Stability of the timing of food intake at daily and monthly timescales in young adults

Cross-sectional observations have shown that the timing of eating may be important for health-related outcomes. Here we examined the stability of eating timing, using both clock hour and relative circadian time, across one semester (n = 14) at daily and monthly time-scales. At three time points ~ 1 month apart, circadian phase was determined during an overnight in-laboratory visit and eating was photographically recorded for one week to assess timing and composition. Day-to-day stability was measured using the Composite Phase Deviation (deviation from a perfectly regular pattern) and intraclass correlation coefficients (ICC) were used to determine individual stability across months (weekly average compared across months). Day-to-day clock timing of caloric events had poor stability within individuals (~ 3-h variation; ICC = 0.12–0.34). The timing of eating was stable across months (~ 1-h variation, ICCs ranging from 0.54–0.63), but less stable across months when measured relative to circadian timing (ICC = 0.33–0.41). Our findings suggest that though day-to-day variability in the timing of eating has poor stability, the timing of eating measured for a week is stable across months within individuals. This indicates two relevant timescales: a monthly timescale with more stability in eating timing than a daily timescale. Thus, a single day’s food documentation may not represent habitual (longer timescale) patterns

Assessing day-to-day regularity of sleep: theoretical and practical implications of available metrics

Objectives Sleep regularity has emerged as an important factor for health in recent years. Metrics differ in their approach to quantifying sleep regularity: Interdaily Stability (IS), Social Jet Lag (SJL), and Standard Deviation (SD) assess variability relative to the mean, whereas the more recent metrics Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI) capture variability on a circadian timescale (i.e., between consecutive days). We systematically assessed and compared these metrics using a range of simulations. Methods Daily sleep patterns were generated for 8 weeks, with later and longer sleep on weekends (0:00-9:00) than weekdays (23:00-6:00). Random variation in sleep timing was systematically increased from 0 min to 360 min in 30-min steps. Mean values and 95% confidence intervals (CIs) were calculated across 10,000 iterations for IS, SJL, SD, CPD, and SRI. Missing data were generated by removing 24h entries, either randomly or non-randomly (e.g., 50% earliest/latest sleep onsets). Results With increasing variation in sleep onset time, all metrics reflected higher irregularity, except SJL, which is sensitive to weekly but not daily changes in sleep timing. As expected, 95% CIs were generally wider for consecutive metrics CPD and SRI than for overall metrics IS and SD. Over the first 14 days, average estimates of IS changed as much as 50% while CPD and SRI remained stable, indicating that IS tends to overestimate how regular sleep patterns are when based on relatively few days. For missing data, 95% CIs were generally wider for consecutive than overall metrics, while their average estimates were more stable, especially for 50% of missing data. The amount of tolerable missing data (e.g., not affecting mean estimates) decreased substantially with increasing non-randomly missing data or variation in sleep timing. Conclusions Overall metrics require relatively many days for an accurate estimate, whereas consecutive metrics such as CPD and SRI are sensitive to daily changes and can better reflect the regularity of patterns that are based on only a few sleep episodes. The right choice of metric may depend on study length, anticipated regularity of the study population, likelihood and distribution of missing data as well as whether the outcome of interest is local vs. global (i.e., accident vs. chronic illness). Future work will examine the metrics’ sensitivity to shift work, nights with no sleep, naps, and fragmented sleep patterns

Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

Study Objectives: Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods: Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results: SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions: Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question

Distinct Non-Additive Effects of Acute and Chronic Sleep Loss and Circadian Phase on Inadvertent Attentional Failures

Introduction : In modern societies, many humans are exposed to a combination of acute and chronic sleep loss and circadian misalignment. We previously reported non-additive effects of these three factors on performance measures derived from the psychomotor vigilance task [Cohen et al., Sci Transl Med, 2010]. Here we tested whether this result were specific to this task or whether it reflects a more fundamental principle of sleep-wake regulation by quantifying the impact of these three factors on the occurrence of spontaneous attentional failures (AF) derived from continuous electro-oculographic (EOG) recordings in that same study. Methods : Nine volunteers (age 21-34 years, 4F) completed a three-week forced desynchrony protocol consisting of 12 consecutive 42.85-h sleep-wake cycles with 32.85 h of scheduled wakefulness and 10 h of scheduled sleep (sleep:wake ratio of 1:3.3). Over three weeks, the protocol therefore induced an increasing chronic sleep loss while allowing extended wake and sleep episodes to occur at all circadian phases. Continuous EOG recordings were obtained. AF were quantified as number of 30-s epochs with visually scored slow eye movements per hour of wakefulness. Data were analyzed by time since scheduled wake onset in 4.1-h bins (acute sleep loss), study week (chronic sleep loss), and circadian phase derived from plasma melatonin. Results : During the first 8.2 h of a wake episode, the number of AF (nAF) was low (< 1/h), irrespective of study week. Across the subsequent wake bins, nAF increased steadily, with the rate of increase being higher in weeks 2 and 3 compared to week 1 (mixed ANOVA, Week x Wake bin, p=0.001). At the end of the wake episode, nAF was 2.0±0.4/h (±SEM), 3.8±0.5/h, and 3.0±0.8/h in weeks 1, 2, and 3, respectively (effect of Week in final wake bin, p<0.0001). Moreover, the effects of acute and chronic sleep loss on nAF were magnified during the biological night (Phase x Wake bin, Phase x Week, both p<0.0002). Conclusion: A single episode of extended recovery sleep can temporarily–i.e., for the first few hours of subsequent wakefulness–restore to baseline levels attentional impairment associated with chronic sleep loss. Chronic sleep loss speeds up the accumulation of inadvertent AF during a wake episode. Thus, acute and chronic sleep loss in combination with circadian phase should be considered as distinct factors that interact in a non-additive way to affect waking function

