Health-related Behaviors and Technology Usage among College Students Bridget F. Melton, EdD; Lauren E. Bigham, EdS; Helen W. Bland, PhD; Matthew Bird, MS; Ciaran Fairman, BS Objective: To examine associations between technology usage and specific health factors among college students. Methods: The research employed was a quantitative, descriptive, cross-sectional design; undergraduate students enrolled in spring 2012 general health education courses were recruited to participate. To explore college students’ specific technology usage and health-related behaviors, a 28-item questionnaire was utilized. Results: Statistical significant dif-

T

echnology use virtually permeates all aspects of 21st century society, with the college student population embracing, as well as, flourishing under increasing access. As pioneers in digital consumption,1 98% of college students report owning a computer, with 60% owning a desktop. In relation to portable technological devices, 96% of college students report owning a cell phone and 84% report owning an iPod or mp3 player.2 Internet usage is reported to be highest among the young adult population, with rates rising from 74% in 2000 to 93% in 2009.3 Additionally, almost half (45%) of 18-to-29 year-olds have access to the Internet on their cell phones and do the majority of their online browsing through a mobile device.4,5 Whereas access to technology has risen substantially among the young adult population, the various types of technologies utilized and their reported frequencies of use are also notable. In relation to traditional television viewing, 58% of young adults report watching television almost every day.6

Bridget F. Melton, Associate Professor in Health and Kinesiology, Georgia Southern University, Statesboro, GA; Lauren E. Bigham Doctorate Candidate in Counseling Psychology, University of Georgia, Athens, GA; Helen W. Bland, Professor in the Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA; Matthew Bird, Graduate Research Assistant, Department of Health and Kinesiology, Georgia Southern University, Statesboro, GA; Ciaran Fairman, Graduate Research Assistant, Department of Health and Kinesiology, Georgia Southern University, Statesboro, GA. Correspondence Dr Melton; [email protected]

510

ferences of technology usage were found between 3 of the 4 health-related behaviors under study (BMI, sleep, and nutrition) (p < .05). Conclusion: As technology usage continues to evolve within the college student population, health professionals need to understand its implications on health behaviors. Key words: obesity, physical activity, exercise, colleges, technology usage Am J Health Behav. 2014;38(4):510-518 DOI: http://dx.doi.org/10.5993/AJHB.38.4.4

More specifically, data from the US Department of Labor’s 2011 American Time Use Survey (ATUS) suggest teenagers watch 2.2 hours of television per day.7 However, after assessing online video viewing by young adults, Madden6 found that 90% reported using the Internet to watch television online. In a study by Salaway, Caruso, and Nelson,8 students reported spending an average of 18 hours per week on-line. As technological access and usage increases among the college student population, researchers have also begun to assess the influence of technology on this population’s health, health-related-behaviors, and health status.9-11 Generally speaking, watching television, surfing the Internet, and playing video games have been defined traditionally as sedentary behaviors.12 Problematically, previous research also has reported a negative association between increased participation in sedentary behaviors and several health problems, including obesity,13 insulin resistance,14 mental health15 and mortality.16 Other research has investigated specific health behaviors (BMI, physical activity levels, sleep and nutrition) that might be influenced by technology, of which an overview follows. Obesity/Body Mass Index Numerous studies have attempted to link technology usage with increasing rates of obesity among adolescents.17-19 For the adolescent population, the prevelance of obesity has risen from 15.5% in 2000 to 18% in 2010.20 The rise in reported rates of obesity parallels the increase in technology usage

Melton et al among adolescents.21 The proportion of teens who report being daily users of the Internet has grown from 42% in 2000 to 63% in 20093. Bélanger et al17 reported that adolescents, who were classified as heavy Internet users (>2 hours a day), were also at a higher risk for becoming overweight. Previous research suggests similar results with high levels of Internet use being associated with obesity.18 Physical Activity Whereas college students generally report higher levels of physical activity when compared to adults, 36.3% of 18-to-24 year-olds meet the national recommendations for physical activity as set forth by the Centers for Disease Control and Prevention (CDC).22,23 Nevertheless, physical activity decreases during the adolescent years.24 Such decreases in physical activity, or the resulting increases in sedentary behaviors, also have been associated with reported screen time among adolescents.25-27 The evolution of technology and introduction of smart phones and tablets have led to a shift in how one views ‘screen time’ with people being exposed to various technologies that can be used on the go.28 Yet, Lepp et al29 confirmed that college students who used their cell phone at high frequency rates were more likely than low frequency users to engage in sedentary behaviors rather than participating in physical activity. Sleep Screen time also has been linked to poor sleeping patterns among adolescents. The presence of media devices in the bedroom has been associated with a delayed bedtime and a significant loss in sleep.29 Researchers have reported similar results with high levels of Internet use being associated with lack of sleep.31-33 The association between technology and screen time with irregular sleep patterns and reduced academic performance has highlighted the need to focus on adolescent behavior in relation to technology use.34

