Multilevel interventions can be uniquely effective at addressing minority health and health disparities, but they pose substantial methodological, data analytic, and assessment challenges that must ...be considered when designing and applying interventions and assessment. To facilitate the adoption of multilevel interventions to reduce health disparities, we outline areas of need in filling existing operational challenges to the design and assessment of multilevel interventions. We discuss areas of development that address overarching constructs inherent in multilevel interventions, with a particular focus on their application to minority health and health disparities. Our approach will prove useful to researchers, as it allows them to integrate information related to health disparities research into the framework of broader constructs with which they are familiar. We urge researchers to prioritize building transdisciplinary teams and the skills needed to overcome the challenges in designing and assessing multilevel interventions, as even small contributions can accelerate progress toward improving minority health and reducing health disparities. To make substantial progress, however, a concerted and strategic effort, including work to advance analytic techniques and measures, is needed.
To adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children.
Parents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were ...asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT's performance with optimized parameters was compared with the visual identification, commonly used Sadeh's sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group.
On average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh's algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P<0.001). Both DT (564±77 min/day) and Sadeh's algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P<0.001).
The DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment ("PhysActBedRest").
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The objective of this paper is to examine the relationship between the development of executive function (EF) and obesity in children and adolescents. We reviewed 1,065 unique abstracts: 31 from ...PubMed, 87 from Google Scholar, 16 from Science Direct, and 931 from PsycINFO. Of those abstracts, 28 met inclusion criteria and were reviewed. From the articles reviewed, an additional 3 articles were added from article references (N=31). Twenty-three studies pertained to EF (2 also studied the prefrontal and orbitofrontal cortices (OFCs); 6 also studied cognitive function), five studied the relationship between obesity and prefrontal and orbitofrontal cortices, and three evaluated cognitive function and obesity. Inhibitory control was most often studied in both childhood (76.9%) and adolescent (72.7%) studies, and obese children performed significantly worse (P<0.05) than healthy weight controls on various tasks measuring this EF domain. Although 27.3% of adolescent studies measured mental flexibility, no childhood studies examined this EF domain. Adolescents with higher BMI had a strong association with neurostructural deficits evident in the OFC. Future research should be longitudinal and use a uniform method of EF measurement to better establish causality between EF and obesity and consequently direct future intervention strategies.
Objective
To evaluate the association between adverse family experiences (AFEs) during childhood and adolescent obesity and to determine populations at highest risk for AFEs.
Methods
A ...cross‐sectional analysis was performed of the 2011‐2012 National Survey of Children's Health, including children aged 10‐17 years. Weighted estimates of 31,258,575 children were based on interviews with 42,239 caregivers. Caregiver reports of nine psychosocial risk factors measured AFEs during childhood. Adolescent overweight and obesity were derived by caregiver‐reported child height and weight.
Results
Nearly one‐third (30.5%) of children had experienced ≥2 AFEs, with geographic variation by state. The prevalence of obesity among children experiencing ≥2 AFEs was 20.4%, when compared with 12.5% among children with 0 AFEs. Adjusted survey regression models were controlled for child, parent, household, and neighborhood characteristics. Children with ≥2 AFEs in childhood were more likely to have obesity (AOR = 1.8; 95% CI = 1.47‐2.17; P < 0.001) than those with no AFEs, with Non‐Hispanic, White children most affected.
Conclusions
Adolescents in this national sample who were exposed to greater numbers of AFEs in childhood also had higher rates of overweight and obesity. Geographic variation and differential associations based on race/ethnicity identified children at greatest risk.
The objective of this technical report is to provide clinicians with evidence-based, actionable information upon which to make assessment and treatment decisions for children and adolescents with ...obesity. In addition, this report will provide an evidence base to inform clinical practice guidelines for the management and treatment of overweight and obesity in children and adolescents. To this end, the goal of this report was to identify all relevant studies to answer 2 overarching key questions: (KQ1) "What are clinically based, effective treatments for obesity?" and (KQ2) "What is the risk of comorbidities among children with obesity?" See Appendix 1 for the conceptual framework and a priori key questions.
