in late life.

DISCUSSION

Understanding how structural racism has influenced the lived experience of Black Americans, including dynamic changes in neighborhood conditions over time, is critical to inform multi-level intervention and policy efforts to reduce pervasive racial and socioeconomic disparities in ADRD.

Comparing GENEActiv against Actiwatch-2 over Seven Nights Using a Common Sleep Scoring Algorithm and Device-Specific Wake Thresholds

Demonstrating inter-device reliability is essential to use devices interchangeably, and accurately integrate, interpret, or compare data from different actigraphs. Despite this, there is a paucity of comparative literature over a timeframe exceeding one night. The aims of this study were to determine an optimal wake threshold for GENEActiv and to evaluate the concordance between Actiwatch-2 and GENEActiv using a common algorithm (Phillips Respironics). Data were collected from 33 individuals (20 female) aged 20-35 years (M= 25.33, SD = 4.69) across a total 213 nights. Participants wore both devices simultaneously and continuously for seven days. The sleep parameters of interest were: total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Exploratory analyses of sensitivity, specificity, overall accuracy, mean bias, and paired samples t-tests indicated an optimal wake threshold of 115 for GENEActiv, compared with Actiwatch-2 at the 40 (medium, default) threshold. Using these thresholds, sensitivity, and overall accuracy of GENEActiv were both good (86% and 78%, respectively), however specificity was relatively low (40%). There were no significant inter-device differences for any sleep parameters, and all absolute mean biases were small. Overall, the findings from this study provide the first empirical evidence to support the reliability of GENEActiv against Actiwatch-2 over multiple nights using a common algorithm with device-specific wake thresholds.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature

Background

Accelerometery is commonly used to estimate physical activity, sleep, and sedentary behavior. In free-living conditions, periods of device removal (non-wear) can lead to misclassification of behavior with consequences for research outcomes and clinical decision making. Common methods for non-wear detection are limited by data transformations (e.g., activity counts) or algorithm parameters such as minimum durations or absolute temperature thresholds that risk over- or under-estimating non-wear time. This study aimed to advance non-wear detection methods by integrating a ‘rate-of-change’ criterion for temperature into a combined temperature-acceleration algorithm.

Methods

Data were from 39 participants with neurodegenerative disease (36% female; age: 45–83 years) who wore a tri-axial accelerometer (GENEActiv) on their wrist 24-h per day for 7-days as part of a multi-sensor protocol. The reference dataset was derived from visual inspection conducted by two expert analysts. Linear regression was used to establish temperature rate-of-change as a criterion for non-wear detection. A classification and regression tree (CART) decision tree classifier determined optimal parameters separately for non-wear start and end detection. Classifiers were trained using data from 15 participants (38.5%). Outputs from the CART analysis were supplemented based on edge cases and published parameters.

Results

The dataset included 186 non-wear periods (85.5% < 60 min). Temperature rate-of-change over the first five minutes of non-wear was − 0.40 ± 0.17 °C/minute and 0.36 ± 0.21 °C/minute for the first five minutes following device donning. Performance of the DETACH (DEvice Temperature and Accelerometer CHange) algorithm was improved compared to existing algorithms with recall of 0.942 (95% CI 0.883 to 1.0), precision of 0.942 (95% CI 0.844 to 1.0), F1-Score of 0.942 (95% CI 0.880 to 1.0) and accuracy of 0.996 (0.994–1.000).

Conclusion

The DETACH algorithm accurately detected non-wear intervals as short as five minutes; improving non-wear classification relative to current interval-based methods. Using temperature rate-of-change combined with acceleration results in a robust algorithm appropriate for use across different temperature ranges and settings. The ability to detect short non-wear periods is particularly relevant to free-living scenarios where brief but frequent removals occur, and for clinical application where misclassification of behavior may have important implications for healthcare decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01633-6.

Digital health tools for the passive monitoring of depression: a systematic review of methods

The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.

Sleep Disturbances, Changes in Sleep, and Cognitive Function in Low-Income African Americans

BACKGROUND

Sleep problems may contribute to the disproportionate burden of Alzheimer's disease and related dementias (ADRD) among African Americans (AAs).

OBJECTIVE

To examine the role of sleep problems in contributing to cognitive function and clinically adjudicated cognitive impairment in a predominantly AA sample.

