Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

Frontiers in Physiology, Год журнала: 2016, Номер 7

Опубликована: Март 9, 2016

Athletes adapt their training daily to optimize performance, as well avoid fatigue, overtraining and other undesirable effects on health. To load, each athlete must take his/her own personal objective subjective characteristics into consideration an increasing number of wearable technologies (wearables) provide convenient monitoring various parameters. Accordingly, it is important help athletes decide which parameters are primary interest wearables can monitor these most effectively. Here, we discuss the available for non-invasive concerning athlete's On basis considerations, suggest directions future development. Furthermore, propose that a combination several effective accessing all relevant parameters, disturbing little possible, optimizing performance promoting

Язык: Английский

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Work–family conflict, family-supportive supervisor behaviors (FSSB), and sleep outcomes.

Journal of Occupational Health Psychology, Год журнала: 2014, Номер 19(2), С. 155 - 167

Опубликована: Апрель 1, 2014

Although critical to health and well-being, relatively little research has been conducted in the organizational literature on linkages between work-family interface sleep. Drawing conservation of resources theory, we use a sample 623 information technology workers examine relationships conflict, family-supportive supervisor behaviors (FSSB), sleep quality quantity. Validated wrist actigraphy methods were used collect objective quantity data over 1 week period time, survey self-reported FSSB, Results demonstrated that combination predictors (i.e., work-to-family family-to-work FSSB) was significantly related both self-report measures quality. Future should further link make interventions targeting as means for improving health.

Язык: Английский

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Assessing sleep using hip and wrist actigraphy

Sleep and Biological Rhythms, Год журнала: 2015, Номер 13(2), С. 172 - 180

Опубликована: Янв. 19, 2015

Язык: Английский

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Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography

SLEEP, Год журнала: 2020, Номер 43(7)

Опубликована: Март 26, 2020

Abstract Study Objectives Multisensor wearable consumer devices allowing the collection of multiple data sources, such as heart rate and motion, for evaluation sleep in home environment, are increasingly ubiquitous. However, validity assessment has not been directly compared to alternatives wrist actigraphy or polysomnography (PSG). Methods Eight participants each completed four nights a laboratory, equipped with PSG several devices. Registered polysomnographic technologist-scored served ground truth sleep–wake state. Wearable providing classification were at both an epoch-by-epoch night level. Data from multisensor wearables (Apple Watch Oura Ring) available electrocardiography triaxial actigraph evaluate quality utility motion data. Machine learning methods used train test classifiers, using wearables. The classifications derived was compared. Results For performance, research ranged d′ between 1.771 1.874, sensitivity 0.912 0.982, specificity 0.366 0.647. strongly correlated level reference sources. Classifiers developed 1.827 2.347, 0.883 0.977, 0.407 0.821. Conclusions epoch can be develop models rivaling existing

Язык: Английский

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Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults

SLEEP, Год журнала: 2017, Номер 40(7)

Опубликована: Июнь 16, 2017

To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements an accelerometer, polysomnography (PSG) and actigraphy. Using wrist-worn PPG to analyze accelerometer measure movements, stages statistics were automatically computed overnight recordings. Sleep–wake, 4-class (wake/N1 + N2/N3/REM) 3-class (wake/NREM/REM) classifiers trained 135 simultaneously recorded PSG recordings 101 healthy participants validated 80 51 middle-aged adults. Epoch-by-epoch agreement compared actigraphy for a subset validation set. The sleep–wake classifier obtained epoch-by-epoch Cohen’s κ between 0.55 ± 0.14, sensitivity wake 58.2 17.3%, 91.5 5.1%. significantly higher than (0.40 0.15 45.5 19.3%, respectively). achieved 0.46 72.9 8.3%, classifier, 0.42 0.12 59.3 8.5%. moderate and, in particular, good terms suggest that this technique is promising long-term monitoring, although more evidence needed understand whether it can complement clinical practice. It also offers improvement sleep/wake detection over individuals, must be confirmed larger, population.

Язык: Английский

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