Assessing 'readiness' by tracking fluctuations in daily sleep duration and their effects on daily mood, motivation, and sleepiness.

STUDY OBJECTIVES: Consumer sleep trackers issue daily guidance on 'readiness' without clear empirical basis. We investigated how self-rated mood, motivation, and sleepiness (MMS) levels are affected by daily fluctuations in sleep duration, timing, and efficiency and overall sleep regularity. We also determined how temporally specific these associations are. METHODS: 119 healthy university students (64 female, mean age = 22.54 ± 1.74 years) wore a wearable sleep tracker and undertook twice-daily smartphone-delivered ecological momentary assessment of mood, motivation, and sleepiness at post-wake and pre-bedtime timings for 2-6 weeks. Naps and their duration were reported daily. Nocturnal sleep on 2471 nights were examined using multilevel models to uncover within-subject and between-subject associations between sleep duration, timing, efficiency, and nap duration on following day MMS ratings. Time-lagged analyses examined the temporal specificity of these associations. Linear regression models investigated associations between MMS ratings and sleep variability, controlling for sleep duration. RESULTS: Nocturnal sleep durations were short (6.03 ± 0.71 h), and bedtimes were late (1:42AM ± 1:05). Within-subjects, nocturnal sleep longer than a person's average was associated with better mood, higher motivation, and lower sleepiness after waking. Effects of such longer sleep duration lingered for mood and sleepiness till the pre-bedtime window (all Ps < .005) but did not extend to the next day. Between-subjects, higher intraindividual sleep variability, but not sleep duration, was associated with poorer mood and lower motivation after waking. Longer average sleep duration was associated with less sleepiness after waking and lower motivation pre-bedtime (all Ps < .05). Longer naps reduced post-nap sleepiness and improved mood. Controlling for nocturnal sleep duration, longer naps also associated with lower post-waking sleepiness on the following day. CONCLUSIONS: Positive connections between nocturnal sleep and nap duration with MMS are temporally circumscribed, lending credence to the construction of sleep-based, daily 'readiness' scores. Higher sleep duration variability lowers an individual's post waking mood and motivation. CLINICAL TRIAL ID: ClinicalTrials.gov NCT04880629.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study.

Objective: Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021-Jan 2022). Methods: Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22 weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1-2: August 16-29, 2021; Phase 2/week 11-12: October 25-November 7, 2021; Phase 3/week 21-22: January 3-16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing. Results: The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (-2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present. Conclusion: Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

A randomized-controlled trial of a digital, small incentive-based intervention for working adults with short sleep.

STUDY OBJECTIVES: We evaluated the efficacy of a digitally delivered, small and scalable incentive-based intervention program on sleep and wellbeing in short-sleeping, working adults. METHODS: A 22-week, parallel-group, randomized-controlled trial was conducted on 21-40 y participants gifted with FitbitTM devices to measure sleep for ≥2 years, as part of a broader healthy lifestyle study. About 225 short sleepers (141 males; average time-in-bed, TIB < 7h) were randomly assigned in a 2:1 ratio to Goal-Setting or Control groups. The Goal-Setting group received health vouchers (~USD 0.24) for meeting each sleep goal (i.e. increasing weeknight TIB by 30 min/sleeping before midnight).The study spanned three phases: (1) 2-week Baseline, (2) 10-week Intervention, and (3) 10-week Follow-Up. Wellbeing questionnaires were administered on Weeks 1-2, 11-12, and 21-22. RESULTS: Baseline weeknight TIB (mean ±â€…SD) was 387 ±â€…43 min (Goal-Setting) and 399 ±â€…44 min (Control), while bedtime was 00:53 ±â€…01:13 (Goal-Setting), and 00:38 ±â€…00:56 (Control). No difference in sleep outcomes was observed at study endpoints, but exploratory week-by-week analysis showed that on Weeks 3-5, TIB in the Goal-Setting group increased (9-18 min; ps < 0.05) while on Week 5, bedtimes shifted earlier (15 min; p < 0.01) compared to Baseline. Morning sleepiness was reduced in the Goal-Setting group (mean[SEM] = -3.17(1.53); p = 0.04) compared to Baseline, although between-group differences were not significant (p = 0.62). Main barriers to sleeping longer were work hours (35%), followed by leisure activities (23%) and family commitments (22%). CONCLUSION: Our program resulted in encouraging subjective sleep improvements and short-term sleep extension, but sustained transformation of sleep will probably require structural measures to overcome significant obstacles to sleep. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04878380 (hiSG Sleep Health Study (hiSG-SHS); https://clinicaltrials.gov/ct2/show/NCT04878380).

