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Nature Medicine Sep 2020Use of wearable devices that monitor physical activity is projected to increase more than fivefold per half-decade. We investigated how device-based physical activity...
Use of wearable devices that monitor physical activity is projected to increase more than fivefold per half-decade. We investigated how device-based physical activity energy expenditure (PAEE) and different intensity profiles were associated with all-cause mortality. We used a network harmonization approach to map dominant-wrist acceleration to PAEE in 96,476 UK Biobank participants (mean age 62 years, 56% female). We also calculated the fraction of PAEE accumulated from moderate-to-vigorous-intensity physical activity (MVPA). Over the median 3.1-year follow-up period (302,526 person-years), 732 deaths were recorded. Higher PAEE was associated with a lower hazard of all-cause mortality for a constant fraction of MVPA (for example, 21% (95% confidence interval 4-35%) lower hazard for 20 versus 15 kJ kg d PAEE with 10% from MVPA). Similarly, a higher MVPA fraction was associated with a lower hazard when PAEE remained constant (for example, 30% (8-47%) lower hazard when 20% versus 10% of a fixed 15 kJ kg d PAEE volume was from MVPA). Our results show that higher volumes of PAEE are associated with reduced mortality rates, and achieving the same volume through higher-intensity activity is associated with greater reductions than through lower-intensity activity. The linkage of device-measured activity to energy expenditure creates a framework for using wearables for personalized prevention.
Topics: Accelerometry; Energy Metabolism; Exercise; Female; Humans; Male; Middle Aged; Monitoring, Physiologic; Mortality; Wearable Electronic Devices
PubMed: 32807930
DOI: 10.1038/s41591-020-1012-3 -
The Lancet. Psychiatry Jun 2018Disruption of sleep and circadian rhythmicity is a core feature of mood disorders and might be associated with increased susceptibility to such disorders. Previous...
Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank.
BACKGROUND
Disruption of sleep and circadian rhythmicity is a core feature of mood disorders and might be associated with increased susceptibility to such disorders. Previous studies in this area have used subjective reports of activity and sleep patterns, but the availability of accelerometer-based data from UK Biobank participants permits the derivation and analysis of new, objectively ascertained circadian rhythmicity parameters. We examined associations between objectively assessed circadian rhythmicity and mental health and wellbeing phenotypes, including lifetime history of mood disorder.
METHODS
UK residents aged 37-73 years were recruited into the UK Biobank general population cohort from 2006 to 2010. We used data from a subset of participants whose activity levels were recorded by wearing a wrist-worn accelerometer for 7 days. From these data, we derived a circadian relative amplitude variable, which is a measure of the extent to which circadian rhythmicity of rest-activity cycles is disrupted. In the same sample, we examined cross-sectional associations between low relative amplitude and mood disorder, wellbeing, and cognitive variables using a series of regression models. Our final model adjusted for age and season at the time that accelerometry started, sex, ethnic origin, Townsend deprivation score, smoking status, alcohol intake, educational attainment, overall mean acceleration recorded by accelerometry, body-mass index, and a binary measure of childhood trauma.
FINDINGS
We included 91 105 participants with accelerometery data collected between 2013 and 2015 in our analyses. A one-quintile reduction in relative amplitude was associated with increased risk of lifetime major depressive disorder (odds ratio [OR] 1·06, 95% CI 1·04-1·08) and lifetime bipolar disorder (1·11, 1·03-1·20), as well as with greater mood instability (1·02, 1·01-1·04), higher neuroticism scores (incident rate ratio 1·01, 1·01-1·02), more subjective loneliness (OR 1·09, 1·07-1·11), lower happiness (0·91, 0·90-0·93), lower health satisfaction (0·90, 0·89-0·91), and slower reaction times (linear regression coefficient 1·75, 1·05-2·45). These associations were independent of demographic, lifestyle, education, and overall activity confounders.
INTERPRETATION
Circadian disruption is reliably associated with various adverse mental health and wellbeing outcomes, including major depressive disorder and bipolar disorder. Lower relative amplitude might be linked to increased susceptibility to mood disorders.
FUNDING
Lister Institute of Preventive Medicine.
Topics: Accelerometry; Biological Specimen Banks; Circadian Rhythm; Cognition; Cross-Sectional Studies; Female; Humans; Male; Middle Aged; Mood Disorders; Sleep; United Kingdom
PubMed: 29776774
DOI: 10.1016/S2215-0366(18)30139-1 -
Sensors (Basel, Switzerland) Feb 2021Low-cost sensors, i [...].
Low-cost sensors, i [...].
Topics: Accelerometry; Autism Spectrum Disorder; Calibration; Heart Rate; Humans; Monitoring, Ambulatory; Photoplethysmography; Wearable Electronic Devices
PubMed: 33672660
DOI: 10.3390/s21041482 -
Medicine and Science in Sports and... Oct 2015This study aimed to describe the scope of accelerometry data collected internationally in adults and to obtain a consensus from measurement experts regarding the optimal...
PURPOSE
This study aimed to describe the scope of accelerometry data collected internationally in adults and to obtain a consensus from measurement experts regarding the optimal strategies to harmonize international accelerometry data.
METHODS
In March 2014, a comprehensive review was undertaken to identify studies that collected accelerometry data in adults (sample size, n ≥ 400). In addition, 20 physical activity experts were invited to participate in a two-phase Delphi process to obtain consensus on the following: unique research opportunities available with such data, additional data required to address these opportunities, strategies for enabling comparisons between studies/countries, requirements for implementing/progressing such strategies, and value of a global repository of accelerometry data.