student population. Specifically, it is important to understand the potential association between technology usage and college students’ health status. This study’s significance is found in its expansion of the traditional screen time conceptualization of technology usage that has commonly relied by previous research. Screen time, which is defined as the amount of time spent using computers, watching television or DVDs, and/or playing video games,41 may be limited by solely focusing on traditional forms of technology. Rather, the ever-evolving forms of technology, including tablets, laptops, smart phones, and gaming systems; provide access to various technology-based applications that should be assessed in greater detail. Previous literature has been limited in its focus on screen time among young adults with little emphasis on various applications that currently encompass technology use.10,13,16,19 This study sought to examine the relationships that exist among BMI, physical activity, sleep patterns, and nutrition with regards to broad technology usage (ie, Twitter, Facebook, and on-line gaming) among college students in a rural southeastern region of the United States. The purpose of this study was to identify the relationship between technology use and certain health-related behaviors. Specifically, this study attempted to address the following research questions: (1) What is the usage rate by technology type among college students? and (2) Are rates of technology usage significantly associated with better health-related behaviors?

Current Study As health behavior patterns established during college are commonly maintained throughout the adult years,40 there is a need to explore the relationship further between a range of health-related variables and technology usage in the college

METHODS Study Design and Sample The research method employed was a quantitative, descriptive, cross-sectional design. After receiving Institutional Review Board approval, undergraduate students from a midsized southeastern public university were recruited for this study. College students enrolled in spring 2012 general health education courses were recruited to participate in an online research study assessing technology usage and health behavior practices. The general health education course is a requirement of every undergraduate student for graduation along with 2 one-credit hour physical activity courses. Most students take these courses during freshman year. During spring 2012, 2017 out of 4888 freshmen were enrolled in this course (41%). The sampling method employed was a probability, randomized cluster approach where intact classrooms were drawn by random selection. Specifically, 4 of 10 health education courses were chosen randomly. Through the general health education course instructors, 850 students were sent recruitment emails that included a brief description of the study, the necessary Web-link, and a passive consent letter. A total of 2 recruitment prompts were given to the classes. The first was an online an-

Am J Health Behav.™ 2014;38(4):510-518

DOI:

Nutrition Technology use has demonstrated some promise to increase health-related behaviors, particularly in the area of nutrition.35,36 College students’ nutritional behaviors are poor with only 5% of this population meeting the recommendation of 5 servings of fruit and vegetables each day.37 With this technology savvy age group, one might make a potential impact with nutritional technology. A current research investigation has shown promise for handheld technologies to elicit positive changes in diet.38,39

http://dx.doi.org/10.5993/AJHB.38.4.4

511

Health-related Behaviors and Technology Usage among College Students nouncement viewable in the students learning management system. The second prompt was an announcement made in class by the instructor to highlight the online post for the students to participate. Completed surveys were sent via Survey Monkey to a central data-gathering place where identifying markers were removed before being sent to the researchers for data analysis. By submitting a survey confirmation page to the respective instructor, students were given minimal bonus points towards their total grade as an incentive to participate. Out of the 850 students who were initially contacted, 591 completed the survey, a 69.5% response rate. Instrumentation To explore college students’ technology usage and health-related behaviors, a 28-item questionnaire, which included items assessing technologyusage, sleep, nutrition, physical activity, and demographics, was utilized. Items included in this questionnaire were based from previously validated instruments.42-45 Internal consistency reliability conducted on each subscales documented a reliable instrument with Cronbach alpha subscales reported as: sleep (α = .766); nutrition (α = .894); physical activity (α = .626); general technology use (α = .656); and health technology use (α = .620). Internal consistency reliability as measured by Cronbach α > .60 are deemed acceptable.46 Technology usage. To assess technology usage, participants replied to 6 researcher-designed items that assessed whether or not (yes/no response choices) participants had used various technologies (ie, social networking, Twitter, general Internet surfing, gaming, multiplayer online gaming and television watching). Following each positive response, the participant was then asked to estimate number of minutes per week that they spent with the technology. An aggregated General Technology Use variable was created via summation of the 6 general technology variables. Similar questions were asked on health-related technology use (11 items), with an aggregated Health Total Technology use variable likewise created. Sleep and nutrition behaviors. Data were collected on the average number of hours participants slept during the night. Average sleep was calculated by weighing the average number of hours the participant reported for weekdays and weekends.44 Taken from the American College Health Association’s National College Health Assessment, nutritional behaviors were assessed by the number of self-reported servings of fruits and vegetables consumed per day.37 Physical activity measure. Physical activity was assessed using the self-administered short-form of the International Physical Activity Questionnaire (IPAQ).43,45 IPAQ elicits information on time spent in (1) light, (2) moderate, and (3) vigorous physical activity, as measured by the number of physical activity minutes in a usual week. The short form

