22 annotated bibliographies AMA style

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=ujhe20

American Journal of Health Education

ISSN: 1932-5037 (Print) 2168-3751 (Online) Journal homepage: https://www.tandfonline.com/loi/ujhe20

Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle Choices between Users and Nonusers of Mobile Health Apps

Alessandra Sarcona, Laura Kovacs, Josephine Wright & Christine Williams

To cite this article: Alessandra Sarcona, Laura Kovacs, Josephine Wright & Christine Williams (2017) Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle Choices between Users and Nonusers of Mobile Health Apps, American Journal of Health Education, 48:5, 298-305, DOI: 10.1080/19325037.2017.1335630

To link to this article: https://doi.org/10.1080/19325037.2017.1335630

Published online: 11 Jul 2017.

Submit your article to this journal

Article views: 1343

View related articles

View Crossmark data

Citing articles: 5 View citing articles

RESEARCH ARTICLE

Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle Choices between Users and Nonusers of Mobile Health Apps Alessandra Sarconaa, Laura Kovacsb, Josephine Wrightc, and Christine Williamsa

aWest Chester University of Pennsylvania; bMontefiore Medical Group; cLong Island University

ABSTRACT Background: Weight gain and lifestyle behaviors during college may contribute to future health problems. This population may not have sufficient self-monitoring skills to maintain healthy lifestyle behaviors. Purpose: The purpose of this study was to determine the relation- ship between usages of mobile health applications (apps) designed to track diet and physical activity and health-related behaviors of college students. Methods: In a cross-sectional study, 401 university students completed a survey to assess eating behavior, physical activity, and health-related lifestyle choices and mobile health app usage. Results: Mobile health app users had significantly higher scores for eating behavior than nonusers, and the impact of using more than one type of mobile health app significantly improved eating behavior. Most participants also identified app use with feeling healthier, better self-monitoring of food intake and exercise, and having more motivation to eat healthier and increase physical activity. Discussion: Use of mobile health apps may have a positive effect on eating behavior, and demographic background appears to be influential with regard to health-related behaviors. Translation to Health Education Practice: Health Educators need to consider the use of apps as a supplementary component of a health promotion program to assist individuals who want to make improvements in their overall health and to prevent chronic disease.

ARTICLE HISTORY Received 2 February 2017 Accepted 16 May 2017

Background

Young adults attending college are more vulnerable to weight gain than the general population1 due to a decline in exercise and unhealthy dietary habits.2 Weight gain and lifestyle behaviors during college may contribute to over- weight and obesity in adulthood and may increase the risk of future health problems and future chronic disease.2 This population may not have sufficient self-regulatory skills, such as self-monitoring, to maintain healthy lifestyle beha- viors in a college environment.1 With the innovation of technology, a dramatic rise in the development of mobile- based applications (apps) has occurred.3 An app uses a software program operated through a browser from a smartphone, tablet, or other mobile device. There are many mobile apps related to health, and data show that a little more than half of individuals who are smartphone users have downloaded a fitness or health app that assists them in recording, tracking, and analyzing health data.4

Given the recognized relationship between lifestyle beha- viors and chronic disease, mobile-based tracking devices and applications are of high interest because of their poten- tial effect on preventative health care measures including

increased physical activity, improvement in dietary self- monitoring, and beneficial behavioral changes. Smartphone-based applications provide a promising method that can be used to monitor and motivate indivi- duals to engage in a healthy lifestyle.5 A strong interest in the use of dietary assessment tools in mobile health apps was reported among health care providers, especially for patient self-monitoring and formanaging obesity, diabetes, and heart disease.6

Because the use of mobile health apps is continuing to increase, there needs to be more research evaluating their effectiveness in prevention of chronic disease. The increased prevalence of chronic disease is largely due to several risk factors that include physical inactivity, high blood pressure and cholesterol, use of tobacco and alcohol, stress, and obesity.7 Most of these risk factors are modifi- able and can be lessened by health interventions and tools such as mobile health apps to support a healthy lifestyle and behavior change.7 Improving risk factors for chronic disease, such as cardiovascular disease, which is the leading cause of death globally, can decrease morbidity and mor- tality and improve quality of life.8 Participants in a cardiac