RESULTS
The review identified accelerometry data from more than 275,000 adults from 76 studies across 36 countries. Consensus was achieved after two rounds of the Delphi process; 18 experts participated in one or both rounds. The key opportunities highlighted were the ability for cross-country/cross-population comparisons and the analytic options available with the larger heterogeneity and greater statistical power. Basic sociodemographic and anthropometric data were considered a prerequisite for this. Disclosure of monitor specifications and protocols for data collection and processing were deemed essential to enable comparison and data harmonization. There was strong consensus that standardization of data collection, processing, and analytical procedures was needed. To implement these strategies, communication and consensus among researchers, development of an online infrastructure, and methodological comparison work were required. There was consensus that a global accelerometry data repository would be beneficial and worthwhile.
CONCLUSIONS
This foundational resource can lead to implementation of key priority areas and identification of future directions in physical activity epidemiology, population monitoring, and burden of disease estimates.
Topics: Accelerometry; Adult; Data Collection; Delphi Technique; Humans; Motor Activity; Research; Sedentary Behavior
PubMed: 25785929
DOI: 10.1249/MSS.0000000000000661 -
International Journal of Obesity (2005) Nov 2019Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy...
BACKGROUND
Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.
METHODS
Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.
RESULTS
Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day and 49.8 (16.3) kJ day kg. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day kg from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day kg, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day, r ~ 0.90).
CONCLUSIONS
In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.
Topics: Accelerometry; Adult; Aged; Deuterium Oxide; Energy Metabolism; Exercise; Female; Humans; Male; Middle Aged; Thigh; Wrist
PubMed: 30940917
DOI: 10.1038/s41366-019-0352-x -
Aging Clinical and Experimental Research May 2024Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has...
INTRODUCTION
Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.
METHODS
The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.
RESULTS
The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).
CONCLUSIONS
The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.
Topics: Humans; Hand Strength; Male; Female; Aged; Accelerometry; Middle Aged; Wrist; Wearable Electronic Devices; Aged, 80 and over; Machine Learning
PubMed: 38717552
DOI: 10.1007/s40520-024-02757-z -
BMC Medical Research Methodology Feb 2023Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage...
BACKGROUND
Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living.
METHODS
Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count.
RESULTS
Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits.
CONCLUSION
This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.
Topics: Humans; Accelerometry; Energy Metabolism; Actigraphy; Fitness Trackers; Wrist
PubMed: 36829120
DOI: 10.1186/s12874-023-01868-x -
International Journal of Obesity (2005) Nov 2016Sedentary behaviour (SB) is an important risk factor for a number of chronic diseases. Although gaps remain in our knowledge of the elements of SB most associated with... (Review)
Review
BACKGROUND
Sedentary behaviour (SB) is an important risk factor for a number of chronic diseases. Although gaps remain in our knowledge of the elements of SB most associated with reduced health outcomes, measuring SB is important, especially in less active patient populations where treatment-related changes may be seen first in changes in SB.
METHODS
We review current published work in the measurement of SB to make recommendations for SB measurement in clinical studies.
RESULTS
To help move our understanding of the area forward, we propose a set of derived measures of SB that can be easily understood and interpreted.
CONCLUSION
Although there is more work required to determine and validate the most clinically relevant and sensitive measures of SB, there is enough understanding of how to measure SB to enable its inclusion in study protocols.
Topics: Accelerometry; Exercise; Health Behavior; Humans; Motor Activity; Sedentary Behavior; Social Determinants of Health; United Kingdom
PubMed: 27478922
DOI: 10.1038/ijo.2016.136 -
Frontiers in Public Health 2022According to World Health Organization statistics, falls are the second leading cause of unintentional injury deaths worldwide. With older people being particularly... (Review)
Review
According to World Health Organization statistics, falls are the second leading cause of unintentional injury deaths worldwide. With older people being particularly vulnerable, detecting, and reporting falls have been the focus of numerous health technology studies. We screened 267 studies and selected 15 that detailed pervasive fall detection and alerting apps that used smartphone accelerometers. The fall datasets used for the analyses included between 4 and 38 participants and contained data from young and old subjects, with the recorded falls performed exclusively by young subjects. Threshold-based detection was implemented in six cases, while machine learning approaches were implemented in the other nine, including decision trees, k-nearest neighbors, boosting, and neural networks. All methods could ultimately achieve real-time detection, with reported sensitivities ranging from 60.4 to 99.3% and specificities from 74.6 to 100.0%. However, the studies had limitations in their experimental set-ups or considered a restricted scope of daily activities-not always representative of daily life-with which to define falls during the development of their algorithms. Finally, the studies omitted some aspects of data science methodology, such as proper test sets for results evaluation, putting into question whether reported results would correspond to real-world performance. The two primary outcomes of our review are: a ranking of selected articles based on bias risk and a set of 12 impactful and actionable recommendations for future work in fall detection.
Topics: Humans; Aged; Accidental Falls; Smartphone; Algorithms; Machine Learning; Accelerometry
PubMed: 36324447
DOI: 10.3389/fpubh.2022.996021 -
Journal of Dairy Science May 2020Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes... (Review)
Review
Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows.
Topics: Accelerometry; Animals; Behavior, Animal; Cattle; Cattle Diseases; Dairying; Lameness, Animal
PubMed: 32113761
DOI: 10.3168/jds.2019-17123