METHODS

This study (n = 216, 78.8% female; mean age = 67.7 years) examined associations between 1) the level (i.e., measured in 2018) and 2) change over time (from 2013 to 2018; n = 168) in actigraphy-assessed sleep with domain-specific cognitive function and clinically adjudicated cognitive impairment (2018) in a community-dwelling, predominantly AA (96.9%) sample. A comprehensive cognitive battery assessed global cognitive function (3MS) and domain-specific cognitive function (attention, visuo-spatial ability, language, delayed recall, immediate recall, and executive function) in 2018. Sleep was measured in 2013 and 2018 via actigraphy.

RESULTS

Higher sleep efficiency and less wakefulness after sleep onset (WASO; measured in 2018) were associated with greater attention, executive function, and visuospatial ability. Increases in sleep efficiency between 2013 and 2018 were associated with better executive function, language, immediate recall, and visuospatial ability, whereas increases in WASO (2013-2018) were associated with poorer attention, executive function, and visuospatial ability. Level or change in sleep duration were not associated with domain-specific cognitive function, nor were any sleep measures associated with clinically adjudicated cognitive impairment.

CONCLUSION

In a predominantly AA sample of older adults, both the level and change (i.e., worsening) of sleep efficiency and WASO were associated with poorer cognitive function. Improving sleep health may support ADRD prevention and reduce health disparities.

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BACKGROUND

Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep.

OBJECTIVE

This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest.

METHODS

The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each.

RESULTS

Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data.

CONCLUSIONS

We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.

Smart Wearable Device Users' Behavior Is Essential for Physical Activity Improvement

BACKGROUNDS

This study aimed to explore the effect on physical activity and sedentary behavior of users of wearable devices, and understand the association between physical activity and behavior.

METHODS

This study conducted a three-arm, randomized controlled trial for 12 weeks. Healthy adults without experience of using a wearable device were recruited and were randomly assigned to a control group with a mobile app and two experimental groups with different smart wearable devices. Data were collected through questionnaires.

RESULTS

No significant effect of group, time, or group-by-time interaction among groups for physical activity, sedentary time, or sleep quality was found. Wearing duration significantly positively predicted changes in low-intensity and total physical activity. The number of times the device was checked negatively predicted a change in sedentary time.

CONCLUSIONS

The behavior of wearable device users is an essential factor for successfully increasing physical activity and decreasing sedentary time.

Does investing in low-income urban neighborhoods improve sleep?

STUDY OBJECTIVES

Neighborhood disadvantage is associated with poor sleep, which may contribute to and exacerbate racial and socioeconomic health disparities. Most prior work has been cross-sectional and thus it has not been possible to estimate causal effects.

METHODS

We leveraged a natural experiment opportunity in two low-income, predominantly African American Pittsburgh, PA neighborhoods, following a randomly selected cohort of households (n = 676) between 2013 and 2016. One of the neighborhoods received substantial public and private investments (housing, commercial) over the study period, while the other socio-demographically similar neighborhood received far fewer investments. Primary analyses used a difference-in-difference analysis based on neighborhood, to examine changes in actigraphy-assessed sleep duration, efficiency, and wakefulness after sleep onset (WASO), and self-reported sleep quality. Secondary analyses examined whether residents' proximity to investments, regardless of neighborhood, was associated with changes in sleep outcomes.

RESULTS

Resident sleep worsened over time in both neighborhoods with no significant differences among residents between the two neighborhoods. Secondary analyses, including covariate adjustment and propensity score weighting to improve comparability, indicated that regardless of neighborhood, those who lived in closer proximity to investments (<0.1 mile) were significantly less likely to experience decreases in sleep duration, efficiency, and quality, or increases in WASO, compared to those who lived farther away.

CONCLUSIONS

While we did not observe sleep differences among residents between neighborhoods, living closer to a neighborhood investment was associated with better sleep outcomes. Findings have relevance for public health and policy efforts focused on investing in historically disinvested neighborhoods.

Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman's rank-order correlation coefficients and Bland-Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = -0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring.

A Combined Sleep Hygiene and Mindfulness Intervention to Improve Sleep and Well-Being During High-Performance Youth Tennis Tournaments

PURPOSE

To investigate the effects of combined sleep hygiene recommendations and mindfulness on actigraphy-based sleep parameters, perceptual well-being, anxiety, and match outcomes during high-performance junior tennis tournaments.

METHODS

In a randomized crossover design, 17 high-performance junior tennis players completed the baseline, control, and intervention (INT) conditions across 3 separate weeks. The baseline consisted of unassisted, habitual sleep during a regular training week, and the control was unassisted sleep during a tournament week. The players attended a sleep education workshop and completed a nightly sleep hygiene protocol during a tournament week for the INT. Analysis was performed on the weekly means and on the night prior to the first match of the tournament (T-1).

