National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry

A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior

BACKGROUND

Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.

METHODS

Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.

RESULTS

Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.

CONCLUSIONS

This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

Comparison of children's physical activity profiles before and after COVID-19 lockdowns: A latent profile analysis

Physical activity is important for children's health, but moderate to vigorous physical activity (MVPA) declines with age. COVID-19 lockdowns resulted in reduced MVPA and increased sedentary time among children. Characterising children's activity patterns may help identify groups who are most likely to be inactive post-lockdown. Data were combined from a pre-COVID-19 cohort study on children aged 5-6 years (Year1: n = 1299), 8-9 years (Year4: n = 1223) and 10-11 years (Year6: n = 1296) and cross-sectional post-lockdown data from a natural experiment on 10-11-year-olds in 2021 (Year6-W1: n = 393) and 2022 (Year6-W2: n = 436). The proportions of time spent in MVPA, light physical activity (LPA) and sedentary time on weekdays and weekends were derived from accelerometer data. Latent class analysis was used to identify activity profiles pre and post-lockdown, and estimate pre-COVID-19 transitions between Year4 and Year6. We identified six pre-COVID-19 activity profiles in Year6, including a new profile characterised by very low MVPA and high sedentary time (19% of children). There was substantial movement between profiles at Year4 and Year6, with 45% moving to a profile with lower MVPA. Likelihood ratio tests suggested differences in Year6 activity profiles pre and post-lockdown, with a new post-lockdown profile emerging characterised by higher LPA. The percentage of children in the least active profiles (where under 20% meet UK physical activity guidelines), rose post-lockdown, from 34% pre-COVID-19 to 50% in 2021 and 40% in 2022. We also saw gender and socioeconomic gaps widen, and increased separation between high and low physical activity levels. Children's physical activity has changed post-COVID-19, in terms of who is being active and how. The impact varies by activity profile, which is influenced by gender and socio-economic position. A greater understanding of these differences and targeting of low active groups is needed to increase both individual and population levels of physical activity.

Use of latent class analysis as a method of assessing the physical activity level, sedentary behavior and nutritional habit in the adolescents' lifestyle: A scoping review

BACKGROUND

Currently, adolescents' lifestyle is commonly characterized by physical inactivity, sedentary behavior, and inappropriate eating habits in general. A person-oriented approach as Latent Class Analysis (LCA) can offer more insight than a variable-centered approach when investigating lifestyle practices, habits, and behaviors of adolescent population.

OBJECTIVE

The aim of the present study was to assess which variables are mostly used to represent the physical activity level, sedentary behavior SB) and nutritional habit in the adolescents' lifestyle in studies that used the LCA.

DESIGN

Scoping review.

METHODS

The study was a performed in accordance with the proposed criteria for systematic reviews and meta-analyses-Preferred Reporting Items for Systematic Reviews and Meta-Analyses and registered in PROSPERO (CRD42018108444). The original articles were searched in MEDLINE/PubMed, Scopus, ScienceDirect, Web of Science, PsycINFO, and SPORTdiscus. The Quality Assessment Tool analyzed the risk of bias of the included studies.

RESULTS

30 original articles were selected. The physical activity level (28 studies), SB and nutritional habits (18 studies) were the most common variable used to evaluate the adolescent's lifestyle by LCA model. Specifically, physical inactivity and high SB were the manifest variables with higher frequency in the negative latent classes (LCs) in adolescent girls. On the other hand, physical exercises and sports were activities more commonly labeled as positive LCs.

CONCLUSIONS

The LCA models of the most of selected studies showed that physical inactivity, high SB were the most common in the LCs with negative characteristics of the adolescents' lifestyle. Better understanding the results of analyzes of clusters of multivariate behaviors such as the LCA can help to create more effective strategies that can make the lifestyle of adolescents healthier.

Physical activity profiles in Parkinson's disease

Background

Identifying physical activity (PA) profiles of people with Parkinson’s Disease (PD) could provide clinically meaningful knowledge concerning how to tailor PA interventions. Our objectives were therefore to i) identify distinct PA profiles in people with PD based on accelerometer data, ii) explore differences between the profiles regarding personal characteristics and physical function.

Methods

Accelerometer data from 301 participants (43% women, mean age: 71 years) was analysed using latent profile analyses of 15 derived PA variables. Physical function measurements included balance performance, comfortable gait speed and single and dual-task functional mobility.

