Quantitative Research and Murawski et al. Study
Creswell and Creswell (2017) observe that quantitative research is geared towards collecting numerical data and making inferences across larger clusters of subjects or to explore a given phenomenon, thus allowing a researcher to assess the association between two variables of interest. The research by Murawski et al. (2017) is a quantitative study with randomized control trial that is designed to assess the effectiveness of using a mobile app intervention to enhance physical activity and sleep health among adults. The use of a quantitative method with randomised control trial (RCT) best fits the study because it is about assessing the effectiveness of an intervention programme in the treatment of cardiovascular diseases by increasing physical activity and sleep health; a survey that require actual data collection from individuals (DiCenso, Guyatt, & Ciliska, 2014).
The study seeks to determine the association between physical activity and sleep health in the treatment of lifestyle chronic diseases. This paper aims at critiquing the study by Murawski et al. (2017) using the recommended CONSORT 2010 checklist (Schulz, Altman & Moher, 2010) in order to determine the quality of the study in the subject matter.
Studies have shown that enough sleep and physical activity are the major lifestyle behaviours that considerably reduce mortality rates caused by chronic lifestyle diseases (Schmid, Ricci, and Leitzmann, 2015). Worldwide statistics indicate that 32% of adults are not physically active (World Health Organization, 2017) and 24% have poor sleep health (Buman, Philips, Youngstedt, Kline, & Hirshkowitz, 2014). The association between inadequate sleep and physical inactivity has been evidenced in research (Bixer, 2009). However, there is no international research that has assessed both the behavioural changes simultaneously.
The study used a two-arm trial design alongside a combined intervention on sleep and physical activity. A two-arm trial approach was most appropriate for the study because it aimed at evaluating the effectiveness of m-health intervention using an experimental group and a control waitlist group (Hopewell, Dutton, Yu, Chan, & Altman, 2010).
Most importantly, this approach is significant as it enables researchers to compare the effects of an intervention with the non-experimental group thus making possible to arrive at specific conclusions. The allocation ratio used by the researchers was 1:1 in which 80 participants were each allocated to the two groups. Equality in the sample size in both the experimental and placebo group ensures that there is no bias in the outcomes. However, Hutchins, Brown, Mayberry, and Sollecito (2015) found out that the mean intervention effect and effect sizes were equal despite the size of the placebo group. Therefore, the use of an equal allocation ratio doesn’t increase the reliability of the findings but instead makes the analysis more intricate and the study costly.
Association between Physical Activity and Sleep Health
The authors have provided eligibility criteria for potential participants using exclusion criteria. A comprehensive report on the exclusion or inclusion criteria in a quantitative study is crucial as it shows that the nature of participants who provided data for analysis. This ensures that the appropriate subjects were used in the study who are well informed on the study topic. This increases the reliability and validity of the study. The authors have also indirectly mentioned the location in which they gathered data. The exclusion criteria show that those who were not residing in Australia during the study were not eligible. However, the authors do not describe the settings of the locations of data collection due to the nature of the research which involves data collection using a mobile app at any time of physical activity and sleep. Cohen and Crabtree (2008) observe that a description of the setting and context of the location of data collection is necessary as it indicates as to why participants responded in a given manner. This minimizes possible biasness in the respondents’ feedback.
The intervention for each group has been reported in detailed including the method of delivery. Whereas the app component includes the responses, educational resources, individual assessment, and goal setting. The report on detailed procedures on intervention and the manner in which they were administered is vital as it allows replication. According to Hedges and Cooper (2009), replicable studies with relatively similar outcomes of the original research increases the validity of the initial study. Furthermore, studies that are replicable provides a basis for further studies and review since any errors identified in the method during the review can be corrected by conducting another research in which the corrections are accounted for.
The study has thoroughly discussed the expected primary and secondary results and their measures in addition to the statistical approaches used to assess them. For instance, the authors explicitly indicate that all measures were to be evaluated through an online survey after the first three months and later on after six months. Furthermore, the various sub-themes to be measured under secondary and primary outcomes have also been described in details alongside the standardized scores and their interpretation based on previous findings. The process outcomes and mediators and moderators have also been pre-specified and assessed. The provision of pre-specified primary and secondary outcome measures enables the reader to quickly understand the findings in relation to the research question or objective. The inclusion of the various methods or ways of assessment of the outcomes increases the credibility of the conclusions and inferences as they are based on pre-established and standardized models. This also improves the reliability of the results because the standardized models limit the researcher’s bias (Noble and Smith, 2015).
The Study Design
The study does not report of any changes made to the trials after the initiation of the trial except for the changes that the participants were advised to make to their goals after goal review strategies in relation to their achievements. The authors contend that these adjustments will enable the participants to align their goals with the current progress to enhance self-efficacy.
The determination of the sample size has been explained by the authors. The authors have based their sample determination and data collection period of previous observations of different researchers. For instance, Kang, Marshall, Barreira, and Lee (2009) reported small to average increases in physical activity in their meta-analyses of treatments for physical inactivity. Additionally, there is evidence of small to average effect sizes for variations in sleep quality (Ho et al., 2015). Thus, these studies are the basis upon which assumptions of pre-post correlations (0.60), alpha (0.025) were made. Based on these assumptions, the authors settle on a sample size of 60 participants for each group in the case of physical activity and 35 participants for sleep quality. Therefore a sample size of 80 for each group was above the minimum requirement based on the observations and most appropriate for the study because a sufficiently large sample size increases the power and effect size which enables easy detection of differences.
