A longitudinal investigation of breastfeeding planning, initiation, and duration among individuals with pre-pregnancy overweight or obesity.

Individuals with a body mass index (BMI)≥25 kg/m2 are less likely to initiate and continue breastfeeding than are those with BMIs<25. Given the intergenerational health benefits of breastfeeding, it is important to understand breastfeeding behaviors and their correlates among individuals with BMIs≥25. Thus, in an observational cohort with BMI≥25 (N = 237), we aimed to characterize longitudinal relationships among breastfeeding planning, initiation, and duration and their sociodemographic/clinical correlates and determine if pre-pregnancy BMI predicts breastfeeding planning, initiation, and duration. Breastfeeding behaviors, weight/BMI, and sociodemographic/clinical characteristics were assessed in early, mid, and late pregnancy, and at six-months postpartum. Most participants planned to (84%) and initiated (81%) breastfeeding, of which 37% breastfed for ≥6 months. Participants who were married, first-time parents, higher in education/income, and had never smoked tobacco were more likely to plan, initiate, and achieve ≥6 months of breastfeeding. Higher pre-pregnancy BMI was not associated with breastfeeding planning or initiation but was associated with lower adjusted odds of breastfeeding for ≥6 months relative to <6 months. Findings suggest that support aimed at extending breastfeeding among those with elevated pre-pregnancy BMI may be warranted. Future interventions should also address sociodemographic and clinical inequities in breastfeeding.

Allostatic load in early pregnancy and sleep-disordered breathing.

OBJECTIVES: To assess the association between allostatic load in early pregnancy and sleep-disordered breathing (SDB) during pregnancy. METHODS: High allostatic load in the first trimester was defined as ≥ 4 of 12 biomarkers (systolic blood pressure, diastolic blood pressure, body mass index, cholesterol, low-density lipoprotein, high-density lipoprotein, high sensitivity C-reactive protein, triglycerides, insulin, glucose, creatinine, and albumin) in the unfavorable quartile. SDB was objectively measured using the Embletta-Gold device and operationalized as "SDB ever" in early (6-15 weeks) or mid-pregnancy (22-31 weeks); SDB at each time point was analyzed as secondary outcomes. Multivariable logistic regression was used to test the association between high allostatic load and SDB, adjusted for confounders. Moderation and sensitivity analyses were conducted to assess the role of allostatic load in racial disparities of SDB and obesity affected the relationship between allostatic load and SDB. RESULTS: High allostatic load was present in 35.0% of the nuMoM2b cohort. The prevalence of SDB ever occurred among 8.3% during pregnancy. After adjustment, allostatic load remained significantly associated with SDB ever (aOR= 5.3; 3.6-7.9), in early-pregnancy (aOR= 7.0; 3.8-12.8), and in mid-pregnancy (aOR= 5.8; 3.7-9.1). The association between allostatic load and SDB was not significantly different for people with and without obesity. After excluding BMI from the allostatic load score, the association decreased in magnitude (aOR= 2.6; 1.8-3.9). CONCLUSION: The association between allostatic load and SDB was independent of confounders including BMI. The complex and likely bidirectional relationship between chronic stress and SDB deserves further study in reducing SDB.

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research.

BACKGROUND: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. METHODS: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. RESULTS: Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. CONCLUSIONS: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

Associations between skeletal muscle energetics and accelerometry-based performance fatigability: Study of Muscle, Mobility and Aging.