Bedtime procrastination and chronotype differentially predict adolescent sleep on school nights and non-school nights.

OBJECTIVES: Bedtime procrastination (BTP) refers to the tendency to delay sleep beyond an intended bedtime, in favor of continuing evening activities. BTP has been associated with negative sleep outcomes (later timing, shorter duration, poorer quality), and is viewed as a problem of exercising self-control. BTP could be particularly challenging in adolescents, given the combined effects of increasing bedtime autonomy, later chronotype, and a still developing self-control capacity. Thus far, research on BTP has only been based on self-report measures. Here we examined the influence of BTP on adolescent sleep, using objective measures of sleep. METHODS: About 121 adolescents aged 14-19 years completed a survey on BTP, sleep quality, chronotype, and mental health. Subsequently, habitual sleep was actigraphically monitored for up to 2 weeks, and participants completed a temporal discounting task (a proxy for impulsivity). Associations between BTP, chronotype, and actigraphy-measured sleep were examined for school nights and non-school nights separately. RESULTS: Greater BTP was associated with poorer subjective sleep, eveningness chronotype, and higher daytime fatigue, as well as higher anxiety/depression scores. Measured using actigraphy, greater BTP predicted later bedtimes and shorter sleep duration on school nights, even when controlling for chronotype. On non-school nights, eveningness chronotype, but not BTP, predicted later bedtimes and wake-up times. BTP was not correlated with temporal discounting. CONCLUSIONS: Bedtime procrastination exerts significant influence on subjective and objective sleep measures in adolescents. Its effects are most prominent on school nights and can be separated from the effects of chronotype, which has stronger effects on sleep timing on non-school nights.

Limited evidence for enhanced working memory performance and effort allocation in the face of loss versus gain incentives: A preregistered (non) replication.

The motivation to avoid losses is often considered a strong drive of human behavior, affecting decisions in the context of risk, temporal delay, and effort provision. However, studies measuring cognitive performance under loss and gain incentives have yielded mixed findings. In a recent study, we found evidence that losses motivated better working memory performance than gains. Since these results deviated from previous studies, we decided to perform a preregistered, high-powered, replication, and extension study. Eighty-six participants performed a working memory task (N-Back) under Gain and Loss conditions. Pupillometry was recorded during task performance, and subjective effort ratings were taken after each task run. Results showed that contrary to our previous findings, losses did not enhance working memory performance, relative to gains, at any N-Back level. Similarly, while pupil diameter was parametrically modulated by working memory load, no differences between the Gain and Loss conditions were found. Participants did believe that they were exerting more effort during Loss versus Gain conditions, regardless of N-Back level. However, this did not translate to performance differences between Gain and Loss conditions.

Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor.

We conducted a study to understand how dynamic functional brain connectivity contributes to the moderating effect of trait mindfulness on the stress response. 40 male participants provided subjective reports of stress, cortisol assays, and functional MRI before and after undergoing a social stressor. Self-reported trait mindfulness was also collected. Experiencing stress led to significant decreases in the prevalence of a connectivity state previously associated with mindfulness, but no changes in two connectivity states with prior links to arousal. Connectivity did not return to baseline 30 min after stress. Higher trait mindfulness was associated with attenuated affective and neuroendocrine stress response, and smaller decreases in the mindfulness-related connectivity state. In contrast, we found no association between affective response and functional connectivity. Taken together, these data allow us to construct a preliminary brain-behaviour model of how mindfulness dampens stress reactivity and demonstrate the utility of time-varying functional connectivity in understanding psychological state changes.

Minimum number of nights for reliable estimation of habitual sleep using a consumer sleep tracker.

