Computer Science
ITS 836 University of the Cumberlands Mobile Phone Health Apps Usage Article Analysis

ITS 836

University of the Cumberlands

ITS

Question Description

I need an explanation for this Computer Science question to help me study.

Assignment:

Please critique the research article (that is included here, pdf included here) from the following angles: a) Data collection b) Data analysis c) Results, findings and conclusion.

1. A minimum of 500 words is required.

2. APA format needs to be followed (100%). As per University mandate, not following APA formatting can impact your grades negatively.

3. Do your best to refer research articles from peer reviewed journals like IEEE, ACM. A minimum of 3 references are required.

Healthcare mobile apps survey.pdf

With APA References and In-text citations.

Subject:

ITS 836 Data science & Big data analytics

Reference book:

EMC Education Services. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wiley.

Unformatted Attachment Preview

JOURNAL OF MEDICAL INTERNET RESEARCH Carroll et al Original Paper Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach Jennifer K Carroll1, MPH, MD; Anne Moorhead2, MSc, MA, MICR, CSci, FNutr (Public Health), PhD; Raymond Bond3, PhD; William G LeBlanc1, PhD; Robert J Petrella4, MD, PhD, FCFP, FACSM; Kevin Fiscella5, MPH, MD 1 Department of Family Medicine, University of Colorado, Aurora, CO, United States 2 School of Communication, Ulster University, Newtownabbey, United Kingdom 3 School of Computing & Maths, University of Ulster, Newtownabbey, United Kingdom 4 Lawson Health Research Institute, Family Medicine, Kinesiology and Cardiology, Western University, London, ON, Canada 5 Family Medicine, Public Health Sciences and Community Health, University of Rochester Medical Center, Rochester, NY, United States Corresponding Author: Jennifer K Carroll, MPH, MD Department of Family Medicine University of Colorado Mail Stop F496 12631 E. 17th Ave Aurora, CO, 80045 United States Phone: 1 303 724 9232 Fax: 1 303 724 9747 Email: jennifer.2.carroll@ucdenver.edu Abstract Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating. There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentions to change, and actual health behaviors. Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 Health Information National Trends Survey (HINTS), which was designed to provide nationally representative estimates for health information in the United States and is publicly available on the Internet. We used multivariable logistic regression models to assess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentions to change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss. Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+ years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or less than high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of having adopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted a mobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likely to report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P<.01) consumption, physical activity (83.0% vs 65.4%, P<.01), and weight loss (83.4% vs 71.8%, P<.01). Individuals with apps were also more likely to meet recommendations for physical activity compared with those without a device or health apps (56.2% with apps vs 47.8% without apps, P<.01). Conclusions: The main users of health apps were individuals who were younger, had more education, reported excellent health, and had a higher income. Although differences persist for gender, age, and educational attainment, many individual sociodemographic factors are becoming less potent in influencing engagement with mobile devices and health app use. App use was associated with intentions to change diet and physical activity and meeting physical activity recommendations. http://www.jmir.org/2017/4/e125/ XSL• FO RenderX J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p. 1 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH Carroll et al (J Med Internet Res 2017;19(4):e125) doi: 10.2196/jmir.5604 KEYWORDS smartphone; cell phone; Internet; mobile applications; health promotion; health behavior Introduction identifying appropriate use of health apps among population groups. As of 2015, nearly two-thirds (64%) of the American public owned a mobile phone, which is an increase from 35% in 2011 [1]. It is estimated that 90% of the worldwide population will own a mobile phone by 2020 [1]. Current UK data reveals that mobile phone usage is increasing as 66% adults aged more than 18 years owned a mobile phone in 2015, up from 61% in 2014 [2]. Mobile phone ownership is higher among younger people, with 77% ownership for those aged 16-24 years [3]. Although mobile phone ownership is especially high among younger persons and those with higher educational attainment and income [4], those with lower income and educational attainment are now likely to be “mobile phone dependent,” meaning that they do not have broadband access at home and have few other options for Web-based