Adolescent Social Networks, Physical

Activity and Other Health BehaviorsJohn R Sirard10.20.2011

Overview

1. Background

2. Social factors related to physical activity (PA)

3. Social Networks, PA and Screen Time (cross-sectional results)

4. Future Directions (Social Network Analysis and Health – NIH FOA)

1. Background• Physical Activity: Any bodily movement

produced by the contraction of skeletal muscle that increases energy expenditure above a basal level.– Not just sports and planned exercise– A behavior (not fitness or other physiological

outcomes)

% meeting PA recommendation: > 60 min of moderate-vigorous physical activity per day

9th 10th 11th 12th0

5

10

15

20

25

30

MalesFemales

Grade

% m

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PA Prevalence - A New Challenge (Troiano et al. 2008)

• 2009 YRBSS : 18.4% (Males: 24.8%; Females: 11.4%)• Measured by accelerometer…

• % meeting PA recommendation: > 60 min of moderate-vigorous physical activity per day

PA and Health in Youth

• Lipid profile • Blood Pressure• Overweight and obesity• Metabolic Syndrome

• CVD risk factors• Bone health• Depression, anxiety, mood, self-esteem

• QOL and fatigue of cancer survivors

PA and Other Health Behaviors

Trost SG. Physical Education, Physical Activity and

Academic Performance. Fall 2007 Research Brief. Active

Living Research (RWJF)

• “Students whose time in PE or school-based physical activity was increased maintained or improved their grades and scores on standardized achievement tests, even though they received less classroom instructional time than students in control groups”.

2. Social Factors Related to PA

Inter-personal

Intra-personal & Behaviors

Behavior settings

Systemic & Policy Factors

Unhealthy foods easily accessible, heavily

marketed to childrenHealthy foods are more

costly and difficult to obtain.

Urban sprawl, lack of pedestrian infrastructure

Cultural, Religious & Family Values

Reasons for eating; the quantity, quality, and types of foods

eaten; gender roles

Attributes of SelfGenetics.Self Efficacy impacts motivation and attempts to alter, modify, and maintain health behaviors.

EnvironmentExtreme climates,

walking or biking trails, public gyms and pools, public transportation,

School/work environments

Physical ActivityLack of PA and excess sedentary activities

DietUnhealthy dietary behaviors; low intake of fruits and vegetables

Core RelationshipsFamily, peers, authority figures offering different types of support for healthy behaviors

Policy

TechnologyLabor saving devices, automobiles, communication technology

Social Factors and PA

• Social Support for Physical Activity– Emotional Support (encourages you to do physical

activity)– Informational Support– Tangible Support (do physical activities with you, pay

for equipment / fees)

• Perceived support for PA from family and friends positively associated with adolescent PA– typically

Social Networks

Social Networks and Obesity

• Christakis and Fowler (NEJM 2007)

– Framingham Cohort Study with 30 years of follow-up

• “…obesity appears to spread through social ties”.

– National Longitudinal Study of Adolescent Health• Strauss 2003, Trogdon 2008

• Challenges to analysis and interpretation (Lyons 2011)

3. Social Networks, PA and Screen Time (cross-sectional results)

Project EAT 2010

• Eating and Activity among Teens– New cohort of 2,793 middle and high school students from 20

Mpls/St. Paul middle schools and high schools (2009-2010)

– Weight-related behaviors (PA, screen time, diet, weight control practices, disordered eating) and

– Related multi-level correlates (individual, peer, family, school, neighborhood)

– Main “outcome”: weight status

Social Networks and PA in Project EAT 2010Best Male

friend

Male friend 2

Male friend 3

Best Female friend

Female friend 2

Female friend 3

• Purpose: To examine how friends’ PA and screen time is related to an individual adolescent’s PA and screen time by using data from nominated friends

• Hyp: Associations between ego and friend PA and screen time would be strongest between same gender and weakest for opposite gender associations.

• Hyp: Associations between ego and friend behaviors would be stronger for high school versus middle school students.

Example of network connections at one middle school. Triangles indicate Girls, Circles indicate Boys, the numbers in the symbol

represent the grade level.

How many friends are enough?• Sensitivity Analyses

1. Data from all 6 nominated friends (n=251)• From smaller, lower income, more ethnically diverse

schools – not representative of larger Project EAT sample

2. Data from all but one nominated friend (n=585)• Also not representative of larger sample

3. Data from at least 1/3 of nominated friends (n=1655)

4. Data from at least one nominated friend (n=2126)

Table 1: Sample characteristics for n= 2126 Egos with any friends in dataset. Values are mean+SD or n (percent)

  Males (n=983) Females (n=1143) p-valueAge (years) 14.29±2.01 14.11±1.88 0.03Grade

Middle School (n=1114) 504 (51.3) 610 (53.4)0.34

High School (n=1012) 479 (48.7) 533 (46.6)Ethnicity group

White (n=426) 224 (22.8) 202 (17.7)

0.01

African American/Black (n=567) 260 (26.5) 307 (26.9)Latino/Hispanic (n=373) 164 (16.7) 209 (18.3)Asian American (n=409) 195 (19.8) 214 (18.7)Native American (n=87) 40 (4.1) 47 (4.1)Mixed/Other (n=264) 100 (10.2) 164 (14.4)

