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Nov 05, 2022 211 likes •439 views

Explo loring th the Associati tion Between Affect Varia iabil ilit ity and Dail ily Activ ivit ity Patt ttern Metrics in in Chil ildren Chih-Hsiang (Jason) Yang, PhD Eldin Dzubur, PhD Jaclyn. P. Maher, PhD Britni. R. Belcher, PhD,

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Chih-Hsiang (Jason) Yang, PhD Eldin Dzubur, PhD

  • Jaclyn. P. Maher, PhD
  • Britni. R. Belcher, PhD, MPH

Donald Hedeker, PhD

  • Genevieve. F. Dunton, PhD, MPH

Presenting at the Society for Ambulatory Assessment, Syracuse University, NY. 01/30/19

Explo loring th the Associati tion Between Affect Varia iabil ilit ity and Dail ily Activ ivit ity Patt ttern Metrics in in Chil ildren

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Background

  • Prevalent inactivity level among children - global health issue1-2
  • Novel activity pattern metrics also can lead to health benefits4-7

▪ Sporadic (i.e., intermittent) moderate-to-vigorous physical activity (MVPA) ▪ Sedentary breaks

  • Variations in affect may impact everyday activity patterns3

Whether children’s within-person mean and variability in momentary affect predict daily activity pattern metrics? Potential moderation effects?

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Mothers’ and Their Children’s Health (MATCH) Study

▪ 6-wave EMA cohort study across 3 years

  • 192 Children

▪ Age: 8-14 (SD=1.25) ▪ 51% female ▪ 50% Hispanic

Funding: R01HL119255 (Dunton, PI)

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Children’s EMA data collection protocol EMA design for children:

▪ 3 random prompts on weekdays ▪ 7 random prompts on weekend days

EMA platform:

▪ A customized Android app

At least 60 minutes between 2 consecutive prompts

8:00 00 AM AM 10:00 :00 PM PM 11:29 AM 12:36 PM 2:01 PM 4:25 PM 7:33 PM 8:06 AM 5:55 PM

Random prompt for children on a weekend day

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EMA items measuring children’s momentary affect

Positive affect items: ▪ Happy ▪ Joyful Negative affect items: ▪ Mad ▪ Sad ▪ Stressed

  • Averaged scores created for

positive and negative affect

On a 4-point Response Scale

(Ebesutani et al., 2012)

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Some children have relatively more erratic affect than others

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Some children have relatively higher/lower mean affect

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Accelerometry data provide rich information for studying the pattering of movement behaviors

ActiGraph GT2M / GT3X

Day 1 Day 2 Day 3 Day 4 Day 5

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Daily sedentary breaks Daily sporadic MVPA events

(bursts lasting <10 mins)

Sedentary breaks Sporadic MVPA

Two activity outcome measures

Mean=27.59 SD=7.85 Mean=84.58 SD=12.20

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  • Novel statistical software that allows for the subject-level random

effects of time-varying variables to influence subject-level outcomes

  • Two-stage data analysis approach:

▪ Stage 1: Mixed Effects Location Scale Model ▪ Estimate subject-level mean and variation in affect ▪ Stage 2: Multiple Linear Regression Model ▪ Predict mean daily sporadic MVPA and sedentary breaks

Applying MIXWILD Program

MIXed models With Intensive Longitudinal Data

Hedeker and Nordgren (2013)

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Stage 1 – Mixed-effects location scale (Mixregls) model

Modeling Momentary Positive / Negative Affect (Level-1 variable)

Random intercept (mean)

  • f positive / negative affect

Random scale (variability)

  • f positive / negative affect

Stage 2 – Multiple linear regression model

Predicting Daily Averaged Activity Pattern Metrics (Level-2 variable)

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  • Children with similar levels of variations in momentary affect

may have different daily activity patterns ▪ Depending on demographics or mean levels of affect

  • Add interaction terms in the Stage 2 model

Testing Potential Moderation Effects in Stage 2

Affect Variations Activity Patterns Sex / Hispanic / Mean Affect

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Stage 1 Stage 2 EMA Momentary Negative Affect Random Intercept of Negative Affect Random Scale of Negative Affect Children’s Averaged Daily Sporadic MVPA Events

Stage 1 time-varying covariates

  • Weekend vs weekday
  • EMA prompt delivery time
  • Children’s age

Stage 2 time-invariant covariates

  • Children’s sex
  • Hispanic vs non-Hispanic
  • Interactions:

Random intercept x sex Random intercept x Hispanic Random scale x sex Random scale x Hispanic Random intercept x random scale

EMA Momentary Positive Affect Random Intercept of Positive Affect Random Scale of Positive Affect Children’s Averaged Daily Sporadic MVPA Events Control variables Model 1 Model 2

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EMA Momentary Negative Affect Stage 1 Stage 2 Random Intercept of Negative Affect Random Scale of Negative Affect Children’s Averaged Daily Sedentary Breaks

Stage 1 time-varying covariates

  • Weekend vs weekday
  • EMA prompt delivery time
  • Children’s age

Stage 2 time-invariant covariates

  • Children’s sex
  • Hispanic vs non-Hispanic
  • Interactions:

Random intercept x sex Random intercept x Hispanic Random scale x sex Random scale x Hispanic Random intercept x random scale

EMA Momentary Positive Affect Random Intercept of Positive Affect Random Scale of Positive Affect Children’s Averaged Daily Sedentary Breaks Control variables Model 3 Model 4

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Number of level-2 clusters = 192; aLog-transformed at stage 1; *p<.05, ***p<.001.

