Most research on parenting and childhood obesity and obesity-related behaviours has focused on mothers while fathers have been underrepresented. Yet, recent literature has suggested that fathers ...uniquely influence their children's lifestyle behaviours, and hence could also affect their weight status, but this has not yet been scientifically proven. Therefore, the present study aimed to determine whether the association between fathers' weight status and their children's weight status is mediated by fathers' and children's movement behaviours (i.e. physical activity (PA) and screen time (ST)).
Cross-sectional data of 899 European fathers and their children were analyzed. Fathers/male caregivers (mean age = 43.79 ± 5.92 years, mean BMI = 27.08 ± 3.95) completed a questionnaire assessing their own and their children's (mean age = 8.19 ± 0.99 years, 50.90% boys, mean BMI
= 0.44 ± 1.07) movement behaviours. Body Mass Index (BMI, in kg/m
) was calculated based on self-reported (fathers) and objectively measured (children) height and weight. For children, BMI z-scores (SD scores) were calculated to obtain an optimal measure for their weight status. Serial mediation analyses were performed using IBM SPSS 25.0 Statistics for Windows to test whether the association between fathers' BMI and children's BMI is mediated by fathers' PA and children's PA (model 1) and fathers' ST and children's ST (model 2), respectively.
The present study showed a (partial) mediation effect of fathers' PA and children's PA (but not father's ST and children's ST) on the association between fathers' BMI and children's BMI (model for PA; coefficient: 0.001, 95% CI: 0.0001, 0.002; model for ST; coefficient: 0.001, 95% CI: 0.000, 0.002). Furthermore, fathers' movement behaviours (PA and ST) were positively associated with their children's movement behaviours (PA and ST) (model for PA, coefficient: 0.281, SE: 0.023, p < 0.001; model for ST, coefficient: 0.345, SE: 0.025, p < 0.001).
These findings indicate that the influence of fathers on their children's weight status partially occurs through the association between fathers' PA and children's PA (but not their ST). As such, intervening by focusing on PA of fathers but preferably of both members of the father-child dyad (e.g. engaging fathers and their children in co-PA) might be a novel and potentially effective strategy for interventions aiming to prevent childhood overweight and obesity. Longitudinal studies or intervention studies confirming these findings are however warranted to make meaningful recommendations for health intervention and policy.
The Feel4Diabetes-study is registered with the clinical trials registry http://clinicaltrials.gov , ID: 643708 .
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
To assess the relationship between time spent in light physical activity and cardiometabolic health and mortality in adults.
Systematic review and meta-analysis.
Searches in Medline, Embase, ...PsycInfo, CINAHL and three rounds of hand searches.
Experimental (including acute mechanistic studies and physical activity intervention programme) and observational studies (excluding case and case-control studies) conducted in adults (aged ≥18 years) published in English before February 2018 and reporting on the relationship between light physical activity (<3 metabolic equivalents) and cardiometabolic health outcomes or all-cause mortality.
Study quality appraisal with QUALSYST tool and random effects inverse variance meta-analysis.
Seventy-two studies were eligible including 27 experimental studies (and 45 observational studies). Mechanistic experimental studies showed that short but frequent bouts of light-intensity activity throughout the day reduced postprandial glucose (-17.5%; 95% CI -26.2 to -8.7) and insulin (-25.1%; 95% CI -31.8 to -18.3) levels compared with continuous sitting, but there was very limited evidence for it affecting other cardiometabolic markers. Three light physical activity programme intervention studies (n ranging from 12 to 58) reduced adiposity, improved blood pressure and lipidaemia; the programmes consisted of activity of >150 min/week for at least 12 weeks. Six out of eight prospective observational studies that were entered in the meta-analysis reported that more time spent in daily light activity reduced risk of all-cause mortality (pooled HR 0.71; 95% CI 0.62 to 0.83).
Light-intensity physical activity could play a role in improving adult cardiometabolic health and reducing mortality risk. Frequent short bouts of light activity improve glycaemic control. Nevertheless, the modest volume of the prospective epidemiological evidence base and the moderate consistency between observational and laboratory evidence inhibits definitive conclusions.
PURPOSEThis study aimed to compare three objective measures (GT1M ActiGraph, ActivPAL™, and direct observation) of sedentary behavior in preschoolers.
METHODSFifty-two 4- to 6-yr-old preschoolers ...wore an ActivPAL™ and a GT1M ActiGraph for five consecutive days and were videotaped for 1 h during classroom activities at preschool. The area under the receiver operating characteristic curve was calculated to assess the criterion validity of the ActivPAL™ (sitting/lying, with and without standing still) and the GT1M ActiGraph (<100 counts per minute) to estimate sedentary behavior (directly observed sitting behaviors). A two-way repeated-measures ANOVA was used to define the convergent validity of the ActivPAL™ and the GT1M ActiGraph sedentary behavior estimates across the measurement days. The practical utility of the ActivPAL™ was tested in the same sample by asking the parents how their child perceived wearing the ActivPAL™.