Results

Three distinct profiles were identified; “Sedentary” (N = 68), “Light Movers” (N = 115), “Steady Movers” (N = 118). “Sedentary” included people with PD with high absolute and relative time spent in Sedentary behaviour (SB), little time light intensity physical activity (LIPA) and negligible moderate-to-vigorous physical activity (MVPA). “Light Movers” were people with PD with values close to the mean for all activity variables. “Steady Movers” spent less time in SB during midday, and more time in LIPA and MVPA throughout the day, compared to the other profiles. “Sedentary” people had poorer balance (P = 0.006), poorer functional mobility (P = 0.027) and were more likely to have fallen previously (P = 0.027), compared to “Light Movers. The Timed Up and Go test, an easily performed clinical test of functional mobility, was the only test that could distinguish between all three profiles.

Conclusion

Distinct PA profiles, with clear differences in how the time awake is spent exist among people with mild-moderate PD.

Relationship between children physical activity, inflammatory mediators and lymphocyte activation: possible impact of social isolation (COVID-19)

Objective

Lifestyle and body composition may be simultaneously responsible for immune response modulation. This study aimed to compare plasmatic adipokines concentration and lymphocyte cytokine production in children with different daily steps (DS) range, as well as to discuss the potential negative impact of the social isolation during COVID-19 pandemic in this context. DS can be a useful and low-cost way of monitoring children's health status.

Study design

Fifty children were classified into clusters based in DS measured by pedometer: Sedentary Group (DS = 9338 ± 902 steps) and Active Group (DS = 13,614 ± 1003 steps). Plasma and lymphocytes were isolated and cultured to evaluate cytokine production.

Results

Sedentary group presented lower adiponectin (7573 ± 232 pg/mL), higher leptin (16,250 ± 1825 pg/mL) plasma concentration, and higher lymphocyte production of IL-17, IFN-gamma, TNF-, IL-2 in relation to active group, suggesting predominance of Th1 response. Otherwise, the active group presented higher lymphocyte supernatant concentration of IL-10 and higher regulatory T cell (Treg) percentage.

Conclusion

These results indicate that lymphocytes of children performing higher DS have an anti-inflammatory profile, especially of Treg. Besides, the prolonged social isolation in children during the COVID-19 pandemic, limiting physical mobility and exercise, reduces DS and increases adiposity, which could impair the immune system function and raise the susceptibility to inflammatory diseases.

Behavioral classes related to physical activity and sedentary behavior on the evaluation of health and mental outcomes among Brazilian adolescents

Latent Class Analysis can assist researchers interested in a better understanding of behavioral patterns and their association with health outcomes. This study aimed to identify lifestyle latent classes related to distinct domains of physical activity (PA) and sedentary behavior (SB) among adolescents and their association with health outcomes. This cross-sectional study included 217 Brazilian adolescents (15 to 18 years old, 49.3% female). The classes were based on moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), number of steps, sedentary behavior (SB), and screen time (ST). To assess these behaviors, participants wore an accelerometer for one week. ST, demographic characteristics, socioeconomic status, and signs of common mental disorders (CMD) were evaluated through questionnaires. Latent Class Analysis was used to identify lifestyle classes. Three classes were recognized: “Active—Non-sedentary” (class 1) with 28.1% of adolescents; “Inactive—Non-sedentary” (class 2), 48.85%; and “Inactive—Sedentary” (class 3), 23.04%. Sex and signs of CMD were associated with the prevalence of the classes. Female adolescents presented 4.48 (95% CI 2.04–9.77) times more chance of belonging to the “Inactive—Sedentary” (class 3). Adolescents who presented CMD had 11.35 (95% CI 3.45–101.1) times more chance of belonging to the “Inactive—Non-sedentary” (class 2). The interaction between sex and signs of CMD showed that girls with signs of CMD were 9.20 (95% CI 1.17–71.52) more likely to belong to the Inactive—Sedentary class than the “Active—Non-sedentary”. Results indicate that sex and signs of CMD can affect the prevalence of the classes. Our findings highlight that physical inactivity and SB can be associated with signs of CMD, especially in female adolescents.

Physical Activity and Adiposity in a Racially Diverse Cohort of US Infants

OBJECTIVE

Early life physical activity may help prevent obesity, but objective quantification in infants is challenging.