access other than via mobile phone. Therefore, the aim for our study was 3-fold: (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. Given the increasing focus on new models for integrating technology into health care and the need to expand the evidence base on the role of health apps for health and wellness promotion, these research questions are timely and relevant to inform the development of health app interventions. As mobile phone ownership rapidly proliferates, so does the number of mobile phone software apps grown in the marketplace [5]. Apps focused on health promotion are quite common: more than 100,000 health apps are available in the iTunes and Google Play stores [6]. This staggering number speaks to both the huge market and ongoing demand for new tools to help the public manage their diet, fitness, and weight-related goals, and the limitations of the current health care system to provide such resources. A recent study found that 53% of cell phone users owned a smartphone—this translates to 45% of all American adults—and that half of those (or about 1 in 4 Americans) have used their phone to look up health information [7]. There is increasing usage of health apps among health care professionals, patients and general public [8], and apps can play a role in patient education, disease self-management, remote monitoring of patients, and collection of dietary data [9-12]. Using mobile phones and apps, social media also can be easily accessed, and increasing numbers of individuals are using social media for health information with reported benefits and limitations [8]. Despite the massive uptake in mobile phone ownership and health app usage and their potential for improving health, important limitations of health apps are the lack of evidence of clinical effectiveness, lack of integration with the health care delivery system, the need for formal evaluation and review, and potential threats to safety and privacy [6,13-17]. Although previous studies have described the sociodemographic factors associated with mobile health and app use [7,18,19], it is a rapidly changing field with the most recent published reports reflecting data at least four to five years old. Additionally, there is a lack of information on the users of health apps in terms of their sociodemographic and health characteristics and health behaviors. Furthermore, to our knowledge, there have been no previous publications reporting on the association between the use of health apps, behavioral or attitudinal factors (ie, readiness or intentions to change), and health outcomes. This information is important for future health-improving initiatives and for http://www.jmir.org/2017/4/e125/ XSL• FO RenderX Methods Data Source The National Cancer Institute’s Health Information National Trends Survey (HINTS) is a national probability sample of US adults that assesses usage and trends in health information access and understanding. HINTS was first administered in 2002-2003 as a cross-sectional survey of US civilians and noninstitutionalized adults. It has since been iteratively administered in 2003, 2005, 2008, 2011, 2012, 2013, and 2014. We used data from HINTS 4 Cycle 4 data released in June 2015, which corresponded to surveys administered in August-November, 2014. Publicly available datasets and information about methodology are available at the HINTS website [20]. The 2014 iteration reported herein contained questions about whether participants used mobile phone or tablet technology and software apps for health-related reasons. The overall response rate was 34.44%. This study was reviewed and qualified for an Exemption by the American Academy of Family Physicians Institutional Review Board. Participants A total of 3677 individuals completed the 2014 HINTS survey. From this sample, 148 respondents were considered partial completers, in that they completed 50%-79% of the questions in Sections A and B. We included all 3677 respondents in our analysis. We used sampling weights from the HINTS dataset that were incorporated into the regression analyses. Measures Demographics We used participants’ self-report of their age, sex, race, ethnicity, income, level of education, English proficiency, height, and weight. We converted height and weight into body mass index (BMI), using weight (kg)/height (m2)×10,000, and classified participants as obese (≥30), overweight (29.9-26), or normal weight or underweight (<26). J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p. 2 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH Usage of Mobile Devices and Health Apps We used participants’ responses to the 3 questions to characterize the distribution of subjects who used health-related software apps on their mobile devices. The participants were asked whether they had a tablet computer, smartphone, basic cell phone only, or none of the above. We examined factors for those with and without mobile devices, since previous studies have shown differences in seeking health information on the Internet related to access (eg, availability of a computer) [21,22], HINTS dataset is a nationally representative sample, and we wished to put our findings on app use in the larger population context. We categorized participants who had a mobile phone or a tablet device under the label “Device+.” Similarly, participants who did not report having a mobile phone or a tablet device were labeled “Device-.” Of the Device+ group, we also categorized them according to whether they had health apps on their device (Device+/App+) or did not have health apps on their device (Device+/App-). Fruit and Vegetable Intake We assessed fruit and vegetable intake using the 2 questions: amount of fruit consumed per day and amount of vegetables consumed per day (7 response options for each ranging from none to >4 cups per day). We reclassified the response options for both questions into a single dichotomous outcome variable, that is, the subject either (1) meets recommendations for fruit or vegetables (4 or more cups for each) or (2) does not meet recommendations for fruit or vegetables (all other response options). Fruit and vegetable scores were analyzed separately. Physical Activity We assessed physical activity using the 2 questions: (1) in a typical week how many days do you do any physical activity or exercise of at least moderate intensity, such as brisk walking, bicycling at a regular pace, and swimming at a regular pace? (8 response options ranging from none to 7 days per week) and (2) on the days that you do any physical activity or exercise of at least moderate intensity how long do you do these activities? (2 response options for minutes and hours). We reclassified the response options into a single dichotomous outcome variable for physical activity, that is, whether the subject (1) met physical activity recommendations (≥150 minutes per week) or did not meet the physical activity recommendations (<150 minutes per week). Carroll et al in the last year, have you intentionally tried to (1) increase the amount of fruit or 100% fruit juice you eat or drink, (2) increase the amount of vegetables or 100% vegetable juice you eat or drink, (3) decrease the amount of regular soda or pop you usually drink in a week, (4) lose weight, and (5) increase the amount of exercise you get in a typical week? Statistical Analysis The outcome variable (OUTCOME) was a composite derived from 3 survey variables: (1) own a smartphone (an Internet-enabled mobile phone “such as iPhone android BlackBerry or Windows phone” differentiated from a “basic cell phone,” hereafter referred to as “mobile phone”) or device, (2) have health apps on mobile phone or device, and (3) use of health apps. Own a mobile phone or device was a system-supplied derived variable to categorize responses given to question B4 (possession of a mobile phone or tablet device). Have health apps on mobile phone or device (question B5) asked about health apps on a tablet or mobile phone. Use of health apps (question B6a) asked whether the apps on a mobile phone or tablet helped in achieving a health-related goal. OUTCOME consisted of 3 levels: Device-/App- (33.2% of respondents), Device+/App- (44% of respondents), and Device+/App+ (22.77% of respondents). Device referred to having a tablet or mobile phone, and App referred to having a health-related app that ran on a tablet or mobile phone. A total of 93 of 3677 respondents were unable to be classified due to missing data. These people were not used in the analyses. To assess the relationship between OUTCOME and the demographic or health behavior variables, simple unweighted 2-way crosstab tables were generated and tested with a chi-square test of association. We used a cutoff of P<.05 to determine statistical significance for all analyses. We used the R programming language (R-Studio) and SPSS (SPSS Inc) for all data modeling and analysis carried out in this study. Results Principal Findings From the 3677 total HINTS respondents, 3584 answered questions about whether or not they had a tablet computer or mobile phone, or used apps. Figure 1 shows the participants in this study. Intentions to Change Behavior We examined participants’ intentions to change behavior based on the 5 questions (all with yes or no responses): At any time http://www.jmir.org/2017/4/e125/ XSL• FO RenderX J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p. 3 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH Carroll et al Figure 1. Health Information National Trends Survey (HINTS) respondents’ use of mobile phones, tablets, and apps. Demographic Variables Associated With App Use Table 1 compares respondents grouped into Device+/App+, Device+/App-, and Device-, according to sociodemographic characteristics. As shown in Table 1, those who used health apps (compared with those who either did not have apps or did http://www.jmir.org/2017/4/e125/ XSL• FO RenderX not have the necessary equipment) were more likely to be younger, live in metropolitan areas, have more education, have higher income, speak English well, be Asian, and report excellent