RESULTS

Significant differences were observed for increased time in bed, total sleep time, and an earlier bedtime (P < .05) across the INT week. These parameters also significantly improved on T-1 of the INT. A moderate effect size (P > .05, d > 1.00) was evident for decreased worry on T-1 of the INT. Small effect sizes were also evident for improved mood, cognitive anxiety, and sleep rating across the INT week. The match performance outcomes remained unchanged (P > .05).

CONCLUSIONS

Sleep hygiene INTs increase the sleep duration of high-performance junior tennis players in tournament settings, including the night prior to the tournament's first match. The effects on perceptual well-being and anxiety are unclear, although small trends suggest improved mood, despite no effect on generic match performance outcomes.

Violent crime, police presence and poor sleep in two low-income urban predominantly Black American neighbourhoods

OBJECTIVE

To examine violent crime in relation to sleep and explore pathways, including psychological distress, safety perceptions and perceived police presence, that may account for associations.

METHODS

In 2018, 515 predominantly Black American (94%) adults (Pittsburgh, Pennsylvania, USA) provided survey data: actigraphy-assessed sleep duration and wakefulness after sleep onset (WASO). We estimated pathways from violent crime (2016-2018) to sleep through psychological distress, perceptions of safety and perceived adequacy of police presence.

RESULTS

WASO was most strongly associated with violent crimes that were within 1/10 mile of the participant's home and within the month preceding the interview. Violent crimes were associated with lower perceived safety (β=-0.13 (0.03), p<0.001) and greater WASO (β=5.96 (2.80), p=0.03). We observed no indirect associations between crime and either WASO or sleep duration through any of the tested mediators. Crime was not associated with sleep duration.

CONCLUSIONS

We demonstrated that more proximal and more recent violent crimes were associated with reduced perceived safety and worse WASO. Differential exposure to violent crime among Black Americans may contribute to health disparities by reducing residents' perceived safety and sleep health.

The Association between Sleep Duration and Quality with Readmissions: An Exploratory Pilot-Study among Cardiology Inpatients

Background: Readmissions within 30 days of discharge are prominent among patients with cardiovascular disease. Post hospital syndrome hypothesizes that sleep disturbance during the index admission contributes to an acquired transient vulnerability, leading to increased risk of readmission. This study evaluated the association of in-hospital sleep (a) duration and (b) quality with 30-day all-cause unplanned readmission. Methods: This prospective observational cohort study included patients admitted to the coronary care unit of a South Australian hospital between 2016–2018. Study participants were invited to wear an ActiGraph GT3X+ for the duration of their admission and for two weeks post-discharge. Validated sleep and quality of life questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), were administered. Readmission status and questionnaires were assessed at 30 days post-discharge via patient telephone interview and a review of hospital records. Results: The final cohort consisted of 75 patients (readmitted: n = 15, non-readmitted: n = 60), of which 72% were male with a mean age 66.9 ± 13.1 years. Total sleep time (TST), both in hospital (6.9 ± 1.3 vs. 6.8 ± 2.9 h, p = 0.96) and post-discharge (7.4 ± 1.3 h vs. 8.9 ± 12.6 h, p = 0.76), was similar in all patients. Patient’s perception of sleep, reflected by PSQI scores, was poorer in readmitted patients (9.13 ± 3.6 vs. 6.4 ± 4.1, p = 0.02). Conclusions: Although an association between total sleep time and 30-day readmission was not found, patients who reported poorer sleep quality were more likely to be readmitted within 30 days. This study also highlighted the importance of improving sleep, both in and out of the hospital, to improve the outcomes of cardiology inpatients.

Prevalence and correlates of obstructive sleep apnea in urban-dwelling, low-income, predominantly African-American women

STUDY OBJECTIVES

The current study examined the prevalence and correlates of obstructive sleep apnea in a sample of low-income, predominantly African-American women using two waves of data.

METHODS

Participants were adults from two urban neighborhoods who enrolled in the PHRESH Zzz Study (N = 828; Pittsburgh Hill/Homewood Research on Neighborhoods, Sleep, and Health). A subsample who reported never receiving OSA diagnosis completed home sleep apnea testing in 2016 (n = 269, mean age 55.0 years, 79.6% female) and again in 2018 (n = 135). Correlates of OSA tested included demographic and anthropometric variables, health behavior/conditions, psychological distress and general health, smoking status, actigraphy-measured sleep, and neighborhood factors measured at baseline.

