Three decades of continuous wrist-activity recording: analysis of sleep duration

Concordance between subjective and objective measures of infant sleep varies by age and maternal mood: Implications for studies of sleep and cognitive development

Infant habitual sleep has been proposed as an important moderator of development in domains such as attention, memory or temperament. To test such hypotheses, we need to know how to accurately and consistently assess habitual sleep in infancy. Common assessment methods include easy to deploy but subjective parent-report measures (diary/sleep questionnaire); or more labour-intensive but objective motor movement measures (actigraphy). Understanding the degree to which these methods provide converging insights is important, but cross-method agreement has yet to be investigated longitudinally. Moreover, it is unclear whether concordance systematically varies with infant or maternal characteristics that could represent confounders in observational studies. This longitudinal study (up to 4 study visits/participant) investigated cross-method concordance on one objective (7-day actigraphy) and three commonly used subjective (7-day sleep diary, Brief Infant Sleep Questionnaire, Sleep & Settle Questionnaire) sleep measures in 76 typically developing infants (age: 4–14 months) and assessed the impact of maternal characteristics (stress, age, education) and infant characteristics (age) on cross-method concordance. In addition, associations between objective and subjective sleep measures and a measure of general developmental status (Ages & Stages Questionnaire) were investigated. A range of equivalence analyses (tests of equivalence, correlational analyses, Bland-Altman plots) showed mixed agreement between sleep measures. Most importantly, cross-method agreement was associated with maternal stress levels and infant age. Specifically, agreement between different measures of night waking was better for mothers experiencing higher stress levels and was higher for younger than older infants; the reverse pattern was true for day sleep duration. Interestingly, objective and subjective measures did not yield the same patterns of association with developmental domains, indicating that sleep method choice can influence which associations are found between sleep and cognitive development. However, results converged across day sleep and problem-solving skills, highlighting the importance of studying day sleep in future studies. We discuss implications of sleep method choice for investigating sleep in the context of studying infant development and behaviour.

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A range of equivalence analyses showed mixed agreement between subjective and objective sleep measures.

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Cross-method agreement was associated with maternal stress levels and infant age.

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Objective and subjective measures did not yield the same patterns of association with developmental domains except for day sleep duration.

Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study

BACKGROUND

Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation.

OBJECTIVE

This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work.

METHODS

A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement.

RESULTS

Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm.

CONCLUSIONS

We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

Abstract

Sleep quality and duration as well as activity-rest-cycles at individual level are crucial for maintaining physical and mental health. Although several methods do exist to monitor these parameters, optimal approaches are still under consideration and technological development. Wrist actigraphy is a non-invasive electro-physical method validated in the field of chronobiology to record movements and to allow for monitoring human activity-rest-cycles. Based on the continuous recording of motor activity and light exposure, actigraphy provides valuable information about the quality and quantity of the sleep–wake rhythm and about the amount of motor activity at day and night that is highly relevant for predicting a potential disease and its targeted prevention as well as personalisation of medical services provided to individuals in suboptimal health conditions and patients. Being generally used in the field of sleep medicine, actigraphy demonstrates a great potential to be successfully implemented in primary, secondary and tertiary care, psychiatry, oncology, and intensive care, military and sports medicines as well as epidemiological monitoring of behavioural habits as well as well-being medical support, amongst others.

Prediction of disease development and individual outcomes

Activity-rest-cycles have been demonstrated to be an important predictor for many diseases including but not restricted to the development of metabolic, psychiatric and malignant pathologies. Moreover, activity-rest-cycles directly impact individual outcomes in corresponding patient cohorts.

Targeted prevention

Data acquired by actigraphy are instrumental for the evidence-based targeted prevention by analysing individualised patient profiles including light exposure, sleep duration and quality, activity-rest-cycles, intensity and structure of motion pattern.

Personalised therapy

Wrist actigraphic approach is increasingly used in clinical care. Personalised measurements of sedation/agitation rhythms are useful for ICU patients, for evaluation of motor fatigue in oncologic patients, for an individual enhancement of performance in military and sport medicine. In the framework of personalised therapy intervention, patients can be encouraged to optimise their behavioural habits improving recovery and activity patterns. This opens excellent perspectives for the sleep-inducing medication and stimulants replacement as well as for increasing the role of participatory medicine by visualising and encouraging optimal behavioural patterns of the individual.

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r = .86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

Changes in accelerometer-measured sleep during the transition to retirement: the Finnish Retirement and Aging (FIREA) study

STUDY OBJECTIVES

Retirement is associated with increases in self-reported sleep duration and reductions in sleep difficulties, but these findings need to be confirmed by using more objective measurement tools. This study aimed at examining accelerometer-based sleep before and after retirement and at identifying trajectories of sleep duration around retirement.

METHODS

The study population consisted of 420 participants of the Finnish Retirement and Aging study. Participants' sleep timing, sleep duration, time in bed, and sleep efficiency were measured annually using a wrist-worn triaxial ActiGraph accelerometer on average 3.4 times around retirement. In the analyses, sleep on nights before working days and on nights before days off prior to retirement were separately examined in relation to nights after retirement.

