The Effectiveness of an Online Learning Strategy on Changing Physical Activity Counseling Practice in Nurses

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Abstract

Background

Nurses may be well poised for providing physical activity guidance and support to patients.

Purpose

The purposes of this study were to examine the effectiveness of a concise, evidence based online learning modules strategy (OLMS) for improving nurses’ physical activity counselling.

Methods

68 nurses were randomly assigned to either an OLMS group or control group. The OLMS group completed a series of six online learning modules aimed at improving physical activity counselling practice.

Results

The OLMS group, compared to the control group, showed a trend for improvement in Physical Activity Counselling Practice ( p = .063) after controlling for baseline values, and significant improvement in (a) Self-efficacy for Physical Activity Counselling ( p = .001), (b) Knowledge of Physical Activity Guidelines, ( p = .031), and (c) Perceived Benefits of Physical Activity Counselling ( p = .014) over the course of the intervention. No significant change was found for Barriers for Providing Physical Activity Counselling ( p > .05).

Conclusions

The OLMS tested may be an effective means for improving self-efficacy, knowledge, and perceived benefits of physical activity counselling, suggesting the utility of online learning strategies for improving nurses’ physical activity counselling practice. Given barriers to providing physical activity counselling were not affected by the intervention, future interventions and policy change could target these barriers specifically in order to give nurses more tools and time for reaching patients and addressing physical activity counselling in practice.

Related collections

Most cited references 35

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One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants

Regina Guthold, Gretchen Stevens, Leanne Riley … (2018)

Insufficient physical activity is a leading risk factor for non-communicable diseases, and has a negative effect on mental health and quality of life. We describe levels of insufficient physical activity across countries, and estimate global and regional trends.

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Different ways to estimate treatment effects in randomised controlled trials

Twisk J, Bosman L, Hoekstra T … (2018)

Background Regarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data. Methods Longitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment. Results It was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect. Conclusion Regarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model.