Design and methods of the mobile assessment of cognition, environment, and sleep (MACES) feasibility study in newly diagnosed breast cancer patients.

Endocrine therapy (ET) for breast cancer treatment is associated with cognitive complaints, but their etiology is poorly understood. To address this, we developed and implemented an ambulatory assessment protocol consisting of wearable activity monitors, brief surveys of affect, context, and perceived impairments, and ultra-brief performance-based measures of cognition. Newly diagnosed, ER/PR+, stage 0-III, female breast cancer patients, were recruited. Ambulatory assessments were conducted on smart phones and wearable activity monitors were used to monitor sleep and physical activity. Participants were asked to complete five 7-day measurement bursts (one before starting ET and one each month for 4 consecutive months while on ET). We observed a consent rate of 36%, 27 women completed the study. Of the women that withdrew, 91% dropped prior to the midpoint of follow up. There were no significant differences in demographics, clinical breast cancer characteristics, sleep or physical activity patterns, or measures of cognition between women who completed versus withdrew. Women who did not complete the study provided fewer valid days of baseline data. In conclusion, while some women may be overwhelmed with their cancer diagnosis, we did not identify any predictive characteristics of women whom did not complete the study. This novel method enables the prospective study of psychological changes associated with cancer treatment, capturing a wide array of information about behavior, experience, and cognition, thus providing a picture of the lived experiences of cancer patients before and during exposure to ET.

Actigraphic sleep dimensions and associations with academic functioning among adolescents.

STUDY OBJECTIVES: There is mixed evidence regarding associations of sleep duration with academic functioning in adolescents and a lack of research on other sleep dimensions, particularly using objective sleep measures. We examined associations of multiple actigraphic sleep dimensions with academic functioning among adolescents. METHODS: Data were from the sleep sub-study of the age 15 wave of the Future of Families and Child Wellbeing Study (n=774-782), a national, diverse sample of teens. Adolescents wore wrist-actigraphs for ~1 week and completed a survey reporting grades and school-related problems. Regression models assessed whether average sleep duration, timing, maintenance efficiency, and SD-variability were associated with self-reported academic functioning in cross-sectional analyses adjusted for demographic characteristics, depressive symptoms, and anxious symptoms. RESULTS: Later sleep timing (hrs) and greater sleep variability (SD-hrs) were associated with poorer academic outcomes, including sleep onset variability with higher odds of receiving a D or lower (OR=1.29), sleep onset (ß=-.07), sleep offset (ß=-.08), and sleep duration variability (ß=-.08) with fewer A grades, sleep offset with lower GPA (ß=-.07), sleep offset (OR=1.11), sleep duration variability (OR=1.31), and sleep onset variability (OR=1.42) with higher odds of being suspended or expelled in the past two years, and sleep duration variability with greater trouble at school (ß=.13). Sleep duration, sleep maintenance efficiency, and sleep regularity index were not associated with academic functioning. CONCLUSION: Later sleep timing and greater sleep variability are risk factors for certain academic problems among adolescents. Promoting sufficient, regular sleep timing across the week may improve adolescent academic functioning.

Effects of a 1-hour per night week-long sleep extension in college students on cardiometabolic parameters, hydration status, and physical activity: A pilot study.