Performance fatigability is typically experienced as insufficient energy to complete daily physical tasks, particularly with advancing age, often progressing toward dependency. Thus, understanding the etiology of performance fatigability, especially cellular-level biological mechanisms, may help to delay the onset of mobility disability. We hypothesized that skeletal muscle energetics may be important contributors to performance fatigability. Participants in the Study of Muscle, Mobility and Aging completed a usual-paced 400-m walk wearing a wrist-worn ActiGraph GT9X to derive the Pittsburgh Performance Fatigability Index (PPFI, higher scores = more severe fatigability) that quantifies percent decline in individual cadence-versus-time trajectory from their maximal cadence. Complex I&II-supported maximal oxidative phosphorylation (max OXPHOS) and complex I&II-supported electron transfer system (max ETS) were quantified ex vivo using high-resolution respirometry in permeabilized fiber bundles from vastus lateralis muscle biopsies. Maximal adenosine triphosphate production (ATPmax ) was assessed in vivo by 31 P magnetic resonance spectroscopy. We conducted tobit regressions to examine associations of max OXPHOS, max ETS, and ATPmax with PPFI, adjusting for technician/site, demographic characteristics, and total activity count over 7-day free-living among older adults (N = 795, 70-94 years, 58% women) with complete PPFI scores and ≥1 energetics measure. Median PPFI score was 1.4% [25th-75th percentile: 0%-2.9%]. After full adjustment, each 1 standard deviation lower max OXPHOS, max ETS, and ATPmax were associated with 0.55 (95% CI: 0.26-0.84), 0.39 (95% CI: 0.09-0.70), and 0.54 (95% CI: 0.27-0.81) higher PPFI score, respectively. Our findings suggested that therapeutics targeting muscle energetics may potentially mitigate fatigability and lessen susceptibility to disability among older adults.

The association between multidimensional sleep health and gestational weight gain.

BACKGROUND: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed. OBJECTIVES: This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG). METHODS: Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined 'healthy' sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD) and high (>+1 SD). RESULTS: Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null. CONCLUSIONS: Future research should determine whether sleep health is an intervention target for healthy GWG.

Validation of the Pittsburgh Performance Fatigability Index in the Study of Muscle, Mobility and Aging.

BACKGROUND: The Pittsburgh Performance Fatigability Index (PPFI) quantifies the percent decline in cadence using accelerometry during standardized walking tasks. Although PPFI has shown strong correlations with physical performance, the developmental sample was relatively homogenous and small, necessitating further validation. METHODS: Participants from the Study of Muscle, Mobility and Aging (N = 805, age = 76.4 ±â€…5.0 years, 58% women, 85% White) wore an ActiGraph GT9X on the nondominant wrist during usual-paced 400 m walk. Tri-axial accelerations were analyzed to compute PPFI (higher score = greater fatigability). To evaluate construct and discriminant validity, Spearman correlations (rs) between PPFI and gait speed, Short Physical Performance Battery (SPPB), chair stand speed, leg peak power, VO2peak, perceived fatigability, and mood were examined. Sex-specific PPFI cut-points that optimally discriminated gait speed using classification and regression tree were then generated. Their discriminate power in relation to aforementioned physical performance were further evaluated. RESULTS: Median PPFI score was 1.4% (25th-75th percentile range: 0%-21.7%), higher among women than men (p < .001). PPFI score was moderate-to-strongly correlated with gait speed (rs = -0.75), SPPB score (rs = -0.38), chair stand speed (rs = -0.36), leg peak power (rs = -0.34) and VO2peak (rs = -0.40), and less strongly with perceived fatigability (rs = 0.28-0.29), all p < .001. PPFI score was not correlated with mood (|rs| < 0.08). Sex-specific PPFI cut-points (no performance fatigability: PPFI = 0%; mild performance fatigability: 0% < PPFI < 3.5% [women], 0% < PPFI < 5.4% [men]; moderate-to-severe performance fatigability: PPFI ≥ 3.5% [women], PPFI ≥ 5.4% [men]) discriminated physical performance (all p < .001), adjusted for demographics and smoking status. CONCLUSION: Our work underscores the utility of PPFI as a valid measure to quantify performance fatigability in future longitudinal epidemiologic studies and clinical/pharmaceutical trials.

Associations between sleep health and obesity and weight change in adults: The Daily24 Multisite Cohort Study.

