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Out with the old, in with the new: Assessing change in screen time when measurement changes over time.

ABSTRACT: We examined if screen time can be assessed over time when the measurement protocol has changed to reflect advances in technology. Beginning in 2011, 929 youth (9-12 years at time one) living in in New Brunswick (Canada) self-reported the amount of time spent watching television (cycles 1-13), using computers (cycles 1-13), and playing video games (cycles 3-13). Using longitudinal invariance to test a shifting indicators model of screen time, we found that the relationships between the latent variable reflecting overall screen time and the indicators used to assess screen time were invariant across cycles (weak invariance). We also found that 31 out of 37 indicator intercepts were invariant, meaning that most indicators were answered similarly (i.e., on the same metric) across cycles (partial strong invariance), and that 28 out of 37 indicator residuals were invariant indicating that similar sources of error were present over time (partial strict invariance). Overall, across all survey cycles, 76% of indicators were fully invariant. Whereas issues were noted when new examples of screen-based technology (e.g., iPads) were added, having established partial invariance, we suggest it is still possible to assess change in screen time despite having changing indicators over time. Although it is not possible to draw definitive conclusions concerning other self-report measures of screen time, our findings may assist other researchers considering modifying self-report measures in longitudinal studies to reflect technological advancements and increase the precision of their results.

SUBMITTER: Gunnell KE

PROVIDER: S-EPMC5766751 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Out with the old, in with the new: Assessing change in screen time when measurement changes over time.

Gunnell Katie E KE Brunet Jennifer J Bélanger Mathieu M

Preventive medicine reports 20171224

We examined if screen time can be assessed over time when the measurement protocol has changed to reflect advances in technology. Beginning in 2011, 929 youth (9-12 years at time one) living in in New Brunswick (Canada) self-reported the amount of time spent watching television (cycles 1-13), using computers (cycles 1-13), and playing video games (cycles 3-13). Using longitudinal invariance to test a shifting indicators model of screen time, we found that the relationships between the latent var ...[more]

PMID: 29340268

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Project description:BackgroundIt is well known that safe delivery in a health facility reduces the risks of maternal and infant mortality resulting from perinatal complications. What is less understood are the factors associated with safe delivery practices. We investigate factors influencing health facility delivery practices while adjusting for multiple other factors simultaneously, spatial heterogeneity, and trends over time.MethodsWe fitted a logistic regression model to Lot Quality Assurance Sampling (LQAS) data from Uganda in a framework that considered individual-level covariates, geographical features, and variations over five time points. We accounted for all two-covariate interactions and all three-covariate interactions for which two of the covariates already had a significant interaction, were able to quantify uncertainty in outputs using computationally intensive cluster bootstrap methods, and displayed outputs using a geographical information system. Finally, we investigated what information could be predicted about districts at future time-points, before the next LQAS survey is carried out. To do this, we applied the model to project a confidence interval for the district level coverage of health facility delivery at future time points, by using the lower and upper end values of known demographics to construct a confidence range for the prediction and define priority groups.ResultsWe show that ease of access, maternal age and education are strongly associated with delivery in a health facility; after accounting for this, there remains a significant trend towards greater uptake over time. We use this model together with known demographics to formulate a nascent early warning system that identifies candidate districts expected to have low prevalence of facility-based delivery in the immediate future.ConclusionsOur results support the hypothesis that increased development, particularly related to education and access to health facilities, will act to increase facility-based deliveries, a factor associated with reducing perinatal associated mortality. We provide a statistical method for using inexpensive and routinely collected monitoring and evaluation data to answer complex epidemiology and public health questions in a resource-poor setting. We produced a model based on this data that explained the spatial distribution of facility-based delivery in Uganda. Finally, we used this model to make a prediction about the future priority of districts that was validated by monitoring and evaluation data collected in the next year.

Project description:Introduction:Organizational readiness to change may be a key determinant of implementation success and a mediator of the effectiveness of implementation interventions. If organizational readiness can be reliably and validly assessed at the outset of a change initiative, it could be used to assess the effectiveness of implementation-support activities by measuring changes in readiness factors over time. Methods:We analyzed two waves of readiness-to-change survey data collected as part of a three-arm, randomized controlled trial to implement evidence-based health promotion practices in small worksites in low-wage industries. We measured five readiness factors: context (favorable broader conditions); change valence (valuing health promotion); information assessment (demands and resources to implement health promotion); change commitment (an intention to implement health promotion); and change efficacy (a belief in shared ability to implement health promotion). We expected commitment and efficacy to increase at intervention sites along with their self-reported effort to implement health promotion practices, termed wellness-program effort. We compared means between baseline and 15?months, and between intervention and control sites. We used linear regression to test whether intervention and control sites differed in their change-readiness scores over time. Results:Only context and change commitment met reliability thresholds. Change commitment declined significantly for both control (-0.39) and interventions sites (-0.29) from baseline to 15?months, while context did not change for either. Only wellness program effort at 15?months, but not at baseline, differed significantly between control and intervention sites (1.20 controls, 2.02 intervention). Regression analyses resulted in two significant differences between intervention and control sites in changes from baseline to 15?months: (1) intervention sites exhibited significantly smaller change in context scores relative to control sites over time and (2) intervention sites exhibited significantly higher changes in wellness program effort relative to control sites. Discussion:Contrary to our hypothesis, change commitment declined significantly at both Healthlinks and control sites, even as wellness-program effort increased significantly at HealthLinks sites. Regression to the mean may explain the decline in change commitment. Future research needs to assess whether baseline commitment is an independent predictor of wellness-program effort or an effect modifier of the HealthLinks intervention.

Dataset Information

Out with the old, in with the new: Assessing change in screen time when measurement changes over time.

Publications

Out with the old, in with the new: Assessing change in screen time when measurement changes over time.

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