Determinants of Physical Activity and Screen Time Trajectories in 7th to 9th Grade Adolescents—A Longitudinal Study

Krist, Lilian; Roll, Stephanie; Stroebele‐Benschop, Nanette; Rieckmann, Nina; Müller‐Nordhorn, Jacqueline; Bürger, Christin; Willich, Stefan N.; Müller‐Riemenschneider, Falk

doi:10.3390/ijerph17041401

Cited by 11 publications

(7 citation statements)

References 55 publications

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“…This study found a significant relationship between technology consumption time and the child’s age. Other studies found that older children are more likely to spend more time using technology than younger children [ 21 , 60 , 72 ]. This association may be attributed to the fact that older children are more skilled in using technology and able to access different types of technology [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Technology Use and Physical Activity among Typically-Developing Children

et al. 2020

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Objectives: This study aimed to investigate the relationship between technology use and physical activity level and to measure the association between sociodemographic characteristics of the participants, technology use, and physical activity level among Saudi children. Materials and Methods: This cross-sectional study was conducted among 458 parents of typically-developing Saudi children (6–12 years). A translated validated questionnaire used for data collection consisted of three parts: Children’s Physical Activity Questionnaire (CPAQ), Questionnaire on the Impact of Technology on Children (used to investigate the impact of technology on children’s physical activity) and sociodemographic questions (e.g, children’s age and sex, age, educational level, marital status of parents and monthly income). Data were analyzed using Pearson correlation and Mann-Whitney U test to assess the relationship between technology use and physical activity level. A chi-squared test was used to assess the relationship between technology use and sociodemographic variables. Statistical significance was set at p < 0.05. Results: Mean age of the sampled children was (8.44 ± 2.07). Data analysis revealed that high use of technology was significantly associated with low level of activity. Pearson’s correlation analysis showed a negative relationship between a high level of activity and technology use (r = −0.138, p = 0.047). Ownership of a device was significantly associated with higher technology time consumption. Regression analysis revealed that age of the child, educational level of the parents, screen time use, and owning electrical devices significantly predicted the level of practicing physical activity among children of sampled parents (p < 0.05). Conclusions: practicing inadequate physical activity among children could be influenced by educational level of parents, screen time use, and owning electrical devices. Therefore, parental involvement is required to reduce time of exposure to technology screens among children.

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Section: Discussionmentioning

confidence: 99%

The Relationship between Technology Use and Physical Activity among Typically-Developing Children

et al. 2020

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“…Decreasing MVPA trajectories have been reported previously. Krist et al, in a longitudinal study of grade 7-9 adolescents (n = 2122, 50.2% females), found that 17% of participants were members of a decreasing MVPA trajectory measured using self-report hours per week (Krist et al 2020). Faroq et al identified only decreasingtotalvolume of MVPA(measuredwith accelerometers) trajectories in boys (n = 217) and girls (n = 214) between ages 7 and 15 years (Farooq et al 2018).…”

Section: Heterogeneity In Mvpa and Screen Time Trajectoriesmentioning

confidence: 99%

“…An additional argument against a one-size-fits-all public health approach relates tosexdifferences in MVPA and sedentary time. Generally, adolescent boys accumulate more MVPA and are more likely than girls to exhibit favourable MVPA trajectories (i.e., increasing or consistently high MVPA levels) (Krist et al 2020). Further, boys accumulate more sedentary time, especially computer and television (TV) time (i.e., screen time) than girls (Roberts et al 2017;Ohannessian 2009) and they are less likely to exhibit favourable screentime trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…Physical activity in trajectory investigations is measured using highly variable methods (i.e., objective (Farooq et al 2018;Kwon et al 2015a) or subjective (Findlay et al 2009(Findlay et al , 2010Howie et al 2016;Kwon et al 2015aKwon et al , 2015b; parent-and/or child-reported (Findlay et al 2009(Findlay et al , 2010; as organized physical activity (Findlay et al 2009), unorganized physical activity (Findlay et al 2010), or team participation (Kwon et al 2015a); sex-specific (Farooq et al 2018;Findlay et al 2009Findlay et al , 2010 or whole-sample (Audrain-McGovern et al 2012;Parker et al 2019a)), and this methodological variety hinders consistent conclusions about physical activity development during adolescence. In regard to sedentary or screen time (i.e., a subset of total sedentary time), most studies examine screen time trajectories measured in hours daily orweekly, and report stable high, moderate orlow,as well as increasing or decreasing trajectories (da Silva et al 2017;Krist et al 2020;Kwon et al 2015aKwon et al , 2015bMcVeigh et al 2016;Yang et al 2019). Differing results across studies create ambiguity inthe literature and raise questions about the extent to which lack of consistency in methodology underpins the differing results.…”

Section: Introductionmentioning

confidence: 99%

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Contemporaneous trajectories of physical activity and screen time in adolescents

Riglea

Doré

O’Loughlin

et al. 2021

Appl. Physiol. Nutr. Metab.