OBJECTIVES: To examine cross-sectional and longitudinal associations of individual sleep domains and multidimensional sleep health with current overweight or obesity and 5-year weight change in adults. METHODS: We estimated sleep regularity, quality, timing, onset latency, sleep interruptions, duration, and napping using validated questionnaires. We calculated multidimensional sleep health using a composite score (total number of "good" sleep health indicators) and sleep phenotypes derived from latent class analysis. Logistic regression was used to examine associations between sleep and overweight or obesity. Multinomial regression was used to examine associations between sleep and weight change (gain, loss, or maintenance) over a median of 1.66 years. RESULTS: The sample included 1016 participants with a median age of 52 (IQR = 37-65), who primarily identified as female (78%), White (79%), and college-educated (74%). We identified 3 phenotypes: good, moderate, and poor sleep. More regularity of sleep, sleep quality, and shorter sleep onset latency were associated with 37%, 38%, and 45% lower odds of overweight or obesity, respectively. The addition of each good sleep health dimension was associated with 16% lower adjusted odds of having overweight or obesity. The adjusted odds of overweight or obesity were similar between sleep phenotypes. Sleep, individual or multidimensional sleep health, was not associated with weight change. CONCLUSIONS: Multidimensional sleep health showed cross-sectional, but not longitudinal, associations with overweight or obesity. Future research should advance our understanding of how to assess multidimensional sleep health to understand the relationship between all aspects of sleep health and weight over time.

Mid-pregnancy sleep disturbances are not associated with mid-pregnancy maternal glycemia.

Background: In pregnancy, epidemiological data have consistently shown strong associations between sleep quality and duration and maternal glycemia. However, other sleep disturbances such as difficulty falling asleep and staying asleep are common in pregnancy. They may contribute to impaired maternal glycemia through sympathetic nervous system activity, systemic inflammation, and hormonal pathways. However, there is little research examining associations between these specific sleep disturbances and maternal glycemia. Objective: This study aimed to investigate the associations of sleep disturbances during mid-pregnancy and mid-pregnancy maternal glycemia and gestational diabetes subtypes. Study Design: This is a secondary data analysis of the Comparison of Two Screening Strategies for Gestational Diabetes trial. Participants (n = 828) self-reported the frequency of sleep disturbances (i.e., trouble falling asleep, trouble staying asleep, waking several times per night, and waking feeling tired or worn out) in mid-pregnancy. Gestational diabetes was diagnosed using either the International Associations of Diabetes and Pregnancy Study Groups or Carpenter-Coustan approach. We defined gestational diabetes subtypes based on the degree of insulin resistance and beta-cell dysfunction. We used multinomial logistic regression to examine associations of sleep disturbances with gestational diabetes status (i.e., normal, mild glycemic dysfunction, and gestational diabetes) and gestational diabetes subtypes (i.e., neither insulin resistance or beta-cell dysfunction, insulin resistance only, beta-cell dysfunction only, and insulin resistance and beta-cell dysfunction). Results: A total of 665 participants (80%) had normal glycemia, 81 (10%) mild hyperglycemia, and 80 (10%) had gestational diabetes. Among participants with gestational diabetes, 62 (78%) had both insulin resistance and beta-cell dysfunction, 15 (19 %) had insulin resistance only, and 3 had beta-cell dysfunction only or neither insulin resistance nor beta-cell dysfunction. Sleep disturbance frequency was not associated with maternal glycemia or gestational diabetes subtypes. Conclusions: Sleep disturbances in mid-pregnancy were not associated with maternal glycemia during mid-pregnancy. Future research should collect data on sleep disturbances at multiple time points in pregnancy and in combination with other sleep disturbances to determine whether sleep plays any role in maternal glycemic control.

Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research.

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results: Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study.

Background: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG). Methods: We conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined "healthy" sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD), and high (>+1 SD). Results: Nearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG). Conclusions: Multidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG. Synopsis: Study question: What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What's already known?: Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?: We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain.

Formative Development of ClockWork for the Postpartum Period: A Theory-Based Intervention to Harness the Circadian Timing System to Address Cardiometabolic Health-Related Behaviors.