CONTACT Alessandra Sarcona [email protected] West Chester University of PA, 303 Sturzebecker Health Science Center, 855 South New Street, West Chester, PA 19383. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ujhe

AMERICAN JOURNAL OF HEALTH EDUCATION 2017, VOL. 45, NO. 5, 298–305 https://doi.org/10.1080/19325037.2017.1335630

© SHAPE America

rehabilitation center who used a mobile health app had significantly higher adherence and completion rates than those who did not use devices.9 Research like this that evaluates the use of mobile health apps for health promo- tion and disease prevention is limited but foreseen to be on the upsurge.

Most of the popular mobile health and fitness applica- tions available provide a focus on fitness and self- monitoring.10 Dietary self-monitoring is consistently associated with weight loss in behavior-based programs; however, adherence is a recognized challenge with tradi- tional paper-and-pencil monitoring.11 Adherence to a self-monitoring weight management intervention was significantly higher in a group using a smartphone app compared to a paper diary.12 Dietary self-monitoring on a smartphone app and the positive acceptability of compu- ter recording methods for energy intake may lead to increased compliance with tracking caloric consumption- 13 and demonstrate the potential for long-term weight loss.11 It has been suggested that individuals, especially those of younger generations, preferred the use of mobile phones for dietary and weight loss interventions com- pared to other web-based tools.13 Weight loss is a key aspect in prevention of chronic disease and therefore is of great interest in mobile health apps for assisting in this area of health promotion and disease prevention.

To date, there is limited research that has focused on the effects of utilizing mobile-based health and fitness applications for the college-age population, rendering a gap in information on the potential benefits of utilizing this technology in a technology-savvy population.14

Approximately half of college students who completed the 2013 National College Health Assessment indicated a greater need for health-related information, and uni- versity-based smartphone applications may help stu- dents better access this information.15 Stress management, nutrition/diet, and physical activity/fit- ness were the most common health topics that college students selected as most important to them.15 Young adulthood presents an ideal time for health interven- tions to reduce the effect of health problems and risk factors for chronic disease in later life. Because young adults are high users of mobile devices, interventions that use this technology may improve engagement.

Purpose

The purpose of this study was to evaluate differences in eating behaviors, physical activity, and health-related lifestyle choices between users and nonusers of mobile health apps among college students. The following research questions were addressed: (a) “Is there an association between gender, age, race/ethnicity, and

body mass index [BMI] with mobile health app use?” (b) “What is the relationship between mobile health app use and lifestyle, physical activity, and eating beha- vior?” (c) “What is the impact of type of mobile health app(s) on lifestyle, physical activity, and eating beha- vior?” (d) “What is the influence of the independent variables gender, age, and race/ethnicity on the depen- dent variables lifestyle, physical activity, and eating behavior?” The authors hypothesized that users of mobile health apps have more positive eating behaviors and health-related lifestyle choices and increased phy- sical activity compared to nonusers.

Methods

A survey tool was used to evaluate health related beha- viors of participants; it included the following validated questionnaires: the 1998 Lifestyle and Habits Questionnaire–brief version,16 which assesses lifestyle behaviors/attitudes and tested among young adults (18–25 years); The Godin-Shephard Leisure-Time Physical Activity Questionnaire,17 which measures fre- quency of physical activity; and the Eating Behavior Inventory (EBI),18 which is designed to assess behaviors associated with weight loss and weight management. In addition to the questionnaires, the survey asked demo- graphic information including age, race/ethnicity, gen- der, and self-reported height and weight. In addition, the following question was asked: “Within the past 12 months, have you used a mobile-based health or fitness app?” If the response was “yes,” the following questions were asked: “What application(s) do you use?”; responses included MyFitness Pal, Jawbone, FitBit, Supertracker, and/or other, please specify (more than one response was acceptable); and “How do you feel about using your mobile-based health or fitness app? (Check all that apply)”; choices included a list of posi- tive and negative possibilities.

