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

This paper critiques 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 study is underpinned by the Social Cognitive Theory and uses a purposive sampling method. The sample size of both the intervention and the control group is 80 for each group. The study reports detailed procedures on participant selection and data collection. The authors have provided a comprehensive step-wise process on the intervention and data collection. The study has established techniques to be used in the implementation and assessment of a theory m-health intervention integrated with individualized support aimed at promoting both physical activities and sleep health in Australian adults.

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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.,

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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

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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

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respondents were encouraged to provide their opinions freely. The report on the duration of
the intervention is vital as it gives an indication of the amount of data obtained and whether
the sample is a true representative of the entire population and whether it’s possible to
generalize the findings. However, the researchers did not provide a basis for using emails and
messaging system to gather data.
The study also reports the use of goal review strategies in which the participants are emailed
customized weekly summary of their performance in the previous week and are allowed to
re-assess and adjust their goals accordingly to meet the recent progress and promote self-
efficacy. The detailed report on the data collection and method and any adjustments on the
same improve the richness of the data. Additionally, the fact that the respondents checked
their progress performance and assessed than about the goals they had initially set gives the
accurate reflection of the opinions of the participants (James et al., 2016) thus minimizing
any possible biasness due to the researchers’ interference. This also increases the validity of
the interpretations of the researcher. However, the researchers only relied on the initial
participants, and the credibility and reliability of their findings were to be based on the fact
that the results were compared with a control group and the differences determined.
However, the credibility and reliability of the findings could have been strengthened the more
if there was a repeat of the same experiment with different participants due to their inability
to control the settings of the participants while keying in their feedbacks.
There was data collection during and after the study. For instance, the participants could key
in data at any time, and then they are sent the weekly summary of their progress in order to
work towards achieving the objectives they had initially set or re-align them accordingly. The
continual collection of data enables early detection of errors and correction accordingly thus
increasing the authenticity of the findings.

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There were more than one data coders. The random allocation of the participants to the two
study groups was done by an independent researcher. Furthermore, an independent
statistician supervised the analysis of the primary outcomes. According to Zohrabi (2013), the
use of multiple data coders in research is significant as it points out to a comprehensive
understanding of the study topic. The primary analyses were aimed at assessing the variations
in the study variables between the control and intervention group. An explicit process of data
coding and analysis has been provided for each identified theme in primary outcomes such as
physical activity, and sleep quality, and secondary outcomes such as stress, depression and
anxiety, resistance training among others. According to James et al. (2016), detailed
descriptions of the coding and memoing process is an indication of the researcher’s
perception, assessment, and understanding of the data. This further increases the credibility of
the outcomes.
The researchers report having used the Generalised Linear Mixed Models (GLMM) in
approximating the differences between sleep quality and physical activity between the
groups. Furthermore, the effect of missing data on the findings was assessed using the Pattern
Mixture Modelling. The collected data was keyed into Qualtrics in the form of a text file and
later imported to statistics software for analyses. A summary of the outcomes will be sent to
the participants as well. The use of software by researchers to store, assess, and code
qualitative data in addition to obtaining comments on the outcomes by the participants
strengthens the validity of the inferences made by the researcher. This is because it is an
assurance that the actual perspectives of the participants were represented in the study and not
manipulated by the researchers to meet their objectives (Pagel & Kwiatkowski, 2010).
The authors in their reporting have not included participant quotations to illustrate the various
themes identified from the outcomes. According to Lopresti, Hood, & Drummond (2013), the

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use of direct quotations from participants in qualitative research is significant as it
strengthens the transparency and trustworthiness of the outcomes and inferences. This might
be due to the nature of the study because all the primary and secondary variables were
measured using standardized instruments which captured quantitative information only.
However, the inclusion of direct quotations on measurements that were based on the Linkert
scale could have been included to at least strengthen the transparency and trustworthiness of
the results and interpretations. But the study has not provided the data findings of the research
but a rationale and techniques related to implementation and assessment of a theory m-health
intervention.
The study doesn’t include the findings from the participants, but instead, it has provided the
measurement and assessment instruments to be used to measure every sub-theme identified in
the study. It can be said that there is the possibility of clarity among the primary themes and
coherence between the expected data and the outcomes because under each sub-theme there
is presented a standardized instrument to be used in the assessment and measurement of the
participant feedback. Furthermore, references are made on the effectiveness of each
measurement as used in other studies in addition to summary interpretations. For instance,
under the theme of mediators and moderators, the authors have indicated that the sub-theme
sleep hygiene will be measured using a 13-item Sleep Hygiene Index (SHI) designed by
Mastin, Bryson, & Corwyn (2006). The authors further demonstrate the standard
interpretation of the scores and that it has an internal consistency of alpha 0.66 and test-retest
reliability of 0.71 in addition to the interpretations of its correlations.
The authors have presented the major themes to be considered in the findings. The major
themes include primary outcomes, secondary outcomes, process outcomes, mediators and
moderators, and power and sample size. Under these themes, several sub-themes have been

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discussed in detail with each one having a description of the standardized measurement
instrument, validity and reliability and the global interpretation of the scores.
The study has established techniques to be used in the implementation and assessment of a
theory m-health intervention integrated with individualized support aimed at promoting both
physical activities and sleep health in Australian adults. Based on the demonstrated
trustworthiness of the study, it is expected that the outcomes will provide a wealth of
knowledge to the health professionals in the development and implementation of m-health
interventions to help reduce the incidence of chronic diseases. There is a need for adequate
information on attrition in m-health interventions so as future studies have large
representative samples (Ho et al., 2015).

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