METHODS

A total of 506 infants were examined from 2013 to 2016. Infants wore accelerometers for 4 days at ages 3, 6, 9, and 12 months. Daily log-transformed physical activity counts were computed, averaged, and standardized across assessments. A linear mixed model was used to examine trends in standardized physical activity counts as well as associations between physical activity and BMI z score, sum of subscapular and triceps skinfold thickness for overall adiposity (SS+TR), and their ratio for central adiposity (SS:TR).

RESULTS

Among infants, 66% were black and 50% were female. For each additional visit, standardized physical activity counts increased by 0.23 (CI: 0.18 to 0.27; P < 0.0001). This translates to 126.3 unadjusted physical activity counts or a 4% increase for each visit beyond 3 months. In addition, a 1-SD increase in standardized physical activity counts (550 unadjusted physical activity counts) was associated with a 0.01-mm lower SS:TR (95% CI: -0.02 to -0.001; P = 0.03). However, standardized physical activity counts were not associated with BMI z score or SS+TR.

CONCLUSIONS

Physical activity increased over infancy and was associated with central adiposity. Despite limitations, researchers should consider objective measurement in infants.

From Total Volume to Sequence Maps: Sophisticated Accelerometer Data Analysis

PURPOSE

To date, epidemiological studies have focused on the potential health effects of total volume of physical activity (PA) or sedentary behavior (SB). However, two persons may have the same volume of PA or SB but accumulated in a completely different sequence. The pattern of accumulating PA and SB might be more important for health effects than the total volume. Therefore, the aim was to develop a sophisticated algorithm translating accelerometer data into detailed sequence maps considering how PA and SB are accumulated throughout the day.

METHODS

We developed a novel algorithm to convert accelerometer counts into a sequence map based on behavior states defined by a combination of intensity (SB, light, moderate, and vigorous intensity) and duration (sporadic accumulation or in bouts of different duration). In addition, hierarchical cluster analysis was applied to identify clusters of children with similar behavioral sequence maps.

RESULTS

Clustering resulted in seven clusters of children with similar PA and SB sequence maps: an average cluster (33% of children); a cluster with relatively more SB, light, and moderate PA in bouts (SB and PA bouters, 31%); a cluster characterized by more sporadic SB and light PA (light activity breakers, 26%); and four smaller clusters with 7% of the children or less.

CONCLUSION

This novel algorithm is a next step in more sophisticated analyses of accelerometer data considering how PA and SB are accumulated throughout the day. The next step is identifying whether specific patterns of accumulating PA and SB are associated with improved health outcomes.

Hour-by-hour physical activity patterns of adults aged 45-65 years: a cross-sectional study

Background

Limited information exists on hour-by-hour physical activity (PA) patterns among adults aged 45–65 years. Therefore, this study aimed to distinguish typical hour-by-hour PA patterns, and examined which individuals typically adopt certain PA patterns.

Methods

Accelerometers measured light and moderate-vigorous PA. GIS-data provided proportions of land use within an 800 and 1600 m buffer around participant’s homes. Latent class analyses were performed to distinguish PA patterns and groups of individuals with similar PA patterns.

Results

Four PA patterns were identified: a morning light PA pattern, a mid-day moderate-vigorous PA pattern, an overall inactive pattern and an overall active pattern. Groups of individuals with similar PA patterns differed in ethnicity, dog ownership, and the proportion of roads, sports terrain, larger green and blue space within their residential areas.

Conclusions

Four typical hour-by-hour PA patterns, and three groups of individuals with similar patterns were distinguished. It is this combination that can substantially contribute to the development of more tailored policies and interventions. PA patterns were only to a limited extent associated with personal and residential characteristics, suggesting that other factors such as work time regimes, family life and leisure may also have considerable impact on the distribution of PA throughout the day.

Combined Patterns Of Physical Activity And Screen-Related Sedentary Behavior Among Chinese Adolescents And Their Correlations With Depression, Anxiety And Self-Injurious Behaviors

Background and purpose

There are increasing concern about independent associations between physical activity, screen-based sedentary behavior (SSB), and psychological problems, but only a few studies have attempted to explore combined patterns of physical activity and SSB in adolescents and their correlations with psychological problems. This study was aimed at identifying combined patterns of moderate-to-vigorous physical activity (MVPA) and SSB and examining the prevalence of different combined patterns and their correlations with depression, anxiety, and self-injurious behavior among Chinese adolescents.