OBJECTIVES: Short sleep duration is associated with poor physical health in college students. Few studies examine the effects of sleep extension on physical health in this population, who are susceptible to sleep loss. We examined health effects of a 1-week, 1-hour nightly sleep extension in college students. METHODS: Twelve healthy undergraduate college students (83% female; age 20.2 ± 1.5years) completed a study consisting of sleeping typically for 1week ("Habitual"), then extending sleep by ≥1 hour/night during the second week ("Extension"). Sleep and physical activity actigraphy were collected throughout. Following each week, participants completed cardiometabolic assessments including a meal response and provided a urine sample for markers of hydration. RESULTS: In Extension compared to Habitual, average sleep duration increased (mean change±SEM, +42.6 ± 15.1 minutes; p = .005), while subjective sleepiness (-1.8 ± 0.8 units; p = .040), systolic blood pressure (-6.6 ± 2.8 mmHg; p = .037), postprandial glucose area under the curve (-26.5 ± 10.2 mg/dL × h; p = .025) and time to baseline (-83.0 ± 46.4 minutes; p = .031) after the meal response, sedentary time (-44.3 ± 15.7 minutes; p = .018), and percentage of wake in moderate-to-vigorous activity (-0.89% ± 0.35%; p = .030) decreased. Participants who increased average sleep duration by ≥20 minutes (n = 9) were better hydrated according to urine osmolality (-187.0 ± 68.4 mOsm/kg; p = .026) and specific gravity (-0.01 ± 0.002 g/mL; p = .012) and had reduced odds of dehydration according to urine osmolality (≥800 mOsm/kg; -67%; OR=0.03; p = .035). CONCLUSIONS: This pilot study's findings suggest that sleep extension may improve cardiometabolic functioning and hydration, and alter sedentary behavior and physical activity, in college students. Sleep extension may be employed to improve multiple aspects of health in this sleep-deprived population.

Interactive Screen-Based Activities Predict Worse Actigraphic Sleep Health That Night Among Adolescents.

PURPOSE: To determine the micro-longitudinal effects of duration and timing of screen-based activities on sleep within and between adolescents. METHODS: Daily survey and actigraphy data from the age 15 wave of the Future of Families and Child Wellbeing Study were analyzed using multilevel modeling. Four hundred seventy five adolescents provided three or more days of valid daily survey and nighttime sleep data. RESULTS: Within-person results showed that on days when adolescents played video games more than their daytime average ± SE (1.3 ± 1.2 hours), sleep onset (6 ± 2 minutes, p < .01) and midpoint (4 ± 2 minutes, p < .02) were delayed for each additional hour of gaming. Between-person results showed that for each hour adolescents used screens to communicate with friends across the day, sleep onset was later (11 ± 3 minutes, p < .01), sleep midpoint was later (8 ± 3 minutes, p < .01), and sleep duration was shorter (-5 ± 2 minutes, p < .03). Adolescents who used screens to communicate with friends or play video games in the hour before bed had later sleep onset (30 ± 14 minutes, p < .03) and midpoint (25 ± 13 minutes, p < .05). DISCUSSION: Among adolescents, passive screen usage such as browsing the Internet or watching videos may not affect sleep timing or duration, but limiting interactive screen-based activities could protect adolescent sleep health and well-being.

Secondary analysis of a randomized trial testing community health educator interventions for diabetes prevention among refugees with depression: effects on nutrition, physical activity and sleep.

BACKGROUND: Refugees have high levels of psychological distress that hamper lifestyle change efforts. We previously reported that community health educator (CHE) diabetes prevention interventions decreased HbA1c and depressive symptoms among Cambodian-American refugees with depression; this paper reports health behavior outcomes of those interventions. METHODS: Participants were aged 35-75, Khmer speaking, at risk for diabetes, and met study criteria for likely depression by either a) antidepressant medication and/or b) prolonged elevated depressive symptoms. Participants were randomized to one of three CHE interventions: 1) lifestyle intervention called Eat, Walk, Sleep (EWS), 2) EWS plus medication therapy management with a pharmacist/CHE team (EWS + MTM), or, 3) social services (SS; control). Physical activity and sleep were measured with 7 days of actigraphy. Nutrition was measured as carbohydrates as reported in a culturally tailored food frequency questionnaire. Assessments were at baseline, end point (12 months), and follow-up (15 months). RESULTS: The n = 188 participants were 78% female, average age of 55 years, half had a household income < $20,000, and modal education was 7.0 years. Individuals in the two treatment groups that received the EWS intervention significantly increased their brown rice consumption (p < .001, Cohen's d = 0.76) and their moderate-to-vigorous activity (p = .039, d = 0.32). No intervention changed sleep duration, timing, efficiency or wake after sleep onset. Across groups, individuals who increased brown rice consumption, increased vigorous activity and decreased total sleep time variability showed decreased HbA1c, with small effect sizes. CONCLUSIONS: CHEs may improve nutrition and physical activity in refugees with depression but more intensive interventions may be required to impact sleep. Improvements in all three behaviors appear to be associated with HbA1c lowering TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT02502929.

Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

GOAL AND AIMS: Commonly used actigraphy algorithms are designed to operate within a known in-bed interval. However, in free-living scenarios this interval is often unknown. We trained and evaluated a sleep/wake classifier that operates on actigraphy over ∼24-hour intervals, without knowledge of in-bed timing. FOCUS TECHNOLOGY: Actigraphy counts from ActiWatch Spectrum devices. REFERENCE TECHNOLOGY: Sleep staging derived from polysomnography, supplemented by observation of wakefulness outside of the staged interval. Classifications from the Oakley actigraphy algorithm were additionally used as performance reference. SAMPLE: Adults, sleeping in either a home or laboratory environment. DESIGN: Machine learning was used to train and evaluate a sleep/wake classifier in a supervised learning paradigm. The classifier is a temporal convolutional network, a form of deep neural network. CORE ANALYTICS: Performance was evaluated across ∼24 hours, and additionally restricted to only in-bed intervals, both in terms of epoch-by-epoch performance, and the discrepancy of summary statistics within the intervals. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Performance of the trained model applied to the Multi-Ethnic Study of Atherosclerosis dataset. CORE OUTCOMES: Over ∼24 hours, the temporal convolutional network classifier produced the same or better performance as the Oakley classifier on all measures tested. When restricting analysis to the in-bed interval, the temporal convolutional network remained favorable on several metrics. IMPORTANT SUPPLEMENTAL OUTCOMES: Performance decreased on the Multi-Ethnic Study of Atherosclerosis dataset, especially when restricting analysis to the in-bed interval. CORE CONCLUSION: A classifier using data labeled over ∼24-hour intervals allows for the continuous classification of sleep/wake without knowledge of in-bed intervals. Further development should focus on improving generalization performance.

Actigraphic Sleep Variability is Associated With Lower Positive Mood in Adolescents.

PURPOSE: Poor sleep health is associated with lower positive mood in adolescents, and more variable sleep is associated with more negative mood. There is a lack of research on the associations between sleep variability and positive mood in adolescents. We investigated whether several types of sleep variability, measured with actigraphy, were associated with positive mood reported on a daily diary in adolescents. METHODS: Data were collected from a substudy of the Year 15 wave of the Future of Families and Child Wellbeing Study (n = 580; 53% female, mean age ± standard deviation [SD] = 15.4 ± 0.5 years, range 14.7-17.7). Adolescents wore an actigraphy device (M ± SD = 5.6 ± 1.4 nights per adolescent, range: 3-10) and completed daily diaries (M ± SD = 5.5 ± 1.4 days per adolescent, range: 3-9) for ∼1 week, where they rated their levels of happiness and excitement during that day from 0 (not at all) to 4 (extremely). Happiness and excitement were averaged into "positive mood." Separate linear regression models assessed whether actigraphy-measured variability of sleep duration, onset, and offset (residual individual standard deviation, riSD), sleep regularity index, social jetlag, and free night catch-up sleep were associated with average positive mood per person. Analyses adjusted for age, birth sex, race/ethnicity, household income, and the primary caregiver's education level. RESULTS: Greater variability in sleep duration (p = .011, ß = -0.11) and lower sleep regularity index (p = .034, ß = 0.09) were associated with lower ratings of positive mood. There were no other significant associations (p ≥ .10). DISCUSSION: Variable and irregular sleep are associated with lower levels of positive mood in adolescence, which may increase the risk of poor emotional health in adulthood.

