An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data

Baek, Jonggyu; Banker, Magaret; Jansen, Erica C.; She, Xichen; Peterson, Karen E.; Pitchford, E. Andrew; Song, Peter

doi:10.1007/s12561-021-09309-3

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Nightly sleep measures were estimated from the actigraphy data with 60‐second epoch lengths using a pruned dynamic programming (PDP) algorithm developed in R (R Foundation for Statistical Computing) [25]. This method has been evaluated against polysomnography, the gold standard for sleep assessment, and compared with manual detection of sleep/wake times in the present cohort with > 95% correlation [25]. The algorithm incorporated the self‐reported bedtimes and wake times to improve accuracy and it was used to calculate weekday (Sunday through Thursday night) and weekend (Friday and Saturday) sleep duration (minutes) and weekday and weekend sleep midpoint (the midpoint of sleep onset and wake time; reported in decimal hours).…”

Section: Methodsmentioning

confidence: 99%

“…Nightly sleep measures were estimated from the actigraphy data with 60-second epoch lengths using a pruned dynamic programming (PDP) algorithm developed in R (R Foundation for Statistical Computing) [25]. This method has been evaluated against polysomnography, the gold standard for sleep assessment, and compared with manual detection of sleep/wake times in the present cohort with > 95% correlation [25]. The algorithm incorporated the self-reported bedtimes and wake times to improve…”

Section: Sleep Measuresmentioning

confidence: 99%

“…For this analysis, we used sleep data from T1 participants who provided ≥ 4 days of valid wear days (a valid wear day was defined as < 4 hours of non-wear time); of those who agreed to wear the actigraph at this time, 96% wore it for 7 days. Nightly sleep measures were estimated from the actigraphy data with 60-second epoch lengths using a pruned dynamic programming (PDP) algorithm developed in R (R Foundation for Statistical Computing) [25]. This method has been evaluated against polysomnography, the gold standard for sleep assessment, and compared with manual detection of sleep/wake times in the present cohort with > 95% correlation [25].…”

Section: Sleep Measuresmentioning

confidence: 99%

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Sleep duration and timing are prospectively linked with insulin resistance during late adolescence

Jansen

Burgess

Chervin

et al. 2023

Obesity

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Objective: The aim of this study was to evaluate whether short sleep duration or later sleep timing is a risk factor for insulin resistance (IR) in late adolescence.Methods: Mexico City adolescents enrolled in a longitudinal birth cohort (ELEMENT) took part in two study visits during peri-puberty that occurred approximately 2 years apart. IR was assessed with serum glucose and insulin. Four groups were defined using puberty-specific cut points: no IR over the follow-up period, transition from normal to IR, transition from IR to normal, and IR at both time points. Baseline sleep assessments were measured with 7-day wrist actigraphy. Multinomial logistic regression models were used to evaluate associations between sleep duration and timing with homeostatic model assessment of insulin resistance categories, adjusting for age, sex, and baseline pubertal status.Results: Adolescents who were ≥ 1 hour below the sleep duration recommendations-for-age were 2.74 times more likely to develop IR (95% CI: 1.0-7.4). Similarly, adolescents who were in the latest category of sleep midpoint (>4:33 a.m.) were more likely than those with earliest midpoints (1 a.m.-3 a.m.) to develop IR (odds ratio = 2.63, 95% CI: 1.0-6.7). Changes in adiposity over follow-up did not mediate sleep and IR.Conclusions: Insufficient sleep duration and late sleep timing were associated with development of IR over a 2-year period in late adolescence.

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Section: Methodsmentioning

confidence: 99%

Section: Sleep Measuresmentioning

confidence: 99%

Section: Sleep Measuresmentioning

confidence: 99%

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Sleep duration and timing are prospectively linked with insulin resistance during late adolescence

Jansen

Burgess

Chervin

et al. 2023

Obesity

Self Cite

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“…Algorithms are already pervasive in assessing lifestyle behaviors and risks. 15–18 They now segment and estimate sleep durations using clinical-grade sensors, use heart rate variability signals (derived from electrocardiograms) for the non-invasive detection of diabetes, etc. 15,16 We are beginning an era of care delivery where the progression of these algorithms is towards the consumption of signals that go from reference diagnostics and known chemical markers to social phenotypes that health conditions seem to shape.…”

Section: Ensuring Equitable Access To Care—a Unified Health Algorithm...mentioning

confidence: 99%

“…15–18 They now segment and estimate sleep durations using clinical-grade sensors, use heart rate variability signals (derived from electrocardiograms) for the non-invasive detection of diabetes, etc. 15,16 We are beginning an era of care delivery where the progression of these algorithms is towards the consumption of signals that go from reference diagnostics and known chemical markers to social phenotypes that health conditions seem to shape. 19,20 If the ownership and custody of such a unified health algorithm is with an individual and not any one stakeholder, this could be dropped into any open-source network promoting collaboration amongst stakeholders, reducing wastage from overlapping tasks, and opening the gateway for collaboration amongst all players within the arena.…”

Section: Ensuring Equitable Access To Care—a Unified Health Algorithm...mentioning

confidence: 99%

A unified health algorithm that teaches itself to improve health outcomes for every individual: How far into the future is it?

et al. 2022

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The single biggest factor driving health outcomes is patient behavior. The CHR Model (County Health Rankings Model) weights socioeconomic factors, lifestyle behaviors, and physical environment factors collectively at 80% in driving impact on health outcomes, to the 20% weight for access to and quality of clinical care. Commercial determinants of health affect everyone today and unhealthy choices worsen pre-existing economic, social, and racial inequities. Yet there is a disproportionate focus on therapeutic intervention to the exclusion of shaping patient behaviors to improve healthcare. If the recent pandemic taught us a critically important lesson, it is the imperative to look beyond clinical care. According to the Centers for Disease Control and Prevention (CDC), long-standing systemic health and social inequities put various groups of people at higher risk of getting sick and dying from COVID-19, including many racial and ethnic minority groups. The virus was simply more efficient in detecting such vulnerabilities than the guardians of these physiologies. These insights from the pandemic come at the heel of a confluence of three major accelerants that may radically reshape our approaches to hot-spotting vulnerabilities and managing them before they manifest in a derangement or disease. They are the recent strides in behavioral economics and behavior science; advances in remote monitoring and personal health technologies; and developments in artificial intelligence and data sciences. These accelerants allow us to imagine a previously impossible vision—we can now build and maintain a unified health algorithm for every individual that can dynamically track the two interdependent streams of risk, clinical and behavioral.

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Associations between exposure to phthalates, phenols, and parabens with objective and subjective measures of sleep health among Mexican women in midlife: a cross-sectional and retrospective analysis

Zamora

Peterson

Goodrich

et al. 2023

Environ Sci Pollut Res

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An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data

Cited by 10 publications

References 26 publications

Sleep duration and timing are prospectively linked with insulin resistance during late adolescence

Sleep duration and timing are prospectively linked with insulin resistance during late adolescence

A unified health algorithm that teaches itself to improve health outcomes for every individual: How far into the future is it?

Associations between exposure to phthalates, phenols, and parabens with objective and subjective measures of sleep health among Mexican women in midlife: a cross-sectional and retrospective analysis

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