Parents' Perceptions of and Responses to School-Based Body Mass Index Screening Programs-A Systematic Review

What Are the Challenges of School Nurses in South Korea in Managing Obese Children From Low-Income Households?

Given that the obesity rate among school-age children is increasing, school nurses can play a vital role in managing obesity and encouraging healthy living in school settings. Obese children from low-income backgrounds are more vulnerable than other students and require more careful attention and intervention. This qualitative study aimed to explore and understand the barriers recognized by school nurses in managing obesity in low-income household children. A focus group interview was conducted with 17 school nurses working at an elementary school. Children, home, school, political and structural, and social areas were revealed as intricate factors in obesity management. This study can help understand school nurses' obstacles in managing obese children from low-income families and can help them prepare practical measures to overcome these obstacles.

A scoping review of school-based anthropometric measurement

Though anthropometric measurement (AM) frequently occurs in school settings, it is not without risks to child wellbeing. The aim of this scoping review was to examine how AM in school settings takes place and is reported on to make recommendations on best practices. We identified and extracted data from 440 studies published since 2005 that conducted AM in school (pre-school through secondary/high school) settings. Privacy and sensitivity of AM were unclear in over 90% of studies. Thirty-one studies (7.0%) reported protecting student privacy, while nine (2.0%) reported public measurement. Only five studies reported sensitivity regarding AM (1.1%). Exactly who conducted AM was not specified in 201 studies (45.7%). Sixty-nine studies did not provide a weight status criteria citation (19.2%), and 10 used an incorrect citation (2.7%). In summary, serious shortcomings in the reporting of how AM is conducted and by whom, along with details concerning weight status classification, are evident. There is considerable room for improvement regarding the reporting of key methodological details. We propose best practices for AM in school settings, which also double as conditions that should be met before AM takes place in school settings.

School-level self-reported versus objective measurements of body mass index in public high school students

Population-level surveillance of student weight status (particularly monitoring students with a body mass index (BMI) ≥95th percentile) remains of public health interest. However, there is mounting concern about objectively measuring student BMI in schools. Using data from the nation's largest school district, we determined how closely students' self-reported BMI approximates objectively-measured BMI, aggregated at the school level, to inform decision-making related to school BMI measurement practices. Using non-matched data from n = 82,543 students with objective height/weight data and n = 7676 with self-reported height/weight from 84 New York City high schools (88% non-white and 75% free or reduced-price meal-eligible enrollment), we compared school-level mean differences in height, weight, BMI, and proportion of students by weight status, between objective and self-reported measures. At the school-level, the self-reported measurement significantly underestimated weight (-1.38 kg; 95% CI: -1.999, -0.758) and BMI (-0.38 kg/m2; 95% CI: -0.574, -0.183) compared to the objective measurement. Based on the objective measurement, 12.1% of students were classified as having obesity and 6.3% as having severe obesity (per CDC definition); the self-report data yielded 2.5 (95% CI: -1.964, -0.174) and 1.4 (95% CI: -2.176, -0.595) percentage point underestimates in students with obesity and severe obesity, respectively. This translates to 13% of students with obesity and 21% of students with severe obesity being misclassified if using self-reported BMI. School-level high school students' self-reported data underestimate the prevalence of students with obesity and severe obesity and is particularly poor at identifying highest-risk students based on BMI percentile.

BMI Data Collection and Communication Practices in a Multistate Sample of Head Start Programs