RESULTS

18.0% of all 2016 participants reported receiving physician diagnoses of OSA. Among those who completed in-home assessment, 19.3% had AHI ≥15 and 33.8% had AHI ≥5 plus one or more sleep symptoms. Estimates of the prevalence of OSA in all 2016 participants were 33.8%-45.7% based on physician diagnoses and AHI results, depending on the criteria used. Age, gender, BMI, blood pressure, habitual snoring, neighborhood walkability, actigraphy-measured sleep characteristics, and smoking were concurrently associated with OSA in 2016. Changes in AHI categories from 2016 to 2018 were documented.

CONCLUSIONS

Low-income African Americans, including women, are a high-risk group for OSA, but remain under-diagnosed and under-treated. The current findings show a high prevalence of OSA in African-American women and are among the first to demonstrate that both individual and neighborhood factors are implicated in OSA prevalence.

Estimation of Physical Activity Intensity in Spinal Cord Injury Using a Wrist-Worn ActiGraph Monitor

OBJECTIVES

To derive accelerometer count thresholds for classifying time spent in sedentary, light intensity, and moderate-to-vigorous physical activity (MVPA) in manual wheelchair users (MWUs) with spinal cord injury (SCI).

DESIGN

Participants completed 18 activities of daily living and exercises for 10 minutes each with a 3-minute break between activities while wearing a COSMED K4b2 portable metabolic cart and an ActiGraph activity monitor on the dominant wrist. A linear regression was computed between the wrist acceleration vector magnitude and SCI metabolic equivalent of task (MET) for 80% of the participants to obtain thresholds for classifying different activity intensities, and the obtained thresholds were tested for accuracy on the remaining 20% of participants. This cross-validation process was iterated for 1000 times to evaluate the stability of the thresholds on data corresponding to different proportions of sedentary, light intensity, and MVPA. MET values of 1.5 or lower were classified as sedentary behavior, MET values between 1.5 and 3 were classified as light intensity, and MET values of 3 or higher were classified as MVPA. The final thresholds were then validated on an out-of-sample independent dataset.

PARTICIPANTS

MWUs (N=17) with SCI in the out-of-sample validation data set.

INTERVENTIONS

Not applicable.

SETTING

Research lab, community MAIN OUTCOME MEASURES: Accelerometer thresholds to classify sedentary, light intensity, and MVPA were obtained and their accuracy tested using cross-validation and an out-of-sample dataset.

RESULTS

The threshold between sedentary and light intensity was 2057 counts-per-minute, and the threshold between light intensity and MVPA was 11,551 counts per minute. Based on the out-of-sample validation, the obtained thresholds had an overall accuracy of 85.6%, with a sensitivity and specificity of 95.3% and 97.4% for sedentary behavior, 87.8% and 84.5% for light intensity, 68.5% and 96.3% for MVPA, respectively.

CONCLUSION

Accelerometer-based thresholds can be used to accurately identify sedentary behavior. However, thresholds may not provide accurate estimations of MVPA throughout the day when participants engage in more resistance-based activities.

Broken Windows, Broken Zzs: Poor Housing and Neighborhood Conditions Are Associated with Objective Measures of Sleep Health

African Americans and socioeconomically disadvantaged individuals have higher rates of a variety of sleep disturbances, including short sleep duration, poor sleep quality, and fragmented sleep. Such sleep disturbances may contribute to pervasive and widening racial and socioeconomic (SES) disparities in health. A growing body of literature demonstrates that over and above individual-level SES, indicators of neighborhood disadvantage are associated with poor sleep. However, there has been scant investigation of the association between sleep and the most proximal environments, the home and residential block. This is the first study to examine the association between objective and self-reported measures of housing and block conditions and sleep. The sample included 634 adults (mean age = 58.7 years; 95% African American) from two low-income urban neighborhoods. Study participants reported whether they experienced problems with any of seven different housing problems (e.g., broken windows) and rated the overall condition of their home. Trained data collectors rated residential block quality. Seven days of wrist actigraphy were used to measure average sleep duration, efficiency, and wakefulness after sleep onset (WASO), and a sleep diary assessed sleep quality. Multivariate regression analyses were conducted for each sleep outcome with housing or block conditions as predictors in separate models. Participants reporting "fair" or "poor" housing conditions had an adjusted average sleep duration that was 15.4 min shorter than that of participants reporting "good" or "excellent" conditions. Those reporting any home distress had 15.9 min shorter sleep and .19 units lower mean sleep quality as compared with participants who did not report home distress. Poor objectively measured block quality was associated with 14.0 min shorter sleep duration, 1.95% lower sleep efficiency, and 10.7 additional minutes of WASO. Adverse housing and proximal neighborhood conditions are independently associated with poor sleep health. Findings highlight the importance of considering strategies that target upstream determinants of sleep health disparities.

Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer

OBJECTIVE

To examine agreement between multiple commercial activity monitors (CAMs) and a validated actigraph to measure sleep.

METHODS

Thirty adults without sleep disorders wore an Actiwatch Spectrum (AW) and alternated wearing 6 CAMs for one 24-h period each (Fitbit Alta, Jawbone Up3, Misfit Shine 2, Polar A360, Samsung Gear Fit2, Xiaomi Mi Band 2). Total sleep time (TST) and wake after sleep onset (WASO) were compared between edited AW and unedited CAM outputs. Comparisons between AW and CAM data were made via paired t-tests, mean absolute percent error (MAPE) calculations, and intra-class correlations (ICC). Intra-model reliability was performed in 10 participants who wore a pair of each AW and CAM model.

RESULTS

Fitbit, Jawbone, Misfit, and Xiaomi overestimated TST relative to AW (53.7-80.4 min, P ≤ .001). WASO was underestimated by Fitbit, Misfit, Samsung and Xiaomi devices (15.0-27.9 min; P ≤ .004) and overestimated by Polar (27.7 min, P ≤ .001). MAPEs ranged from 5.1% (Samsung) to 25.4% (Misfit) for TST and from 36.6% (Fitbit) to 165.1% (Polar) for WASO. TST ICCs ranged from .00 (Polar) to .92 (Samsung), while WASO ICCs ranged from .38 (Misfit) to .69 (Samsung). Differences were similar between poor sleepers (Pittsburgh Sleep Quality Index global score >5; n = 10) and good sleepers. Intra-model reliability analyses revealed minimal between-pair differences and high ICCs.

CONCLUSIONS

Agreement between CAMs and AW varied by device, with greater agreement observed for TST than WASO. While reliable, variability in agreement across CAMs with traditional actigraphy may complicate the interpretation of CAM data obtained for clinical or research purposes.

Food Insecurity is Associated with Objectively Measured Sleep Problems

Objective/Background: Food Insecurity (FI) can be a profound source of stress, which may increase the risk for sleep disturbance. This is the first study to examine the association between FI and objectively and subjectively measured sleep. Participants: The sample included 785 adults living in two low-income neighborhoods (mean age = 56; 95% African American). Methods: FI was measured using a validated 10-item survey that assesses conditions and behaviors that characterize households when they lack financial resources to meet basic food needs. Sleep duration, efficiency, wakefulness after sleep onset (WASO), and variability in sleep duration were measured via actigraphy. Sleep quality was assessed via sleep diary. Sleep outcomes were analyzed as a function of FI, adjusting for covariates. Psychological distress was tested as a potential mediator. Results: Greater FI was associated with shorter actigraphy-assessed sleep duration (B = -2.44; SE = 1.24; i.e., 24 minutes shorter for the most as compared to least insecure group), poorer sleep efficiency (B = -.27; SE = .13); p's < .05), and poorer subjective sleep quality (B = -.03; SE = .01; p < .01). Greater FI was also associated with greater likelihood of short (<7 hours; OR = 1.11; CI: 1.02-1.21) and long sleep (>9 hours; OR = 1.19; CI: 1.01-1.39), compared to the recommended sleep duration of 7-9 hours. Psychological distress partially mediated the association between FI and subjective sleep quality. Conclusions: Addressing or mitigating food insecurity may present a novel opportunity for improving sleep health among low-income populations.

A preliminary study of a composite sleep health score: associations with psychological distress, body mass index, and physical functioning in a low-income African American community

OBJECTIVES

Although multiple individual sleep measures (eg, sleep duration, satisfaction) have been linked to a wide range of physical and mental health conditions, scant research has examined how individual sleep dimensions may act independently or additively to influence health. The current study investigates associations of 5 sleep dimensions (duration, satisfaction, efficiency, timing, and regularity), analyzed separately and simultaneously, with psychological distress, body mass index, and physical functioning among a low-income, predominantly African American population.

DESIGN

We constructed a composite sleep health (SH) score from the sum of scores, representing "good' and "poor" ranges of 5 sleep measures (range 0-5).

SETTING

Two low-income, predominantly African American neighborhoods in Pittsburgh.

PARTICIPANTS

Participants included 738 community-dwelling adults (78% female and 98% black).

