Mobile Apps Utilization in Modern Health Care

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Mobile Apps Utilization in Modern Health Care

Carroll et al. (2017) examined the current tendencies in applying mobile apps in modern health care. The present research is relevant, since mobile app analytics, aimed at monitoring and exploring the behavior of mobile app users, is at its nascent stage (Han et al., 2016, p. 984). Moreover, over the past 20 years, the world has witnessed the explosion in the number of smart devices, and their use in health care remains understudied (Li et al., 2019, p. 3351).

The study explored the essential characteristics of mobile apps users as among the U.S. representatives, including their sociodemographic status and health condition. Furthermore, the researchers assessed the attitudinal and behavioral predictors of the use of health apps (Carroll et al., 2017, para. 9). It is necessary to observe that the authors focus on behavioral prognosis is an undeniable advantage of this research. Indeed, understanding of the mechanisms of therapeutic change in using mobile apps remains an urgent issue in modern health care (Domhardt et al., 2019, p. 221). Thus, the authors aspired to provide a comprehensive analysis of the issue under consideration.

The data collection involved the survey carried out by the National Cancer Institute as of June 2015. The scholars focused on questions about whether participants used software apps for health-related reasons, and what devices they used, with the total response rate of 34.44% (Carroll et al., 2017, para. 10). To analyze the demographic characteristics of health apps users, the authors referred to the participants self-report data.

The researchers targeted such parameters as age, sex, race, ethnicity, income, level of education, English proficiency, height, and weight (Carroll et al., 2017, para. 12). Subsequently, Carroll et al. calculated their body mass index to classify them as obese, overweight, and healthy weight or underweight. Furthermore, the scholars analyzed the apps users preferences in terms of using a particular device. Carroll et al. also investigated the users physical activity, as well as their fruit and vegetables consumed per day. However, there were significant interferences in the data collection process. Initially, the research embraced 3677 respondents, but the study eventually excluded 93 users due to data gaps.

The data analysis relied on the multivariable logistic regression models. The variables, applied in this study, were the participants possession of a smartphone as against basic cell phones or other devices, having health apps installed, and using them. The analysis revealed the following distribution of responses: Device-/App- (33.2% of respondents), Device+/App- (44% of respondents), and Device+/App+ (22.77% of respondents) (Carroll et al., 2017, para. 14).

The researchers assessed the relationship between these variables, the demographic characteristics, and health behavior intentions by generating the unweighted 2-way crosstab tables and testing it with the chi-square criterion. The statistical significance was determined using the P<.05 cutoff. The researchers used the R programming language and SPSS for data simulation and analysis. At the same time, the data analysis was possibly subject to distortion due to the potential existence of unknown confounding factors associated with app use and health behavior. Hence, the data interpretation was not devoid of the influence of such confounding variables.

The study findings demonstrated that the respondents younger age, more elaborated educational background, and a higher income influenced the rate of their use of health apps. As to predicting the use of mobile apps, the participants sex, race, and gender displayed a mild-to-moderate effect. In terms of behavioral and attitudinal prognosis, the study found that apps users had more intentions to improve fruit and vegetable consumption, physical activity, and weight loss (Carroll et al., 2017, para. 25). Moreover, health apps users more likely intended to meet recommendations to increase the amount of physical exercise.

It is necessary to remark that the authors provided reasonable substantiation for a part of their findings. For instance, age and education determined the level of respondents literacy in terms of information technologies. At the same time, the study found reliable gender differences in terms of health apps usage but did not provide a feasible explanation for this finding. The authors made only an assumption that this fact might reflect differences in health-seeking behavior (Carroll et al., 2017, para. 30). However, the reason for the revealed gender factor remained unclear.

The study had several limitations, which one should take into consideration. Firstly, the research did not embrace the participants responses over time. Furthermore, the scholars used only the limited scope of questions suggested by the applied health information survey. Last but not least, the study did not clarify the cause-effect relationship between mobile apps and behavior dynamics. That is to say, the researchers did not find out whether motivated participants tended to use apps more actively, or it was the use of apps that increased their motivation.

Hence, the obtained findings delineated the prospects for further research in this direction to refine the abovementioned limitations. The researchers concluded that the present study contributed to the existing scientific literature by providing relevant information on the cohorts of individuals, who are likely and unlikely to use health apps. These findings can be applied to streamline clinical interventions, promote further development and expansion of mobile technologies in health care.

References

Carroll, J.K., Moorhead, A., Bond, R., LeBlanc, W.G., Petrella, R.J., & Fiscella, K. (2017). Who uses mobile phone health apps and does use matter? A secondary data analytics approach. Journal of Medical Internet Research, 19(4), e125. Web.

Domhardt, M., Geßlein, H., von Rezori, R.E., & Baumeister, H. (2019). Internet- and mobile-based interventions for anxiety disorders: A meta-analytic review of intervention components. Depression and Anxiety, 36(3), 213224. Web.

Han, S. P., Park, S., & Oh, W. (2016). Mobile app analytics: A multiple discrete-continuous choice framework. MIS Quarterly, 40(4), 983-1008.

Li, B., Zhao, S., Zhang, R., Shi, Q., & Yang, K. (2019). Anomaly detection for cellular networks Using big data analytics. IET Communications, 13(20), 33513359.

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