Introduction
Youth physical activity in the United States
Both the National Association for Sport and Physical Education (NASPE) and the World Health Organization (WHO) advocate for children to engage in at least 60 minutes of moderate to vigorous physical activity (MVPA) daily to enhance health-related fitness.
Only 42% of children aged 6-11 in the United States meet recommended fitness guidelines, with adolescent participation even lower, estimated between 8-17% In contrast, Western European countries report significantly higher activity levels, with 97% of children and 62-82% of adolescents meeting similar fitness standards.
Research indicates that only a fraction of 15-year-olds meet international physical activity recommendations, with a notable decline in activity levels over recent decades, particularly from childhood to adolescence in the US compared to other countries This drop in youth physical activity in the US raises significant concerns, as numerous studies highlight the positive impact of physical activity on children's health and academic performance These benefits may stem from enhanced cognitive function and memory, as well as improvements in physical and psychosocial well-being While it is suggested that school attendance may play a role in linking physical and psychosocial wellness to academic success, this relationship has yet to be formally investigated.
Characterizing health-related fitness
Health-related fitness is defined as, “A state of being that reflects a person’s ability to perform specific forms of physical activity/exercise or functions, and is related to present
2 and future health outcomes.” 28(p.3-2) This construct was first introduced by the Task Force on Youth Fitness in 1977 in response to youth fitness assessments promoted by the
American Alliance for Health, Physical Education, Recreation and Dance
The AAHPER/AAHPERD emphasizes the connection between physical fitness and sports performance, distinguishing it from functional health Central to this concept is the Fitnessgram, a widely recognized criterion-referenced fitness assessment created by the Cooper Institute for Aerobics Research, utilized in schools and research worldwide The five key components of fitness are cardiorespiratory endurance, muscular endurance, muscular strength, body composition, and flexibility Body composition is often assessed using BMI as a proxy; while BMI serves as a good indicator of health-related fitness, it should not be considered a direct measure of fitness or physical ability.
Cardiorespiratory endurance, muscular endurance, muscular strength, and flexibility are evaluated through various physical ability tests, including timed runs and specific assessments for strength and flexibility Therefore, body composition should be regarded as a separate concept from other components of fitness.
Youth health-related fitness and school attendance
Recent studies indicate that higher fitness levels can enhance workplace attendance, mirroring findings in adult populations Specifically, cardiorespiratory fitness and physical activity are positively linked to increased attendance at work Furthermore, interventions aimed at improving fitness in adults have proven effective in boosting workplace attendance rates.
Causal mechanisms may include reduced risk of cardiovascular
Research indicates a strong link between improved fitness and increased attendance in youth, highlighting the role of insulin sensitivity, hypertension, and metabolic syndrome in reducing illness-related absences Studies have shown that better diet and physical activity can mitigate health issues and psychosocial challenges associated with overweight and obesity These findings suggest that enhancing fitness levels could lead to higher attendance rates among young populations.
Research consistently shows a positive or neutral relationship between youth fitness and educational outcomes, indicating that reallocating instructional time to aerobic physical activity does not harm children's academic performance A 2010 CDC review of 50 studies found that half demonstrated positive associations between school-based physical activity and academic indicators, with only 1.5% showing negative impacts The review concluded that physical activity may enhance, rather than hinder, academic achievement Additionally, a meta-analysis of 59 studies from 1947 to 2009 revealed a significant positive correlation between school-based physical activity programs and cognitive performance, with experimental studies showing a greater effect size compared to observational studies.
To the author’s knowledge, only 5 studies have examined the relationship between fitness and attendance among youth, and their findings are consistent in demonstrating a positive association.19,22,32-34
A cross-sectional study by Mohar involving primary and middle school children (n=24) revealed a significant inverse relationship between moderate to vigorous physical activity (MVPA) and mean days absent, with averages of 6.99 (SD=0.42), 3.90 (SD=2.50), and 3.34 (SD=0.25) days absent per year for the lowest, middle, and highest physical activity tertiles, respectively Similarly, Shannonhouse's non-randomized controlled trial indicated that a game-based physical activity intervention led to increased attendance, showing an average of 8.82 (SD=6.78) days absent for the experimental group compared to 10.03 (SD=7.86) for the control group (p=0.056) Additionally, Kristjánsson found a significant positive association between individual student physical activity and attendance, further supporting the link between physical activity and school attendance.