MEASUREMENTS

Actigraphy-based measures of sleep duration, regularity, timing, and efficiency, and self-reported sleep satisfaction. Outcomes included self-reported psychological distress, physical functioning, and measured body mass index (BMI).

RESULTS

Each 1-unit higher SH score was associated with 0.55-unit lower psychological distress score (range 0-24) and 2.23-unit higher physical functioning score. Participants with at least 2, 3, or 4 sleep dimensions in the "healthy" range, vs fewer, had lower psychological distress scores. Greater sleep satisfaction was associated with higher physical functioning, and longer sleep duration was associated with lower physical functioning. Neither the composite SH score nor any of the individual sleep dimensions were associated with BMI.

CONCLUSIONS

Assessing multiple sleep dimensions may provide a more comprehensive understanding of associations of sleep with psychological distress than assessing any single sleep dimension. Although no sleep measures were related to BMI in the current sample, analyses should be replicated in other samples to determine generalizability.

Neighborhood disadvantage is associated with actigraphy-assessed sleep continuity and short sleep duration

Study Objectives

Neighborhood disadvantage has been linked to poor sleep. However, the extant research has primarily focused on self-reported assessments of sleep and neighborhood characteristics. The current study examines the association between objective and perceived neighborhood characteristics and actigraphy-assessed sleep duration, efficiency, and wakefulness after sleep onset (WASO) in an urban sample of African American adults.

Methods

We examined data from predominantly African American adults (n = 788, mean age 55 years; 77% female) living in two low-income neighborhoods. Perceived neighborhood characteristics included safety, social cohesion, and satisfaction with one's neighborhood as a place to live. Objective neighborhood conditions included walkability, disorder, street lighting, and crime levels. Sleep duration, efficiency, and WASO were measured via 7 days of wrist-worn actigraphy. Analyses estimated each of the sleep outcomes as a function of perceived and objective neighborhood characteristics. Individual-level sociodemographics, body mass index, and psychological distress were included as covariates.

Results

Greater perceived safety was associated with higher sleep efficiency and shorter WASO. Greater neighborhood disorder and street lighting were associated with poorer sleep efficiency and longer WASO and greater likelihood of short sleep duration (<7 versus 7-9 hr as referent). Higher levels of crime were associated with poorer sleep efficiency and longer WASO, but these associations were only evident in one of the neighborhoods.

Conclusions

Both how residents perceive their neighborhood and their exposure to objectively measured neighborhood disorder, lighting, and crime have implications for sleep continuity. These findings suggest that neighborhood conditions may contribute to disparities in sleep health.

Assessing Physical Activity and Sedentary Behavior under Free-Living Conditions: Comparison of Active Style Pro HJA-350IT and ActiGraphTM GT3X

Various accelerometers have been used in research measuring physical activity (PA) and sedentary behavior (SB). This study compared two triaxial accelerometers—Active style Pro (ASP) and ActiGraph (AG)—in measuring PA and SB during work and nonwork days in free-living conditions. A total of 50 working participants simultaneously wore these two accelerometers on one work day and one nonwork day. The difference and agreement between the ASP and AG were analyzed using paired t-tests, Bland–Altman plots, and intraclass coefficients, respectively. Correction factors were provided by linear regression analysis. The agreement in intraclass coefficients was high among all PA intensities between ASP and AG. SB in the AG vertical axis was approximately 103 min greater than ASP. Regarding moderate-to-vigorous-intensity PA (MVPA), ASP had the greatest amount, followed by AG. There were significant differences in all variables among these devices across all day classifications, except for SB between ASP and AG vector magnitude. The correction factors decreased the differences of SB and MVPA. PA time differed significantly between ASP and AG. However, SB and MVPA differences between these two devices can be decreased using correction factors, which are useful methods for public health researchers.

Objective changes in activity levels following sleep extension as measured by wrist actigraphy

OBJECTIVE/BACKGROUND

It is widely established that insufficient sleep can lead to adverse health outcomes. Paradoxically, epidemiologic research suggests that individuals who report habitual nightly sleep greater than 9 h also are at risk for adverse health outcomes. Further, studies have shown that long sleepers have decreased activity levels, which may partially explain the relationship between long sleep duration and mortality. The influence of sleep extension (longer time in bed) on levels of daily activity has not yet been established. The current study examined whether a week of sleep extension altered activity levels within the subsequent daily waking active and sleep period in order to determine whether increased time in bed indeed is related to decreased activity levels.