Instruments

The Lifestyle and Habits Questionnaire includes 13 questions with responses using a Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. “I am able to manage the stress in my life” is an example of an item from this survey16; higher scores were asso- ciated with more positive lifestyle habits. The Godin- Shepard Leisure-Time Physical Activity Questionnaire assessed self-reported physical activity for a typical 7- day week; it included times per week individuals par- ticipated in a list of exercises categorized as strenuous, moderate, or mild intensity. A total weekly score using activities in the moderate and strenuous categories

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 299

was computed based on the formula devised by Godin and Shephard.17 A final score in units, calculated using activities in the strenuous and moderate inten- sity categories, was advised by the authors, because these correlate with more health benefits than the mild intensity category.17 The EBI included 20 ques- tions using a 5-point Likert scale as previously described; higher scores were associated with more positive eating behaviors. “I carefully watch the quan- tity of food I eat” is an example of a survey item. This survey has been shown to be consistently sensitive to behavioral weight management interventions, but it appears that the amount of change in EBI scores has decreased slightly over time.18 The researchers deter- mined that some questions in the EBI may not be appropriate for college students; therefore, 20 gradu- ate students tested the EBI tool for face and content validity. Six items were changed to be more represen- tative of college students’ eating behaviors, and 6 items were deleted by the graduate students because they felt that these statements were not relative to college students. According to O’Neill et al,19 the EBI had an internal consistency with an acceptable Cronbach’s alpha coefficient of .74. In the current study, a similar Cronbach’s alpha coefficient of .70 was found.

Sample and recruitment

A convenience sample of individuals was recruited from one suburban and one urban university campus after receiving university institutional review board study approval. For both campuses, total enrollment for graduate and undergraduate students is 18 693 (34% male and 66% female). The researchers set up tables in high-traffic areas of both campuses and recruited students to complete the survey, where healthy snacks were offered as an incentive. The stu- dents completed the informed consent and the survey was distributed by the researchers using Survey Monkey (Survey Monkey Inc., San Manteo, CA; http://www.surveymonkey.com); most participants accessed the survey using their cell phones or on the laptop located at the information table. Inclusion cri- teria included participants who were students at the university and age 18 or above; only students less than 18 years old were excluded.

Data analysis

Version 23 of the Statistical Package for the Social Sciences (IBM Corp.; Armonk, NY) was utilized for all statistical analyses, and all significance levels were

set at P ≤ .05, except where noted when a more strin- gent level of .01 was set. Preliminary assumption testing was conducted to check for normality, linearity, uni- variate and multivariate outliers, homogeneity of var- iance–covariance matrices, and multicollinearity. In addition to descriptive results, the statistical analyses used included a chi-square to explore the relationship between gender, age, race/ethnicity, and BMI and mobile health app use; analysis of variance and multi- variate analysis of variance where the dependent vari- ables included scores from the lifestyle, physical activity, and EBI questionnaires; and independent vari- ables including the categorical variables app use, type of app, gender, age, and race/ethnicity.

Results

Table 1 shows demographic data. The majority of par- ticipants were non-Hispanic white, female, and between the ages of 18 and 22 years of age. The female-to-male ratio of 2.7:1 was almost consistent with the 2:1 female-to-male ratio for the university. Table 2 evaluates the first research question, “Is there an association between gender, age, race/ethnicity, and BMI with mobile health app use?” A chi-square test for

Table 1. Demographic characteristics of participants (n = 401). Demographic Information Sample

Gender Male 107 (27%) Female 294 (73%)

Age (years) 18–22 277 (69%) ≥23 124 (31%)

Race/ethnicity Non-Hispanic white or Euro American 242 (61%) Non-white: South Asian or Indian American 7 (2%) Black, Afro-Caribbean or African American 72 (18%) Middle Eastern or Arab American 9 (2%) East Asian or Asian American 13 (3%) Other/Hispanic 37 (9%) I’d prefer not to answer 21 (5%)

Table 2. Demographic information related to mobile health app use. App user Yes No

All subjects 185 (46%) 216 (54%) Gender Male 41 (22.2%) 66 (30.6%) Female 144 (77.8%) 150 (69.4%)

Age (years) 18–22 126 (68.1%) 151 (69.95) ≥23 59 (31.9%) 65 (30.1%)