.15±0.0.024), where self-reported frequency of physical activity (1 = almost never, 2 less than once a week, 3 = once a week, 4 = 2-3 times a week, 5 = 4-5 times a week, and
Research indicates a significant relationship between fitness levels and school attendance A study found that students who engaged in physical activity almost daily reported a reduction in skipped classes, with a decrease of 15 on a 1-5 scale Additionally, Welk et al identified a moderate positive correlation (r=.38) between cardiovascular health-related fitness and school attendance Blom et al further demonstrated that students with higher fitness levels had a 3.31 times lower odds of having eight or more absences per year Despite these findings, only one study utilized longitudinal data to explore the fitness-attendance relationship, and no research has assessed the effects over multiple years.
Cross-sectional studies do not establish the temporality between exposure and outcome, making longitudinal research essential for accurately assessing the causal relationship between fitness and attendance By utilizing longitudinal data collected over multiple years, researchers can better understand the potential causal pathways linking fitness levels to attendance patterns.
1.3.1 School-level differences in the fitness-attendance association
Research on the impact of school-level factors on the relationship between fitness and attendance is limited Existing literature indicates that contextual factors, such as neighborhood poverty and the built environment, significantly influence children's participation in physical activities at school and in their communities Safe and attractive neighborhoods can enhance opportunities for physical activity, while community norms may affect parental decisions regarding school attendance Additionally, perceptions of neighborhood safety can play a role in attendance rates Despite these insights, only one study has examined the fitness-academic association while accounting for school-area poverty, and no research has explored how contextual factors may serve as antecedents or confounders in the fitness-attendance relationship Therefore, further investigation into the effects of school contextual factors on this relationship is needed.
1.3.2 Gender modification of the fitness-attendance association
Numerous studies demonstrate low self-esteem in adolescent girls is significantly associated with both lower physical activity levels 21 and attendance, 25,26 attributed in part
Gender may act as an effect measure modifier (EMM) in the relationship between fitness and attendance, with self-esteem potentially playing a more significant role for girls than boys Research suggests that psychosocial factors, particularly for girls, can hinder participation in physical activity, aligning with Social Cognitive Theory (SCT) This indicates that the influences of self-esteem and other factors vary by gender, impacting the fitness-attendance dynamic.
To date, there is a lack of research specifically focusing on gender as a moderating factor in the relationship between fitness attendance and academic performance However, six studies have explored how gender influences the association between fitness and academics, with four of these studies indicating that the effects are more pronounced for females.
A study by Bezold revealed that girls who experienced a significant increase in fitness relative to their peers (0.36 percentile points per year) also demonstrated the most notable rise in academic ranking (1.06 percentile points per year) While one study indicated no significant gender differences, another study observed stronger effects for boys, although it focused on a younger sample of elementary school-aged children.
1.3.3 Health-related fitness and chronic absenteeism
Chronic absenteeism affects 10-15% of students nationwide, equating to approximately 5-7.5 million children who miss 10% or more of the school year, which is at least 20 days This issue tends to escalate as students grow older and is closely linked to factors such as race, ethnicity, and socioeconomic status.
Chronic absenteeism negatively impacts academic performance and long-term graduation rates, while reducing it may help close racial and ethnic achievement gaps Research by Musser et al indicates that improving attendance from chronic absenteeism to average levels can lead to a 17% and 26% reduction in the achievement gap for non-Hispanic white and minority 4th graders in English and math standardized tests, respectively Additionally, the positive correlation between fitness and attendance suggests that enhanced physical fitness may also contribute to lowering chronic absenteeism among children.
To the author’s knowledge, no studies have addressed the causal effects of fitness on chronic absenteeism in youth.
Summary and gaps in current literature
The literature to date hypothesizes fitness improves cognition and/or psychosocial health in youth, which may promote attendance Most research demonstrates a positive association between fitness and attendance.19,22,32-34
Most research on fitness and attendance consists of cross-sectional studies, which cannot establish causal relationships due to the unknown timing of exposure and outcomes Additionally, many studies only adjusted for basic factors such as gender, socioeconomic status, age, and race/ethnicity, neglecting other potential confounders like psychosocial influences, substance use, family dynamics, and the impact of the school and neighborhood environment This oversight is significant, especially considering evidence that school-level factors significantly affect student outcomes Furthermore, these studies did not analyze individual student data within the school context.
Eight neighborhoods present challenges in separating individual factors from area-level influences Furthermore, there is a lack of research investigating how gender may modify the relationship between fitness attendance and chronic absenteeism, as well as the long-term causal effects of fitness on absenteeism while considering contextual factors.
Overview of the dissertation
This dissertation aimed to prospectively investigate how changes in fitness levels influence student attendance, considering gender differences and contextual factors It also sought to analyze the causal relationship between fitness changes and chronic absenteeism The findings are intended to guide school-based policies focused on improving attendance The research utilized NYC Fitnessgram data, encompassing around 350,000 individual students across six cohorts monitored over four consecutive years, from grades 5 to 8, between 2006 and 2013.