METHODS

A total of 26 healthy volunteers wore wrist accelerometer devices (Actiwatch 2.0, Philips) in order to objectively measure sleep and activity for six days during their normal schedules and for six days during a sleep extension (10 h time in bed) intervention.

RESULTS

There were no significant or clinically-relevant differences in 24-h activity or activity during the active or sleep period between baseline and sleep extension conditions. There were no main or interaction effects of day and condition when daily activity counts were compared between baseline and sleep extension conditions for the 24 h period (Day: F_{(5, 21)} = 1.92, p = 0.12; Condition: F_(1,25) = 2.93, p = 0.09; Day by Condition: F_(5,21) = 0.32, p = 0.83), Active Waking Period (Day: F_(5,25) = 1.53, p = 0.18; Condition: F_(1,25) = 0.26, p = 0.61; Day by Condition: F_(5,21) = 0.55, p = 0.74) or Nightly Sleep (Day: F_(5,21) = 0.86, p = 0.51; Condition: F_(1,25) = 1.78, p = 0.19; Day by Condition: F_(5,21) = 0.79, p = 0.56) periods. In contrast, there was a main effect of condition when examining sleep duration by day between conditions (Day: F_(5,21) = 1.60, p = 0.16; Condition: F_(1,25) = 167.31, p < 0.001; Day by Condition: F_(5,21) = 2.31, p = 0.07), such that sleep duration was longer during the sleep extension condition.

DISCUSSION

Sleep duration increased during six days of a sleep extension protocol but activity levels remained similar to their baseline (normal) sleep schedule. The current findings suggest that extending time in bed alone does not alter waking activity counts in young healthy adults. The link between extended sleep and adverse health outcomes may be attributable to other phenotypic factors, or other biological correlates of extended sleep and poor health.

Relationship between sleep disturbance and self-care in adults with type 2 diabetes

AIMS

Type 2 diabetes (T2D) is a metabolic disorder requiring intensive self-care to maintain optimal glycemic control. Sleep disturbance is common in T2D patients and likely impairs glycemic control. Our aim was to examine the relationship between sleep disturbance and self-care in adults with T2D.

METHODS

This study used a correlational design. Sixty-four adults with T2D were recruited. Validated instruments were used to measure self-care, subjective sleep disturbance, and covariates (e.g., diabetes distress, self-efficacy, fatigue, and daytime sleepiness). Over an 8-day period, the ActiGraph-wGT3X was used to measure objective sleep outcomes (e.g., total sleep time and number of awakenings). Bivariate correlation and multiple linear regression analyses were conducted.

RESULTS

The mean age of the participants was 60.6 (SD 6.8) years (range 50-78), and 51.6% were women. Controlling for covariates, subjective sleep disturbance (β = - 0.26), diabetes distress (β = - 0.39), and daytime sleepiness (β = - 0.21) were strong predictors of diabetes self-care (R² = 0.51, p < 0.001). When objective sleep parameters were used, the number of awakenings (β = - 0.23) also predicted self-care along with diabetes distress, fatigue, and daytime sleepiness (R² = 0.57, p < 0.001).

CONCLUSIONS

Subjective sleep disturbance and frequent nocturnal awakenings are associated with worse diabetes self-care in adults with T2D. Healthcare providers are recommended to include comprehensive sleep assessment at every clinical visit. Diabetes educators may consider including sleep-related education in their diabetes self-management classes and/or counseling sessions.

Exercise during early pregnancy is associated with greater sleep continuity

OBJECTIVES/BACKGROUND

Pregnant women report disturbed sleep beginning in early pregnancy. Among nonpregnant populations, exercise has been associated with improved sleep; however, research in pregnant samples has been equivocal. We examined whether varying degrees of exercise were associated with better nocturnal sleep among pregnant women during early gestation.

PARTICIPANTS

172 pregnant women.

METHODS

Self-reported sleep and exercise and objective sleep were collected during early gestation: T1 (10-12 weeks), T2 (14-16 weeks), and T3 (18-20 weeks) from 172 pregnant women. Exercise was categorized into three time-varying groups: 0 metabolic equivalent minutes per week (MET-min/week), 1 to < 500 MET-min/week, or ≥ 500 MET-min/week. Linear mixed-effects models were employed to test hypotheses.