Socioeconomic StatusMissing (n= 64) 31 (3.2) 33 (2.9)

0.61Low (n=611) 286 (29.1) 325 (28.4)Low-middle(n=526) 249 (25.3) 277 (24.2)Middle(n=717) 320 (32.6) 397 (34.7)

Upper-Middle + High (n=208) 97 (9.8) 111 (9.7)

Table 1 (cont’d): Sample characteristics for n= 2126 Egos with any friends in dataset. Values are mean+SD or n (percent)

  Males (n=983) Females (n=1143) p-valuesWeight Status * Not overweight (n=1272) 566 (57.6) 706 (61.8)

0.67 Overweight (n=854) 417 (42.4) 437 (38.2)

Physical Activity (hrs/wk) 6.83±4.84 4.98±4.40 <.001Screen Time (hrs/wk) 45.15±28.85 36.51±23.92 <.001

Number of friends 2.64+sd 2.62+sd 0.XXNumber of Male friends 1.49+sd 1.01+sd 0.XXNumber of Female friends 1.15+sd 1.61+sd 0.XXOverweight status is defined as > 85th percentile on the Centers for Disease Control and Prevention’s age and gender specific Body Mass Index curvesBMI = kg / m2

Table 2. Estimated change in male’s physical activity or screen time for every 1 hour increase in friends’ mean (or best

friend’s) physical activity or screen time

Male

FriendsFemale Friends

Male Best Friend

Female Best Friend

Male Ego n=838 n=668 n=483 n=389

Physical Activity 0.059 (.037) 0.114 (.042) 0.061 (.042) 0.034 (.045)

p-value 0.111 0.007 * 0.149 0.448

Screen Time 0.024 (.042) 0.111 (.054) -0.025 (.043) 0.110 (.072)

p-value 0.559 0.038 0.558 0.129

Adjusted for Ego’s age, SES, race/ethnicity, BMI, and number of friends available* Significant interaction with school level (middle vs. high school)

?

Table 3. Estimated change in female’s physical activity or screen time for every 1 hour increase in friends’ mean (or best

friend’s) physical activity or screen time

Male

FriendsFemale Friends

Male Best Friend

Female Best Friend

Female Ego n=723 n=1007 n=404 n=612

Physical Activity 0.122 (.025) .182 (.0439) .086 (.026) .128 (.052)

p-value <.0001 <.0001 0.0009 0.0142 *

Screen Time .097 (.039) .091 (.043) -.010 (.032) .022 (.056)

p-value 0.012 0.032 0.75 0.696

Adjusted for Ego’s age, SES, race/ethnicity, BMI, and number of friends available* Significant interaction with school level (middle vs. high school)

Strengths and Limitations

• Strengths– Large, diverse sample– Novel method and analyses

• Limitations– Cross-sectional– Incomplete network data

Conclusions

• Several associations between adolescent and friend PA and screen time

• Consistent associations for female PA support previous research on youth PA and sports participation

• Limited associations between Male’s PA and screen time vs. friends’ PA and screen time

4. Future Directions

NIH Funding Opportunity Announcement

• PAR-10-145, “Social Network Analysis and Health”

• Mechanism: R01• Due: May 11, 2012• Start: April 2013

Overall Research Goal

• Do changes in friend networks from 8th through 10th grade predict changes in weight-related behaviors of individuals as well as sub-networks (cliques) within a school? – Longitudinal, multiple time points– More complete network data

• Include multi-level predictors of PA and screen time as mediators of the hypothesized behavior changes – individual-level psychosocial factors, family/home

environment, school, social and physical environments of neighborhood.

Possible Research Questions• How are these weight-related behaviors transmitted within school-

based peer networks? – If an active person in 8th grade becomes friends with a group of inactive

individuals in 9th grade, does this individual become less active themselves or do they maintain their activity level?

– Does the network become more like the new friend or is there no effect? – Why?

– What role do PA, sport, TV viewing, video games play in selecting new friend(s)

– What are the characteristics of the individual and those in the network and how do those factors mediate behavior change of the individual or the network?

poorly written Specific Aims1. To determine if changes in an individual’s social network (to a

network with different weight-related behaviors) results in changes in the individual’s weight-related behavior that is similar to the network’s behavior

2. To determine if changes in the weight-related behaviors of an individual(s) within a clique influences the behaviors of others in the clique.

3. To identify the… A. Individual and network characteristics that promote, maintain, or

increase PA within the networkB. Individual and network characteristics that promote, maintain, or

increase screen time within the network

Additional Research Questions(School-level Analysis)

• Does the “spread” of student athletes within non-sports clubs/organizations and across sub-networks (cliques) affect the overall physical activity level of the school?

• Collect rosters for school-based sports teams and selected formal non-sports clubs (e.g., student government, newspaper, yearbook, band). – Hyp: School-level physical activity will be greater in schools with a

greater “spread” of student athletes within non-sport groups/clubs, and across cliques due to the added exposure of other students to those student athletes, compared to schools where student athletes stay within limited insular cliques.