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Quantitative Study to Decrease Prevalence of Alcohol Use Amongst Young Adults
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Young adults are exposed to alcohol consumption, and this shows that the focus of the research is to check the effectiveness of smartphone messaging in their livelihoods. The research question is focused on using smartphone message intervention to determine whether young adults will change their alcohol consumption (Bertholet et al., 2017). The question was the key point in analyzing the extraneous variables in the selected study. The extraneous variables are defined as all other variables that are not defined as the independent variable and can cloud the results. The variables are socio-economic conditions of the respondents, level of education, income level, and friendships among young adults. The variables are dependent variables and have the ability to manipulate the independent variable, as they will help to cloud the findings. The variables will be controlled through random sampling, and this is because they are linked together with alcohol consumption. For example, friendship among friends can be a motivator in alcohol consumption, and it is a variable that can either lead to increased alcohol consumption or reduced consumption. The second way to control the level of education variable is matching, whereby the link will be focused on ensuring that the respondents are matched with the level of drinking.
Research instruments are the best tools that are involved in the collection of data, and the research will entail mobile surveys as a process of getting data on the level of drinking. According to a study by Lauckner et al. (2019) and Nicolas Bertholet et al. (2017), the research can involve the application of breathalyzers and Blood Alcohol Content (BAC) as a second method to measure alcohol consumption. (Lauckner et al., 2019; Bertholet et al. 2017). The validity estimate involved in the study can be proposed to be test reviews on the alcohol dependency scale on the alcohol measurement methods, where the data will be subjected to analysis to determine whether the past results are the best in the research study. The reliability measure and the best tool will be the alcohol dependency scale, and the idea is to look for the internal consistency of alcohol consumption. At times, some of the respondents might give unreliable information, and this calls for a reliability test which will be factored in the alcohol dependence scale. According to a study by Sobell and Sobell (1995), the drinking measure that will be applied will be the DSML, and this is because it has the elements of monitoring progress. The purpose of the measure is depicted to be a daily recall of drinking, whereby it will help in self-reporting on alcohol consumption (Sobell & Sobell, 1995).
The intervention selected is the effectiveness of smartphone messaging on reducing the prevalence of alcohol consumption. The intervention is a method that is impactful in checking whether the students who use smartphones have an opportunity to reduce the use of alcohol (Kazemi et al., 2017). The intervention, according to the past research, was recommended to be applied as it offers great and positive feedback on reducing the alcohol consumption rate (Crane et al., 2018). The last aspect in the proposal that is key and needs attention is the data collection procedures, whereby it will depend on the mobile surveys that will be conducted on the selected participants. The participants will be selected through random sampling, as it will help to control the extraneous variables that were identified. The data will be collected through the administered breathalyzers on the participants.
The data collection will be started after getting the participants who regularly engage in alcohol consumption. The argument in this context is that data collection will depend on the mobile surveys, as it is also the simplest process to ensure that the data is valid for the research. The other part of the data collection procedures is that the association between smartphone messaging and level of drinking will be analyzed keenly, as some participants might have wrong information, and this limits the results that might be anticipated in the research (Kazemi et al. 2017).
References
Bertholet, N., Daeppen, J. B., McNeely, J., Kushnir, V., & Cunningham, J. A. (2017). Smartphone Application for Unhealthy Alcohol Use: A pilot study. Substance abuse, 38(3), 285-291. DOI: 10.1080/08897077.2017.1281860
Crane, D., Garnett, C., Michie, S., West, R., & Brown, J. (2018). A smartphone app to reduce excessive alcohol consumption: Identifying The Effectiveness of Intervention Components in a Factorial Randomized Control Trial. Scientific Reports, 8(1), 1-11. https://doi.org./10.1038/s41598-018-22420-8
Sobell, L., & Sobell, M. (1995). Alcohol Consumption Measures. National Institute on Alcohol Abuse and Alcoholism. https://pubs.niaaa.nih.gov/publications/assessingalcohol/measures.htm
Lauckner, C., Taylor, E., Patel, D., & Whitmire, A. (2019). The Feasibility of Using Smartphones and Mobile Breathalyzers to Monitor Alcohol Consumption among People Living with HIV/AIDS. Addiction science & clinical practice, 14(1), 1-11. https://doi.org/10.1186/s13722-019-0174-0
Kazemi, D. M., Borsari, B., Levine, M. J., Li, S., Lamberson, K. A., & Matta, L. A. (2017). A Systematic Review of the mHealth Interventions to Prevent Alcohol and Substance Abuse. Journal of health communication, 22(5), 413-432. DOI: 10.1080/10810730.2017.1303556
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