Donald Hedeker

Purpose : To determine the association between meeting 24-hour movement guidelines and markers of adiposity in children, including body mass index, waist circumference, waist-to-height ratio, and percentage body fat. Methods : The sample included 910 children aged 6–10 years from Northern Portugal. Meeting the 24-hour movement guidelines was defined as (1) moderate-to-vigorous physical activity (MVPA): ≥60 minutes per day, (2) sleep: 9 to 11 hours/night, and (3) screen time: ≤2 hours per day. Moderate-to-vigorous physical activity and sleep duration were assessed with wrist-worn accelerometry over 7 days, and screen time was assessed by parental report. Results : A total of 5.4% of the sample met none of the guidelines, whereas 23% met all 3 guidelines. Children who met individual guidelines (MVPA, screen time, and sleep) had significantly lower levels of body mass index z -scores and waist circumference, whereas those who met the MVPA and sleep guidelines had lower waist-to-height ratio. Those who met the MVPA guideline had lower percentage body fat than those who did not. There were significant linear trends (all P < .01) between the number of guidelines met (0, 1, 2, and 3) and lower levels of all markers of adiposity. Conclusions : We found significant associations between meeting 24-hour guidelines and lower levels of adiposity among Portuguese children.

PURPOSE: Evaluate the longitudinal relationship between financial toxicity (FT), health-related quality of life (HR-QOL), and psychological distress for patients with cancer. METHODS: Adults with cancer applying for foundation financial assistance or receiving treatment participated in a longitudinal survey study at any point in their cancer journey and 1, 3, and 6 months later. Validated surveys measured FT (Comprehensive Score for FT [COST]), HR-QOL (Functional Assessment of Cancer Therapy-General [FACT-G]), and psychological distress (Brief-Profile of Mood States [Brief-POMS]). Multilevel ordinal logistic and linear regression models assessed longitudinal relationships. RESULTS: Out of 1,112 approached, 732 (66%) completed the enrollment survey with subsequent response rates of 66%, 58%, and 52% at 1, 3, and 6 months, respectively. Participants had a mean age of 68 years. Most were non-Hispanic White (86%), male (57%), Medicare beneficiaries (82%), and had hematologic malignancies (53%). Most common cancer types were multiple myeloma and prostate cancer. Most (80%) had FT at enrollment (grade 1+ or COST <26), and FT was higher in earlier calendar months. Access to a financial counselor was associated with lower FT for those who desired a referral (adjusted odds ratio [aOR], 0.18). Previous (OR, 1.58) and current (OR, 1.73) intravenous chemotherapy were associated with higher FT. In multivariable multilevel models clustered by participants, higher FT was significantly associated with lower HR-QOL and greater psychological distress. The probability of any FT decreased from 79% at enrollment to 72% at 1 month and 68% at 3 and 6 months. CONCLUSION: Patients with various cancer types had associations between higher FT, lower HR-QOL, and more psychological distress across repeated measures over 6-month intervals. FT screening should be repeated across the cancer care continuum, especially early in the calendar year. This will allow interventions to improve patients' emotional and physical well-being.

The legacy of the Soviet childcare system has contributed to persistently high rates of child institutionalization across many countries in Eastern Europe, the Caucasus and Central Asia. Although countries throughout the region have adopted deinstitutionalization and family reunification policies, most programs provide limited support to address children's mental health needs or family functioning during the critical transition from institutional care to family and community reintegration. This study compares and optimizes 3 prevention and early intervention approaches to improve mental health outcomes for children with a history of institutional care. Using the multiphase optimization strategy, the study experimentally evaluates which interventions-family strengthening, trauma-focused child mental health services, or economic empowerment-yield meaningful improvements in child mental health. This randomized clinical trial used a full 2³ (2×2×2) factorial design. A total of 439 children aged 7 to 12 years with a history of institutional placement and subsequent family reunification, along with 305 primary caregivers (parents or kin caregivers), were recruited from the 3 largest cities in Azerbaijan. Participants were randomly assigned to 1 of 8 experimental conditions, each receiving different combinations of the 3 interventions plus usual care. The family strengthening intervention consisted of a 12-session multifamily group program designed to improve parent-child relationships and supportive parenting. The asset-based economic empowerment intervention included opening a Matched Child Development Account with a US $50 seed deposit, 2:1 matched savings up to US $20 per month, and an 8-week financial education program for caregivers and children. Data on child mental health outcomes (self-esteem, depression, posttraumatic stress disorder, attention-deficit/hyperactivity disorder, internalizing and externalizing problems), cognitive and social processes (executive functioning, working memory), social and academic functioning (eg, grades), family functioning, and economic well-being were collected at baseline, 1-year, and 2-year follow-ups using multi-informant surveys (child, caregiver, and teacher reports), digital cognitive tasks (Wechsler Intelligence Scale for Children, Fifth Edition and Cambridge Neuropsychological Test Automated Battery), and clinical assessments. Recruitment occurred between 2022 and 2023, with 2-year follow-up data collection completed in 2025. Intervention effects analyses are ongoing and are expected to be completed in 2026. This study will identify the most effective and efficient combination of interventions to support the mental health of children reintegrating from institutional care. The findings are expected to provide important evidence to support deinstitutionalization and family reunification initiatives in the region and strengthen the global evidence base on effective, scalable community-based interventions addressing multilevel determinants of mental health among at-risk children in low- and middle-income countries.