Light sensitivity as a physiological factor that promotes irregular sleep/wake patterns: a model-based investigation

Introduction: Irregular sleep is a health risk factor. However, we currently have a poor understanding of physiological factors that contribute to individuals having irregular sleep/wake patterns. We used a validated mathematical model of sleep-wake and circadian physiology to systematically examine the influence of circadian, sleep homeostatic, and light sensitivity parameters on sleep regularity. Materials and Methods: Sleep-wake patterns were generated by a computational model, assuming a 5-day work schedule with enforced wakefulness from 7:00 to 19:00. We introduced daily random variation σ in the model’s sleep-onset threshold to mimic observed intra-individual variability in sleep/wake patterns. The Sleep Regularity Index (SRI) was calculated, ranging from 0 (random pattern) to 100 (perfectly regular pattern). Eight model parameters were varied to determine their effects on SRI: circadian period (τ); circadian amplitude (νvc); sleep homeostatic time constant (χ); and five light sensitivity parameters: delay bias of the phase response curve (b), sensitivity of the dose response curve (p), retinal output strength (G), photoreceptor recovery rate (β), and photoreceptor activation rate (α0). Results: Sleep regularity was meaningfully affected by six of the eight parameters. Responses occurred in three clusters: (1) light sensitivity parameters G and β had no effect on SRI; (2) circadian amplitude νvc modulated the effect of σ, such that weaker amplitude resulted in lower SRI (less regular patterns) for the same σ; and (3) the remaining five parameters τ, χ, b, p, and α0 all generated maximal SRI scores (most regular patterns) for default parameter values, with lower SRI scores when parameters deviated from default values. Conclusions: This is the first study to systematically investigate potential mechanisms of irregular sleep using mathematical modeling. Our findings suggest that irregular sleep can result from individual differences in the sensitivity to the timing and intensity of light exposure, as well as differences in circadian and sleep homeostatic parameters

Performance and Alertness after combined exposure to chronic and acute sleep loss and circadian misalignment

Objectives Many individuals are exposed to combinations of acute and chronic sleep loss as well as repeated circadian misalignment in real life. A key question is whether the effects of chronic sleep loss accumulated during the work week can be completely eliminated by long sleep bouts during the weekend. Insight in the recovery process of performance and mood from sleep loss is needed to increase safety in shiftwork and other work environments. Methods and materials Ten healthy volunteers (3 females, mean (SD) age of 28.3 (4.2) years) were studied during a 65- day inpatient stay that included (i) three baseline 24.0-hr days (16-hr wake), (ii) a constant routine protocol (CR1, 41.33-hr wake), (iii) a forced desynchrony (FD) protocol consisting of 12 consecutive 28-hr sleep-wake cycles (18.67-hr wake), (iv) a CR protocol (CR2, 33-52-hr wake) that ended such that the individual´s circadian phase of awakening of the next segment would be the same as during baseline, (v) a 5-day recovery segment with 24-hr days (16-hr wake), and (vi) a CR protocol (CR3; 40.1-40.5-hr wake). Performance was tested every two hours whenever the individual was awake with a 10-min Psychomotor Vigilance Task (PVT), a 2-min Addition test (ADD, number correct) and Visual Analog Scales (VAS) that included Alert-Sleepy scale. The ADD test results were expected to increase across the protocol since there is a learning component. Linear or Generalized linear mixed models were used to compare: (i) Baseline Wake Periods (WP) 2 and 3 vs. last 2 Recovery WP; (ii) CR1 vs CR3; (iii) 1st 6 WP vs 2nd 6 WP of FD; and (iv) 1st 2 vs. last 2 Recovery WP. Additional details of the protocol and original study results are in Gronfier et al 2007 (PNAS). Results PVT median RT and lapses worsened from BL to the end of Recovery, from CR1 to CR3, from 1st to 2nd 6 WP of FD. ADD correct results increased from BL to the end of Recovery, from CR1 to CR3, from 1st to 2nd 6 WP of FD, and from the 1st to last 2nd WP of Recovery. VAS alertness improved from CR1 to CR3. Conclusions The worsening of PVT median and lapses suggests an effect of combined exposure to acute sleep deprivation and circadian misalignment. To what extent this is due to incomplete recovery and/or other elements of the protocol requires further investigation. The stable or improved subjective alertness during these same times is consistent with the known discrepancy between subjective and objective metrics under these condition