Methods

Junior and senior high school students (N = 13,659; mean age 15.18±1.89) were recruited. Latent class analysis was conducted to identify combined patterns of MVPA and SSB. Associations between subgroups of MVPA and SSB and socio-demographic characteristics were assessed by logistic regression. Their correlation with depression, anxiety, and self-injurious behaviors was assessed by analysis of variance with analysis stratified by gender.

Results

Four latent classes were identified: high MVPA/low SSB group (64.7%), low MVPA/low SSB (26.7%), low MVPA/high SSB (4.8%), and low MVPA/moderate SSB (3.9%). Generally, the high MVPA/low SSB class was a relatively healthy group. The low MVPA/high SSB class was at risk of enduring depression, anxiety, and self-injurious behavior, with boys being more at risk than girls.

Conclusion

Four latent subgroups of MVPA and SSB were identified in Chinese adolescents. The findings highlight the potential role of concurrent MVPA and SSB, with gender-specific characteristics in the primary prevention of adolescent depression, anxiety, and self-injurious behaviors.

Agreement Between GT3X Accelerometer and ActivPAL Inclinometer for Estimating and Detecting Changes in Different Contexts of Sedentary Time Among Adolescents

Background: This investigation aimed to analyze the agreement between the GT3X accelerometer and the ActivPAL inclinometer for estimating and detecting changes in sedentary behavior of different contexts among adolescents. Methods: Secondary data from an intervention using standing desks in the classroom conducted within 2 sixth-grade classes (intervention [n = 22] and control [n = 27]) were used. The intervention took place over 16 weeks, with activity assessments (ActivPAL and GT3X) being performed 7 days before and in the last week of the intervention. Baseline information from both groups was considered for cross-sectional analysis (209 valid days), while data from 20 participants (intervention group) were used for longitudinal analysis. Results: The authors observed that GT3X overestimated sedentary time at school (16.8%), after school (13.5%), and during weekends (7.3%) compared with ActivPAL (P < .05). Outside the school (after school [r = −.188] and on weekends [r = −.260]), there was a trend to higher overestimation among adolescents with less sedentary behavior. Longitudinally, the GT3X was unable to detect changes resulting from an intervention in school hours (ActivPAL = −34.7 min·9 h−1 vs GT3X = +6.7 min·9 h−1; P < .05). Conclusions: The authors conclude that GT3X (cut-point of <100 counts·min−1) overestimated sedentary time of free-living activities and did not detect changes resulting from a classroom standing desk intervention in adolescents.

Profiles of children's physical activity and sedentary behaviour between age 6 and 9: a latent profile and transition analysis

BACKGROUND

Physical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and large proportions of older children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles are associated with other covariates and how they change over time.

METHODS

Valid accelerometer data were collected for 1132 children aged 6 and 1121 at age 9, with 565 children with data at both ages. We calculated the proportions of total wear time spent in sedentary, light and MVPA activity on both weekdays and weekends. Latent profile (class) analysis was applied separately to the two age groups to identify activity profiles. We then used latent transition analysis to explore transitions between profiles at the two time points.

RESULTS

We identified five profiles of activity at age 6 and six profiles at age 9. Although profiles were not directly equivalent, five classes captured similar patterns at both ages and ranged from very active to inactive. At both ages, active profiles, where the majority achieved the recommended MVPA guidelines, were more likely to be active at weekends than on weekdays. There was substantial movement between classes, with strongest patterns of movement to classes with no change or a decrease in MVPA. Transition between classes was associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities.

CONCLUSIONS

This paper is the first to apply latent profile analysis to the physical activity of UK children as they move through primary school. Profiles were identified at ages 6 and 9, reflecting different weekday and weekend patterns of physical activity and sedentary time. There was substantial movement between profiles between ages 6 and 9, mostly to no change or less active profiles. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted.

Profiles of Physical Fitness Risk Behaviours in School Adolescents from the ASSO Project: A Latent Class Analysis