This study employed advanced analytic techniques, specifically multilevel longitudinal data analysis, to effectively analyze a dataset featuring repeated measures of individuals grouped within schools while considering neighborhood and contextual factors The research utilized both 3-level linear and logistic adjusted models to investigate the causal relationship between fitness and attendance, focusing on days absent and chronic absenteeism Additionally, the analysis accounted for potential confounding variables, including changes in BMI and sociodemographic factors.
Aim 1 To characterize individual-level and between-school variation in student health – related fitness and attendance, using the NYC Fitnessgram dataset (2006/07-2012/2013)
Hypothesis 1a suggests that as fitness levels decline—measured through aerobic capacity, muscular strength, and endurance tests—students will experience an increase in absenteeism over the course of a year Additionally, this absenteeism is expected to rise with higher grade levels and greater poverty levels in the school area.
Hypothesis 2b suggests that variations between schools, particularly influenced by factors such as school-area poverty, will contribute a modest yet significant portion to the overall variability in attendance rates, specifically in terms of the number of days students are absent per year.
Aim 2 To analyze the causal effects of change in health-related fitness on subsequent attendance in 6 cohorts of NYC Department of Education (DOE) middle school students followed consecutively over 4 years (grades 5-8)
Hypothesis 2a Higher positive change in individual-level fitness
Cardiorespiratory and muscular endurance, along with muscular strength fitness composite percentile scores, are significant predictors of absenteeism over a year This relationship holds true even after considering various individual and school-level factors, as well as accounting for clustering effects and time-dependent interactions.
Hypothesis 2b Gender will modify the relationship between change in fitness and 1-year lagged attendance Fitness will be a stronger predictor of attendance in females compared with males
Aim 3 To analyze the causal effects of change in fitness on subsequent chronic absenteeism in 6 cohorts of NYC DOE middle school students followed consecutively over 4 years (grades 5-8)
Hypothesis 3a School chronic absenteeism will decrease with increasing fitness and decreasing grade levels
Hypothesis 3b suggests that an increase in individual-level fitness, measured through cardiorespiratory and muscular endurance as well as muscular strength composite percentile scores, is associated with a decreased likelihood of chronic absenteeism over a one-year period.
≥20 days absent per year) after accounting for potential individual- and school-level confounders, as well as accounting for clustering by individual and school, and time-dependent interactions
This dissertation is structured into five chapters, starting with Chapter 2, which explores individual-level and between-school variations in fitness and attendance using the NYC Fitnessgram dataset from 2006/07 to 2012/13 Chapter 3 analyzes the causal effects of changes in fitness on subsequent attendance among six cohorts of NYC DOE middle school students over four years, also examining gender as a modifying factor in these effects Finally, Chapter 4 presents findings on the longitudinal causal effects of fitness changes on chronic absenteeism in NYC middle school students, tracked over the same four-year period.
Chapters 2 through 4, and public health policy recommendations targeting both student fitness and attendance are discussed in Chapter 5
This dissertation is the first to prospectively examine how changes in fitness impact youth attendance, utilizing multilevel, repeated measures data The findings provide compelling evidence for public health interventions that encourage at least 60 minutes of daily physical activity for children aged 6-17, as well as the importance of quality physical education before, during, and after school In the US, schools face pressure to replace physical education with non-physical instructional time due to the focus on high-stakes testing; in 2006, fewer than 10% of middle schools offered daily physical education for all grades Understanding the long-term, causal relationship between fitness and attendance can support public health policies aimed at enhancing school-based physical activity programs.
Data sources and study population
The NYC Fitnessgram dataset, managed by the NYC Department of Education (DOE) and the Department of Health and Mental Hygiene (DOHMH), includes annual fitness assessments for around 870,000 public school students in grades K-12, starting from the 2006-07 school year.
12 have both strong reliability and validity 28,29 Morrow et al also demonstrated reliability and validity of the Fitnessgram across testing sites 77
Individual student Fitnessgram data spanning multiple years were connected using a unique identifier Since previous research has not explored the relationship between fitness and attendance over several years, a one-year lag was implemented This approach is informed by earlier longitudinal studies indicating that fitness can enhance academic performance, and it aims to increase the size of the analytic sample by incorporating repeated fitness assessments from middle school students.
The study's primary outcomes focused on annual absenteeism, specifically tracking the number of days absent per year and identifying chronic absenteeism as students missing 20 or more days Attendance data was sourced from the NYC Fitnessgram dataset, which links year-end attendance records to unique student identifiers Additionally, the dataset includes student admission and discharge dates that fall before or after the school year ends.