Race/ethnicity Non-Hispanic white or Euro American 119 (66.9%) 123 (60.9%) Non-white 59 (33.1%) 79 (39.1%)

Body mass index Normal 92 (57.5%) 106 (58.6%) Overweight–obese 68 (42.5%) 75 (41.4%)

300 A. SARCONA ET AL.

independence (with Yates’s continuity correction) indi- cated no significant association between gender and mobile health app use, χ2 (1, n = 401) = 3.17, P = .08, phi = −0.10. There was no significant difference for age and mobile health app use, χ2 (1, n = 401) = 0.08, P = .78, phi = −0.02; for race/ethnicity and mobile health app use, χ2 (1, n = 380) = 1.21, P = .27, phi = −0.06; and for BMI and mobile health app use, χ2 (1, n = 341) = 0.008, P = .93, phi = −0.011.

Table 3 displays the perceived positive and negative feelings in relation to mobile-based health or fitness apps by participants with a history of or current use of mobile health apps; each participant may have made multiple responses. Most participants identified app use with feeling healthier, more motivation to eat healthier and increased physical activity, and better tracking of exercise and food intake. There was a 4:1 positive to negative response regarding how participants felt about their mobile health apps.

A one-way between-groups multivariate analysis of variance (MANOVA) was performed to investigate the second research question, “What is the relationship between mobile health app use and lifestyle, physical activity, and eating behavior?” Three dependent vari- ables were used: lifestyle scores, physical activity scores, and eating behavior scores. The independent variable was mobile health app use. Preliminary assumption testing found no serious violations. There was a statis- tically significant difference between mobile app users and nonusers on the combined dependent variables, F (3, 397) = 3.93, P = .009; Wilks lambda = 0.97; partial eta squared = 0.03. When the results for the dependent variables were considered separately, the only differ- ence to reach statistical significance was EBI score, F (1, 399) = 9.98. An inspection of the mean scores indicated that mobile health app users reported higher EBI scores than participants who did not use mobile health apps; refer to Table 4.

A one-way between-groups analysis of variance was conducted to explore research question 3, “What is the

impact of type of mobile health app(s) on lifestyle, physical activity and eating behavior?” Subjects were divided into three groups according to type of app: group 1: My Fitness Pal (n = 53), group 2: Fit Bit (n = 41), group 3: Other (n = 57), group 4: greater than one app (n = 36). There was no significance noted for lifestyle and physical activity scores. There was a statis- tically significant main effect due to the type of mobile health app on EBI scores, F (3, 183) = 5.3, P = .002. There was a medium effect size of .08, calculated using eta squared. Post hoc comparisons using Tukey’s hon- estly significant difference test indicated that the mean score for group 4 (65.42 ± 8.31) was significant com- pared to group 1 (59.11 ± 7.26), P = .002, group 2 (60.02 ± 8.23), P = .019, and group 3 (59.54 ± 8.34), P = .004; see Figure 1.

Research question 4, “What is the influence of the independent variables gender, age, and race/ethnicity on the dependent variables lifestyle, physical activity, and eating behavior?” was assessed using 3 MANOVA tests. For the first MANOVA, the inde- pendent variable was gender (male = 107; female = 294) and the 3 dependent variables were lifestyle scores, physical activity scores, and eating behavior scores. Preliminary assumption testing found that physical activity and lifestyle scores violated the equality of variances; therefore, a more stringent alpha of .01 was used to test for significance. There was a statistically significant difference between males and females on the combined dependent variable, F (3, 397) = 12.83, P < .001; Wilks lambda = 0.91; partial eta squared = 0.09, which is a moderately high effect size. When the results for the dependent variables were considered separately, they were all statistically significant: lifestyle score F (1, 399) = 22.54, partial eta squared = 0.05 (moderate effect size); physical activity score F (1, 399) = 6.61, partial eta squared = 0.02 (small effect size); EBI score F (1, 399) = 6.61, partial eta squared = 0.02 (small effect size). Males reported higher lifestyle scores and phy- sical activity scores and females reported higher EBI scores; refer to Table 5.

