Susan Malone

Abstract

There is an inconsistent conclusion regarding the relationship of social isolation and loneliness with poor sleep. We investigated the associations of social isolation and loneliness with new-onset insomnia symptoms in a nationally-representative sample of 9,430 adults aged ≥50 who were free of any insomnia symptoms/sleep disorders at baseline (wave 12/13) and followed up to 4 years from the Health and Retirement Study. Social isolation was measured by Steptoe's Social Isolation Index. Loneliness was measured by the revised 3-item UCLA-Loneliness Scale. Insomnia symptoms were quantified using the modified Jenkins Sleep Questionnaire. During a mean follow-up of 3.52 years, 1,522 (16.1%) participants developed at least one insomnia symptom. Cox models showed that loneliness was associated with the onset of difficulties initiating or maintaining sleep, early-morning awakening, nonrestorative sleep, and at least one of these symptoms after adjusting for potential covariates; while social isolation was not associated with the onset of difficulties maintaining sleep, early-morning awakening, or at least one insomnia symptom after adjusting for health indicators. These results are consistent in sensitivity analyses and stratified analyses by age, sex, race/ethnicity, and obesity. Public health interventions aimed at fostering close emotional relationships may reduce the burden of poor sleep among middle-aged and older adults.

Abstract

Objective: Automated insulin delivery (AID) may benefit individuals with long-standing type 1 diabetes where frequent exposure to hypoglycemia impairs counterregulatory responses. This study assessed the effect of 18 months AID on hypoglycemia avoidance and glucose counterregulatory responses to insulin-induced hypoglycemia in long-standing type 1 diabetes complicated by impaired awareness of hypoglycemia. Methods: Ten participants mean ± standard deviation age 49 ± 16 and diabetes duration 34 ± 16 years were initiated on AID. Continuous glucose monitoring was paired with actigraphy to assess awake- and sleep-associated hypoglycemia exposure every 3 months. Hyperinsulinemic hypoglycemic clamp experiments were performed at baseline, 6, and 18 months postintervention. Hypoglycemia exposure was reduced by 3 months, especially during sleep, with effects sustained through 18 months (P ≤ 0.001) together with reduced glucose variability (P < 0.01). Results: Hypoglycemia awareness and severity scores improved (P < 0.01) with severe hypoglycemia events reduced from median (interquartile range) 3 (3-10) at baseline to 0 (0-1) events/person·year postintervention (P = 0.005). During the hypoglycemic clamp experiments, no change was seen in the endogenous glucose production (EGP) response, however, peripheral glucose utilization during hypoglycemia was reduced following intervention [pre: 4.6 ± 0.4, 6 months: 3.8 ± 0.5, 18 months: 3.4 ± 0.3 mg/(kg·min), P < 0.05]. There were increases over time in pancreatic polypeptide (Pre:62 ± 29, 6 months:127 ± 44, 18 months:176 ± 58 pmol/L, P < 0.01), epinephrine (Pre: 199 ± 53, 6 months: 332 ± 91, 18 months: 386 ± 95 pg/mL, P = 0.001), and autonomic symptom (Pre: 6 ± 2, 6 months: 6 ± 2, 18 months: 10 ± 2, P < 0.05) responses. Conclusions: AID led to a sustained reduction of hypoglycemia exposure. EGP in response to insulin-induced hypoglycemia remained defective, however, partial recovery of glucose counterregulation was evidenced by a reduction in peripheral glucose utilization likely mediated by increased epinephrine secretion and, together with improved autonomic symptoms, may contribute to the observed clinical reduction in hypoglycemia.

Abstract

Background: This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID). Methods: Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen’s d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months. Results: Sleep improved from baseline to 18 months (shorter sleep latency [P <.05, d = 1.74], later sleep offset [P <.05, d = 0.90], less wake after sleep onset [P <.01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62). Conclusion: AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements. This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Abstract