Many behavioral interventions designed to improve health outcomes are delivered in group settings. To date, however, group interventions have not been evaluated to determine if the groups generate ...interaction among members and how changes in group interaction may affect program outcomes at the individual or group level.
This article presents a model and practical tool for monitoring how social ties and social structure are changing within the group during program implementation. The approach is based on social network analysis and has two phases: collecting network measurements at strategic intervention points to determine if group dynamics are evolving in ways anticipated by the intervention, and providing the results back to the group leader to guide implementation next steps. This process aims to initially increase network connectivity and ultimately accelerate the diffusion of desirable behaviors through the new network. This article presents the Social Network Diagnostic Tool and, as proof of concept, pilot data collected during the formative phase of a childhood obesity intervention.
The number of reported advice partners and discussion partners increased during program implementation. Density, the number of ties among people in the network expressed as a percentage of all possible ties, increased from 0.082 to 0.182 (p < 0.05) in the advice network, and from 0.027 to 0.055 (p > 0.05) in the discussion network.
The observed two-fold increase in network density represents a significant shift in advice partners over the intervention period. Using the Social Network Tool to empirically guide program activities of an obesity intervention was feasible.
IMPORTANCE: Prevention of obesity during childhood is critical for children in underserved populations, for whom obesity prevalence and risk of chronic disease are highest. OBJECTIVE: To test the ...effect of a multicomponent behavioral intervention on child body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) growth trajectories over 36 months among preschool-age children at risk for obesity. DESIGN, SETTING, AND PARTICIPANTS: A randomized clinical trial assigned 610 parent-child pairs from underserved communities in Nashville, Tennessee, to a 36-month intervention targeting health behaviors or a school-readiness control. Eligible children were between ages 3 and 5 years and at risk for obesity but not yet obese. Enrollment occurred from August 2012 to May 2014; 36-month follow-up occurred from October 2015 to June 2017. INTERVENTIONS: The intervention (n = 304 pairs) was a 36-month family-based, community-centered program, consisting of 12 weekly skills-building sessions, followed by monthly coaching telephone calls for 9 months, and a 24-month sustainability phase providing cues to action. The control (n = 306 pairs) consisted of 6 school-readiness sessions delivered over the 36-month study, conducted by the Nashville Public Library. MAIN OUTCOMES AND MEASURES: The primary outcome was child BMI trajectory over 36 months. Seven prespecified secondary outcomes included parent-reported child dietary intake and community center use. The Benjamini-Hochberg procedure corrected for multiple comparisons. RESULTS: Participants were predominantly Latino (91.4%). At baseline, the mean (SD) child age was 4.3 (0.9) years; 51.9% were female. Household income was below $25 000 for 56.7% of families. Retention was 90.2%. At 36 months, the mean (SD) child BMI was 17.8 (2.2) in the intervention group and 17.8 (2.1) in the control group. No significant difference existed in the primary outcome of BMI trajectory over 36 months (P = .39). The intervention group children had a lower mean caloric intake (1227 kcal/d) compared with control group children (1323 kcal/d) (adjusted difference, −99.4 kcal 95% CI, −160.7 to −38.0; corrected P = .003). Intervention group parents used community centers with their children more than control group parents (56.8% in intervention; 44.4% in control) (risk ratio, 1.29 95% CI, 1.08 to 1.53; corrected P = .006). CONCLUSIONS AND RELEVANCE: A 36-month multicomponent behavioral intervention did not change BMI trajectory among underserved preschool-age children in Nashville, Tennessee, compared with a control program. Whether there would be effectiveness for other types of behavioral interventions or implementation in other cities would require further research. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01316653
The objective of this technical report is to provide clinicians with actionable evidence-based information upon which to make treatment decisions. In addition, this report will provide an evidence ...base on which to inform clinical practice guidelines for the management and treatment of overweight and obesity in children and adolescents. To this end, the goal of this report was to identify all relevant studies to answer 2 overarching key questions: (KQ1) "What are effective clinically based treatments for obesity?" and (KQ2) "What is the risk of comorbidities among children with obesity?" See Appendix 1 for the conceptual framework and a priori Key Questions.
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