Why so slangry (sleepy and angry)? Shorter sleep duration and lower sleep efficiency predict worse next-day mood in adolescents.

PURPOSE: The goal of this study was to evaluate the relationships of actigraphic nighttime sleep duration and quality with next-day mood among urban adolescents using a micro-longitudinal design. METHODS: A subsample (N = 525) of participants from the Fragile Families & Child Wellbeing Study (mean age: 15.4 years; 53% female; 42% Black non-Hispanic, 24% Hispanic/Latino, 19% White non-Hispanic) in the United States between 2014 and 2016 concurrently wore a wrist actigraphic sleep monitor and rated their daily mood in electronic diaries for about 1 week. Multilevel models tested the within-person temporal associations of nightly sleep duration and sleep maintenance efficiency with next-day reports of happiness, anger, and loneliness. The models also tested the between-person associations of sleep variables and mood. Models adjusted for sociodemographic and household characteristics, weekend, and school year. RESULTS: After nights when adolescents obtained longer sleep duration than their usual, they reported lower ratings of anger (B = -.03, p < .01) the next day. After nights when adolescents had higher sleep maintenance efficiency than their usual, they reported higher ratings of happiness (B = .02, p < .01) the next day. Adolescents who had longer average sleep duration reported lower ratings of anger (B = -.08, p < .01) and loneliness (B = -.08, p < .01) compared to others. There was no within-person association of sleep duration or efficiency with loneliness. Sleep duration was not associated with happiness between adolescents, and sleep maintenance efficiency was not associated with any mood measure between adolescents. CONCLUSIONS: Improvements to nightly sleep may help increase happiness and decrease anger the following day in adolescents. Promoting sleep health is recommended to improve mood.

Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study.

OBJECTIVES: The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor. METHODS: Wrist actigraphy measures of participants (N = 289, Mage = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment). RESULTS: Exploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (ß [95% confidence interval] = 0.63 [0.19, 1.08]). CONCLUSIONS: Actigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.

Psychological distress and health behaviours among Cambodian Americans at risk for developing diabetes.

This paper reports secondary data analysis of associations between psychological distress and health behaviours among Cambodian Americans. Data are from baseline assessments from a diabetes prevention trial. All participants met stucriteria for depression and were free of diabetes. Participants (n = 191) completed surveys, a food frequency assessment, and wore sleep and physical activity actigraphy devices for 7 days. A factor analysis of symptoms of post-traumatic stress, baksbat (a Cambodian culture-bound syndrome), depression, and anxiety yielded a single factor named 'psychological distress'. Multivariate models controlling for psychotropic medications were run for the following outcomes: sleep actigraphy, self-reported sleep, physical activity actigraphy, self-reported physical activity, nutrition, and substance use. For actigraphy, higher distress was associated with lower moderate/vigorous physical activity and higher mean variability of 24 h total sleep time. Higher distress was also associated with worse self-reported sleep quality as indicated by standard, and culturally-specific, sleep indicators. Higher distress was also associated with lower use of food labels, lower carbohydrate consumption, and higher alcohol consumption as a coping mechanism. Interventions to mitigate diabetes risk in high-distress populations may benefit from strategies to decrease psychological distress. The sequelae of complex trauma may transcend discrete psychiatric diagnoses.

Worse sleep health predicts less frequent breakfast consumption among adolescents in a micro-longitudinal analysis.