Demographic variables included gender, race/ethnicity (non-Hispanic white, non-
Hispanic black, Hispanic, Asian or Pacific Islander, Native American, and other
The data is derived from annual DOE demographic surveys conducted with parents, which include information on race, ethnicity, and place of birth—specifically whether students are from NYC, other parts of the United States, or born abroad This demographic information is linked to Fitnessgram data using unique student identifiers.
The study examined the impact of obesity status changes on attendance, recognizing a positive correlation between obesity and attendance, as supported by existing literature It differentiated obesity from other fitness variables, noting that components like aerobic capacity, muscular strength, and endurance are significant predictors of academic performance across all weight categories While BMI serves as an indicator of health-related fitness, it is not a standalone measure of physical ability Instead, physical fitness is evaluated through various tests assessing cardiorespiratory endurance, muscular endurance, strength, and flexibility To accurately assess the relationship between fitness and attendance, changes in obesity status were controlled in the analysis In New York City, student height and weight data are collected during physical education classes as part of the Fitnessgram assessment, with BMI calculated based on CDC growth chart norms Obesity in youth is defined as a BMI at or above the 95th percentile for their respective age and gender.
A categorical poverty variable for school areas was established, categorizing the percentage of households in the school zip code living below the federal poverty threshold into four levels: low (30%) This data was sourced from the American Community Survey (ACS) covering the years 2007-2013 The area poverty statistics were then connected to individual student Fitnessgram records using the school zip code.
Inclusion criteria for this study were active enrollment status in a NYC public school for
≥2 consecutive years while in grades 6-8 during the study period (2006/7-2012/13) in districts 1-32 (i.e the schools that have Fitnessgram measurements; nE7,397)
To ensure a consistent observation period across varying school years, students (n=6,225) who were enrolled for less than n-5 days were excluded, where n represents the maximum enrollment days (ranging from 292 to 297 days) Additionally, students who did not participate in the Fitnessgram test for two consecutive years (n=4,464) and those from schools with poor quality fitness data (n=50) were also removed from the analysis Furthermore, students who changed schools (n=1,977) were excluded to maintain accurate school clustering in the study After applying these criteria, the final sample consisted of 349,381 unique 6th to 8th graders across 624 schools, with a demographic breakdown of 51% female, 77% born in NYC, 38% Hispanic, 28% Non-Hispanic Black, and 17% Non-Hispanic White.
220,769, and 186,135 student-years contributed 6 th , 7 th and 8 th grade data, respectively)
Individual- and school-level differences in the fitness-attendance
Background
2.1.1 Physical activity and school outcomes
In the United States, only 42% of children aged 6-11 meet the recommended 60 minutes of moderate to vigorous physical activity (MVPA) daily, as advised by the National Association for Sport and Physical Education (NASPE) and the World Health Organization (WHO) Alarmingly, this percentage drops to just 8-17% for adolescents aged 12-19 Over the past several decades, physical activity levels have significantly decreased, with a more pronounced decline observed from childhood to adolescence in the US compared to other countries These concerning national trends are also reflected in New York City.
(NYC), where 43%, 35% and 20% of youth ages 6-10, 11-12, and 14-18 meet physical activity guidelines 87,88
Concerns about low levels of physical activity among youth in the US are growing, as research highlights the significant health and academic benefits of regular exercise Studies show that physical activity can enhance cognition and memory while also improving physical and psychosocial wellness Recent literature further emphasizes the importance of youth health-related fitness in promoting overall well-being and academic success.
Research indicates that fitness can enhance student attendance, aligning with similar studies that link fitness to improved attendance in adult workplaces Notably, there is a positive correlation between cardiorespiratory fitness and physical activity levels in adults, suggesting that maintaining fitness may lead to better attendance outcomes across different age groups.
16 workplace attendance 37,38,42 Furthermore, interventions targeting improvements in adult fitness have demonstrated an increase in workplace attendance.35,36,41,43
2.1.2 Effects of individual and school-level factors on the fitness-attendance association
Individual-level factors associated with the fitness-attendance relationship include gender, individual household socioeconomic status, age or grade level, obesity status, race/ethnicity, home language, and place of birth.19,22,33,34,51
The impact of school contextual effects on the relationship between fitness and attendance remains largely unexplored Research indicates that school contextual factors, including area poverty and the built environment, significantly influence children's participation in physical activities both at school and in their neighborhoods Neighborhood characteristics can enhance opportunities for safe and accessible physical activities Additionally, school contextual effects can affect student attendance, as community norms and parental attitudes may influence decisions regarding school absences Furthermore, perceptions of neighborhood safety can also play a role in student attendance Given that neighborhoods can impact both physical activity and school attendance, it is crucial to assess these contextual factors as potential antecedents or confounders in this relationship However, existing literature lacks studies that examine the fitness-attendance association while incorporating school-area measures.