Variable Negative affecta predicting Daily short MVPA bouts Positive affect predicting Daily short MVPA bouts Intercept 43.164*** 29.030*** Sex (male=1)

  • 0.113
  • 1.368

Hispanic (=1) 1.211

  • 1.519

Random intercept (mean) 0.222 0.144 Random intercept x sex 2.564 1.012 Random intercept x Hispanic

  • 3.062
  • 1.031

Random scale (variability) 5.445*

  • 0.852

Random scale x sex

  • 3.500

2.830* Random scale x Hispanic

  • 4.027

0.811 Random intercept x random scale

  • 1.070
  • 1.229*

Stage 2 estimates – Linear regression modeling (outcome=short MVPA bouts)

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Number of level-2 clusters = 192

Variable Negative affect predicting Daily sedentary breaks Positive affect predicting Daily sedentary breaks Intercept 84.760*** 85.591*** Sex (male=1)

  • 2.530
  • 2.983

Hispanic (=1) 0.678

  • 0.021

Random intercept (mean) 1.699 0.553 Random intercept x sex

  • 1.085
  • 0.588

Random intercept x Hispanic

  • 2.274

0.961 Random scale (variability) 3.663 0.181 Random scale x sex 0.902 3.633 Random scale x Hispanic

  • 2.944
  • 0.144

Random intercept x random scale

  • 1.456
  • 1.241

Stage 2 estimates – Linear regression modeling (outcome=sedentary breaks)

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(variability in positive affect)

Sex and mean levels of positive affect moderate the relation between positive affect variability and sporadic MVPA events

(variability in positive affect) (mean positive affect)

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  • High mean levels and more consistency in momentary positive affect

were associated with more sporadic MVPA events in children.

  • Sex moderated the relation between positive affect variability &

sporadic MVPA : ▪ Girls: more consistency in positive affect

  • > more sporadic MVPA
  • More variability in negative affect predicted more sporadic MVPA

events in children.

  • Mean levels and variability in momentary affect (both positive and

negative) did not predict sedentary breaks.

  • These preliminary results should be interpreted with caution.

Conclusions

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References:

  • 1. Tremblay, M. S., Barnes, J. D., Copeland, J. L., & Esliger, D. W. (2005). Conquering Childhood Inactivity: Is the Answer in the

Past? Medicine & Science in Sports & Exercise, 37(7), 1187–1194.

  • 2. Nader, P. R., Bradley, R. H., Houts, R. M., McRitchie, S. L., & O’Brien, M. (2008). Moderate-to-Vigorous Physical Activity

From Ages 9 to 15 Years. JAMA, 300(3), 295–305.

  • 3. Schwarzfischer, P., Gruszfeld, D., Stolarczyk, A., Ferre, N., Escribano, J., Rousseaux, D., … Grote, V. (2019). Physical Activity

and Sedentary Behavior From 6 to 11 Years. Pediatrics, 143(1), e20180994.

  • 4. Maher, J. P., Dzubur, E., Nordgren, R., Huh, J., Chou, C.-P., Hedeker, D., & Dunton, G. F. (2019). Do fluctuations in positive

affective and physical feeling states predict physical activity and sedentary time? Psychology of Sport and Exercise, 41, 153–161.

  • 5. Jefferis, B. J., Parsons, T. J., Sartini, C., Ash, S., Lennon, L. T., Papacosta, O., … Whincup, P. H. (2018). Objectively measured

physical activity, sedentary behaviour and all-cause mortality in older men: does volume of activity matter more than pattern of accumulation? Br J Sports Med, bjsports-2017-098733.

  • 6. Osei-Tutu, K. B., & Campagna, P. D. (2005). The effects of short- vs. long-bout exercise on mood, VO2max., and percent

body fat. Preventive Medicine, 40(1), 92–98.

  • 7. Saint‐Maurice, P. F., Troiano, R. P., Matthews, C. E., & Kraus, W. E. (2018). Moderate‐to‐Vigorous Physical Activity and

All‐Cause Mortality: Do Bouts Matter? Journal of the American Heart Association, 7(6).

  • 8. Benatti, F. B., & Ried-Larsen, M. (2015). The Effects of Breaking up Prolonged Sitting Time: A Review of Experimental
  • Studies. Medicine and Science in Sports and Exercise, 47(10), 2053–2061.
  • 9. Dunton, G. F., Liao, Y., Dzubur, E., Leventhal, A. M., Huh, J., Gruenewald, T., … Intille, S. (2015). Investigating within-day and

longitudinal effects of maternal stress on children’s physical activity, dietary intake, and body composition: Protocol for the MATCH study. Contemporary Clinical Trials, 43.

  • 10. Hedeker, D. (under review). MIXWILD: A new freeware program for multilevel statistical modeling of intensive longitudinal

data.

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  • Population-based data are needed
  • Examining novel features of both affect and device-based

activity data in EMA studies may advance tailored interventions to promote healthier behavioral patterns among children

  • MixWILD can be used to ask new research questions in intensive

longitudinal studies ▪ The subject-level random effects -> health outcomes

  • Individuals’ mean levels and variability in time-varying variables (i.e.,

affect) should be considered as meaningful dimensions in theories of physical activity motivation

Implications and future directions

For more information: chihhsiy@usc.edu

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Thank you for your attention!