BACKGROUND: Poor self-reported sleep health has been linked to not consuming breakfast in adolescents, but it is unknown whether poor sleep measured objectively predicts next-day breakfast consumption within adolescents. We investigated within- and between-person associations of objectively measured sleep dimensions and subjective sleep quality with adolescent breakfast consumption. METHODS: Data were collected from a micro-longitudinal substudy of the Year 15 wave of the Fragile Families and Child Wellbeing Study (n = 590). Adolescents wore an actigraphy device and completed daily diaries for ~ 1 week (M ± SD = 5.6 ± 1.4 nights per adolescent, range: 3-9), where they rated their sleep quality and reported whether they had eaten breakfast that day, with no specific definition of breakfast provided (M ± SD = 5.5 ± 1.4 days per adolescent, range: 3-9). Separate mixed models assessed whether actigraphy-measured sleep duration (linear and quadratic, sleep duration x sleep duration), timing, maintenance efficiency, and subjective quality predicted odds of breakfast consumption both within and between adolescents. Variability of sleep duration and timing (standard deviation per person), sleep regularity index (SRI), and social jetlag were tested as additional between-person predictors. Analyses with predictors other than sleep duration were adjusted for sleep duration. RESULTS: Following nights when adolescents had shorter or longer sleep duration (p = .005; curvilinear association), later sleep onset, or later sleep midpoint (both p = .025) than their own usual, they had lower odds of consuming breakfast the next day (within-person associations). Adolescents who on average had later sleep onset (p = .013) or midpoint (p = .013) or who reported lower sleep quality (p = .011) had lower average odds of consuming breakfast (between-person associations). Adolescents with greater variability of sleep duration (p = .005), midpoint (p = .004), or offset (p < .001) had lower average odds of consuming breakfast (between-person associations). Sleep maintenance efficiency (within or between adolescents), SRI, and social jetlag were not associated with breakfast consumption (all p > .10). CONCLUSIONS: Multiple dimensions of sleep health are associated with breakfast consumption, both within and between adolescents. Poor sleep and dietary behaviors in adolescence may negatively impact future metabolic health.

Sleep parenting practices are associated with infant self-soothing behaviors when measured using actigraphy.

OBJECTIVE/BACKGROUND: Night wakings are common during infancy, with variability in infant self-soothing or requiring parent involvement to fall back asleep. Reasons for variable soothing behaviors are unclear and may be influenced by early-life sleep parenting practices. The study applied a novel method using sleep actigraphy in mother-father-infant triads to quantify infant-only wake bouts (i.e., "self-soothing") and examined associations between sleep parenting practices from 6 to 24 weeks of age. PATIENTS/METHODS: Mother-father-infant triads (N = 20) wore sleep monitors for 1 week at infant ages 6, 15, and 24 weeks. Data were time-matched within families to quantify infant-only wake bouts (when infants woke in the night and parents remained asleep) to be indicative of infant self-soothing. Mothers reported on sleep parenting practices (e.g., feeding to sleep, putting infants to bed while awake) using the Brief Infant Sleep Questionnaire at each age. Mixed models tested associations between parenting practices and infant-only wake bouts. RESULTS: Most mothers (69.5%) reported typically feeding their infants to sleep at bedtime; 27.1% reported typically putting infants to bed while still awake. Parenting practices associated with a greater proportion and frequency of infant-only wake bouts included not typically feeding infants to sleep during night wakings, putting infants to bed while still awake (at 24 weeks of age), and using a greater proportion of low-, rather than high-, stimulus soothing strategies during night wakings. CONCLUSION: These data suggest parenting practices, such as putting infants to bed when drowsy but still awake, are important strategies for promoting infant self-soothing during night wakings.

Neighborhood Disadvantage Is Associated with Lower Quality Sleep and More Variability in Sleep Duration among Urban Adolescents.