RESULTSResults indicated a poor classification accuracy for both devices (area under the receiver operating characteristic curve, 0.6) to measure sedentary behavior based on the direct observation, with and without the inclusion of standing. Time defined as sedentary behavior (sitting/lying) was lower for the ActivPAL™ compared with the GT1M ActiGraph (mean bias, 7.7%; limits of agreement, −29.01% to 13.6%). According to the parental reports, 38% of the preschoolers had skin irritation due to wearing the ActivPAL™ for consecutive days.
CONCLUSIONSLow classification accuracy was found for the ActivPAL™ and the GT1M ActiGraph to measure sedentary behavior in preschoolers. No correction factor can be suggested to make the sedentary estimates of the GT1M ActiGraph and the ActivPAL™ convergent as no systematic bias and wide limits of agreement were found. Furthermore, the practical utility of the ActivPAL™ was perceived to be lower compared with the ActiGraph accelerometer in preschoolers.
The 24-h day-containing physical activity, sedentary behaviour and sleep-in pre-school children has not yet been extensively investigated. The aim of the current study was to investigate ...pre-schoolers' compliance with the 24-h movement behaviour guidelines (i.e., three hours/day total physical activity, a maximum of one hour/day of screen time and 10⁻13 h sleep/night). In total, 595 pre-schoolers (53.3% boys, mean age: 4.2 years) provided complete data for the three behaviours. Physical activity was objectively measured with accelerometers, while screen time and sleep were parent-reported through questionnaires. The proportion of pre-schoolers complying with the 24-h movement behaviour guidelines was calculated on weekdays and on weekend days. Low compliance rates were found: 10.1% on weekdays and only 4.3% on weekend days. The majority of pre-schoolers complied with the sleep duration guidelines (>90% on weekdays and weekend days), followed by the screen time guidelines (61% on weekdays and 28% on weekend days). The lowest compliance rates were found for physical activity (<20% on weekdays and weekend days). Overall, low percentages of pre-schoolers complying with the 24-h movement behaviour guidelines were found, and the lowest compliance was found for physical activity.
High levels of childhood obesity have been observed globally over the last three decades. Preschools are promising settings to implement obesity prevention interventions in the early years. The aim ...of this study was to test the feasibility of a cluster randomised controlled trial of the ToyBox-Scotland preschool obesity prevention intervention.
Six preschools in predominantly deprived areas of Glasgow, UK, were randomised to either the ToyBox intervention (
= 3) or usual curriculum control group (
= 3). The intervention ran for 18 weeks from March-June 2018, and consisted of practitioner-led physical activity and sedentary behaviour sessions in preschools, with an additional interactive home component. Primary outcome measures were intervention fidelity, recruitment rates, attrition rates, and compliance with trial procedures. Secondary outcomes were body mass index (BMI)
-score, bioelectrical impedance analysis (BIA), objectively measured physical activity and sedentary time via activPAL accelerometer, and parent-reported home eating, snacking, and water consumption.
The preschool component of the intervention was implemented with high fidelity (64%), while the home component was implemented with low fidelity (41%). A cluster-level recruitment rate of 10% was achieved, and the individual-level recruitment rate was 18% (42/233 children, mean age 4.4 years; 17 girls). The attrition rate was 14%, and compliance rates varied considerably by the outcome. Compliance was highest for BMI (86%), while 19% of the sample returned valid accelerometer data for both baseline and follow-up and the parental questionnaire response rate was 23%. Both intervention and control groups showed small increases in BMI
-scores at follow-up of 0.02 and 0.06, respectively. Both groups had small decreases in physical activity and increases in sedentary time at follow-up.
Before progression to an effectiveness trial, additional procedures should be considered to improve recruitment rates, compliance with outcome measures, and implementation of the home-based component of the ToyBox-Scotland intervention.
ISRCTN12831555.
To date, the scientific literature on socioeconomic correlates and determinants of physical activity behaviours has been dispersed throughout a number of systematic reviews, often focusing on one ...factor (e.g. education or parental income) in one specific age group (e.g. pre-school children or adults). The aim of this umbrella review is to provide a comprehensive and systematic overview of the scientific literature from previously conducted research by summarising and synthesising the importance and strength of the evidence related to socioeconomic correlates and determinants of PA behaviours across the life course.
Medline, Embase, ISI Web of Science, Scopus and SPORTDiscus were searched for systematic literature reviews and meta-analyses of observational studies investigating the association between socioeconomic determinants of PA and PA itself (from January 2004 to September 2017). Data extraction evaluated the importance of determinants, strength of evidence, and methodological quality of the selected papers. The full protocol is available from PROSPERO (PROSPERO2014:CRD42015010616).
