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PLoS Medicine Oct 2021Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale....
BACKGROUND
Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort.
METHODS AND FINDINGS
In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry.
CONCLUSIONS
In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.
Topics: Accelerometry; Adult; Aged; Biological Specimen Banks; Cohort Studies; Cross-Sectional Studies; Female; Humans; Male; Mental Disorders; Middle Aged; Multifactorial Inheritance; Reproducibility of Results; Risk Factors; Self Report; Sleep; United Kingdom
PubMed: 34637446
DOI: 10.1371/journal.pmed.1003782 -
Biostatistics (Oxford, England) Apr 2021Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using...
Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a $450$-m outdoor walk of $32$ study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online.
Topics: Accelerometry; Gait; Humans; Monitoring, Ambulatory; Walking
PubMed: 31545345
DOI: 10.1093/biostatistics/kxz033 -
JNCI Cancer Spectrum Mar 2023
Topics: Humans; Sedentary Behavior; Cancer Survivors; Neoplasms; Exercise; Accelerometry
PubMed: 36869677
DOI: 10.1093/jncics/pkad020 -
Scientific Reports Jan 2021Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are...
Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
Topics: Accelerometry; Adolescent; Adult; Aged; Algorithms; Deep Learning; Female; Humans; Machine Learning; Male; Middle Aged; Polysomnography; Sleep; Sleep Stages; Sleep Wake Disorders; Wearable Electronic Devices; Young Adult
PubMed: 33420133
DOI: 10.1038/s41598-020-79217-x -
Frontiers in Public Health 2022The purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in...
OBJECTIVES
The purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions.
DESIGN
A cross-sectional study and repeated measures.
METHODS
A total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure.
RESULTS
Based on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 ( < 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, < 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts.
CONCLUSIONS
The smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition.
Topics: Humans; Male; Female; Accelerometry; Smartphone; Reproducibility of Results; Social Conditions; Cross-Sectional Studies; China
PubMed: 36582382
DOI: 10.3389/fpubh.2022.1009022 -
Scientific Reports Jul 2022Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on...
Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a standalone Python package for research use. We believe that this material will provide a useful resource for the research community, accelerate digital health science and facilitate clinical applications of wearable accelerometry.
Topics: Acceleration; Accelerometry; Exercise; Software; Wearable Electronic Devices
PubMed: 35831446
DOI: 10.1038/s41598-022-16003-x -
International Journal of Environmental... Aug 2019Various accelerometers have been used in research measuring physical activity (PA) and sedentary behavior (SB). This study compared two triaxial accelerometers-Active... (Comparative Study)
Comparative Study
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 -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.
Topics: Actigraphy; Adult; Exercise; Female; Health Surveys; Humans; Japan; Male; Middle Aged; Reproducibility of Results; Sedentary Behavior
PubMed: 31450754
DOI: 10.3390/ijerph16173065 -
Sensors (Basel, Switzerland) Oct 2023The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer... (Review)
Review
The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.
Topics: Humans; Smartphone; Biomechanical Phenomena; Reproducibility of Results; Gait; Accelerometry
PubMed: 37896708
DOI: 10.3390/s23208615 -
Journal of Occupational Health Jan 2021Objectively measured sedentary behavior (SB) on weekdays and weekends has been mainly assessed in white-collar workers, while data in blue-collar workers are sparse.... (Comparative Study)
Comparative Study
OBJECTIVES
Objectively measured sedentary behavior (SB) on weekdays and weekends has been mainly assessed in white-collar workers, while data in blue-collar workers are sparse. Therefore, this study presented the difference in accelerometer-measured SB levels between weekdays and weekends, stratified by white- and blue-collar occupations.
METHODS
This study was a sub-analysis of accelerometer data from 73 workers (31 blue-collar and 42 white-collar) at a Japanese manufacturing plant. SB was defined as ≤1.5 metabolic equivalents estimated using an accelerometer, and compared between weekdays and weekends by using mixed models adjusted for confounders. The proportion of workers who sat for ≤8 h/day on weekdays and weekends were compared using McNemar's test.
RESULTS
In white-collar workers, SB time on weekdays was significantly longer than that on weekends (598 vs 479 min/day, P < .001). In blue-collar workers, there was no significant difference in SB time between weekdays and weekends (462 vs 485 min/day, P = .43). The proportion of workers who achieved the recommended SB levels (≤8 h) was only 4.8% for white-collar workers on weekdays and 54.8% on weekends (P = .04), while that of blue-collar workers was 45.2% and 58.1% respectively (P > .99).
CONCLUSIONS
White-collar workers were exposed to significantly longer SB time on weekdays than on weekends, which was not the case in blue-collar workers. It may be rather challenging for white-collar workers to limit their SB time to the level recommended by the latest guidelines for better health, especially on weekdays.
Topics: Accelerometry; Adolescent; Adult; Aged; Exercise; Female; Humans; Male; Manufacturing and Industrial Facilities; Middle Aged; Occupations; Sedentary Behavior; Time Factors; Work; Young Adult
PubMed: 34275174
DOI: 10.1002/1348-9585.12246 -
Frontiers in Public Health 2020Regular physical activity (PA) and reduced sedentary behavior (SB) are positively related to children's health and considered as pillars of a healthy lifestyle....
Regular physical activity (PA) and reduced sedentary behavior (SB) are positively related to children's health and considered as pillars of a healthy lifestyle. Full-day schools with their afterschool programs (ASPs) have an impact on children's daily PA and SB. Studies investigating PA and SB in ASPs, which compare PA and SB between the organizational forms full-day and half-day schools, are rare. The aim of this study is to describe elementary school children's PA and SB during ASPs and to compare the results to other time periods of the day, e.g., teaching hours and leisure time. Additionally, PA and SB of children in full-day and half-day schools are compared. Further, relevant factors influencing the achievement of the World Health Organization's (WHO's) PA guidelines for children, e.g., time spent in ASPs, are investigated. PA and SB of 332 German students ( = 198 full-day school children; = 134 half-day school children) from 11 different elementary schools were measured via accelerometry for 5 consecutive days within one school week in 2017. PA and SB during ASPs and other times of the day were analyzed via one-way and factorial ANOVA, correlation, and logistic regression. Children attending full-day schools show the highest percentage of moderate-to-vigorous PA (MVPA) (13.7%) and the lowest percentage of SB (49.5%) during ASPs, in comparison with teaching hours and leisure time. In the afternoon hours, full-day school children show 20 min less SB than half-day school children. Children spending more time in ASPs obtain significantly more SB ( = 0.23) and less MVPA ( = -0.15). Further, they less likely reach WHO's PA guidelines odds ratio (OR = 0.98). Peers and the choice as well as offer of extracurricular activities promote PA in ASPs. Media availability leads to higher SB in leisure time. ASPs help to be more active and less sedentary. Time spent in ASPs should be limited, so that full-day school children still have the possibility to join other PA offers in leisure time. ASP time should contain a certain minimum amount of MVPA in line with ASP guidelines.
Topics: Accelerometry; Child; Exercise; Germany; Humans; Schools; Sedentary Behavior
PubMed: 32984249
DOI: 10.3389/fpubh.2020.00463