Study Objectives: To determine the minimum number of nights required to reliably estimate weekly and monthly mean sleep duration and sleep variability measures from a consumer sleep technology (CST) device (Fitbit). Methods: Data comprised 107 144 nights from 1041 working adults aged 21-40 years. Intraclass correlation (ICC) analyses were conducted on both weekly and monthly time windows to determine the number of nights required to achieve ICC values of 0.60 and 0.80, corresponding to "good" and "very good" reliability thresholds. These minimum numbers were then validated on data collected 1-month and 1-year later. Results: Minimally, 3 and 5 nights were required to obtain "good" and "very good" mean weekly total sleep time (TST) estimates, while 5 and 10 nights were required for monthly TST estimates. For weekday-only estimates, 2 and 3 nights were sufficient for weekly time windows while 3 and 7 nights sufficed for monthly time windows. Weekend-only estimates of monthly TST required 3 and 5 nights. TST variability required 5 and 6 nights for weekly time windows, and 11 and 18 nights for monthly time windows. Weekday-only weekly variability required 4 nights for both "good" and "very good" estimates while monthly variability required 9 and 14 nights. Weekend-only estimates of monthly variability required 5 and 7 nights. Error estimates made using data collected 1-month and 1-year later with these parameters were comparable to those associated with the original dataset. Conclusions: Studies should consider the metric, measurement window of interest, and desired reliability threshold to decide on the minimum number of nights required to assess habitual sleep using CST devices.

Reopening after lockdown: the influence of working-from-home and digital device use on sleep, physical activity, and wellbeing following COVID-19 lockdown and reopening.

STUDY OBJECTIVES: COVID-19 lockdowns drastically affected sleep, physical activity, and wellbeing. We studied how these behaviors evolved during reopening the possible contributions of continued working from home and smartphone usage. METHODS: Participants (N = 198) were studied through the lockdown and subsequent reopening period, using a wearable sleep/activity tracker, smartphone-delivered ecological momentary assessment (EMA), and passive smartphone usage tracking. Work/study location was obtained through daily EMA ascertainment. RESULTS: Upon reopening, earlier, shorter sleep and increased physical activity were observed, alongside increased self-rated stress and poorer evening mood ratings. These reopening changes were affected by post-lockdown work arrangements and patterns of smartphone usage. Individuals who returned to work or school in-person tended toward larger shifts to earlier sleep and wake timings. Returning to in-person work/school also correlated with more physical activity. Contrary to expectation, there was no decrease in objectively measured smartphone usage after reopening. A cluster analysis showed that persons with relatively heavier smartphone use prior to bedtime had later sleep timings and lower physical activity. CONCLUSIONS: These observations indicate that the reopening after lockdown was accompanied by earlier sleep timing, increased physical activity, and altered mental wellbeing. Moreover, these changes were affected by work/study arrangements and smartphone usage patterns.

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data.

Using polysomnography over multiple weeks to characterize an individual's habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81-0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

COVID-19-related mobility reduction: heterogenous effects on sleep and physical activity rhythms.

STUDY OBJECTIVES: Mobility restrictions imposed to suppress transmission of COVID-19 can alter physical activity (PA) and sleep patterns that are important for health and well-being. Characterization of response heterogeneity and their underlying associations may assist in stratifying the health impact of the pandemic. METHODS: We obtained wearable data covering baseline, incremental mobility restriction, and lockdown periods from 1,824 city-dwelling, working adults aged 21-40 years, incorporating 206,381 nights of sleep and 334,038 days of PA. Distinct rest-activity rhythm (RAR) profiles were identified using k-means clustering, indicating participants' temporal distribution of step counts over the day. Hierarchical clustering of the proportion of days spent in each of these RAR profiles revealed four groups who expressed different mixtures of RAR profiles before and during the lockdown. RESULTS: Time in bed increased by 20 min during the lockdown without loss of sleep efficiency, while social jetlag measures decreased by 15 min. Resting heart rate declined by ~2 bpm. PA dropped an average of 42%. Four groups with different compositions of RAR profiles were found. Three were better able to maintain PA and weekday/weekend differentiation during lockdown. The least active group comprising ~51% of the sample, were younger and predominantly singles. Habitually less active already, this group showed the greatest reduction in PA during lockdown with little weekday/weekend differences. CONCLUSION: In the early aftermath of COVID-19 mobility restriction, PA appears to be more severely affected than sleep. RAR evaluation uncovered heterogeneity of responses to lockdown that could associate with different outcomes should the resolution of COVID-19 be protracted.

Losses Motivate Cognitive Effort More Than Gains in Effort-Based Decision Making and Performance.