Table 3. Respondents’ feelings about mobile health or fitness apps. Response Question n

Positive It helps me keep track of my exercise and food intake 125 It helps me manage my weight better 60 It makes me feel healthier 80 It motivates me to eat healthier and increase my physical activity

91

It helps me have a more positive body image 44 Negative It makes me feel obsessive about my exercise and food

intake 41

It creates anxiety/guilt if I do not reach my exercise or food intake goals

34

It interferes with my daily activities and/or social life 12 It makes me neurotic about my body image 18

Table 4. Comparison of lifestyle, physical activity, and eating behavior scores on students who use mobile health apps versus non app users.

Lifestyle Physical Activity EBIa

Group Mean SD P Mean SD P Mean SD P

App users (n = 185)

48.87 7.49 .095 59.91 25.72 .117 60.51 8.27 .007*

Nonusers (n = 216)

47.55 8.15 55.71 27.53 57.88 8.33

aEBI indicates Eating Behavior Inventory. *Significant at P ≤ .05.

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 301

The second MANOVA was performed to investigate the relationship between age and lifestyle scores, phy- sical activity scores, and eating behavior scores. There was a statistically significant difference between age on the combined dependent variables, F (3, 397) = 17.432, P < .001; Wilks lambda = 0.89, with a large effect size (partial eta squared = 0.12). When the results for the dependent variables were considered separately, age and EBI score, F (1, 399) = 30.88, partial eta squared = 0.07 (moderate effect size), and physical activity score, F (1, 399) = 7.50, partial eta squared = 0.02 (small effect size), were statistically significant. Participants who were older reported higher EBI scores than younger subjects, and participants who were younger reported higher physical activity scores than older subjects; refer to Table 5.

The third MANOVA was performed to examine the relationship between race/ethnicity and scores for lifestyle, physical activity, and eating behavior. See Table 1 to view original race/ethnicity distribution. To perform the analysis with a more even distribu- tion, the independent variable was collapsed to form 2 groups: group 1 = white (n = 242) and group 2 = non-white (South Asian or Indian American, Black, Afro-Caribbean or African American, Middle Eastern

or Arab American, East Asian or Asian American, and other; n = 138). Preliminary assumption testing found no serious violations. There was a statistically significant difference between group 1 and group 2 on the combined dependent variables, F (3, 376) = 3.05; Wilks lambda = 0.98; P = .003; partial eta squared = 0.02 (small effect size). When the results for the dependent variables were considered sepa- rately, the only difference to reach statistical signifi- cance was physical activity score, F (1, 378) = 8.97, with a small effect size of 0.02. An inspection of the mean scores indicated that participants who self-iden- tified as white reported higher physical activity scores than non-white participants; refer to Table 5.

Discussion

With the innovation of technology, mobile-based health apps provide a promising new technique that can be used to improve modern-day health interven- tions. However, little is known about the potential effects of utilizing this technology for the college-aged population. The purpose of this study was to evaluate differences in eating behaviors, physical activity, and health-related lifestyle choices between users and

59.11

60.02

59.54

65.42

55

56

57

58

59

60

61

62

63

64

65

66

1 = My Fitness Pal 2 = Fit Bit 3 = Other 4 = > One App

Mean EBI Score

Figure 1. Eating behavior inventory scores based on type of mobile app.

Table 5. Comparison of lifestyle, physical activity, and eating behavior scores on students’ gender, age, and race/ethnicity. Lifestyle Physical activity EBIa

Group Mean SD P Mean SD P Mean SD P

Male (n = 101) 51.17 7.74 .000* 64.14 24.61 .003* 51.17 7.74 .011* Female (n = 294) 47.06 7.64 55.28 27.16 47.06 7.64 Younger, 18–22 years (n = 277) 47.70 8.22 .084 60.08 27.54 .000* 57.59 7.96 .006* Older, 23+ years (n = 124) 49.17 6.95 52.22 24.16 62.45 8.41 White non-Hispanic (n = 242) 48.61 7.68 .120 61.33 25.97 .003* 59.22 8.12 .543 Non-white (n = 138) 47.30 8.09 52.93 26.76 58.67 8.86

aEBI indicates Eating Behavior Inventory. *Significant at P ≤ .01.

302 A. SARCONA ET AL.

nonusers of mobile health apps and to determine whether demographic factors were associated with app use and health behaviors.

