Kinesiology Research Day 2021 Poster Presentations | School of Kinesiology Student Council

Household chaos in rural families: Exploring the relationship between child physical activity, sedentary behavior, and screen-time with the home environment

KIN Student Presenter

Stephanie Grace, PhD Student

Abstract

INTRODUCTION: Regular physical activity (PA) and limited sedentary behavior (SB) and screen-time are essential for children’s health. Household chaos (HHchaos), defined as disorganization characterized by noise and crowding, has been linked with negative health and behavioral outcomes in children.

MAIN PURPOSE: To examine associations between child PA, SB and screen-time with HHchaos in rural children.

METHODS: One-hundred-and-five parent/child dyads were enrolled in NU-HOME, a family-based, obesity prevention RCT. Baseline data from participants were analyzed using SAS 9.4.

RESULTS: Children were 8.96±1.05 years old, 58% female and 53% normal weight. Mean daily total PA, SB, and screen-time were 259.1±58.22 minutes, 499.9±77.46 minutes, and 2.1±1.42 hours, respectively. Unadjusted HHchaos scores (mean=5.04±3.6) were not significantly associated with child PA or SB but were positively associated with child screen-time (p<0.01). CONCLUSIONS: Our findings related to screen-time align with current literature. However, the relationship between HHchaos with PA and SB need to be explored further.

Comparison between Dance-Based Exergaming and Aerobic Dance on Young Adults’ Physiological and Psychological Outcomes

KIN Student Presenter

Wenxi Liu, PhD Student

Abstract

Purpose: In order to better develop effective physical activity programs among young adults, this study explored the effects of a dance-based exergaming on Chinese college students’ energy expenditure, enjoyment and self-efficacy in comparison with the traditional aerobic dance exercise.

Method: A one-group repeated-measures design was used for this study. A total of 40 college students (33 females; Mage = 21.16 years, SD = 2.1) completed two separate 20-minutes exercise sessions with a 10-minute interval on the same day: (1) Xbox 360 Kinect Just Dance exergaming session; and (2) traditional instructor-led aerobic dance exercise session. Participants’ energy expenditure (Kcal) was measured by the ActiGraph GT9X Link accelerometers, and their perceived enjoyment and self-efficacy were assessed via validated surveys following each session. The dependent t-test was used to examine the mean differences for all outcomes between two sessions, with the significance level set at 0.05.

Results: Dependent t-test indicated significant differences on participants’ enjoyment (t = -1.83, p = 0.04, Cohen’s d = 0.30). Specifically, participants in the dance-based exergaming session reported higher level of enjoyment (M = 3.96, SD = 0.65) as compared to aerobic dance session (M = 3.61, SD = 0.54). However, there was no significant difference for energy expenditure (Mexergame = 106.76 vs. Maerobic = 104.14) and self-efficacy (Mexergame = 4.06 vs. Maerobic = 3.95) between two sessions.

Conclusion: The findings suggested that, in dance-based exergaming play session, college students had comparable energy expenditure as the traditional aerobic dance session while experiencing more fun and enjoyment. This suggests that exergaming can be a fun exercise alternative for promoting physical activity among young adults. Future randomized control trials are needed to further conclude the effects of exergaming among this population.

Changes to Physical Activity During a Global Pandemic: A Mixed Methods Analysis Among A Diverse Population-Based Sample of Emerging Adults in the U.S.

KIN Student Presenter

Amanda Folk, PhD Student

Abstract

Purpose: Emerging adults’ lives have changed because of the COVID-19 pandemic. Physical
activity (PA) behaviors need to be examined to inform interventions and improve health.

Methods: Responses to the C-EAT (COVID-19 Eating and Activity over Time) study in 720
emerging adults (age=24.7±2.0yrs) were analyzed. This mixed-methods study quantitatively
examined self-report PA (mild PA, moderate-to-vigorous PA (MVPA), and total PA) and
sociodemographic variables longitudinally from 2018 and 2020. Qualitative responses on how
COVID-19 impacted PA (collected in 2020) were analyzed using a grounded theory approach.

Results: All PA intensity levels were lower during COVID-19 than 2018 (p’s <.0001). Over half
of the sample reported a decrease in MVPA (53.8%) and total PA (55.6%); 42.6% reported a
decrease in mild PA. Those of high SES reported different extents of change in total PA (p=.001)
compared to those of lower SES. Most (83.6%) participants reported COVID-19 influenced PA.
The most common explanations were decreased gym access, effects on outdoor PA, and increased
dependence on at-home PA.