Human behavior is more strongly driven by the motivation to avoid losses than to pursue gains (loss aversion). However, there is little research on how losses influence the motivation to exert effort. We compared the effects of loss and gain incentives on cognitive task performance and effort-based decision making. In three experiments, participants performed a cognitively effortful task under gain and loss conditions and made choices about effort expenditure in a decision-making task. Results consistently showed significant loss aversion in effort-based decision making. Participants were willing to invest more effort in the loss compared to the gain condition (i.e., perform a longer duration task: Experiments 1 and 2; or higher task load: Experiment 3). On the other hand, losses did not lead to improved performance (sustained attention), or higher physiological effort (pupil diameter) in Experiments 1 and 2. In Experiment 3, losses did enhance working memory performance, but only at the highest load level. Taken together, these results suggest that loss aversion motivates higher effort investment in effort-based decision-making, while the effect of loss aversion during a performance may depend on the task type or effort level.

Dissociable influences of implicit temporal expectation on attentional performance and mind wandering.

Mind wandering at critical moments during a cognitive task degrades performance. At other moments, mind wandering could serve to conserve task-relevant resources, allowing a brief mental respite. Recent research has shown that, if target timing is predictable, mind wandering episodes coincide with moments of low target likelihood. Conversely, mind wandering can be avoided at moments when targets are expected. In the current study, we tested whether mind wandering can be guided by implicit temporal expectations when target timing is less predictable. In two experiments (Experiment 1: N = 37, Experiment 2: N = 61), participants performed a sustained attention task in which target events were preceded by a variable pre-target interval (foreperiod). As time passes over the foreperiod duration, implicit target expectation increases, given that it has not yet appeared. In Experiment 1, all foreperiod durations were equally probable (uniform distribution: 2-10 s). This resulted in faster responses when targets were preceded by long compared to short foreperiods (foreperiod-effect). In contrast, mind wandering, assessed by thought probes inserted following short or long foreperiods, did not follow this pattern. In Experiment 2, alterations in the foreperiod distribution (left or right-skewed) resulted in changes in the behavioral foreperiod-effect, but mind wandering was unaffected. Our findings indicate that implicit timing strongly affects behavioral response to target events, but has no bearing on the mind wandering. Contrastingly, mind wandering did correlate with performance deterioration due to fatigue (time-on-task), suggesting that the thought probe method was sufficiently sensitive to behaviorally relevant changes in mental state.

Pace yourself: Neural activation and connectivity changes over time vary by task type and pacing.

Performance deterioration over time, or time-on-task (TOT) effects, can be observed across a variety of tasks, but little attention has been paid to how TOT-related brain activity may differ based on task pacing and cognitive demands. Here, we employ a set of three closely related tasks to investigate the effect of these variables on fMRI activation and connectivity. When participants dictated the pace of their own responses, activation and network connectivity within the dorsal attention network (DAN) increased over short time scales (~2-3 min), a phenomenon that was not observed when participants had no control over their pace of work. Reaction time slowing was also the most pronounced in this self-paced task. In contrast, TOT-related changes in default-mode network (DMN) activity and connectivity, DAN-DMN anti-correlations, and pupil diameter did not differ based on pacing or task instructions. Over a longer (~10 min) time scale, task-positive activation and connectivity decreased in all paradigms, in agreement with older findings. These results highlight dynamic patterns of resource allocation that have not previously been observed in fMRI experiments, and speak to the idea that the brain may strategically allocate resources depending on the task at hand and the time scale of work.

Sleep deprivation, effort allocation and performance.

Sleep deprivation causes physiological alterations (e.g., decreased arousal, intrusion of micro-sleeps), that negatively affect performance on a wide range of cognitive domains. These effects indicate that cognitive performance relies on a capacity-limited system that may be more challenged in the absence of sleep. Additionally, sleep loss can result in a lower willingness to exert effort in the pursuit of performance goals. Such deficits in motivation may interact with the effects of capacity limitations to further stifle cognitive performance. When sleep-deprived, cognitive performance is experienced as more effortful, and intrinsic motivation to perform dwindles. On the other hand, increasing motivation extrinsically (e.g., by monetary incentives) can inspire individuals to allocate more task-related effort, and can partially counter performance deficits associated with sleep deprivation. In this chapter, we review current research on the interplay between sleep deprivation, effort and performance. We integrate these findings into an effort-based decision-making framework in which sleep-related performance impairments may result from a voluntary decision to withdraw effort. We conclude with practical implications of this framework for performance in healthy populations (e.g., work productivity) and clinical conditions.

Reward supports flexible orienting of attention to category information and influences subsequent memory.