BACKGROUND: Day-to-day variability in sleep patterns and eating timing may disrupt circadian rhythms and has been linked with various adverse cardiometabolic outcomes. However, the extent to which variability in sleep patterns and eating timing relate to atherosclerotic development in subclinical stages remains unclear. METHODS AND RESULTS: Generally healthy adults (N=62, 29.3±7.3 years, 66% female) completed 14 days of sleep and dietary assessments via wrist accelerometry and photo-assisted diet records, respectively. Variability in sleep duration, sleep onset, eating onset (time of first caloric consumption), eating offset (time of last caloric consumption), and caloric midpoint (time at which 50% of total daily calories are consumed) were operationalized as the SD across 14 days for each variable. Separate regression models evaluated the cross-sectional associations between sleep and eating variability metrics with end-diastolic carotid intima-media thickness (CIMT) measured via ultrasonography. Models adjusted for age, sex, systolic blood pressure, sleep duration, and total energy intake. Each 60-minute increase in sleep duration SD and sleep onset SD were associated with a 0.049±0.016 mm (P=0.003) and 0.048±0.017 mm (P=0.007) greater CIMT, respectively. Variability in eating onset and offset were not associated with CIMT; however, each 60-minute increase in caloric midpoint SD was associated with a 0.033±0.015 mm greater CIMT (P=0.029). Exploratory post hoc analyses suggested that sleep duration SD and sleep onset SD were stronger correlates of CIMT than caloric midpoint SD. CONCLUSIONS: Variability in sleep patterns and eating timing are positively associated with clinically relevant increases in CIMT, a biomarker of subclinical atherosclerosis, in early adulthood.

Abstract

BACKGROUND: Social isolation is prevalent and associated with dementia, yet the directionality and mechanisms are less understood. This study examined the association between social isolation and cognitive functioning and explored the mediating role of sleep disturbance on the social isolation-cognition relationship. METHODS: Data from 5 753 dementia-free Americans aged ≥50 of 2006 (T1), 2010 (T2), and 2014 (T3) waves of the Health and Retirement Study. Social isolation was measured by the Steptoe Social Isolation Index. Cognitive functioning was measured by the Telephone Interview of Cognitive Status. Sleep disturbance was measured with the modified Jenkins Sleep Scale. We used cross-lagged panel models to determine the associations between social isolation, sleep disturbance, and cognitive functioning. RESULTS: Social isolation is significantly associated with subsequent cognitive functioning (T1 to T2: β = -0.055, standard error [SE] = 0.014, p < .001; T2 to T3: β = -0.044, SE = 0.016, p < .001). Lower cognitive functioning is significantly associated with greater subsequent social isolation (T1 to T2: β = -0.101, SE = 0.020, p < .001; T2 to T3: β = -0.058, SE = .011, p < .001). Sleep disturbance at T2 partially mediated the effect of social isolation (T1) on cognitive functioning (T3), accounting for 6.2% of the total effect (β = -0.003, SE = 0.001, p < .01). CONCLUSIONS: Social isolation may deteriorate cognitive functioning and vice versa. The association between social isolation and cognition is partially explained by sleep disturbance.

Abstract

Background Improving the recruitment and retention of underrepresented groups in all research areas is essential for health equity. However, achieving and retaining diverse samples is challenging. Barriers to recruitment and retention of diverse participants include socioeconomic and cultural factors and practical challenges (e.g., time and travel commitments). Objectives The purpose of this article is to describe the successful recruitment and retention strategies used by two related studies within a P20 center funded by the National Institute of Nursing Research focused on precision health research in diverse populations with multiple chronic conditions, including metabolic syndrome. Methods To address the complexity, biodiversity, and effect of metabolic syndrome and multiple chronic conditions, we developed culturally appropriate, multipronged recruitment and retention strategies for a pilot intervention study and a longitudinal observational pilot study within our P20 center. The following are the underlying principles that guided the recruitment and retention strategies: (a) flexibility, (b) active listening and bidirectional conversations, and (c) innovative problem solving. Results The intervention study (Pilot 1) enrolled 49 participants. The longitudinal observational study (Pilot 2) enrolled 45 participants. Women and racial/ethnic minorities were significantly represented in both. In Pilot 1, most of the participants completed the intervention and all phases of data collection. In Pilot 2, most participants completed all phases of data collection and chose to provide biorepository specimens. Discussion We developed a recruitment and retention plan building on standard strategies for a general medical population. Our real-world experiences informed the adaption of these strategies to facilitate the participation of individuals who often do not participate in research - specifically, women and racial/ethnic populations. Our experience across two pilot studies suggests that recruiting diverse populations should build flexibility in the research plan at the outset.

Abstract

The bioethical concept of best interest standard is cited in courts across America and considered to be an effective method of managing pediatric health care decision-making. Although the best interest standard is referred to in an abundance of nursing, medical, legal, and bioethical literature, refinement and a clear definition of the concept are lacking in the context of school health. An exhaustive and methodical search was conducted across six databases revealing 41 articles from the past decade. The Wilsonian methodology was used to analyze, refine, and clarify the concept of best interest standard by presenting original case vignettes (model, contrary, related, and borderline) and an innovative conceptual model as it applies to school nursing. This concept analysis provides school nurses with a deeper understanding of the best interest standard to navigate the complex nature of making school health care decisions.