Suresh and Chandrashekara (2012) note that a detailed procedure of sample size determination is critical in research as it indicates the statistical power of the findings, and hence their reliability and credibility. Furthermore, sample size determination affects the sample size of a study and thus dictates the amount of information to be collected. This has a bearing on the precision or confidence level of the study.
The researchers are to allocate the participants to the groups in a random manner after their baseline evaluation using sealed and opaque envelopes. Permuted block randomization was is to be adopted and block sizes of four and eight used under the guidelines recommended by Kang, Ragan, and Park (2008). Permuted block randomization will be the most appropriate method for the study which has equal sample sizes both for control and intervention group. This is because this approach will not only ensure equality across the groups but also uniformity in the primary outcomes. The selection of block sizes of four and eight are large enough and appropriate for the study because large block sizes help protect against the researcher foretelling the sequence of the intervention (Kahan & Morris, 2012). Thus, this ensures that the randomization process is not biased due to the investigator’s preferences (Efird, 2010).
Eligibility criteria for participants
The study has disclosed the allocation concealment mechanism used during randomization. Opaque sealed envelopes designed by the BM were used in permuted block randomization. Furthermore, details of the procedure used to conceal the sequence have also been provided. For instance, the use of opaque sealed envelopes. Allocation concealment was necessary for the randomization process because it prevents selection bias which will have an impact on the group subjects to receive the intervention.
Furthermore, this ensures that bias randomization is avoided (Kang, Ragan, and Park, 2008). Minimal bias in quantitative research strengthens the credibility of the study. The investigators report that the allocation sequence was assigned to an independent researcher not included in the study, and was also responsible for group allocations. This disclosure is very significant to the reader because it shows that the researcher did not influence the randomization and group allocation process. As a result, any group comparisons or findings will be dependable in addition to the inferences made from the outcomes. The study reports that none of the trial participants were blinded to the group assignment including the leader of the groups. Based on the nature of the study, blinding was not necessary as the participants could not access progressive reports of other participants either in the same group or a different one. Karanicolas, Farrokhyar, and Bhandari (2010) observes that in situations where blinding is impossible, then the study groups should be treated equally as much as possible and be far apart from each other as much as possible. However, blinding was done to group allocation during the analysis of primary outcomes and supervised by an independent statistician. This is important as it will ensure that there is minimal bias and the validity of the findings is maximized.
The Generalised Linear Mixed Models (GLMM) were used for estimating the differences between groups in physical activity and sleep quality both for secondary and primary outcomes. Moreover, the Pattern Mixture Modelling will be used in sensitivity analyses to evaluate the effect of missing data on the results. The authors justify the combination of the two models in the analyses by arguing that they will complement each other in their inherent weakness of assumptions regarding the effect of missing data. Baseline measures of the findings will be adjusted based on the estimations derived using the GLMM
The study provides a flow diagram of the random allocation of the participants to respective groups and the relevant intervention. The chart also demonstrates the intended primary outcome after analysis. The study has also used tables to show the characteristics of the clinical interventions for each group. For examples table on the constructs of the cognitive behavioral theory and the corresponding descriptions of the intervention components. The use of a flow chart diagram and tables enables the reader to easily understand the significant part of the study which is recruitment, data collection, and analysis.
Intervention and Method of Delivery
Furthermore, it makes the study to be replicable. The dates for the start of recruitment and follow-up have also been reported in the study (from May 2017 until the sample size is achieved). This is important as it shows that availability of time for the study and whether the participants did have sufficient time to provide adequate information for the study (Turner, Shamseer, Altman, Schulz, & Moher, 2012).
The study provides the expected primary and secondary results of both study groups alongside the estimated effect size and accuracy. The different constructs under each result have been supplied in details and the valid and reliable instruments to be used in the assessment. This is appropriate for the study because it measures several constructs of lifestyle behavior. The effect sizes have also been determined based on previous reviews. For instance, the effect size of physical activity is d= 0.45, and for sleep, it is d =0.65. Such high effect sizes are appropriate for the study for they ensure that the sample sizes are adequate and the group differences are reliable (Hutchins et al., 2015).
Conclusion
The study by Murawski et al. (2017) has significantly met the checklist criteria of CONSORT for RCT. This implies that methodological evidence provided in the study meets the quality standards of reporting in public healthcare. As a result, the proposed mobile app can be an effective intervention in the treatment of chronic lifestyle diseases by emphasizing on the need for physical activity and sleep health.
Furthermore, this is the only known study that has assessed the effectiveness of a mobile intervention which considers multiple behavioural changes namely sleep and physical activity, in a quantitative RCT in adults who are physically inactive and experiencing poor sleep health. The findings of this study will provide additional knowledge in the prevention and treatment of chronic diseases by using a mobile app which can reach an extensive population. Further studies should be conducted using a comparator condition in which either physical activity or sleep activities are individually measured to ascertain the magnitude of each intervention component.
References
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