2.1.3 Reporting attendance at the individual- and school-levels
Based on education reports and the scientific literature, attendance data are typically
17 aggregated at the school-level.32,73,74,89-91
For example, the NYC DOE reported an average daily student attendance rate of 92% for all students, and 95%, 94% and 93% for
In the 2014-15 academic year, data from 6th, 7th, and 8th grade students citywide revealed that relying on school-aggregated attendance rates can obscure the true extent of chronic absenteeism For instance, a school may report an average daily attendance rate of 90%, yet 40% of its students could still be classified as chronically absent This discrepancy highlights the importance of utilizing student-level data to accurately understand attendance patterns and address absenteeism effectively.
≥20 days per year), depending on the composition of the student population present on a given day 72
This study analyzed the NYC Fitnessgram dataset from 2006/7 to 2012/13 to examine the relationship between fitness levels and student attendance among middle school students The research hypothesized that higher fitness levels would correlate with increased attendance, while lower grades and reduced school-area poverty would also contribute positively to attendance rates Additionally, it was expected that variations in student attendance could be partially attributed to differences between schools and the poverty levels of their respective areas.
Methods
2.2.1 Data source, collection, management and study population
Data were drawn from the NYC Fitnessgram dataset jointly managed by NYC
Department of Education (DOE) and Department of Health and Mental Hygiene
(DOHMH), and comprised of annual fitness assessments collected by DOE for
Each year, approximately 870,000 students in grades K-12 attend New York City public schools, a trend that has been consistent since the 2006-07 school year The New York City Department of Education (NYC DOE) stands as the largest school district in the United States, catering to around 1.1 million students across more than 1,800 schools, which includes about 170,000 middle school students annually NYC schools are required to maintain a minimum of 85% attendance.
Fitnessgram measurements on their students each year 93 Individual student fitness data from multiple years are linked in the dataset by a unique student identifier
The NYC Fitnessgram dataset provides valuable attendance data, which is collected at the end of the school year and connected to Fitnessgram records using unique student identifiers Additionally, the dataset includes student admission and discharge dates that occurred before or after the school year's conclusion.
Inclusion criteria for this study were active enrollment status in a NYC public school for
During the study period from 2006/07 to 2012/13, students in grades 6-8 from districts 1-32 were analyzed based on their Fitnessgram measurements, with a total of 7,397 students included To ensure consistent observation, students who were enrolled for less than five days were excluded, resulting in the removal of 6,225 participants Additionally, those who did not complete the Fitnessgram test for at least two consecutive years (4,464 students) and those from schools with poor quality fitness data (50 students) were also excluded Finally, students who changed schools during the study were removed from the analysis to maintain data integrity.
Figure 2.1 Sample Selection Flow Chart
The analysis included 20 accounts for school clustering, resulting in a final sample of 349,381 unique 6th to 8th graders across 624 schools The demographic breakdown showed that 51% of the students were female, with 77% born in New York City Additionally, 38% identified as Hispanic, 28% as Non-Hispanic Black, and 17% as Non-Hispanic White Student-year contributions were significant, with 177,281 from 6th grade, 220,769 from 7th grade, and 186,135 from 8th grade.
The study focused on age- and gender-specific changes in fitness composite percentile scores, derived from mean performance in aerobic capacity (PACER), muscle strength, and endurance tests (curl-up and push-up), categorized into five groups: >20% increase, 10-20% increase, 20% decrease compared to the previous year The PACER test requires individuals to run back and forth across a 20-meter distance at an incrementally increasing pace, while the push-up and curl-up tests are also conducted at a specified pace, with students aiming to complete as many repetitions as possible Successful completion of these assessments is determined by achieving scores within age- and gender-specific Healthy Fitness Zones, consistent with prior longitudinal research linking fitness levels to academic outcomes.
Several variables pertaining to student attendance available in the NYC Fitnessgram dataset were examined in order to ensure a consistent period of observation across
A study involving 21 students initially considered the number of days present per year as a variable for total attendance; however, analysis showed many students reported over 20 days more than the maximum possible attendance for the school years 2006/7 to 2008/9 This discrepancy suggested inconsistent data entry practices across schools, potentially including summer attendance In 2010, the establishment of the NYC Mayor’s Interagency Task Force on Chronic Absenteeism aimed to enhance attendance data collection and reporting, which likely contributed to the improved accuracy of attendance records following this period.