Spontaneous attentional failures reflect multiplicative interactions of chronic sleep loss with acute sleep loss and circadian misalignment

Objectives: Acute and chronic sleep loss and circadian timing interact such that, depending on their combination, small or very large performance decrements are observed in tasks of attention. Here, we tested whether such nonlinear interactions extend to a physiological measure of spontaneous visual attentional failures, indicating a fundamental principle of sleep-wake regulation. Methods: Nine healthy volunteers completed an in-laboratory 3-week forced desynchrony protocol consisting of 12 consecutive 42.85-hour cycles with a sleep-wake ratio of 1:3.3. The protocol induced increasing chronic sleep loss, while extended wake (32.85 hours) and sleep episodes (10 hours) occurred at multiple circadian phases. Attentional failure rate was quantified from continuous electrooculograms (number of 30-second epochs with slow eye movements/h of wakefulness) as a function of time since scheduled wake (acute sleep loss), week of study (chronic sleep loss), and circadian (melatonin) phase. Results: During the first ∼8 hours awake, attentional failure rate was low, irrespective of the week. During the following wake hours, attentional failure rate increased steadily but at a faster rate in weeks 2 and 3 compared to week 1. The effects of acute and chronic sleep loss on attentional failure rate were magnified during the biological night compared to the biological day. Conclusions: A single extended sleep episode can only temporarily reverse attentional impairment associated with chronic sleep loss. Multiplicative effects of acute and chronic sleep loss-further amplified during the biological night-substantiate the interaction of 2 homeostatic response mechanisms and caution against underestimating their disproportionate combined impact on performance, health, and safety

Effects of Prior Time Awake and Time Asleep on Sleep Structure: Analyses across Forced Desynchrony Protocols with Different Sleep/Wake Cycle Durations

Introduction : Sleep timing and structure are affected by prior wake duration, length of time in current sleep episode and circadian phase. To investigate the first two effects independent of circadian phase, we conducted a meta-analysis of sleep recorded during forced desynchrony (FD) protocols with different prior wake and sleep episode durations. Methods : Fifty healthy young (19-34 years; 16F) participants with no medical, psychological or sleep disorders and free of medications and caffeine were studied in inpatient protocols with sleep/wake cycle (T) durations and prior wake durations: T=20 hr (Wyatt 1999), T=28 hr (Gronfier 2007), T=42.85 hr (Wyatt 2004, Grady 2010) each with a 2:1 wake: sleep ratio, and T=42.85hr (Cohen 2010) with a 3.3:1 wake:sleep ratio. Sleep was scored using Rechtschaffen and Kales criteria. During FD, sleep episodes are distributed evenly across the full circadian cycle. The amount of each stage of sleep (~600 sleep episodes) was averaged within subject by length of time since scheduled sleep onset in 1-hr bins; this averaging minimized circadian effect. Mixed models were performed for the effects of prior wake and length of time since scheduled sleep onset for different metrics of Slow Wave Sleep (SWS; NREM stages 3+4), REM sleep, and Wake. Results : SWS decayed approximately exponentially across sleep episodes with the exception of a sharp decrease in the 2nd hr and a sharp rise in the 3rd hr corresponding to the occurrence of the first REM sleep episode. SWS in the 1st hour had a non-linear dose-response relationship to prior wake duration. The time constant of a saturating exponential fit to SWS was different for T=20 hr than other T-cycle lengths. REM sleep values were lowest in the 1st hour, highest in the 2nd hour and then stable at an intermediate level for remaining hours; there were no differences in the 1st hour by prior wake duration. These results potentially demonstrate SWS-REM competitive homeostatic interactions. Wake exhibited the highest values in the first hour, low values for the 2nd and 3rd hour, and monotonically rising values for the remainder of the sleep episode. The initial value, minimum and slope for Wake all had non-linear inverse dose response relationships with prior wake duration. Conclusion: There are non-linear dose-response effects of prior time awake and prior time asleep on sleep stages

Pouvons-nous objectiver la raideur musculaire du patient fibromyalgique ?

Viscoelastic stiffness in the ankles of nine female subjects with fibromyalgia and nine control subjects was quantified by assessing passive sinusoidal movement. Increased elastic muscle stiffness in the ankles as well as different viscous muscle stiffness in the two ankles was observed in the subjects with fibromyalgia. In conclusion, measuring the resistance to the passive sinusoidal movement of the ankles could offer a valuable method of quantifying the subjective feeling of stiffness described by people with fibromyalgia