Differential social and contextual environments may contribute to adolescent sleep disparities, yet most prior studies are limited to self-reported sleep data and have not been conducted at a national level, limiting the variation in neighborhood contexts. This study examined the association between neighborhood disadvantage and objective measures of adolescent sleep. A racially and geographically diverse sample of American adolescents (N = 682) wore wrist-worn accelerometers, "actigraphs," for ≥ 5 nights. Neighborhood disadvantage was calculated using a standardized index of neighborhood characteristics (proportion of female-headed households, public assistance recipients, households in poverty, adults without high school degrees, and unemployed). Adolescents in more disadvantaged neighborhoods spent more time awake after falling asleep (4.0 min/night, p < .05), a greater percentage of nighttime sleep intervals awake (1%, p < .01), and had less consistent sleep duration (11.6% higher standard deviation, p < .05). Sleep duration and timing did not differ across neighborhood groups. These findings demonstrate that adolescents who live in more disadvantaged neighborhoods have lower quality, less consistent sleep.

Daily associations between family interaction quality, stress, and objective sleep in adolescents.

OBJECTIVE: To better understand the short-term impact of family interactions on adolescent sleep, this study examined daily associations between family interaction quality and sleep duration, sleep maintenance efficiency, and subjective sleep quality. METHODS: Participants were 517 diverse youth (Mage = 15.4 years, Range = 15-18) in the Fragile Families and Child Wellbeing Study who completed daily reports of family interaction quality, mood and subjective sleep quality and provided actigraphy-based estimates of sleep duration and sleep maintenance efficiency. RESULTS: Following days when youth reported that they got along with their parents, they slept 26 minutes (95% CI [2.93, 49.88]) longer. Youth who reported greater quality interactions with other family members on average had longer sleep duration, and youth who reported higher levels of family stress perceived lower sleep quality. CONCLUSIONS: Findings highlight the importance of considering daily family life, including the quality of brief family interactions, as an entry point for future sleep interventions for adolescents.

Secondhand smoke exposure is longitudinally associated with shorter parent-reported sleep duration during childhood.

BACKGROUND: Secondhand smoke exposure has been cross-sectionally associated with worse sleep health outcomes in children and shorter sleep duration in adolescents. OBJECTIVES: We assessed longitudinal and cross-sectional associations between secondhand smoke (SHS) exposure and shorter sleep duration in children from the Fragile Families and Child Wellbeing Study, a longitudinal birth cohort. We additionally examined whether associations would persist after controlling for potential confounders. PARTICIPANTS: Mothers (n = 4898) were recruited using a stratified random sample of large United States cities and oversampling for nonmarital births. MEASUREMENTS: Mothers were asked about whether they smoked during pregnancy, whether their child spent time with someone who is smoking, and their child's weekday sleep duration. Sociodemographic factors, asthma diagnosis, and bedtime routines were assessed as potential confounders. Data collected at ages 3, 5, and 9 years were analyzed using multivariable regression models (N = 1912; 51.6% boys). RESULTS: SHS exposure at age 3 predicted 15.0 fewer minutes at age 5 (P = .001) and 12.3 fewer minutes at age 9 (P = .003). SHS exposure at age 9 was cross-sectionally associated with 14.4 fewer minutes of sleep duration at age 9 (P = .002). Findings persisted after controlling for potential confounders. CONCLUSION: These results provide associational support for the hypothesis that SHS exposure may have long-term consequences for childhood sleep duration. Future studies should investigate the relationship between SHS exposure and shorter sleep duration using objective measurements of serum cotinine and sleep actigraphy and by exploring potential mechanisms.

Too Jittery to Sleep? Temporal Associations of Actigraphic Sleep and Caffeine in Adolescents.

Caffeine consumption has been linked to poor sleep health in adolescents, but it is unknown whether poor sleep predicts caffeine consumption, and/or whether caffeine consumption predicts poor sleep, particularly when sleep is measured objectively. Data were collected from a micro-longitudinal sub-study of the age 15 wave of the Fragile Families and Child Wellbeing Study (n = 589). Adolescents wore an actigraphy device and completed daily surveys for ~1 week. Daily surveys assessed subjective sleep quality and caffeinated beverage consumption (0 = no caffeine, 1 = any caffeine). Separate mixed models assessed whether actigraphy-measured sleep duration, timing, maintenance efficiency, and subjective quality predicted next-day caffeinated beverage consumption within and between adolescents. Variability (standard deviation) of sleep duration and timing, sleep regularity index, and social jetlag were tested as additional between-person predictors. Lagged models tested whether daily caffeinated beverage consumption predicted sleep that night (n = 458). Adolescents with more variable sleep duration and midpoint had higher average odds of consuming caffeinated beverages compared to others. After adolescents consumed ≥1 caffeinated beverage, they had later sleep onset that night and wake time the next morning than usual versus when they did not consume caffeine. Curbing caffeinated beverage consumption may aid in the maintenance of regular sleep schedules and advance sleep timing in adolescents.