Conclusion: Results indicate emerging adults would benefit from behavioral interventions and
health promotion efforts in response to the pandemic to facilitate PA engagement with a focus on
activities that can be easily performed in the home or in safe neighborhood spaces.

Examining Movement Characteristics in Individuals with Stroke During Object Hitting Task

KIN Student Presenter

Sarah Yoost, Undergraduate Student

Abstract

Introduction: Bimanual coordination after stroke is a complex interaction between motor and
visuospatial impairments of the affected and unaffected arm. This research involved a secondary
analysis of data collected using a validated object hitting task on the Kinarm robot that requires
motor abilities and visuospatial skills. The analysis aimed to identify movement characteristic
differences between the stroke and control groups.
Methods: Pre-existing data from 49 participants with stroke and 45 controls were collected. Data
for the stroke group was collected within one month of stroke. The object hitting task on the
Kinarm robot is a 2-minute 18 seconds task which has the participant hit as many of the 300 balls
dropping from 10 equally spaced bins as possible. The speed and number of balls dropping
increased throughout the task. This secondary analysis looked at average hand speeds,
percentage of moving time, number of speed maxima of each hand. Regression analysis was
used to determine how movement characteristics could explain the number of balls hit with each
hand and the total number of balls hit.
Results: Strong relationships in the stroke group were found between the number of speed
maxima (r 2 =0.4787), the percentage of time moving (r 2 =0.3816), and the average hand speed
(r 2 =0.2896) of the affected hand with the number of balls hit with the affected arm. These
relationships were not seen in the control group. The stroke group moved their affected hand less
and with lower speeds during the task than the control group, resulting in fewer balls hit.
Conclusions: The movement characteristics explained the number of balls hit for the stroke
group but not the control group. This indicates the success on the task may be dependent on
motor abilities in individuals with stroke but might depend more to visuospatial skills not
measured by these variables in controls.

Machine Learning Fails to Improve Marathon Time prediction Compared to Multiple Linear Regression

KIN Student Presenter

Nick Foreman, Masters Student & Anton Hesse, PhD Student

Abstract

Introduction: Predicting sport performance is common for professional athletes but also
interests recreational athletes, and previous approaches have primarily used a multiple
regression (REG) approach. Newer prediction techniques from machine learning (ML) and more
powerful computers may result in more accurate predictions than regression, even for more
inexperienced runners.
Purpose: To compare REG and ML methods in predicting marathon time using physiological
testing variables.
Methods: Recreationally active college students (n=127, 88 female) from 2016-2018 performed
physiological testing both before and after training for a marathon. Prior to marathon training,
participants completed a 2-mile time trial (2MI), hydrostatic weighing for body fat percentage,
steady state exercise (a 6-minute run at 75% velocity of 2MI), and a graded exercise test for
VO 2MAX ; these tests were repeated following marathon training. Changes between pre- and post-
training variables were expressed as delta values and also included in the dataset. Marathon
performance was predicted with REG using principal component analysis (PCA) and backwards
elimination, and ML methods included random forest (RF), recursive partitioning (RPART), and
bagging (BAG). ML models were run with and without PCA for comparison. Ensembles (ENS) of
available regression methods in R’s caret library were also used. Prediction approaches were compared with an out-of-sample root mean squared error (RMSE) on ~20% of the data (n = 28).
Results: The RMSE for the different methods were: REG: 0.440; RPART: 0.509; RF: 0.548; BAG:
0.550; ENS: 0.528. PCA increased RMSE in RPART (1.027), RF (0.677), and BAG (0.705).
Discussion: REG outperforms RF, RPART, and BAG when predicting marathon time, and ML
prediction worsens when PCA is applied. Ensembles of ML models fail to improve marathon
time prediction over individual ML models.