Individuals with body mass index (BMI) ≥ 25 kg/m2 before pregnancy have greater difficulty losing the weight gained during pregnancy, and this postpartum weight retention predicts higher risk for cardiometabolic disease. The postpartum period involves substantial disruptions in circadian rhythms, including rhythms related to eating, physical activity, sleep, and light/dark exposure, each of which are linked to obesity and cardiometabolic disease in non-pregnant adult humans and animals. We posit that a multi-component, circadian timing system-based behavioral intervention that uses digital tools-ClockWork-will be feasible and acceptable to postpartum individuals and help promote weight- and cardiometabolic health-related behaviors. We provide data from stakeholder interviews with postpartum individuals (pre-pregnancy BMI ≥ 25; n = 7), which were conducted to obtain feedback on and improve the relevance and utility of digital self-monitoring tools for health behaviors and weight during the postpartum period. Participants perceived the ClockWork intervention and digital monitoring app to be helpful for management of postpartum weight-related health behaviors. They provided specific recommendations for increasing the feasibility intervention goals and improving app features for monitoring behaviors. Personalized, easily accessible interventions are needed to promote gestational weight loss after delivery; addressing circadian behaviors is an essential component of such interventions. Future studies will evaluate the efficacy of the ClockWork intervention and associated digital tools for improving cardiometabolic health-related behaviors linked to the circadian timing system during the postpartum period.

Association of Eating and Sleeping Intervals With Weight Change Over Time: The Daily24 Cohort.

Background We aim to evaluate the association between meal intervals and weight trajectory among adults from a clinical cohort. Methods and Results This is a multisite prospective cohort study of adults recruited from 3 health systems. Over the 6-month study period, 547 participants downloaded and used a mobile application to record the timing of meals and sleep for at least 1 day. We obtained information on weight and comorbidities at each outpatient visit from electronic health records for up to 10 years before until 10 months after baseline. We used mixed linear regression to model weight trajectories. Mean age was 51.1 (SD 15.0) years, and body mass index was 30.8 (SD 7.8) kg/m2; 77.9% were women, and 77.5% reported White race. Mean interval from first to last meal was 11.5 (2.3) hours and was not associated with weight change. The number of meals per day was positively associated with weight change. The average difference in annual weight change (95% CI) associated with an increase of 1 daily meal was 0.28 kg (0.02-0.53). Conclusions Number of daily meals was positively associated with weight change over 6 years. Our findings did not support the use of time-restricted eating as a strategy for long-term weight loss in a general medical population.

Sleep characteristics modify the associations of physical activity during pregnancy and gestational weight gain.

OBJECTIVE: Excessive gestational weight gain (eGWG) is associated with adverse long-term maternal outcomes. Most lifestyle interventions that incorporate physical activity have been ineffective at reducing eGWG. The purpose of this study was to determine if sleep modified the relationships between physical activity change from the 2nd to 3rd trimester and the odds of excessive gestational weight gain (eGWG). METHODS: This was a secondary data analysis of a prospective cohort study of pregnant birthing people with overweight or obesity (n = 105). We estimated physical activity energy expenditure (PAEE) in the 2nd and 3rd trimesters of pregnancy and sleep characteristics (i.e., sleep quality, daytime dysfunction, sleep efficiency, sleep duration) in the 2nd trimester of pregnancy with validated measures. We used regression models with sleep and PAEE change (increase/stable vs. decrease) interaction terms to examine the impact of sleep on PAEE change and eGWG. RESULTS: Mean GWG was 37.02 ± 16.76 lbs. and 80% of participants experienced eGWG. Eighteen percent of participants increased their PAEE from the 2nd to the 3rd trimester. Increasing (vs. decreasing) PAEE was associated with lower log-odds of eGWG only among participants that slept at least 8 h/night (p = 0.06), had at least 85% sleep efficiency (p = 0.03), or reported less daytime dysfunction (p = 0.08). Sleep quality did not moderate the association between PAEE change and eGWG. CONCLUSIONS: Weight management interventions in pregnancy should consider screening for and addressing poor sleep in the second trimester.

Development of a Novel Accelerometry-Based Performance Fatigability Measure for Older Adults.