The principal finding of this study was that mobile health app users reported significantly higher EBI scores or more positive eating behaviors compared to nonusers. In addition, use of mobile health apps was positively related to increased lifestyle scores; however, no significance was noted. The findings of this study correlate with the findings of Dallinga et al20 in which app use was positively related to an overall improve- ment in lifestyle and eating behavior. Mobile health app use was also positively related to participants feeling better about themselves, feeling like an athlete, and motivating others to participate in running and losing weight.20 Two studies have shown that the use of mobile-based apps in a weight loss program increased compliance and led to better health outcomes21 and participants lost significantly more weight than the standard group not using a mobile-based app.22

The types of mobile-based apps utilized among the college population vary dramatically based on popular- ity, features, etc. Therefore, mobile-based apps were examined in reference to their effects on lifestyle beha- vior, physical activity, and eating behavior. Results showed that participants who used more than one type of mobile health app had significantly higher EBI scores or more positive eating behaviors compared to use of individual apps. This aspect of mobile-based technology is limited in previous studies; however, the interaction between varying features of mobile-based apps may be an underlying element that is key to this type of technology-based health intervention. Middelweerd et al evaluated college students’ prefer- ences regarding a physical activity application for smartphones. The majority of the students preferred apps that coached and motivated them, included tai- lored feedback toward personally set goals, and involved competition with friends.23 Another study found that college students were most influenced by apps that were free, were easy to use, provided visual/ auditory cues, and had game-like rewards.14 Most stu- dents used apps that coincided with their specific goals, such as developing an exercise routine or improving eating habits.14 Participants may benefit further from technology interventions if mobile apps specifically cater to their health/lifestyle needs.

This study evaluated demographics such as age, gender, race/ethnicity, and BMI relative to mobile health app use and health-related behaviors. The influence of age is critical to examine because of the notable increase in technology use seen in younger populations. In this study, older participants

reported higher EBI scores and younger participants reported participating in more frequency of physical activity than older subjects; however, no significance was found between age and mobile app use. A study by Bhuyan et al outlined that the majority of the population who used health-related mobile apps were younger than 65 years old. Those who were older were less likely to use apps for achieving health behavior goals.24 It is noted that no participants in our study were older than 65 and only 15% were older than 26 years of age. In a study conducted by Gorton et al, researchers found the greatest support for mobile phone–delivered weight loss interventions among younger participants (16–30 years) compared to older age groups (31–50 years and 50–70+ years).- 25 This is also consistent with the findings of Dallinga et al, who found that app users were significantly younger compared to nonusers.20 These results sug- gest that intervention success may improve with a focus on younger populations and providing more instruction on using apps among older populations.

Male participants in this study scored signifi- cantly higher for lifestyle and physical activity scores, and females scored higher for eating beha- viors. There was no significance between gender and mobile health app use. Research is limited with gender-specific data collection in regard to health behavior.14 Further research regarding mobile health interventions that can be utilized to increase specific types of behaviors in males and females could lead to a large improvement in overall health.

Ethnic minority college women appear to be par- ticularly vulnerable to high rates of overweight and obesity26,27; therefore, race/ethnicity and BMI were evaluated in this study. No differences in this study were found among whites versus non-whites in mobile health app use, and participants who self- identified as white reported higher physical activity scores. Rodgers et al evaluated ethnic minority col- lege women using mobile technology to promote healthy eating. Results revealed that adherence decreased over the course of the study and those with a higher BMI had lower satisfaction using the technology, but those with higher body dissatisfac- tion had the greatest adherence.28 This study did not find that overweight and obese individuals used mobile health apps more than normal-weight persons; however, Bhuyan et al found that obese respondents in their study were 3.2 times more likely than normal-weight subjects to use apps for achieving health behavior goals.24 Effective strategies for treating high rates of obesity are vital to decrease the prevalence of chronic diseases.