Investigating Relationships among Preschool Children’s Perceived Competence, Motor Skills and Physical activity: A Cross-Lagged Panel Model

KIN Student Presenter

Suryeon Ryu, PhD Student

Abstract

Background: Motor skill competence (MSC) and perceived competence (PC) are primary correlates
linked with physical activity (PA) participation, yet there is limited evidence of the mutual longitudinal or
temporal associations among these variables, especially in preschoolers. This study’s purpose, therefore, was to examine the bidirectional relationships between MSC and PA; MSC and PC; and PC and PA in
preschoolers over time.
Methods: Participants were 65 preschoolers (Mage = 4.45, SD = 0.46) recruited from two underserved
schools. All assessments of MSC, PC, and PA were measured in identical conditions at schools at
baseline (T1) and at the end of the eighth week (T2). We employed a cross-lagged model approach to
understand the bidirectional relationships of MSC, PC, and PA.
Results: Results showed that T1 MSC significantly predicted T2 MSC (p < 0.01) and T1 MSC
significantly predicted T2 PA only in girls (p = 0.03). Additionally, a cross-lagged effect of T1 MSC
and T2 PC was only observed in boys (p = 0.03). Lastly, a significant association for T1 MVPA and T2
PC was only observed in girls (p = 0.04).
Conclusions: Bidirectional relationships between the variables in preschoolers were not observed.
However, significant gender differences were observed in each cross-lagged model.

Investigating Relationships among Preschool Children’s Perceived Competence, Motor Skills and Physical activity: A Cross-Lagged Panel Model

KIN Student Presenter

Sarissa Falk, Masters Student

Abstract

Sports analytics is broadly described as the process of analyzing data, making decisions, and
implementing new strategies (Silva, 2016). Professional sports have been utilizing analytics to
increase player performance since the 1970’s. Analytics within sports has expanded beyond
player performance and has reached the business sector of sport. The primary priorities of
business analytics within professional sports are revenue generation and customer acquisition
(Bouchet et al., 2016). A common strategy to generate revenue is through customer retention
which can be studied by spectator attendance behavior. Previous research has utilized analytics
to determine factors that influence spectator attendance. When evaluating spectator attendance
within the Canadian Hockey League, findings suggest that the most popular days to attend games
are weekends and Mondays; team characteristics of winning and fighting drive attendance; and
precipitation reduces attendance (Paul et al., 2019). Although research has shown how business
analytics is applied in professional sports by examining spectator attendance behavior, minimal
research has investigated how this can be applied to collegiate athletics. With a focus of utilizing
business analytics and examining spectator attendance, our study aimed to determine the factors
that contributed to a decrease in game day attendance for the Gopher Men’s Ice Hockey team.
The University of Minnesota Athletics Department provided data on each of the men’s hockey
games from 2014-2019. Using a multiple linear regression analysis, we attempted to find a
relationship between the dependent variable, scanned attendance, and the independent variables.
Season tickets and old WCHA opponents were shown to positively predict scanned attendance,
and day of game and snow precipitation negatively predicted attendance. Our findings
demonstrate how business analytics can be applied to collegiate athletics to make better business,
marketing, and ticketing decisions through evidence-based data.

Recreational Screen Time Behaviors during the COVID-19 Pandemic in the U.S.: A Mixed-Methods Study among a Diverse Population-based Sample of Emerging Adults

KIN Student Presenter

Brooke Wagner, PhD Student

Abstract

It is important to build understanding of how screen time behaviors have changed during the COVID-19
pandemic in order to inform the design of health promotion interventions.

PURPOSE: The purpose of this mixed-methods study was to quantify and describe changes in recreational screen time from 2018 to 2020 among a diverse sample of emerging adults.

METHODS: The sample of emerging adults (n=716) reported their average weekly recreational screen time in 2018 as part of the EAT 2010-2018 (Eating and Activity over Time) longitudinal study and again during the COVID-19 pandemic in 2020. Additionally, participants qualitatively reported how events related to COVID-19 had influenced their screen time.

RESULTS: Weekly recreational screen time significantly increased from 25.9±11.9 hours in 2018 to
28.5±11.6 hours during COVID-19 (p<0.001). The form of screen time most commonly reported to
increase was TV shows and streaming services (n=233). Commonly reported reasons for changes in
screen time were boredom (n=112) and a desire to connect with others (n=52). In contrast, some
participants reported trying to reduce screen time because of its negative impact on their mental health
(n=32).

CONCLUSION: Findings suggest that screen time and mental health may be intertwined during
the pandemic as it may lead to poorer mental health for some emerging adults, while screen time may
be beneficial in promoting connectedness for others. Health professionals and public health messaging
may be able to promote specific forms of screen time to encourage social connection during the COVID-19 pandemic and beyond.