The aim of the present investigation was to describe profiles of adolescents’ fitness level, identify latent classes of fitness-related risk behaviours, and describe their sociodemographic and environmental predictors. In total, 883 adolescents (16.4 ± 1.4 years; 167.3 ± 10.4 cm; 62.8 ± 13.5 kg; 62.2% males) were assessed for personal and lifestyle information and for physical fitness components. Eleven possible fitness determinants and seven predictors were included. Latent class analysis (LCA) was used to determine fitness-related risk behaviours. Logistic regressions predicted class membership and assessed associations with fitness levels and fitness components. Five latent classes were recognised: 1—virtuous, 30.7% of respondents; 2—low physical activity/sport, 18.8%; 3—incorrect alcohol/food habits, 25.8%; 4—health risk/overweight, 15.9%; 5—malaise/diseases, 8.8%. Sex, age, parents’ overweightness/obesity and education, and school type predicted most classes significantly. Compared to class 1, class 2 had higher odds of having all poor fitness components except upper body maximal strength; class 4 had higher risk of low muscular endurance; and class 5 was likely to have lower maximal strength, muscular endurance, and speed/agility. Educating adolescents to reach a sufficient practice of PA/sport could help decreasing the risk of low health-related fitness more than discouraging them from using alcohol, addressing proper food behaviours and habits, and helping them understand their psychophysical malaise symptoms.

Physical activity phenotyping with activity bigrams, and their association with BMI

Background

Analysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person's activity levels change across (brief) time points. These variables can, for instance, differentiate between two people spending the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.

Methods

We use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar.

Results

We found several associations between changes in bigram frequencies and BMI. For instance, a one standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m2 lower BMI [95% confidence interval (CI): -3.47, -1.26], after accounting for the time spent in sedentary, low, moderate and vigorous activity.

Conclusions

Activity bigrams are novel variables that capture how a person's activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits.

Novel patterns of physical activity in a large sample of preschool-aged children

Background

Moderate-to-vigorous physical activity (MVPA), shown to be associated with health benefits, is not well-characterized in preschool-aged children. MVPA is commonly described as a threshold amount to achieve. We examined a novel way to characterize MVPA patterns in preschool-aged children by gender and age.

Methods

Preschool-aged children from Nashville, TN and Minneapolis, MN wore triaxial accelerometers. Four distinct MVPA patterns were identified: isolated spurt (IS), isolated sustained activity (ISA), clustered spurt (CS), and clustered sustained activity (CSA). Multivariable linear regression models were used to test associations of gender and age with each pattern.

Results

One thousand one hundred thirty-one children (3.9 years old, 51% girls, 30% overweight, 11% obese, and 76% Hispanic) wore accelerometers for 12.9 (SD = 1.4) hours/day for 6.7 (SD = 0.7) days. Children spent 53% of wear time in sedentary behavior and 13% in MVPA. On average, boys and girls achieved > 90 min/day of MVPA (98.2 min, SD = 32.3). Most MVPA (80%) was obtained in spurt-like (IS and CS) MVPA; however, girls spent a higher proportion of MVPA in IS and CS, and lower proportion of time in CSA (all p < 0.001). Controlling for gender, an increase of 1-year in age corresponded to a 1.5% increase in CSA (p < 0.05).

Conclusions

How MVPA was obtained varied depending on the gender and age of the child. On average, boys spent more time in sustained MVPA than girls and MVPA was more sustained in older children. Utilizing these patterns could inform PA practice and policy guidelines.

Trial registration

NCT01316653, date of registration: March 3, 2011; NCT01606891, date of registration: May 23, 2012.

Cardiometabolic Correlates of Physical Activity and Sedentary Patterns in U.S. Youth

INTRODUCTION

Daily or weekly averages of physical activity and sedentary behavior could mask patterns of behavior throughout the week that independently affect cardiovascular health. We examined associations between day-to-day physical activity and sedentary behavior latent classes and cardiovascular disease (CVD) risk factors in U.S. youth.

METHODS

Data were from 3984 youth ages 6-17 yr from the National Health and Nutrition Examination Survey (2003-2006) and from previously published accelerometry latent classes characterizing average counts per minute and percent of wear time in moderate-to-vigorous physical activity (MVPA) and sedentary behavior. Multiple linear regression was used to examine associations of the classes with waist circumference, systolic and diastolic blood pressure, HDL-C and LDL-C, triglycerides, glucose, and insulin.

RESULTS

Participants spent 50.4% of the day in sedentary behavior and 5.3% of the day in moderate-to-vigorous physical activity. Average counts per minute were 516.4 for a 7-d period. Significant differences in CVD risk factors were between extreme classes with few differences observed in intermediate classes. Youth in latent class 4 (highest average counts per minute) had lower systolic blood pressure (-4.11 mm Hg, 95% confidence interval [CI] = -7.74 to -0.55), lower glucose (-4.25 mg·dL, 95% CI = -7.84 to -0.66]), and lower insulin (-6.83 μU·mL, 95% CI = -8.66 to -4.99]) compared with youth in class 1 (lowest average counts per minute). Waist circumference was lower for the least sedentary class (-2.54 cm, 95% CI = -4.90 to -0.19) compared with the most sedentary class. Some associations were attenuated when classes were adjusted for mean physical activity or sedentary level.