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 303

Limitations

There were several strengths to this study, such as large sample size and range of diversity of subjects. Limitations include a wide variety of mobile health apps utilized by participants and use of a survey versus actual measure- ment of health-related outcomes. Additional research exploring the evidentiary gaps discussed above is now needed to produce effective behavior change theory– based applications to aid in improved health. The sample was predominately female; however, it was almost con- sistent with the university ratio of female to male but cannot be generalized for all universities. Our study did not find differences in gender regarding use of mobile health apps, but further study on types of apps and moti- vation to use appsmay reveal different preferences among gender. This study evaluated a psychological component of using an app to track health behaviors. With a 4:1 positive to negative response regarding how participants felt about their mobile based apps, future research on specific behavioral responses may be useful. Dennison et al reported participants’ thoughts and feelings regard- ing their use of health and fitness apps. Accuracy, legiti- macy, security, effort required, and how it affected their mood were factors influencing app use.29 It is also impor- tant to consider negative feelings that may be associated with using an app for monitoring health. Participants in this study noted feelings of obsession with their exercise and food intake, anxiety/guilt when exercise or food intake goals were not met, interference with daily activ- ities and/or social life, and neurosis about body image. Similar findings among participants in a study by Gowin et al discussed negative feelings related to app use such as guilt, avoidance, shame, or feeling stressed and becoming obsessed, but users still had positive comments regarding app use for health and fitness.14 Tracking health behaviors has also been associated with eating disorder symptoma- tology, and this is an area that requires more research.30

Translation to Health Education Practice

Mobile-based apps are a leading innovation in this new age of technology. The benefits of using mobile apps for health promotion programs are innumerable because up to 92% of young adults own smart phones, as do the majority of low-socioeconomic status and minority populations.31 This is an ideal population when imple- menting health promotion programs to prevent chronic disease and reduce health care costs. Targeting these populations when focusing on technology-based Health Education programs is essential at reducing health dispa- rities and striving to reach Healthy People 2020 goals.

According to the results of this study, these self- monitoring devices may have an immeasurable effect on behavioral interventions concerning eating beha- vior. Most participants identified mobile health app use with feeling healthier, feeling motivated, and improved self-monitoring. App users were found to have more positive eating behaviors than nonusers, and the impact of using more than one type of mobile-based health app significantly improved eating behavior. Healthy eating habits and exercise are a vital component to health promotion and chronic disease prevention. Though social determinants have a strong influence on one’s health, programs aimed at improv- ing nutrition and exercise via mobile health apps may improve quality of life and longevity. Health Education and health promotion programs must be ingenuous, and using technology that is accessible all hours of the day for participants may promote adherence. Barriers are low for using cell phones; people use smartphones and apps everywhere and at any time. Certified Health Education Specialists need to consider the use of apps as a supplementary component to assist individuals who want to make improvements in their overall health and prevent chronic disease.

More specific randomized controlled trials utilizing dif- ferent types of mobile apps associated with health out- comes may help to further understand the fundamental relationship between improved health and mobile technol- ogy. Studies analyzing app usage adherence and app pre- ference among target populations are necessary for effective Health Education and promotion programming. Leveraging mobile-based technology to improve health offers exciting and unlimited avenues in combatting chronic disease.

References

1. Strong KA, Serena LP, Anderson E, Winett R, Davy M. Weight gain prevention: identifying theory-based tar- gets for health behavior change in young adults. J Am Diet Assoc. 2008;108:1708–1715.

2. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Weight changes, exercise, and dietary patterns during freshman and sophomore years of col- lege. J Am Coll Health. 2005;53:245–251.

3. Deeks A, Lombardo C, Michelmore J, Teede H. The effects of gender and age on health related behaviors. BMC Public Health. 2009;9:213-220.

4. Krebs P, Duncan DT. Health app use among U.S. mobile phone owners: a national survey. JMIR Mhealth Uhealth. 2015;3(4):e101.

5. Moss ASMM, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak. 2012;12(67):1–31.

304 A. SARCONA ET AL.

6. Bonilla C, Brauer P, Royall D, Keller H, Hanning RM, DiCenso A. Use of electronic dietary assessment tools in primary care: an interdisciplinary perspective. BMC Med Inform Decis Mak. 2015;15(4):1–13.

7. Santoro E, Castelnuovo G, Zoppis I, Mauri G, Sicurello F. Social media and mobile applications in chronic disease prevention and management. Front Psychol. 2015;6:567.