Nineteen reviews were included. Moderate methodological quality emerged. For adults, convincing evidence supports a relationship between PA and socioeconomic status (SES), especially in relation to leisure time (positive relationship) and occupational PA (negative relationship). Conversely, no association between PA and SES or parental SES was found for pre-school, school-aged children and adolescents.
Available evidence on the socioeconomic determinants of PA behaviour across the life course is probable (shows fairly consistent associations) at best. While some evidence is available for adults, less was available for youth. This is mainly due to a limited quantity of primary studies, weak research designs and lack of accuracy in the PA and SES assessment methods employed. Further PA domain specific studies using longitudinal design and clear measures of SES and PA assessment are required.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A questionnaire on explanatory variables for each behavior of the 24-h movement behaviors (i.e., physical activity, sedentary behavior, sleep) was developed based on three levels of the ...socio-ecological model, i.e., the intrapersonal level, interpersonal level and the physical environmental level. Within these levels, different constructs were questioned, i.e., autonomous motivation, attitude, facilitators, internal behavioral control, self-efficacy, barriers, subjective norm, social modeling, social support, home environment, neighborhood, and work environment. The questionnaire was tested for test-retest reliability (i.e., intraclass correlation (ICC)) for each item and internal consistency for each construct (i.e., Cronbach's Alpha Coefficient) among a group of 35 healthy adults with a mean age of 42.9 (±16.1) years. The total questionnaire contained 266 items, consisting of 14 items on general information, 70 items on physical activity, 102 items on sedentary behavior, 45 items on sleep and 35 items on the physical environment. Seventy-one percent of the explanatory items showed moderate to excellent reliability (ICC between 0.50 and 0.90) and a majority of constructs had a good homogeneity among items (Cronbach's Alpha Coefficient ≥ 0.70). This newly developed and comprehensive questionnaire might be used as a tool to understand adults' 24-h movement behaviors.
Physical activity, sedentary behavior, and sleep guidelines for preschool children were already established and integrated into the 24 h movement behavior guidelines in 2017. The aim of the current ...study was to investigate correlates of meeting or not meeting the physical activity, sedentary behavior, and sleep guidelines in Belgian preschool children. In total, 595 preschool children (53.3% boys, 46.7% girls, mean age: 4.2 years) provided complete data for the three behaviors and potentially associated correlates. Physical activity was objectively measured with accelerometers. Screen time, sleep duration, and correlates were reported by parents with the use of a questionnaire. Backward logistic regression was used to identify factors associated with meeting all guidelines for weekdays and weekend days. In the final model, older preschoolers (OR = 1.89), having a normal weight compared to being underweight (OR = 0.30), having parents that do not watch a lot of television (OR = 0.99), and having a father that attained higher education (OR = 1.91) were associated with meeting all guidelines on weekdays. For weekend days, a significant association was found for attending a sports club (OR = 1.08). Overall, only a few factors were associated with meeting the guidelines. A more comprehensive measurement of preschool children's potential correlates of physical activity, sedentary behavior, and sleep is warranted.
In recent years, more attention has been paid towards the study of 24-h movement behaviors (including physical activity (PA), sedentary behavior (SB) and sleep) in preschoolers instead of studying ...these behaviors in isolation. This study aimed to evaluate the feasibility of using wrist- vs. thigh-worn accelerometers and to report accelerometer-derived metrics of 24-h movement behaviors in preschoolers. A convenience sample of 16 preschoolers (50.0% boys, 4.35 years) and one of their parents were recruited for this study. Preschoolers had to wear the ActivPAL accelerometer (attached to the upper thigh) and Axivity accelerometer (using a wrist band) simultaneously for 7 consecutive days and for 24 h a day. Parents completed an acceptability survey. In total, 16 preschoolers (100.0%) had a minimum of 6 days of valid wrist-worn data, while only 10 preschoolers (62.5%) had a minimum of 6 days of valid thigh-worn data (p = 0.002). When looking at the acceptability, 81.3% of parents indicated that it was easy for their child to wear the Axivity for 7 consecutive days, and 93.8% of parents indicated so for the ActivPAL (p = 0.88). However, some parents stated that the wristband of the Axivity accelerometer was big, which might have affected data collection. Significant differences were found for the measurement of total volume of PA, SB and sleep across 24 h. Total PA was 464.44 min/day (±64.00) with the ActivPAL compared with 354.94 min/day (±57.46) with the Axivity (p < 0.001). The volume of SB was 290.94 min/day (±55.05) with the ActivPAL compared with 440.50 min/day (±50.01) with the Axivity (p < 0.001). The total volume of sleep was also significantly different between both devices (p = 0.001; ActivPAL: 684.63 min/day ± 51.96; Axivity: 645.69 min/day ± 46.78). Overall, parents perceived both devices to be feasible to use for preschoolers. However, future studies are required to validate both devices for the measurement of preschoolers’ 24-h movement behaviors since significant differences in the classification of PA, SB and sleep were found in this small sample.
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