INTRODUCTION: Efforts to study performance fatigability have been limited because of measurement constrains. Accelerometry and advanced statistical methods may enable us to quantify performance fatigability more granularly via objective detection of performance decline. Thus, we developed the Pittsburgh Performance Fatigability Index (PPFI) using triaxial raw accelerations from wrist-worn accelerometer from two in-laboratory 400-m walks. METHODS: Sixty-three older adults from our cross-sectional study (mean age, 78 yr; 56% women; 88% White) completed fast-paced ( n = 59) and/or usual-paced 400-m walks ( n = 56) with valid accelerometer data. Participants wore ActiGraph GT3X+ accelerometers (The ActiGraph LLC, Pensacola, FL) on nondominant wrist during the walking task. Triaxial raw accelerations from accelerometers were used to compute PPFI, which quantifies percentage of area under the observed gait cadence-versus-time trajectory during a 400-m walk to a hypothetical area that would be produced if the participant sustained maximal cadence throughout the entire walk. RESULTS: Higher PPFI scores (higher score = greater fatigability) correlated with worse physical function, slower chair stands speed and gait speed, worse cardiorespiratory fitness and mobility, and lower leg peak power (| ρ | = 0.36-0.61 from fast-paced and | ρ | = 0.28-0.67 from usual-paced walks, all P < 0.05). PPFI scores from both walks remained associated with chair stands speed, gait speed, fitness, and mobility, after adjustment for sex, age, race, weight, height, and smoking status; PPFI scores from the fast-paced walk were associated with leg peak power. CONCLUSIONS: Our findings revealed that the objective PPFI is a sensitive measure of performance fatigability for older adults and can serve as a risk assessment tool or outcome measure in future studies and clinical practice.

Allostatic Load, Single, and Dual Chronic Conditions: Evidence from the National Health and Nutrition Examination Survey.

Background: Allostatic load (AL) is defined as a cumulative burden of chronic stress and life events, which involves the interaction of different physiological systems at varying degrees of activity. AL is suspected of contributing to health disparities among different populations. Suppressed or overactive physiological systems can interrupt AL affecting proper tissue and organ function leading to disease. The objective of our study was to determine the association of AL with dual chronic conditions. Methods: We used data from the National Health and Nutrition Examination Survey (NHANES). For the current analysis, we used the data cycles of 2007-2010, which is the most recent data that collected comprehensive measures of the composite AL outcome variable. Descriptive, bivariate, and multivariable logistic regression, with stepwise forward variable selection method (P < 0.05), were conducted using STATA/IC 15.0. Results: AL levels were high among 20% of the respondents (n = 2179). Having a lower income to poverty ratio, being married, physical inactivity, experiencing sleep problems, and a history of smoking were significantly associated with high AL (P < 0.05). Non-Hispanic blacks [odds ratio (OR): 1.8; 95% confidence interval (CI): 1.6-2.4] and Mexicans and other Hispanics (OR: 1.4; 95% CI: 1.1-1.7) had higher AL compared to Caucasians. Having cardiovascular disease (CVD) (OR: 1.7; 95% CI: 1.4-2.2) and diabetes (OR: 4.7; 95% CI: 3.8-5.7) independently, as well as both CVD and diabetes (OR: 3.1; 95% CI 2.7-3.6), were associated with higher odds of AL. We conducted an age-adjusted regression model that indicated higher odds of elevated AL among females with diabetes independently (OR: 1.4; 95% CI: 1.2-1.9) and with both CVD and diabetes (OR: 1.6; 95% CI: 1.2-2.1) compared to men. Conclusions: Despite the significant impact and association of AL with overall health, there is minimal evidence of its risk factors and linkage to disease burden. Modifiable lifestyle factors were associated with a higher AL. There is a critical need to support ethnic and gender contextual interventions to reduce the burden of AL on chronic conditions.

Sleep Health Characteristics among Adults Who Attempted Weight Loss in the Past Year: NHANES 2017-2018.