Background: Although there is a federal mandate to collect anthropometric data in Head Start (HS), little is currently known about the processes used to collect the height and weight measurements across programs and how the results are communicated to parents/guardians. The goal of this study was to understand anthropometric data collection and dissemination procedures in a sample of HS programs serving children 3-5 years. Methods: A convenience sample of HS Health or Nutrition managers were recruited via personal contacts and HS state directors to complete an electronic survey. Quantitative data were analyzed using descriptive statistics (means, standard deviations and frequencies). Open-ended questions were coded using thematic analysis. All protocols and procedures were approved by the Institutional Review Board at Miami University. Results: Approximately half of the programs reported that they have a protocol in place to guide measurements (57.1%) and those measurements are primarily taken by HS staff (64.5%). Most programs explain measurements to parents (82.3%) and report that collecting height/weight data is helpful in supporting children's health (76.0%). Most programs (80.3%) provide resources to parents of children with overweight or obesity. Four themes emerged from open-ended responses: (1) Role of Community Partners (e.g., providing information that conflicts with others); (2) Communicating Children's Weight Status with Families (e.g., using sensitive communication methods); (3) Challenges Measuring Children's Weight Status (e.g., accuracy of data, children's awareness); and (4) Family Reaction to Weight Status Communication (e.g., positive or negative experiences). Conclusion: Opportunities for quality improvement include wider use of standardized, written protocols and policies on data collection and enhanced communication practices to share information with parents.

Prevention of eating disorders: 2021 in review

This review of 19 prevention-related publications in Eating Disorders during 2021 is framed by four models: (1) Mental Health Intervention Spectrum: health promotion → types of prevention → case identification/referral → treatment; (2) the prevention cycle: rationale and theory, shaped by critical reviews → clarifying risk and protective factors → program innovation and feasibility studies → efficacy and effectiveness research → program dissemination; (3) links between disordered eating (DE) and eating disorders (EDs); and (4) Kraemer and Jacobi's criteria for variable, causal risk factors (RFs). Twelve articles demonstrated the strengths and limitations of cross-sectional investigations of RFs for DE and EDs: five explored the role of trauma in activating genetic and psychological RFs (e.g., anxiety and experiential avoidance), while five suggest other variables meriting further evaluation: ergogenic supplement use, "feeling fat," internalized weight bias, food insecurity, and compulsive exercising. One article presents a pilot program that reduced risk factors in a high-risk group: professional ballerinas. Implications of the absence of efficacy, effectiveness, dissemination, and advocacy studies, along with the need to develop tailored programs for various high-risk groups, are discussed.

Structural family factors and bullying at school: a large scale investigation based on a Chinese adolescent sample

Backgrounds

Various family factors have been identified in association with school bullying and the involvement of children and adolescents in bullying behaviors.

Methods

A total of 11,919 participants (female = 6671, mean age = 15) from 22 middle schools in Suzhou City, China completed the questionnaire. The associations between structural family factors (family socio-economic status, living arrangement, number of siblings, whether they were local residents/migrants, had an urban/rural hukou [a household registration system in China], parental and maternal education levels, and other various bullying-related constructs (i.e. bullying witnessing, bullying involvement, bystander intervention, and fear of being bullied) were all examined. Odds ratios (ORs) adjusted for covariates were calculated for the four bullying-related constructs (bullying witness, bullying involvement, bystander intervention, and reactions to being bullied) using structural family factors.

Results

The result showed that all demographic household characteristics were associated with bullying at school except for being from a single-child family. Adolescents from rural families witnessed more bullying incidents than those from local families (OR = 1.35, 95% CI: [1.09, 1.68]). Adolescents who come from migrant families (OR = 1.12, 95% CI: [1.07, 1.43]) with a rural hukou (OR = 1.31, 95% CI: [1.00, 1.74]) and low parental education levels (OR = 1.42, 95% CI: [1.01, 2.57]) were more likely to be bullies. Adolescents who came from migrant families (OR = 1.37, 95% CI: [1.03, 1.82]), with low maternal education levels (OR = 1.42, 95% CI: [1.06, 1.91]) engaged in more negative bystander intervention behaviors. Furthermore, adolescents with less educated mothers experienced a higher fear of being bullied (never versus sometimes: OR = 1.33, 95% CI: [1.00, 1.85]; never versus usually OR = 1.39, 95% CI: [1.01, 1.20]).

Conclusions

A systematic examination of the relationship between school bullying and demographic household characteristics may be used to inform school policies on bullying, such as training management on the importance of paying attention to adolescents from disadvantage household backgrounds. Identifying demographic factors that may predict bullying can also be used to prevent individuals from becoming involved in bullying and reduce the related negative consequences from being bullied.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12367-3.