To ensure a consistent observation period, the Fitnessgram dataset utilized the total days enrolled as a key variable, calculated by data specialists based on the duration from admission to discharge This calculation included the first and last days of school for students whose admission and discharge dates fell outside the school year Since the analysis clustered students by school, it effectively excluded those who switched schools during the study period, making the days enrolled variable a crucial exclusion criterion for the study.
The days absent variable was thoroughly analyzed to confirm its suitability as an outcome measure Univariate descriptive analyses indicated that the range of days absent among students was reasonable, with no instances surpassing the total school days Mean attendance rates were calculated and compared to the NYC DOE attendance categories, which reflect the percentage of days attended A new variable was developed from the total days absent in the Fitnessgram dataset, categorized into 10% increments to align with the DOE's attendance metrics, including categories for no show and perfect attendance The comparison revealed that, from 2006/07 to 2012/13, the attendance categories of the DOE and the newly created Fitnessgram variable matched on average 98%.
97.9%-99.3%) An additional average of 1% of Fitnessgram attendance rate values matched within 10% across the DOE attendance rates (range: 0.6%-2.4%)
Recent studies comparing year-specific rates of chronic and severe absenteeism among NYC students revealed consistency with values derived from the Fitnessgram days absent variable.
In the 2011/12 academic year, the rates of chronic and severe absenteeism among NYC 6th to 8th graders were 19% and 6%, respectively, based on the Fitnessgram days absent variable Chronic absenteeism is defined as missing 10% or more of school days (20 days or more), while severe absenteeism is defined as missing 20% or more (40 days or more).
Similarly, chronic and severe absenteeism rates for the same year published by the NYC Mayor’s Interagency Task Force on Truancy, Chronic Absenteeism & School
Chronic absenteeism rates among middle school students showed a significant decline, decreasing by approximately 1% per year, from 26% in the 2006/07 school year to 18% in 2012/13 This trend aligns with findings in the literature, which also report a similar annual decrease of about 1% in chronic absenteeism rates during the same period.
The analysis confirmed that the Fitnessgram days absent variable, a discrete measure (refer to Appendix D for the univariate descriptive plot), effectively reflects student attendance and is suitable as the primary outcome variable for this study.
Demographic variables included gender, race/ethnicity (non-Hispanic white, non-
Hispanic black, Hispanic, Asian or Pacific Islander, Native American, and other
The data collected from annual DOE demographic surveys reveals the racial backgrounds of students, including those identified as multiracial or with parents who refused to disclose their race Additionally, the information encompasses students' places of birth, categorizing them as born in NYC, born in other parts of the United States, or foreign-born This demographic information is linked to Fitnessgram data through unique student identifiers.
Demographic survey data from year one was applied to all other years of data for the same individual
The study examined changes in obesity status among NYC students, categorizing them into four groups: transitioning from obese to not obese, consistently not obese, consistently obese, and transitioning from not obese to obese Height and weight measurements were taken during routine physical education classes as part of the Fitnessgram assessment To assess obesity levels, age- and gender-specific BMI for children was calculated using a standardized formula.
Obesity status in children was determined using CDC growth chart norms tailored for specific gender and age in months, which allowed for the calculation of BMI percentiles According to these standards, obesity is classified as a BMI at or above the 95th percentile for youth within the same gender and age group.
A categorical school-area poverty variable was based on average percentage of households in the school zip code living below the federal poverty threshold (low
(30%) area poverty) drawing from the American Community Survey (ACS) 2007-2013 Area poverty data was linked to individual student Fitnessgram records based on school zip code
Results
Table 2.1 outlines the demographic, fitness, and school characteristics of the study population, which consisted of slightly more females (n=7,355; 51%) The majority identified as Hispanic (n=4,453; 38%) and Non-Hispanic Black (n=363; 28%) Additionally, most students were English-speaking (n=7,727; 57%) and a significant portion were born in New York City (n=9,251; 77%).
In a comprehensive analysis of student fitness over multiple years, 37% of students exhibited less than a 10% change in fitness levels, while 20% experienced over a 20% increase Additionally, 12% saw a moderate increase of 10-20% in their fitness scores Conversely, 19% of students faced a significant decrease of more than 20% in fitness, and 12% had a 10-20% decline Notably, the majority of students (73%) maintained a consistent non-obese status throughout the years, while 17% were consistently obese Only 5% transitioned from obese to non-obese, and 4% shifted from non-obese to obese.