Time for bed! Earlier sleep onset is associated with longer nighttime sleep duration during infancy.

OBJECTIVE/BACKGROUND: Clinical recommendations include putting infants to bed using a consistent bedtime routine at an appropriate hour to promote longer nighttime sleep. Actigraphy was used in this exploratory study to examine how bedtime routines and nighttime sleep onset were associated with nighttime total sleep time (TST) and efficiency from 6 to 24 weeks of age. PATIENTS/METHODS: Infants (n = 24) wore sleep actigraphs for three, one-week periods at 6, 15, and 24 weeks of age. Nighttime TST, sleep efficiency, sleep onset and offset were quantified. Mothers reported on infant bedtime routines using the Brief Infant Sleep Questionnaire at each age. Multilevel models examined between- and within-person associations. RESULTS: As infants aged, sleep onset was earlier, and bedtime routines became shorter (p's < 0.05). Infants fell asleep between 7 and 8:00PM on 24% of the nights. Most mothers (70%) reported that they often fed infants to sleep for the night. For every 1 h earlier in infants' usual sleep onset, nighttime TST was 34.4 min longer that night (p < 0.01). Infants with earlier than usual sleep onset had slightly earlier sleep offset the next morning (8.4 min for every 1 h earlier in onset; p = 0.02). Between-person analyses showed similar patterns. Infants with a more consistent bedtime routine and who were not typically fed to sleep at bedtime had longer nighttime TST at 6 weeks, with a trend or no association at later ages. CONCLUSION: Infants who fell asleep earlier also slept longer at night. Keeping infants up later in hopes of them sleeping in longer may be counterproductive.

Patterns of infant-only wake bouts and night feeds during early infancy: An exploratory study using actigraphy in mother-father-infant triads.

BACKGROUND: Infants' ability to fall back to sleep without parental involvement may reduce nighttime feeding frequency. OBJECTIVE: We describe the associations between infant-only wake bouts ("self-soothing") and nighttime feeds using actigraphy from 6 to 24 weeks of age. METHODS: Mother-father-infant triads (N = 20) wore sleep monitors, and mothers recorded infant night feeds, when infants were 6, 15 and 24 weeks of age. Actigraphy data were matched within-families to quantify infant-only wake bouts (infants woke; mothers/fathers remained asleep). Mixed models tested associations between infant-only wake bouts and night feeding frequency. RESULTS: The proportion of infant-only wake bouts/night increased from 6 to 15 weeks of age (6 weeks: 52% [95% CI: 45-59]; 15 weeks: 64% [57-71]; 24 weeks: 62% [55-69]; P < .01). For every 10% increase in the proportion of infant-only wake bouts/night, there were 0.36 fewer feeds/night (P < .01) at 24 weeks; these concurrent associations were not found at 6 and 15 weeks. The proportion of infant-only wake bouts/night at 6 weeks predicted a faster rate of decline in the number of feeds/night from 6 to 24 weeks (P < .01). CONCLUSION: Infants' ability to fall back to sleep without parent involvement at 6 weeks was associated with the trajectory of nighttime feeding frequency across early infancy.

Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach.

BACKGROUND: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep. PURPOSE: To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters. PARTICIPANTS AND METHODS: We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses. RESULTS: Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant. CONCLUSION: Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.