8. Lloyd-Jones DM, Leip EP, LarsonMG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006;114:791–798.

9. Bert F, Giacometti M, Gualano MR, Siliquini R. Smartphones and health promotion: a review of the evidence. J Med Syst. 2014;38:1–11.

10. Higgins JP. Smartphone applications for patient’s health and fitness. Am J Med. 2016;129:11–19.

11. Hutchesson MJ, Rollo ME, Callister R, Collins CE. Self- monitoring of dietary intake by young women: online food records completed on computer or smartphone are as accurate as paper-based food records but more acceptable. J Acad Nutr Diet. 2015;115:87–94.

12. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper dairy: pilot randomized con- trolled trial. J Med Internet Res.2013;15(4):e32.

13. Wharton CM, Johnston CS, Cunningham BK, Sterner D. Dietary self monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial. J Nutr Educ Behav. 2014;46:440–444.

14. Gowin M, Cheney M, Gwin S, Wann TF. Health and fitness app use in college students: a qualitative study. Am J Health Educ. 2015;46:223–230.

15. Miller T, Chandler L, Mouttapa M. A needs assess- ment, development, and formative evaluation of a health promotion smartphone application for college students. Am J Health Educ. 2015;46:207–215.

16. Dinzeo TJ, Thayasivam U, Sledjeski EM. Lifestyle and habits questionnaire—brief version: relationship to quality of life and stress in college students. Prev Sci. 2014;15:103–114.

17. Godin G, Shephard RJ. A simple method to assess exercise behavior in the community. Can J Appl Sport Sci. 1985;10:141–146.

18. O’Neil PM, Rieder S. Utility and validity of the Eating Behavior Inventory in clinical obesity research: a review of the literature. Obes Rev. 2005;6:209–216.

19. O’Neil PM, Currey HS, Hirsch AA, et al. Development and validation of the eating behavior inventory. J Behav Assess. 1979;1:123–132.

20. Dallinga, JM, Mennes M, Alpay L, Bijwaard H, Baart de la Faille-Deutekom M. App use, physical activity and health lifestyle: a cross sectional study. BMC Public Health. 2015;15(833):1–9.

21. Spring B, Duncan JM, Janke EA, et al. Integrating technology into standard weight loss treatment: a ran- domized controlled trial. JAMA Intern Med. 2013;173:105–111.

22. Burke LE, Styn MA, Sereika SM, et al. Using mhealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med. 2012;43:20–26.

23. Middelweerd A, Van Der Laan DM, van Stralen M, et al. What features do Dutch university students prefer in a smartphone application for promotion of physical activity? A qualitative approach. Int J Behav Nutr Phys Act. 2015;12:31.

24. Bhuyan SS, Lu N, Chandak A, et al. Use of mobile health applications for health-seeking behavior among U.S. adults. J Med Syst. 2016;40:153.

25. Gorton D, Dixon R, Maddison R, Mhurchu CN, Jull A. Consumer views on the potential use of mobile phones for the delivery of weight loss interventions. J Hum Nutr. 2011;24:616–619.

26. Freedman MR. Gender, residence and ethnicity affect freshman BMI and dietary habits. Am J Health Behav. 2010;34:513–524.

27. Harris KM, Perreira KM, Lee D. Obesity in the transi- tion to adulthood: predictions across race/ethnicity immigrant generation, and sex. Arch Pediatr Adolesc Med. 2009;163:1022–1028.

28. Rodgers RF, Franko D, Shiyko M, et al. Exploring healthy eating among ethnic minority students using mobile technology: feasibility and adherence. Health Informatics J. 2016;22:440–450.

29. Dennison L, Morrison L, Conway G, Yardley L. Opportunities and challenges for smartphone applica- tions in supporting health behavior change: qualitative study. J Med Internet Res. 2013;15(4):e86.

30. Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: associations with eating disorder symptomatology. Eat Behav. 2017;26:89–92.

31. Pew Research Center. Mobile fact sheet. http://www. pewinternet.org/fact-sheet/mobile/. Published January 12, 2017. Accessed April 21, 2017.

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 305