The analysis encompassed 624 schools, with an average enrollment of 559.91 students, and 58% of these schools classified as small, having fewer than 400 students Additionally, 26% of students attended schools in high-poverty areas, while 22% were in very high-poverty areas.
Table 2.1 Demographic and fitness-change characteristics of the study population (N49
Change in Fitness (all years)
Change in Obesity Status (all years)
N missing Place of Birth r; N missing Area Poverty =7; N missing or > 1 race/ethnicity 7
2.3.2 Descriptive trends in attendance at the individual- and school-level
Table 2.2 illustrates attendance trends based on student and school characteristics, revealing that the average number of days absent per year varies significantly Students with a decrease in attendance of more than 20% had the highest mean absences at 11.91 days (SD 0.79), followed by those with a decrease of 10-20% at 11.14 days (SD 0.16), less than 10% decrease at 10.71 days (SD 0.88), and those with minimal decrease at 10.29 days (SD 0.27) and 10.26 days (SD 0.15).
29 change, 10-20% increase, and >20% increase, respectively, in fitness composite percentile scores from the year prior (Figure 2.2)
Mean days absent per year were highest among females (M.98 (SD.67)), and American Indian, Hispanic and Non-Hispanic Blacks race/ethnic groups (M.00
(SD.80), M.60 (SD.85), and M.28 (SD.05), respectively) Mean days absent were also highest among students who spoke English in the home (M.89 (SD.08)) and who were born in NYC (M.36 (SD.08))
Students at small schools exhibited a higher average number of absentee days compared to their counterparts in non-small schools, with means of 9.96 days (SD 3.4) versus 2.5 days (SD 0.7) Additionally, the variation in absenteeism among different demographic factors was most pronounced among students in schools located in very high-, high-, medium-, and low-poverty areas, with means of 10 days (SD 3.1), 13 days (SD 6.9), 9.49 days (SD 3.1), and 8.51 days, respectively.
Figure 2.2 Days absent across fitness-change categories Selection Flow Chart
Table 2.2 Days absent per year across student- and school-level demographic and fitness-change characteristics
Asian and/or Pacific Islander 5.50 7.65 6.37 8.25
Change in Fitness (all years)
N missing Place of Birth r; N missing Area Poverty =7; N missing or > 1 race/ethnicity 7.*School-level columns account for school-clustering; Student-level columns do not account for school- clustering
At the school level, students with the greatest decrease in fitness (over 20%) exhibited the highest mean days absent (M=65, SD=21), while those with the greatest increase in fitness scores showed the lowest absences (M=78, SD=54) Female students (M=23, SD=51) and those who spoke English at home (M=04, SD=93) also had the highest mean days absent Additionally, students born in New York City had a mean absence of (M=39, SD=55) Mean days absent varied by poverty levels, with students in very high poverty areas having the highest absences (M=10, SD=86), followed by high (M=37, SD=57), medium (M=9.83, SD=22), and low poverty areas (M=8.90, SD=9.26).
In a comparison of school-level attendance, Hispanic students exhibited the highest mean days absent at 27 (SD 18), followed closely by American Indian students with a mean of 85 (SD 43) and Non-Hispanic Black students at 80 (SD 43).
There was no significant difference in the average number of days absent per year between small and non-small schools, with both groups showing a mean absence of 76 days However, the standard deviations varied, with small schools having a standard deviation of 0.89 and non-small schools at 0.99.
2.3.3 Descriptive trends in attendance by fitness, grade, and school-area poverty
Attendance among NYC middle school students decreased from 2006/07 to 2012/13 as students progressed through the grades, particularly showing a significant drop from 7th to 8th grade This trend was evident at both the individual student and school levels.
A notable trend emerged showing that as grade levels increased, student attendance decreased, indicated by a rise in the number of days absent Specifically, students who experienced the most significant improvement in fitness, exceeding 20%, had an average of 9.56 days absent (SD = 11), compared to an average of 9.85 days absent for those with lesser fitness gains.
Students in grades 6, 7, and 8 exhibited an average of 8.81 (SD.81), 11.87 (SD.73), and 10.62 (SD.27) days absent, respectively In contrast, those with a significant decrease in fitness (over 20%) had higher mean absenteeism rates of 10.62 (SD.27), 11.57 (SD.62), and 13.87 (SD.32) for the same grades.
Attendance rates decline as fitness levels decrease and school-area poverty increases Students with a significant drop in fitness (over 20%) from schools in low-poverty areas had an average of 10.11 days absent, compared to 14.04 days for those in high-poverty areas Conversely, students showing a notable improvement in fitness (over 20%) in low-poverty schools had an average of 8.63 days absent, while those in high-poverty schools had 12.44 days absent.