BACKGROUND The legacy of the former Soviet system of childcare has made post-Soviet countries report some of the highest rates of children living in institutions to this day. Often referred to as “social orphans”, many of these children have at least one living parent but are placed in institutions voluntarily by their parents, primarily for socio-economic reasons, including poverty. Given the challenges associated with institutional care on children, including its impact on long-term developmental and mental health outcomes, many countries in the region are implementing deinstitutionalization policies with a strong focus on family reunification. To further support children and families during this transition, these efforts could be strengthened by providing comprehensive support for children's mental health and improving family functioning. OBJECTIVE This protocol outlines the National Institute of Child Health and Human Development-funded Optimizing Prevention Approaches for Children Reintegrating from Orphanages in Azerbaijan study. METHODS This randomized controlled trial employs the Multiphase Optimization Strategy (MOST) to test the effectiveness of three community-based intervention components—family strengthening, child mental health services, and economic empowerment in the form of Child Development Accounts (or Matched Child Saving Accounts)—on improving the mental health outcomes of children with a history of institutional care in the country. RESULTS Recruitment and data collection for this study was complete as of June 2025. The study enrolled 436 children and 305 of their caregiver participants from the three largest cities in Azerbaijan. The majority of caregivers are female (92.46%), predominantly mothers and grandmothers, and families exhibit significant economic instability. Over half of children (52%) exhibit depressive symptoms, nearly 40% exhibit symptoms of PTSD, and over one-third exhibit symptoms of ADHD. Analysis of baseline data suggest that economic instability, parental stress, and family environment appear to play important roles in child outcomes among this population. Specifically, economic deprivation is positively associated with parental distress (β = 0.38, p < .001) which, in turn, was negatively associated with a supportive family environment (β = -0.69, p< .001). A more supportive family environment was linked to fewer child internalizing problems (β = -0.54, p < .001). CONCLUSIONS These findings provide important evidence to support deinstitutionalization and family reunification initiatives in the region and strengthen the global evidence base on effective community-based interventions addressing multilevel determinants of mental health among at-risk children in low- and middle-income countries. These efforts are important because most of the mental health challenges that show up in older age could be prevented if children are intervened on early in their life. CLINICALTRIAL The study is registered with [ClinicalTrials.gov] [NCT05396625]; [https://clinicaltrials.gov/study/NCT05396625]

This study introduces an innovative approach for analyzing longitudinal behavioral data with hidden patterns in mean (location) and intraindividual variability (scale) trajectories, using location-scale regressions with latent classes in both the location and scale parts of the model. A full Bayesian approach using Stan is adopted for the estimation of the model parameters. Using simulation studies, we demonstrate that our latent class model yields more precise and informative results, especially regarding the scale, in data exhibiting hidden patterns. Simulation results also show that our model can achieve unbiased parameter estimates as well as a high correct classification rate without over-identifying latent classes in data lacking hidden heterogeneity. Our study equips researchers with a practical tool for subgrouping subjects based on both mean and within-subject variability trajectories of longitudinal outcomes. As an illustration, the latent class model is applied to calorie intake data from a weight loss management study. The integration of latent classes into intraindividual variability trajectories of calorie intake facilitates an understanding of dietary behavior consistency, aiding in personalized weight management interventions.

