A Pearson tool–time correlation has also been run to shot the latest unadjusted bivariate moms and dad–kid PA matchmaking given that mentioned by the surveys

• Posted on 10/06/2022
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Study

All analyses were conducted using IBM SPSS Statistics version 23. Outliers for the pedometer data were identified as days with <1,000 or >30,000 steps/day for children and <1,000 or >25,000 for adults were set as missing . Outliers for the child PA questionnaire (>6 h/day), and for the other continuous variables (? ± 3.29 SD) were truncated .

Pearson device–time correlations was basically cost shot the unadjusted bivariate relationships between parents’ and you may kid’s PA dating once the measured from the pedometers

Of the 28 variables included in this study, 83 % were missing on at least one value (number of non-missing values for each variable is available in Table 1). Across cases/participants, 55 % were missing on at least one variable, and across the entire dataset, 13 % of the values were missing. Missing and non-missing cases were compared for variables with >10 % missing data. Significant (p < .05) or marginally significant (p < .10) differences existed on parental BMI for parents' and children's steps/day. Importantly, families who participated in the initial assessment and those that returned the pedometers did not differ on parent self-reported leisure time MVPA (t = ?.67, p = .50) or children's parental-proxy reported PA (t = ?.38, p = .38). We therefore assumed at least a partial missing at random mechanism and imputed all of the missing data (including all covariates, predictor variables, criterion variables) using multiple imputation in SPSS. This procedure uses the fully conditional specification method and imputes data using linear regression for continuous variables and logistic regression for binary variables. We used 100 iterations, which resulted in 100 separate datasets . Relevant variables from the wider dataset (i.e., screen time, aerobic fitness, grip strength, dog ownership, walkability of neighborhood) were included as auxiliary variables.

I together with checked which relationships independently by-child and you can mother gender, son and you may father or mother weight reputation, gender homogeneity, pounds standing homogeneity, father or mother studies, household income, and you can town-peak SES. Linear regressions were utilized to evaluate the research concerns and you may partial roentgen shown feeling dimensions. Cohen’s necessary effect types of brief = .10, medium = .30, higher = .50 were utilized to interpret how big is consequences. The brand new covariates for everybody assesses was in fact son age, gender, and weight updates; parent sex, lbs status, and you will degree; domestic income; area-level SES; and you can year. Each investigation integrated anywhere between eleven and you can thirteen details. With respect to the IBM SPSS Analytics SamplePower 3, with eleven covariates (typical combined impression size), that predictor varying (medium impact proportions) and you may a connection identity (short impression size), 413 members had been required to locate outcomes in the power = .80 to have ? = .01. Thus we had been well enough pushed for all analyses.

To address research questions 1 (whether parents’ steps/day was related to children’s steps/day), a linear regression was run with children’s steps/day as the criterion variable and parents’ steps/day and covariates as predictor variables. Coefficients were deemed significant at p < .05. To address research question 2 (potential moderators of the parent–child step/day relationship), children's steps/day was entered as the criterion variable and parent's steps/day and covariates as predictor variables. One by one we tested potential interactions including parent steps*child gender, parent steps*parent gender, parent steps*gender homogeneity, parent steps*child weight status, parent steps*parent weight status, parent steps*weight status homogeneity, parent steps*parent education, parent steps*household income, parent steps*area SES. In the models where the gender homogeneity and weight status homogeneity interactions were tested, these variables were also included as main effects. Before creating the interaction terms, the continuous variables (i.e., parent steps, area-level SES) were centered on their mean . To control for the increased probability of finding a significant result due to running multiple tests, a more stringent significance level was applied (p < .01) to the interactions. For significant or near significant interactions, a simple slopes analysis was performed to determine the beta coefficients and p-values for each group. Beta coefficients for the simple slopes were calculated by hand using the pooled results . The pooled results did not provide sufficient information to calculate the significance of the slopes by hand so the p-value (set at p < .05) was computed using the initial dataset (i.e., before the multiple imputation). To address research question 3 (parent–child PA relationship as measured by questionnaires), children's proxy-reported PA was entered as the criterion variable and parent self-reported leisure time MVPA and the covariates were entered as predictor variables. Coefficients were deemed significant at p < .05.