RESULTS

Both in bed and out bed times were delayed after retirement compared with nights before working days. Sleep duration increased on average by 41 min (95% confidence interval [CI] = 35 to 46 min) from nights before working days and decreased by 13 min (95% CI = -20 to -6 min) from nights before days off compared with nights after retirement. By using latent trajectory analysis, three trajectories of sleep duration around retirement were identified: (1) shorter mid-range sleep duration with increase at retirement, (2) longer mid-range sleep duration with increase at retirement, and (3) constantly short sleep duration.

CONCLUSIONS

Accelerometer measurements support previous findings of increased sleep duration after retirement. After retirement, especially out bed times are delayed, thus, closely resembling sleep on pre-retirement nights before non-working days.

Daytime Sleep as Compensation for the Effects of Reduced Nocturnal Sleep on the Incidence of Hypertension: A Cohort Study

PURPOSE

The role of sleep duration in the development of hypertension remains controversial. Little is known about the combined effects of nocturnal and daytime sleep. We assessed the association between total sleep duration and the development of hypertension in middle-aged and elderly Chinese adults.

PATIENTS AND METHODS

Data were obtained from 3 waves of China Health and Retirement Longitudinal Study surveys. Middle-aged and elderly Chinese initially without hypertension were followed biennially from 2011 to 2015. Sleep duration was self-reported. Hypertension was defined as a systolic and/or diastolic blood pressure ≥ 140/90 (mmHg), the current use of anti-hypertensive medications or self-reported hypertension.

RESULTS

Over 31,392 person-years of follow-up, 2682 of 10,176 participants developed hypertension. The multivariable adjusted hazard ratios (HR) (95% confidence intervals, 95% CI) for the development of hypertension between those with 0-30 minutes and ≥30 minutes daytime sleep vs those without daytime sleep were 0.67 (0.58, 0.77), and 0.73 (0.59, 0.92), respectively. The protective role of longer periods of daytime sleep (>30 minutes) varied between different subgroups. Compared with moderate nocturnal sleepers, long nocturnal sleepers (HR: 1.66, 95% CI=1.25-2.21) had an increased risk of hypertension. Compared with moderate nocturnal sleepers without daytime sleep, HRs (95% CI) for hypertension were 0.52 (0.45, 0.59) for short nocturnal sleep plus short daytime sleep and 0.55 (0.49, 0.62) for short nocturnal sleep plus long daytime sleep. People with extremely short (HR: 1.34, 95% CI=1.22-1.48) and long (HR: 1.28, 95% CI=1.13-1.44) combined sleep periods had an increased risk of hypertension. Consistent results were also found in subgroups stratified by age and gender.

CONCLUSION

Both extremely long and short total sleep periods were associated with an increased risk of hypertension. People with short or moderate nocturnal sleep durations, especially short nocturnal sleep duration, can benefit from habitual daytime sleep to prevent hypertension.

How does cessation of work affect sleep? Prospective analyses of sleep duration, timing and efficiency from the Swedish Retirement Study

Several strands of research indicate that work competes for time with sleep, but to what extent the timing and duration of sleep is affected by work is not known. Retirement offers a quasi-experimental life transition to study this in a within-individual study design. The few existing studies report that people sleep longer and later after retirement but mainly rely on self-reported data or between-individual analyses. We recruited 100 participants aged 61-72 years who were in paid work but would soon retire and measured them in a baseline week with accelerometers, diaries and questionnaires. After 1 and 2 years, the measurements were repeated for the now retired participants. Changes in sleep duration, timing, efficiency, chronotype and social jetlag were analysed using multilevel modelling. Gender, chronotype at baseline and partner's working status were analysed as potential effect modifiers. Sleep duration increased by 21 min, whereas sleep efficiency remained similar. Time of sleep onset and final awakening were postponed by 26 and 52 min, respectively, pushing midsleep forward from 03:17 to 03:37 hours. Changes in duration and timing of sleep were driven by weekday sleep, whereas weekend sleep stayed about the same. Social jetlag decreased but still occurred after retirement. Changes at retirement in sleep duration and timing were smaller for participants with a later chronotype and who had full-time working partners. These findings indicate that paid work generates sleep loss and hinders people from sleeping in line with their biological time.

Sleep Before and After Retirement

Purpose of Review

In this review, we focus on the association of sleep and retirement from two perspectives. Firstly, we examine the role of sleep during the working years on retirement timing. Secondly, we examine how sleep changes during the transition to retirement.

Recent Findings

Persons with sleep difficulties are more likely to retire due to health problems, such as depression and musculoskeletal disorders. Retirement, on the other hand, is associated with both increased sleep duration and decreased sleep difficulties, mainly premature awakenings and nonrestorative sleep.

Summary

Promotion of sleep quantity and quality could be a potential way to support employees’ work ability and possibly even to postpone retirement, at least in relation to early retirement. Possible proposed mechanisms for the improved sleep after retirement include removal of work stress and increased flexibility in time use, which could be targeted in attempt to promote adequate and good quality of sleep already during the working years.