Importance: Race differences in inflammation have been attributed to higher body mass index (BMI); however, few studies have comprehensively examined whether changes in obesity influence changes in C-reactive protein (CRP) by race, particularly following smoking cessation. Objective: As CRP declines after quitting smoking, the current study examined whether longitudinal changes in multiple measures of obesity were differentially associated with CRP change among non-Hispanic Black (NHB) and non-Hispanic White (NHW) older adults who formerly smoked. Design: This is a longitudinal analysis of the Health and Retirement Study, nationally representative study of US adults. Participants were matched on self-reported demographic, socioeconomic, and behavioral factors using propensity score analysis to control for the effects of social disadvantage. C-reactive protein, waist circumference, body weight, and BMI were assessed in 2006, 2010, and 2014. Linear mixed models examined race differences in the effects of time-varying obesity on CRP change (n = 899 observations). Results: C-reactive protein significantly declined over time (β = −0.23, p < .0001). However, no differences in CRP change were found between NHBs (n = 146) and NHWs (n = 245). Only weight was positively associated with CRP change over time (β = 0.002, p < .001). Conclusions and Relevance: In our matched sample, there were no race differences in CRP decline among older adults, and only weight was associated with a lessened decline over time. Our findings suggest that weight alone may be an important predictor of CRP in older adults rather than BMI, which has been studied extensively in previous research.

In the analysis of accelerometer data, a common practice is to exclude days when participants wore their accelerometers for less than some threshold (e.g., 10 hr), also known as nonvalid days. This practice, however, introduces the potential for selection bias and diminished statistical power since only partial data are considered. This article introduces pattern-mixture mixed-effects models as a robust analytical tool tailored to accelerometer data with nonvalid days, with adjustments made to accommodate the time-varying pattern of missingness. We apply pattern-mixture mixed-effects models to accelerometer data from a randomized controlled trial of lifestyle intervention. Simulation studies are conducted to validate these models and further illustrate their benefits. The use of pattern-mixture mixed-effects models for analyzing accelerometer data with nonvalid days yields more precise parameter estimates with reduced biases as compared with standard mixed-effects models using only valid days or all observations. Our findings underscore the potential of pattern-mixture mixed-effects models as a robust and precise method to analyze accelerometer data.

The selection of appropriate statistical models is essential for accurately interpreting the analysis of count data, especially in behavioral medicine. Traditionally, Poisson and Negative Binomial models have been commonly employed, but they may not always be the most optimal choices, particularly when dealing with data with an abundance of zeroes, which can be effectively modeled using zero-inflated and zero-altered (hurdle) models. Additionally, U-shaped distributions where the data are clustered around both ends—low and high counts—with fewer occurrences in the middle, cannot be adequately captured by traditional approaches and further complicate the analysis. This paper critically examines the widespread use of zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models in the context of adolescent substance use data, identifying their potential limitations. Using a dataset from a smoking study of 1263 adolescents who reported smoking behavior across eight waves, we analyzed the sparse count outcome "Days Smoked in the Past Month," with covariates such as sex, age, and GPA recorded at each wave. Through a comprehensive evaluation of smoking behavior count outcomes—employing model identification via the Kolmogorov–Smirnov (KS) test, validation through confirmation studies, and regression analysis guided by Akaike Information Criterion (AIC). The range of models covered includes: ZIP, Poisson hurdle (PH), ZINB, negative binomial hurdle (NBH), zero-inflated negative binomial with fixed r (ZINB-r), negative binomial hurdle with fixed r (NBH-r), zero-inflated beta-binomial (ZIBB), beta-binomial hurdle (BBH), zero-inflated beta-binomial with fixed n (ZIBB-n), beta-binomial hurdle with fixed n (BBH-n), zero-inflated beta-binomial with fixed alpha and beta (ZIBB-ab), beta-binomial hurdle with fixed alpha and beta (BBH-ab), zero-inflated beta negative binomial (ZIBNB), and beta negative binomial hurdle (BNBH). Our study demonstrates the superior model fitting and regression analysis capabilities of the ZIBB and BBH models. Notably, our findings reveal the effectiveness of the ZIBB model in capturing the U-shaped distribution observed in real-world data. This underscores the importance of exploring a wider range of models beyond ZIP and ZINB for count data analysis. This study advocates for the broader application of these more sophisticated models in behavioral medicine, with the goal to enhance the accuracy and reliability of research outcomes.