Preparatory control of attention facilitates the efficient processing and encoding of an expected stimulus. However, this can occur at the expense of increasing the processing cost of unexpected stimuli. Preparatory control can be influenced by motivational factors, such as the expectation of a reward. Interestingly, expectation of a high reward can increase target processing, as well as reduce the cost associated with reorienting. Using a semantic cueing paradigm, we examined the interaction of reward expectation and cue-validity on semantic judgment performance and subsequent memory. Preparatory attention was assessed with pupillometry. Valid category cueing was associated with better semantic judgment performance and better subsequent memory compared to invalidly cued items. Higher reward also resulted in a larger pre-target pupil diameter, which could be indicative of increased preparatory task engagement or arousal. Critically, higher reward also reduced reorienting cost in both semantic judgment and subsequent memory performance. Our findings suggest that reward expectation can facilitate the effective control of preparatory attention for semantic information, and can support optimal goal-directed behavior based on changing task demands.

Sleep deprivation increases the costs of attentional effort: Performance, preference and pupil size.

Sleep deprivation (SD) consistently degrades performance in tasks requiring sustained attention, resulting in slower and more variable response times that worsen with time-on-task. Loss of motivation to exert effort may exacerbate performance degradation during SD. To test this, we evaluated sustained performance on a vigilance task, combining this with an effort-based decision-making task and pupillometry. Vigilance was tested at rest and after sleep deprivation, under different incentive conditions (1, 5 or 15 cents for fast responses). Subsequently, preference measures were collected from an effort-discounting task, in which a commensurate reward was offered for maintaining attentional performance for different durations (1, 5, 10, 20 or 30 min). Vigilance was impaired during SD, in a manner modulated by reward value. Preference metrics showed that the value of available rewards was discounted by task duration, an effect compounded by SD. Pupillometry revealed that arousal was modulated during SD in a value-based manner, and moment-to-moment fluctuations in pupil diameter were directly predictive of performance. Together, these data demonstrate that attentional performance can be interpreted within a value-based effort allocation framework, such that the perceived cost of attentional effort increases after sleep deprivation.

Reward motivation normalises temporal attention after sleep deprivation.

Preparation of attention facilitates speeded responding at time points with a high probability of target occurrence. Conversely, time points with low target probability are disadvantaged due to lower readiness. When targets are uniformly distributed in time, this effect results in higher readiness after longer preparation times (foreperiods). During sleep deprivation, this temporal bias is amplified, resulting in greater performance decrement when stimuli occur at unfavourable times. In this study, we examined whether reward motivation could modulate this increased temporal bias in response speed. Participants (n = 24) performed the psychomotor vigilance task under four reward conditions (0, 1, 5 or 15c per fast response), both after normal sleep (rested wakefulness) and sleep deprivation. To assess temporal preparation (foreperiod-effect), trials were binned based on the lead time prior to target presentation (short foreperiod: 1-6 s; long foreperiod: 6-10 s). As previously observed, the foreperiod-effect (slower reaction time for short foreperiod trials) increased after sleep deprivation. However, this state effect was attenuated with reward, reaching a response speed comparable to that observed in the unrewarded, well-rested condition. The current findings, therefore, suggest that reward improves overall response performance and normalises temporal attention in sleep-deprived individuals.

Motivation alters implicit temporal attention through sustained and transient mechanisms: A behavioral and pupillometric study.

Temporal expectations aid performance by allowing the optimization of attentional readiness at moment of highest target probability. Reward enhances cognitive performance through its action on preparatory and reactive attentional processes. To elucidate how motivation interacts with mechanisms of implicit temporal attention, we studied healthy young adult participants (N = 73) performing a sustained attention task with simultaneous pupillometric recording, under different reward conditions (baseline: 0 c; reward: 10 c/fast response). Target timing was temporally unpredictable (variable foreperiod: 2-10 s, uniformly distributed), in which case implicitly formed timing expectations. Trials were binned according to current foreperiod (FPn ; short: 2-6 s; long: 6-10 s) and preceding foreperiod (FPn-1 ; short: 2-6 s; long: 6-10 s). Overall, performance data showed the expected temporal attention effects, with slower responses after shorter FPn s, particularly when they followed longer FPn-1 s. Moreover, these temporal effects were significantly reduced in the reward condition. While performance improved in all trial types, the largest benefit appeared in trials that were normally most disadvantaged by invalid temporal expectation. Furthermore, reward motivation was accompanied by an increase in sustained (prestimulus) and transient (poststimulus response) pupil diameter. The latter effect was particularly evident following short FPn s. The current findings suggest that reward motivation can improve overall attentional performance and reduce implicit temporal bias, both through preparatory and reactive attentional mechanisms.