Chronic Health Condition Management and School-Based Health Centers in New York: Findings From the 2020 School Health Profiles Survey.

School-based health centers (SBHCs) are associated with numerous positive aspects of student health services. Many schools in the United States (US) do not have transparent policies on chronic health condition (CHC) management. Of particular concern is the underreporting of the delivery of health services in U.S. schools concerning CHC management and its relationship with the presence or absence of a SBHC. Data from the 2020 School Health Profiles (SHP) Survey were examined in New York public secondary schools. Specific health services were reviewed, together with the presence or absence of a SBHC, including daily medication administration, stock rescue medication, case management services, community partners, chronic disease-specific education, and assurance that students with CHCs were enrolled in an insurance program. A significantly greater proportion of schools with a SBHC compared with schools without a SBHC provided: (1) daily medication administration (92.9% vs. 86.5%; p < .001), (2) stock or rescue medication (84.9% vs. 77.4%; p < .001), (3) case management services (83.1% vs. 67.2%; p < .001), (4) disease-specific education for families (63.1% vs. 57.2%; p = .022), (5) student and family connection to community health services (84.2% vs. 76.5%; p < .001), and (6) ensured that a protocol existed whereby students with a CHC were enrolled in an insurance plan if eligible (79.6% vs. 66.8%; p < .001). Findings suggest that data on a national scale include essential facts for states to consider concerning school health policies and practices. Additional research should examine the intricacy of elements connected with school-based health care to understand better the care provided to children with CHCs.

Feasibility, Acceptability, and Preliminary Effectiveness of a Sleep Intervention in Adults at Risk for Metabolic Syndrome With Short Sleep Duration.

BACKGROUND: The prevalence of short sleep duration is rising and is linked to chronic comorbidities, such as metabolic syndrome (MetS). Sleep extension interventions in adults with MetS comorbidities and short sleep duration are limited and vary widely in terms of approach and duration. OBJECTIVES: This pilot study aimed to test the feasibility and acceptability of a personalized 12-week systematic sleep time extension intervention on post-intervention sleep outcomes in middle-aged adults at risk for MetS with actigraphy-estimated short sleep duration. METHODS: A single-arm, 12-week, 12-session systematic sleep time extension intervention was delivered weekly via videoconferencing. Feasibility and acceptability were assessed using retention rates and mean sleep diary completions. Sleep was estimated for 14 consecutive days prior to and immediately following the 12-week intervention using wrist actigraphy. Daytime sleepiness was assessed using the Epworth Sleepiness Scale. Paired sample t -tests modeled changes in study outcomes. RESULTS: Study participants ( N = 41) had a mean age of 52 years and were mostly female and White; 86% attended >80% of sessions, and mean sleep diary completion was 6.7 diaries/week. Significant improvements in sleep from pre- to post-intervention included increased total sleep time, earlier sleep onsets, more regular sleep onsets, a higher sleep regularity index, and reduced daytime sleepiness. Extending sleep, as well as improving sleep timing and regularity in middle-aged adults with actigraphy-estimated short sleep duration and at risk for MetS, is feasible and acceptable. DISCUSSION: Behavioral sleep characteristics may be modifiable and present a novel behavioral paradigm for mitigating MetS risk. This pilot study provides a proof of concept for the feasibility, acceptability, and preliminary effectiveness of a systematic sleep time extension for middle-aged adults at risk for MetS with actigraphy-estimated short sleep duration.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia.

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.

Combined Race and Gender Trend Disparities in Depressive Symptoms among U.S. High School Students: 1999-2019.

In 2019, over 3.2 million adolescents in the U.S. reported depressive symptoms-a number that continues to increase annually. Not only can depression negatively impact an adolescent's academic performance, social development, and cognitive function, but it is also the most common condition associated with suicide. Previous studies have reported prevalence rates and statistical trends by either gender or race. We conducted an in-depth analysis of the current racial and gender trend disparities in adolescent depressive symptoms by examining linear and quadratic trends from the Youth Behavior Risk Survey (N = 158,601) over two decades (1999-2019), stratified by gender and race subgroups, both separately and combined. This novel quantitative method allows for a more nuanced approach when exploring social and cultural influences on adolescent depressive symptoms. We found a significant difference in the prevalence of depressive symptoms between males and females among Black, Hispanic, and multiracial adolescents in each separate year of pooled data. However, an increased prevalence trend over the 20-year period was only seen among the females of these three racial categories. White and Asian female adolescents also exhibited a statistical increase in prevalence of depressive symptoms over time. The only group of males with a significant trend increase over time were White adolescents. Understanding the intersection of gender and race in adolescent depressive symptoms trends enhances evidence for nurses and other healthcare professionals when developing and implementing targeted, effective prevention and intervention measures.

Best Interest Standard in School Health: A Concept Analysis.

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.

Characterizing Glycemic Control and Sleep in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness Initiating Hybrid Closed Loop Insulin Delivery.

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).

Habitual physical activity patterns in a nationally representative sample of U.S. adults.

Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003-2004 and 2005-2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups.

The Relationship Between Youth Cyberbullying Behaviors and Their Perceptions of Parental Emotional Support.

Studies of bullying among youth usually focus on those who are bullied. Understanding the factors that affect youth who exhibit bullying behaviors is equally important. Such knowledge can heighten effectiveness of prevention and interventions at the individual, family, school, and community levels. We performed a secondary data analysis using data from the 2009 to 2010 World Health Organization's (WHO's) Health Behavior in School-Aged Children cross-sectional survey (n = 12,642), the most recent WHO data collected in the USA. Using latent class analysis, we clustered sample participants into categories of children who do not bully, bully with a low cyberbullying element, bully with a moderate cyberbullying element, and bully with a high cyberbullying element. We used multinomial logistic regression to explore the relationships between youth's perception of certain family characteristics (e.g., parental emotional support and socio-demographic characteristics) and the odds ratios of falling into one of the four latent classes generated. Establishing if a relationship exists between youth's perception of parental support factors and their bullying behavior can enhance understanding of variables that might modify adolescents' bullying. Findings of this study point to the importance of parental emotional support as a factor that can affect adolescent cyberbullying behavior. This evidence is useful for parents, education and healthcare professionals, and others involved in young people's lives.