Figure 2.3 Mean days absent by grade across fitness-change a categories b a Based on change in fitness composite percentile scores from the year prior b Based on tabulated mean estimates.
Figure 2.4 Mean days absent by school-area poverty across fitness-change a categories b a Based on change in fitness composite percentile scores from the year prior b Tabulated mean estimates.
2.3.4 Variation in student attendance accounted for by schools
The overall mean days absent across all schools (nschoolsb4) was 11.85 days per year
Table 2.5 illustrates the variances in student attendance both between and within schools The analysis revealed that the variation in attendance among students is significantly greater than that among schools Nonetheless, the Intraclass Correlation Coefficient (ICC) indicates that schools account for a notable 11% of the variance in student attendance, with a statistically significant p-value of less than 0.001.
Incorporating school-area poverty into the conditional model resulted in a notable reduction of the ICC estimate, which decreased to 9% (p20% increase, 10-20%, increase, 20% decrease) were 10.23 (SD.78), 9.75
(SD.57), 9.55 (SD.66), 9.32 (SD.06) and 9.14 (SD=9.86) vs 13.51 (SD= 13.77), 12.33 (SD.19), 11.60 (SD.28), 11.27 (SD.15) and 11.80 (SD.85), for 6 vs 8 th grades, respectively
Figure 3.1 Mean days absent by grade across fitness-change a categories in girls b a Based on change in fitness composite percentile scores from the year prior b Tabulated mean estimates
Figure 3.2 Mean days absent by grade across fitness-change a categories in boys b a Based on change in fitness composite percentile scores from the year prior b Tabulated mean estimates
Table 3.2 Mean in attendance a for New York City public school students in grades 6-
8, by level of fitness-change from the previous year across gender (N49381;
Change in Fitness d n Mean (SD) n Mean (SD) n Mean (SD)
>20% increase 27202 9.14(9.86) 22613 9.53(10.50) 17101 11.80(12.85) a Days absent per year b Observations account for 1-3 years of fitness-change per student c Tabulated mean estimates d Change in fitness composite percentile scores from the year prior.
Figure 3.3 Between- and within school-level variances in attendance in empty models and with fitness as the predictor
3.3.3 Longitudinal individual-level and school-level clustering of attendance
The average attendance rate across all schools was 11.93 days per year, as indicated by the model 1 intercept Variance in attendance was analyzed at both the student and school levels, revealing that between-student variation was significantly higher than within-student and between-school variations in both models Despite the higher variation among students, the Intraclass Correlation Coefficient (ICC) showed considerable clustering at the school level, accounting for 9% in both models Fitness contributed to the explainable variance in attendance, with 3% for between-school, 1% for between-student, and 3% for within-student variances Additionally, the variability in attendance due to fitness was 2.7% for girls and 2.3% for boys across schools, and 0.42% for girls and 0.71% for boys within schools, with 2.9% for girls and 4.2% for boys across observations within students.
3.3.4 Longitudinal causal effects of fitness-change on attendance
Model 2 results indicated a significant association between changes in fitness levels and subsequent attendance (p 1 race/ethnicity 7
4.3.3 Chronic absenteeism prevalence rates by demographics and fitness
Chronic absenteeism rates among students in grades 6, 7, and 8 were reported at 15%, 16%, and 20%, respectively, with significant variations based on fitness levels and demographics Students experiencing a decrease in fitness of over 20% had the highest absenteeism rate at 18%, while those with a 10-20% decrease followed closely at 16% Additionally, girls exhibited an absenteeism rate of 18%, and rates were notably higher among American Indian (21%), Hispanic (19%), and Non-Hispanic Black students (19%) English-speaking students also faced a high absenteeism rate of 19% Furthermore, students from very high poverty areas and those born in New York City or elsewhere in the U.S showed the highest rates of chronic absenteeism, at 22%, 17%, and 17%, respectively.
Table 4.2 Chronic absenteeism overall and across demographic and fitness- change characteristics (N49381 ) n %
Change in Fitness (all years) b
Very High Area Poverty 17283 22.01 a ≥20 days absent per year b Based on change in fitness composite percentile scores from the year prior Nmissing
Place of Birthi; Nmissing Area Poverty=7; Nmissing or > 1 race/ethnicity7
4.3.4 Cross-sectional fitness-change-chronic absenteeism association by grade
Table 4.3 and Figure 4.1 present cross-sectional logistic models examining the relationship between changes in fitness composite percentile scores and chronic absenteeism over one year, categorized by grade Compared to the reference category of a fitness increase greater than 20%, all fitness-change categories showed significant associations with chronic absenteeism (p20% increase in fitness scores from the year prior; all categories p