BACKGROUND: The purpose of this study was to characterize sleep health in adults who attempted weight loss in the prior year. METHODS: We analyzed data from the National Health and Nutrition Examination Survey 2017-2018 exam cycle. We included 4837 US adults who did (n = 1919) or did not (n = 2918) attempt weight loss in the past year. Participants self-reported their sleep regularity, satisfaction, sleepiness, timing, and duration, which we defined as "good" based on the prior literature. We characterized sleep health by weight loss attempts status, current BMI and weight change among participants who attempted weight loss. RESULTS: On average, participants reported good sleep health in 3.21 ± 1.14 out of the five sleep domains. A total of 13% of participants had good sleep health in all five domains. The prevalence of sleep regularity (52%) was lowest, and the prevalence of infrequent sleepiness was highest (72%), relative to other sleep domains. In models adjusting for BMI, sleep health was similar in participants who did and did not attempt weight loss. Among adults who attempted weight loss, good sleep health was inversely associated with current BMI and self-reported weight change. DISCUSSION: This study's findings highlight the importance of considering sleep health when engaging with adults attempting weight loss.

Predictors of Change in Skin Intrinsic Fluorescence in Type 1 Diabetes: The Epidemiology of Diabetes Complications Study.

BACKGROUND: Skin intrinsic fluorescent (SIF) scores are indirect measures of advanced glycation end-products (AGEs). SIF scores are cross-sectionally associated with type 1 diabetes (T1D) complications such as increased albumin excretion rate (AER), coronary artery calcification (CAC) and neuropathy. We assessed predictors of SIF score change in those with T1D. METHODS: Data from the 30-year longitudinal Epidemiology of Diabetes Complications (EDC) study of childhood-onset T1D were used to assess AGEs measured with a SIF score produced by the SCOUT DS® device. SIF scores were assessed twice in 83 participants: between 2007-08 and again between 2010-14. Regression analyses were used to assess independent predictors of SIF score change. RESULTS: At baseline, mean age was 47.9 ± 6.9 years, diabetes duration was 36.7 ± 6.4 years, and median glycosylated hemoglobin (HbA1c) was 7.1 (interquartile range: 6.5, 8.5). During a mean follow-up of 5.2 ± 0.9 years, mean change in SIF score was 2.9 ± 2.8 arbitrary units. In multivariable linear regression models, log HbA1c (P < 0.001), log estimated glomerular filtration rate (eGFR) (P < 0.001), overt nephropathy (defined as AER ≥ 200 µg/min, P = 0.06), and multiple daily insulin shots/pump use (MDI) exposure years (P = 0.02) were independent predictors of SIF score change. CONCLUSIONS: Increases in SIF score over 5 years were related to increased glycemic levels and decreased kidney function (eGFR). MDI and glomerular damage were related to a decreased SIF score. This is one of the first studies with repeated SIF assessments in T1D and provides unique, albeit preliminary, insight about these associations.

Objectively assessed sleep-disordered breathing during pregnancy and infant birthweight.

BACKGROUND: Sleep-disordered breathing (SDB) in pregnancy is associated with adverse maternal outcomes. The relationship between SDB and infant birthweight is unclear. This study's primary aim is to determine if objectively measured SDB in pregnancy is associated with infant birthweight. METHODS: We measured SDB objectively in early (6-15 weeks' gestation) and mid (22-31 weeks' gestation) pregnancy in a large cohort of nulliparous women. SDB was defined as an Apnea-Hypopnea Index ≥5 and in secondary analyses we also examined measures of nocturnal hypoxemia. We used a modified Poisson regression approach to estimate relative risks (RR) of large-for-gestational-age (LGA: >90th percentile for gestational age) and small-for-gestational-age (SGA: <10th percentile for gestational age) birthweights. RESULTS: The prevalence of early-pregnancy SDB was nearly 4%. The incidence of mid-pregnancy SDB was nearly 6.0%. The prevalence of LGA and SGA was 7.4% and 11.9%, respectively. Early-pregnancy SDB was associated with a higher risk of LGA in unadjusted models (RR 2.2, 95% CI 1.3-3.5) but not BMI-adjusted models (aRR 1.0, 95% CI 0.6-1.8). Mid-pregnancy SDB was not associated with SGA or LGA. Mid-pregnancy nocturnal hypoxemia (% of sleep time <90% oxygen saturation) and increasing nocturnal hypoxemia from early to mid-pregnancy were associated with a higher risk of LGA in BMI-adjusted models. SDB and nocturnal hypoxemia were not associated with SGA. CONCLUSIONS: SDB in pregnancy was not associated with an increased risk of LGA or SGA birthweight, independent of BMI. Some measures nocturnal hypoxemia were associated with an increase in LGA risk, independent of BMI. ClinicalTrials.gov Registration number NCT02231398.