Abstract

Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On 11-12 January 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition. The workshop proceeded in 3 parts: part I covered many aspects of genetics and physiology that mediate the links between nutrient intake and health conditions such as cardiovascular disease, Alzheimer disease, and cancer; part II reviewed potential contributors to interindividual variability in dietary exposures and responses such as baseline nutritional status, circadian rhythm/sleep, environmental exposures, sensory properties of food, stress, inflammation, and the social determinants of health; part III presented the need for systems approaches, with new methods and technologies that can facilitate the study and implementation of precision nutrition, and workforce development needed to create a new generation of researchers. The workshop concluded that much research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, the field must proceed with appropriate levels of caution and make sure the factors listed above are all considered, and systems approaches and methods are incorporated. It will be important to develop and train an expanded workforce with the goal of reducing health disparities and improving precision nutritional advice for all Americans.

Abstract

Introduction:Misalignment between lifestyle behaviors and endogenous circadian rhythms is associated with elevated nocturnal blood pressure (BP) in experimental studies; however, less is known about free-living (i.e. nonlaboratory) circadian disruption and nocturnal BP. Additionally, sex-specific cardiovascular implications of circadian disruption are unclear.Objective:To examine the associations between rest - activity rhythms (RAR), a field-based estimate of circadian disruption, and nocturnal BP characteristics in young men and women.Methods:Fifty participants (20 ± 1 years; 20 men/30 women) underwent 24-h ambulatory BP monitoring following 14 days of wrist actigraphy. RAR variables of interdaily stability (day-to-day consistency in RAR), intradaily variability (within-day fragmentation of RAR), and relative amplitude (difference between peak vs. trough activity) were derived from actigraphy. Multivariable regression models of mean nocturnal SBP, DBP, and SBP dipping were generated to test main associations with RAR variables, and sex × RAR interactions. Daytime BP, race, BMI, physical activity, sleep duration, alcohol, caffeine, and sodium intake were considered as covariates.Results:In the full sample, no main associations between RAR and nocturnal BP characteristics were found. Sex interacted with RAR such that in women, higher interdaily stability (β = -5.39, 95% CI = -10.04 to -0.73, P = 0.024) and relative amplitude (β = -4.78, 95% CI = -9.22 to -0.34, P = 0.036) were both associated with lower nocturnal SBP. Sex-stratified multivariable models of nocturnal BP also revealed associations between interdaily stability and relative amplitude with SBP dipping in women (all P ≤ 0.01). No associations were apparent in men.Conclusion:Consistent and high-amplitude RAR are favorably associated with nocturnal BP characteristics in young women.

Abstract

Nocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counter regulation. These individuals also show disturbed sleep, which may result from glycemic dysregulation. Whether use of a hybrid closed loop (HCL) insulin delivery system with integrated continuous glucose monitoring (CGM) designed for improving glycemic control, relates to better sleep across time in this population remains unknown. The purpose of this study was to describe long-term changes in glycemic control and objective sleep after initiating hybrid closed loop (HCL) insulin delivery in adults with type 1 diabetes and hypoglycemia unawareness. To accomplish this, six adults (median age=58 y) participated in an 18-month ongoing trial assessing HCL effectiveness. Glycemic control and sleep were measured using continuous glucose monitoring and wrist accelerometers every 3 months. Paired sample t-tests and Cohen's d effect sizes modeled glycemic and sleep changes and the magnitude of these changes from baseline to 9 months. Reduced hypoglycemia (d=0.47-0.79), reduced basal insulin requirements (d=0.48), and a smaller glucose coefficient of variation (d=0.47) occurred with medium-large effect sizes from baseline to 9 months. Hypoglycemia awareness improved from baseline to 6 months with medium-large effect sizes (Clarke score (d=0.60), lability index (d=0.50), HYPO score (d=1.06)). Shorter sleep onset latency (d=1.53; p<0.01), shorter sleep duration (d=0.79), fewer total activity counts (d=1.32), shorter average awakening length (d=0.46), and delays in sleep onset (d=1.06) and sleep midpoint (d=0.72) occurred with medium-large effect sizes from baseline to 9 months. HCL led to clinically significant reductions in hypoglycemia and improved hypoglycemia awareness. Sleep showed a delayed onset, reduced awakening length and onset latency, and maintenance of high sleep efficiency after initiating HCL. Our findings add to the limited evidence on the relationships between diabetes therapeutic technologies and sleep health. This trial is registered with ClinicalTrials.gov (NCT03215914).