health. There was no significant association between both BMI and smoking status and app use. J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p. 4 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH Carroll et al Table 1. Demographic variables associated with app usage. Demographic variables Device+/App+ b,c n (%) d Device+/App- Device- n (%) n (%) P value Sex (female vs male; na,c=3519) 808 (51.62) 1555 (50.23) 1156 (55.29) .39 Age (18-44 years vs 45+ years; n=3415) 782 (65.62) 1552 (52.25) 1111 (21.92) <.001 Education (high school or less vs some college or college graduate, n=3444) 788 (12.72) 1535 (27.95) 1121 (51.82) <.01 Income (US $0-49,999 vs 50,000 or greater; n=3530) 808 (31.72) 1560 (42.20) 1162 (75.12) <.001 Race or ethnicity (white vs other; n=3273) 763 (71.85) 1453 (78.52) 1057 (83.68) <.01 BMI (normal vs overweight, obese; n=3420) 782 (33.71) 1524 (36.98) 1114 (33.82) .49 Metro vs nonmetro (n=3584) 816 (92.10) 1577 (85.67) 1191 (78.93) <.001 Speak English (very well or well vs not well or not at all; n=3584) 759 (99.37) 1497 (97.13) 1089 (90.37) <.001 Self-rated health (excellent, very good, good vs fair or poor; n=3477) 795 (92.85) 1544 (89.74) 1138 (74.99) <.001 a The sample sizes (n’s) listed for each variable in the far left column represent the total number of respondents across all app-usage categories (Device+/App+, Device +/App-, Device-) who answered that question. b The sample sizes (n’s) listed for each variable within each cell represent the total number of respondents within a given app-usage category (either Device+/App+, Device +/App-, or Device-) who answered that question. c Sample sizes vary for each variable due to missing values. d Population estimates were used for the numerators and denominators in the calculation of percentages. Row percentages do not add to 100%, as the table shows percentages within a given app-usage category (Device+/App+, Device +/App-, or Device-). Association Between the Use of Apps and Intentions to Change Diet, Perform Physical Activity, and Lose Weight Table 2 shows the association between the use of apps (versus Device+/App- or Device-) with intentions to change diet, perform physical activity, or lose weight. As Table 2 shows, participants with apps were significantly more likely to report intentions to improve fruit (P=.01) and vegetable consumption (P<.01), physical activity (P<.01), and weight loss (P<.01) compared with those in the Device+/App- or Device- groups. Table 2. Association between the usage of apps for health-related goal and intentions to change diet, physical activity, or lose weight. Health-related intention a P valuea Device+/App+ Device+/App- Device- n (%) n (%) n (%) Increase fruit 545 (63.76) 885 (58.50) 654 (48.94) .01 Increase vegetables 621 (74.92) 1023 (64.26) 717 (50.02) <.01 Decrease soda 630 (84.96) 1135 (82.76) 754 (77.36) .06 Increase physical activity 707 (82.99) 1237 (65.42) 769 (49.94) <.01 Lose weight 692 (83.36) 1259 (71.75) 881 (60.02) <.01 Significance between participants with apps (Device+/App+) compared with those not using apps or devices (Device+/App- or Device- groups). Association Between the Use of Apps and Meeting Recommendations for Fruit and Vegetable Intake and Physical Activity Table 3 shows the association between the use of apps (versus Device+/App- or Device-) and meeting the recommendations http://www.jmir.org/2017/4/e125/ XSL• FO RenderX for fruit and vegetable intake and physical activity. Participants in the Device+/App+ group were not significantly more likely to meet recommendations for fruit and vegetables compared with those in the Device+/App- or Device- groups; however, they were significantly more likely to exercise more than 2 hours per week. J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p. 5 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH Carroll et al Table 3. Association between the use of apps for health-related goal and meeting recommendations for fr ...
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Running head: MOBILE PHONE HEALTH APPS USAGE

Mobile Phone Health Apps Usage
Student’s Name
Institution Affiliation
Date of submission

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MOBILE PHONE HEALTH APPS USAGE

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The use of health apps on electronic devices has increased with growth in technology.
Many applications have been created, and this research article is used to assess the number of
people that use these apps and how they respond to instructions. The research, the data used was
based on sampled questions. This could have easily been biased depending on the format of the
questions. The response rate was shallow (less than half), the overall rate of response was
approximately 34%, and not all questions were answered. This could imply that people only
responded to the questions they were interested in which would mean the results were a false
(Ayorinde, Williams, Mannion, Song, Skrybant, Lilford, &...

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