Critique of m-health intervention to improve sleep quality and physical activity in adults

Running head: Evidence Based Practice 1
Evidence Based Practice
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Evidence Based Practice 2
The prevalence of chronic diseases has been attributed to the changing lifestyle which seems
to adhere to the economic pressures that demand that people work overtime. As a result, there
is physical inactivity and lack of adequate sleep. Studies have shown that physical inactivity
and not maintaining proper sleep increases mortality rates caused by cardiovascular diseases
(Shiroma & Lee, 2010; Hoevenaar-Blom et al., 2011) among other chronic illnesses (Shan et
al., 2015; Aune et al., 2015). According to the World Health Organisation (2017), 32% of
adults worldwide are physically inactive, 29% of adults sleep for less than six hours (Hoyos,
Glozier, & Marshall, 2015), over 50% do not have regular patterns of sleep, and only 24%
experience quality sleep (Duncan et al., 2016). There exists an abundant proof of the
association between physical inactivity and low sleep quality (Rayward et al., 2017).
Conversely, there exists no known global statistics on the percentage of people who
simultaneously experience inadequate physical activity and low quality sleep. Therefore,
there is need of an intervention that factors in both aspects to make significant help to public
health. Furthermore, an intervention addressing both sleep quality and physical activity is
necessary because of the mutual association that exists between the two variables (Kline,
2014). Thus, the objective of this paper is to critique the recommended m-health intervention
method aimed at reducing the prevalence of chronic disease rates alongside the related
burdens in the adult population by assessing physical activity and sleep quality
simultaneously. The COREQ checklist developed by Tong, Sainsbury, and Craig (2007) has
been used to critique the study by Murawski et al. (2018) on m-health intervention to improve
sleep quality and physical activity in adults.
The authors cited the Social Cognitive Theory (SCT) as the basis on which the study was
underpinned. The authors provide different justifications for the selection of a theory-based
approach. The researchers argue that existing evidence has shown that theory-based
interventions have overtime proved to be more effective in behavior change (Prestwich et al.,

Evidence Based Practice 3
2014). Abraham, Conner, & Norman (2013) assert that SCT is significant in the theoretical
comprehension of variations in behavior because it takes into consideration the interfaces
between the fundamental factors that affect behavior change such as the environmental
processes. Thus, this approach is the most appropriate because the study focuses on both
sleep health and physical activity which are both affected by environmental factors.
The study reports detailed procedures on participant selection. The potential participants were
recruited using digital advertising such as Twitter and Facebook, and electronic print-based
media such as newspapers and magazines that were distributed countrywide. The
advertisements were directed towards target audiences that met the inclusion criteria. This
implies that a purposive sampling method was used in recruiting the participants. This design
guarantees the collection of detailed information that is relevant to the study objective. The
authors also provide in-depth information on the inclusion and exclusion criteria with an
explicit checklist of the reasons for non-participation of some of the people. This minimizes
the possibility of giving non-evidenced accounts.
The sample size of both the intervention and the control group as reported by the researchers
is 80 for each group. This is important since it will enable the potential users of the findings
to examine the diversity of perspectives included in the outcomes. Based on the nature of the
study which involved online survey, the authors could not access the non-participants and
provide the reasons for them not participating in the study. This would have reduced the
possibilities of making statements that are not supported. The researchers could only assume
that their non-participation was due to their inability to meet the inclusion criteria that is
explicitly provided by the researchers.
The study participants were not with the requirement to report to the research center for data
collection, but instead, all the data was collected through online surveys that evaluated both

Evidence Based Practice 4
the primary and secondary results, in addition to the demographic information and
moderating features. All the instructions and access to the app were emailed to the
intervention group and then reminder messages on the regular use of the app sent. The
researcher did not, therefore, have control over the place of data collection since data was
collected via online surveys. Additionally, the researchers pilot-tested the online surveys and
secured them before the actual survey. This prevented any alterations from being made when
the research was in process, thus limiting any intrusions that would compromise the
credibility of the feedbacks. The provision of detailed information on the setting of the study
is fundamental to the readers because it shows whether the responses of the participants were
influenced or not. Thus, the inability of the researchers to determine the presence or absence
of the non-participants while the respondents made their feedbacks via online affects the
credibility of the responses upon which the findings are based, and conclusions derived.
Furthermore, the demographic data collected enables the readers to consider the bearing of
the outcomes and inferences to their situations. The readers will also be able to determine
whether the data is all inclusive and if different views from different groups were factored in
(James et al., 2016).
The researchers have provided a comprehensive step-wise process on the intervention and
data collection. The app was primarily used to monitor and gather data using its components
namely response, self-monitoring, educational resource, and goal-setting. Guidelines on how
these components were utilized in data collection have also been provided. For the first three
months of intervention baseline data was collected using a messaging system that offered
customized feedback on the progress in goal achievement, provoking a review of the goals
and practices of the actual behaviors. Emails were also used to collect data. The provision of
the procedures on data collection is significant because they improve the understanding of the
readers regarding the focus of the researchers and to allow them to ascertain whether the