CONCLUSIONS

There is some indication that patterns, in addition to the total amount of physical activity and sedentary behavior, may be important for cardiovascular health in youth. Longitudinal studies are needed to examine associations between physical activity and sedentary behavior patterns and changes in CVD risk factors.

Physical Activity and Sedentary Time Patterns in Children and Adolescents With Cystic Fibrosis and Age- and Sex-Matched Healthy Controls

BACKGROUND

Regular physical activity (PA) is increasingly recognized as important in the care of patients with cystic fibrosis (CF), but there is a dearth of evidence regarding physical activity levels or how these are accrued in those with CF.

METHODS

PA was measured by a hip-worn accelerometer for 7 consecutive days in 18 children [10 boys; 12.4 (2.8) y] with mild to moderate CF and 18 age- and sex-matched controls [10 boys; 12.5 (2.7) y].

RESULTS

Both children with CF and healthy children demonstrated similar physical activity levels and patterns of accumulation across the intensity spectrum, with higher levels of PA during weekdays in both groups. Forced expiratory volume in 1 second was predicted by high light PA in children with CF compared with low light PA in healthy children.

CONCLUSION

These findings highlight weekends and light PA as areas warranting further research for the development of effective intervention strategies to increase PA in the youth CF population.

Variations in adolescents' motivational characteristics across gender and physical activity patterns: A latent class analysis approach

BACKGROUND

Neglecting to take account of the underlying context or type of physical activity (PA) that underpins overall involvement has resulted in a limited understanding of adolescents' PA participation. The purpose of the present research was to identify male and female adolescents' leisure time PA patterns and examine whether psychological processes derived from self-determination theory differ as a function of the pattern of PA undertaken.

METHODS

Nine hundred ninety-five students (61.2% females, 38.8% males; M age = 13.72 years, SD = 1.25) from eight secondary schools in Dublin, Ireland completed a physical activity recall 7 day diary and measures of intrinsic motivation, competence, relatedness, autonomy and autonomy support. Based on the diary five binary indicators of physical activity were derived reflecting recommended levels of MVPA on a minimum of 3 days, at least three sessions of non-organized physical activity (e.g. jog), team sport, individual sport, and organized non-sport physical activity (e.g. dance). Latent class analysis was used to identify subgroups of adolescents that engaged in similar patterns of physical activity. Profiles of physical activity participation were subsequently compared on motivational characteristics using Kruskal-Wallis tests.

RESULTS

Latent class analysis revealed six distinct classes for girls (Organized Run/Swim & Dance/Gym; Organized Dance; Leisure Active Team Sport; Active Individual Sport; Walk/Run/Outdoor games; Non-Participation) and five for boys (Leisure Active Gym; Leisure Active Individual Sport; Active Team Sport; Active Mixed Type; Non-Participation). Significant differences were found between the classes. Girls characterized by participation in team or individual sport, and boys represented by team sport participation demonstrated significantly higher self-determined motivational characteristics relative to other profiles of physical activity.

CONCLUSION

This research offers a nuanced insight into the underlying type of activities that constitute overall patterns of PA among adolescent boys and girls and further reveals that psychological processes vary dependent on the profile of physical activity undertaken. The findings may be useful for informing interventions aimed at promoting physical activity among young people.

Applying latent class assignments for accelerometry data to external populations: Data from the National Health and Nutrition Examination Survey 2003-2006

Latent class analysis can identify unmeasured mutually exclusive categories (class membership) among participants for either observed categorical or continuous variables. More recently, latent class analysis has been applied to accelerometry to better understand the day-to-day patterns of physical activity and sedentary behavior. Typically, the class assignments are only relevant to the study for which they were derived and not made available for others to use. Using one-week accelerometry (ActiGraph #AM7164) data collected from the National Health and Nutrition Examination Survey during 2003–2006, latent classes of physical activity and sedentary behavior were derived separately for youths 6–17 years and adults >=18 years. The purpose of this article is to provide the latent class assignments developed on this source population (United States) available to others to apply to their studies using similarly collected accelerometry. This method will extend the usefulness of the latent class analysis and allow for comparisons across studies.