RESULTS

A significant main effect for Time (F[2,254] = 9.77, p < 0.0001) and Time*Exercise group interaction were observed for actigraphic sleep efficiency (aSE) (F[4,569] = 2.73, p = 0.0285). At T2, women who reported ≥ 500 MET-min/week had higher aSE than those who reported 0 MET-min/week. Significant main effects for Exercise Group and Time were observed for actigraphic wake after sleep onset (aWASO; F[2,694] = 3.04, p = 0.0483 and F[2,260] = 3.21, p = 0.0419). aWASO was lowest for those reporting 1 to < 500 MET-min/week (t[701] = 2.35, adjusted p = .0489) and aWASO decreased from T1 to T3 (t[258] = 2.53, adjusted p value = 0.036). Lastly, there was a main effect for Time for the PSQI (F[2,689] = 52.11, p < 0.0001), indicating that sleep quality improved over time.

CONCLUSIONS

Some level of exercise among pregnant women appears to be more advantageous than no exercise at all. Moderate exercise, while still unclearly defined, may be a worthwhile adjunct treatment to combat sleep disturbances during pregnancy.

The convergent validity of Actiwatch 2 and ActiGraph Link accelerometers in measuring total sleeping period, wake after sleep onset, and sleep efficiency in free-living condition

PURPOSE

Physical activity (PA) and sleep are important to health; thus, it is important for researchers to have valid tools to measure them. Accelerometers have been proven valid for measuring PA and sleep, but only one device does this simultaneously: the ActiGraph Link (ActiGraph, LLC); however, the sleep-monitoring function has not been validated. This study aimed to evaluate the predictive power of ActiGraph Link sleep parameters against a validated accelerometer (Actiwatch 2, Phillips Respironics Mini-Mitter).

METHODS

A total of 49 Hong Kong adults aged 18-64 provided valid data on both accelerometers on their non-dominant wrist for seven consecutive days. Epochs from both accelerometers were classified as either sleep or awake using seven established algorithms (Cole-Kripke, Sadeh, Sazonov, high sensitivity threshold, medium sensitivity threshold, low sensitivity threshold, and neural network model), and these data were transformed to total sleeping period, wake after sleep onset, and sleep efficiency.

RESULTS

The non-zero count data for both accelerometers (331,103 observations) were strongly correlated with a Spearman correlation of 0.83 (p < 0.001). The total sleeping period was highly correlated (Spearman correlation ranged from 0.74 to 0.90) regardless of the algorithms used. All algorithms yielded insignificant difference in total sleep time measured by the two accelerometers (p > 0.05) with a negligible effect size of d < 0.2. The agreement of sleep/wake status was high for all algorithms, with accuracy ranging from 93.05 % (Sadeh's algorithm) to 96.13 % (Cole-Kripke's algorithm).

CONCLUSIONS

Results showed that the sleep function of the ActiGraph Link performs similar to a validated accelerometer (Actiwatch 2) and provides an opportunity to measure both sleep and PA simultaneously.

Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders

BACKGROUND

Humans spend more than one-fourth of their life sleeping, and sleep quality has been significantly linked to health. However, the objective examination of ambulatory sleep quality remains a challenge, since sleep is a state of unconsciousness, which limits the reliability of self-reports. Therefore, a non-invasive, continuous, and objective method for the recording and analysis of naturalistic sleep is required.

OBJECTIVE

Portable sleep recording devices provide a suitable solution for the ambulatory analysis of sleep quality. In this study, the performance of two activity-based sleep monitors (Actiwatch and MTN-210) and a single-channel electroencephalography (EEG)-based sleep monitor (SleepScope) were compared in order to examine their reliability for the assessment of sleep quality.

METHODS

Twenty healthy adults were recruited for this study. First, data from daily activity recorded by Actiwatch and MTN-210 were compared to determine whether MTN-210, a more affordable device, could yield data similar to Actiwatch, the de facto standard. In addition, sleep detection ability was examined using data obtained by polysomnography as reference. One simple analysis included comparing the sleep/wake detection ability of Actiwatch, MTN-210, and SleepScope. Furthermore, the fidelity of sleep stage determination was examined using SleepScope in finer time resolution.

RESULTS

The results indicate that MTN-210 demonstrates an activity pattern comparable to that of Actiwatch, although their sensitivity preferences were not identical. Moreover, MTN-210 provides assessment of sleep duration comparable to that of the wrist-worn Actiwatch when MTN-210 was attached to the body. SleepScope featured superior overall sleep detection performance among the three methods tested. Furthermore, SleepScope was able to provide information regarding sleep architecture, although systemic bias was found.

CONCLUSION

The present results suggest that single-channel EEG-based sleep monitors are the superior option for the examination of naturalistic sleep. The current results pave a possible future use for reliable portable sleep assessment methods in an ambulatory rather than a laboratory setting.