The Impact of a Randomized Controlled Trial of a Lifestyle Intervention on Sleep Among Latina Postpartum Women.

BACKGROUND: Poor sleep is associated with adverse outcomes among postpartum women. Exercise may improve sleep, but this has not been well examined in the postpartum period. PURPOSE: To examine the impact of a culturally modified, individually tailored lifestyle intervention on sleep outcomes among postpartum Latina women. METHODS: Estudio PARTO was a randomized controlled trial aimed at reducing Type 2 diabetes among Latina women with abnormal glucose tolerance in pregnancy. Participants were randomized to a lifestyle (i.e., diet and exercise; n = 70) or a health and wellness control intervention (n = 78) in late pregnancy (baseline). The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality (PSQI score), onset latency (minutes per night), duration (hours per night), efficiency (percentage of the time in bed asleep), and daytime dysfunction at baseline, 6 weeks, 6 months, and 12 months postpartum. RESULTS: Mean PSQI score (6.56 ± 3.87), sleep duration (6.84 ± 1.75 hr/night), and sleep efficiency (79.70% ± 18.10%) did not differ between the arms at baseline. Mixed-effects models indicated a greater decrease of 1.29 in PSQI score (i.e., improved sleep quality) in the lifestyle versus health and wellness arm (95% confidence interval [CI] = -2.50 to -0.08, p = .04) over follow-up. There was the suggestion of a smaller decrease in sleep duration (mean = 0.48 hr/night, 95% CI = -0.10 to 1.06, p = .10) in the lifestyle versus health and wellness arm. There were no statistically significant differences in other sleep outcomes between arms. CONCLUSIONS: Findings suggest that lifestyle interventions improve sleep quality but not sleep duration, sleep onset latency, sleep efficiency, or daytime dysfunction in postpartum Latina women and, therefore, may hold promise for improving subsequent mental and physical health in this population. CLINICAL TRIALS REGISTRATION: NCT01679210.

The Preliminary Efficacy of a Sleep Self-management Intervention Using a Personalized Health Monitoring Device during Pregnancy.

Background: Sleep disturbances are common during pregnancy and are associated with the development of adverse pregnancy outcomes. Personal health monitors (PHM) can facilitate change in health behaviors, though few studies have examined their use in improving sleep during pregnancy. This pilot study aimed to characterize sleep changes during pregnancy in women participating in a self-management intervention using a PHM.Participants/Methods: Participants with low risk, singleton pregnancies from Western Massachusetts were randomized at 24 weeks gestation to receive sleep education only (n = 12) or sleep education, and PHM intervention (n = 12). The single-session sleep education was given at baseline by a registered nurse. Sleep quality, duration, efficiency, disturbances, daytime sleepiness, and fatigue were assessed at baseline and 12 weeks follow-up using questionnaires. We described mean ± standard deviation within and between-group changes in each sleep outcome from baseline to 12 weeks follow-up.Results: The PHM arm experienced larger sleep quality improvements and daytime sleepiness than the sleep-education only arm, but the differences were not statistically significant. In the PHM arm, the Pittsburgh Sleep Quality Index (PSQI) score decreased (i.e., sleep quality increased) 1.22 ± 2.39 (p = .16), and the Epworth Sleepiness Scale (ESS) score decreased (i.e., daytime sleepiness decreased) 1.11 ± 2.08 (p = .15). In the sleep-education arm PSQI decreased 0.57 ± 2.37 (p = .55) and ESS decreased 1.29 ± 2.93 (p = .29). Neither group experienced statistically significant changes in sleep duration, efficiency, disturbances, or fatigue.Conclusion: Sleep education with PHM may improve or prevent decreases in sleep outcomes during pregnancy. Further investigation in larger trials is warranted.