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Adolescents often report low moderate-to-vigorous physical activity (MVPA) and high screen time. We modeled sex-specific MVPA and screen time trajectories during adolescence and identified contemporaneous patterns of evolution. Data were drawn from two longitudinal investigations. NDIT included 1294 adolescents recruited at age 12-13 who completed questionnaires every 3 months for 5 years. MATCH included 937 participants recruited at age 9-12 who completed questionnaires every 4 months for 7 years. MVPA was measured as the number of days per week being active for at least 5 minutes (NDIT) or 60 minutes (MATCH). In both studies, screen time was measured as the number of hours spent weekly in screen activities. In each study, sex-specific group-based trajectories were modeled separately for MVPA and screen time from grade 7 to 11. Contemporaneous patterns of evolution were examined in mosaic plots. In both studies, five MVPA trajectories were identified in both sexes, and four and five screen time trajectories were identified in boys and girls, respectively. All combinations of MVPA and screen time trajectories were observed. However the contemporaneous patterns of evolution were favorable in 14-31% of participants (i.e., they were members of the stable high MVPA and the lower screen time trajectories). Novelty bullets: • MVPA and screen time trajectories during adolescence and their combinations showed wide variability in two Canadian studies. • Up to 31% of participants showed favorable contemporaneous patterns of evolution in MVPA and screen time. • Using uniform methods for trajectory modeling may increase the potential for replication across studies.

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“…Concerning PA, several studies have reported also different developmental trajectories for PA [43][44][45][46][47][48][49][50]. According to a recent systematic review of distinct trajectories of PA, three or four different trajectory classes were most frequently found among the reviewed studies, and several of the trajectories described a decline in PA during later childhood and adolescence [51].…”

Section: Introductionmentioning

confidence: 99%

Physical activity and psychopathology: are long-term developmental trajectories of physical activity in children and adolescents associated with trajectories of general mental health problems and of attention-deficit hyperactivity (ADHD) symptoms?

Ganjeh,

Hagmayer,

Meyer

et al. 2024

Eur Child Adolesc Psychiatry

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A medium-to-high level of physical activity (PA) may have at least a short-term positive effect on psychopathology in children and adolescents. Hence, the objective of this study was to investigate the long-term effects of PA in non-adult age groups on their general mental health problems and/or ADHD symptoms, using trajectories of concurrent development over a period of 10 years. This study employed data from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) collected at three time points (baseline, Wave 1, Wave 2, over 10 years) from 17,640 children and adolescents. Using parent-reported data from the Strengths and Difficulties Questionnaire (SDQ), different developmental trajectories of general mental health problems (SDQ-total) and ADHD symptoms (SDQ-H/I) were identified with latent class mixed models (LCMM) statistics. This was also applied to parent- and self-reported data of three levels of PA. The latter was assessed according to WHO recommendations. The joint probability of class membership for SDQ-total as well as ADHD symptoms with PA was calculated to generate the concurrent developmental trajectories between variables. Results showed a 4-class trajectory model for both SDQ-total and ADHD symptoms among boys and girls. The majority of children and adolescents showed “low general difficulties” and “low ADHD symptoms” over the period of 10 years. Three distinct trajectories in boys and four distinct trajectories in girls were found for PA. Most of the participants showed an “increasing–decreasing activity” trajectory. No statistically significant correlations were found between the different SDQ-total or ADHD symptom trajectories and the trajectories of PA in the two genders. Taken together, our findings did not indicate any significant relationship between waxing and waning PA course over 10 years and various classes of mental health problems for children and adolescents. In contrast to our cross-sectional findings, no steady long-term medium/high-level of PA was present, which could (at least partly) explain the non-significant findings.

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Determinants of Physical Activity and Screen Time Trajectories in 7th to 9th Grade Adolescents—A Longitudinal Study

Cited by 11 publications

References 55 publications

The Relationship between Technology Use and Physical Activity among Typically-Developing Children

The Relationship between Technology Use and Physical Activity among Typically-Developing Children

Contemporaneous trajectories of physical activity and screen time in adolescents

Physical activity and psychopathology: are long-term developmental trajectories of physical activity in children and adolescents associated with trajectories of general mental health problems and of attention-deficit hyperactivity (ADHD) symptoms?

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