512

of the IPAQ has been found to be valid as well as reliable.43,45 The IPAQ scoring protocol assigns the following metabolic equivalent (MET) energy expenditure values to light, moderate, and vigorous intensity activity: 3.3 METs, 4.0 METs, and 8.0 METs, respectively. Demographic questionnaire. Self-reported height and weight were utilized to calculate body mass index (BMI). Using the CDC guidelines, participants were classified into weight class categories depending on range: underweight as BMI 30.47 Additional demographic information solicited was grade classification, sex, and ethnicity. Analysis Descriptive statistical analyses reported means, standard deviations, frequencies, and percentiles for health behaviors and demographics. Inferential statistics determined statistical significance between technology usage and health behaviors (p < .05). One-way analysis of variance (ANOVA) can be used to compare means of groups where the independent variable is categorical. When using ANOVA to determine linear associations, the independent variable can be an attribute variable which is understood as one that is not manipulated.48 The use of an ANOVA to determine linear association assumes independence, normality of population distribution, and homogeneity of variance. Researchers hypothesized that participants with lower technology usage would have better health-related behaviors (p < .05). Data were analyzed using SPSS version 19.49 RESULTS Participants Participants were 591 students enrolled in spring 2012 general health education courses offered at a medium-sized southeastern university. Exclusion criteria included those who reported a total technology time of 0 minutes per week (technology time was necessary to complete the survey) and those who reported over 7056 minutes of technology use per week (every waking minute per day). Total adjusted response rate was 69.5% (591/850). To protect against nonresponse bias, a total usable, acceptable response rate needs to be 50%-60%49 which this study met. The majority of participants were female (N = 325, 55.0%). Of the respondents, 70.1% (N = 414) were freshmen, 15.2% (N = 90) were sophomores, 9.0% (N = 53) were juniors, and 3.6% (N = 21) were seniors. Most of the respondents identified themselves as White (63.6%, N = 376), with the remaining identifying as African American (25.0%, N = 148), and others (11.7%, N = 56) including Hispanic, Asian, and bi-racial. IPAQ calculations of reported physical activity levels classify persons into low, moderate and high physical activity groupings. The majority of the participants fell into the high physical activ-

Melton et al

Table 1 Distribution of Demographic Characteristics, BMI, Physical Activity Levels and Health Behaviors (N = 591) Demographic Variables



N

(%)

Sex (N = 578) Male 253 (47.8) Female 325 (56.2) Class Rank (N = 591) Freshman 414 (70.1) Sophomore 90 (15.2) Junior 53 ( 9.0) Senior 21 ( 3.6) Ethnicity (N = 580) White 376 (63.6) Black 148 (25.0) Other 56 (11.7) Physical Activity Group (N = 540) Low 21 ( 3.6) Moderate 136 (23.0) High 383 (64.8) BMI (N = 550) Underweight 73 (12.4) Normal 305 (51.6) Overweight 108 (18.3) Obese 64 (10.8) Health-related Variables x SD Sleep Hours per Night (N = 487) Weekday 6.71 1.50 Weekend 8.30 2.21 Average 7.16 1.40 Nutrition Servings per Day (N = 533) Fruit 1.28 1.18 Vegetable 1.47 1.32 Total 2.49 0.93

ity group (N = 383, 64.8%). The remaining participants were low (N = 21, 3.6%) or moderate (N = 136, 23.0%). BMI scores were calculated from self-reported height and weight. The BMI classifications ranged from underweight to obese. Within the study, 12.4% (N = 73) were underweight, 51.6% (N = 305) were a healthy weight, 18.3% (N = 108) were overweight, and 10.8% (N = 64) were obese. Mean sleep hours per night varied between weekday (= 6.71, SD = 1.5) and weekend (= 8.30, SD = 2.21); with average weekly sleep hours per night as 7.16 (SD = 1.40). Participants average fruit intake was 1.28 (SD = 1.18) servings per day, average vegetable servings per day were 1.47 (SD =1.32).

from total social networking with participants spending on average 258.08 (SD = 26.89) minutes per week using social networking websites or Twitter (Table 2). Television watching had the second highest usage with a reported average of 254.09 (SD = 17.37) minutes per week, followed by general internet surfing with an average of 202.66 (SD = 19.35) minutes per week. For health-related technology usage, participants reported an average of 11.34 (SD = 2.81) minutes per week on fitness gaming, followed by Internet surfing for health purposes with 10.89 (SD = 1.56) minutes per week.

Technology Usage among College Students Leading reported general technology usage came

Associations between Technology Usage and Health-related Behaviors Numerous significant associations were found between technology usage and health-related be-

Am J Health Behav.™ 2014;38(4):510-518

DOI:

http://dx.doi.org/10.5993/AJHB.38.4.4

513

Health-related Behaviors and Technology Usage among College Students

Table 2 Technology Usage Reported by Total Weekly Minutes, Means and Standard Deviations (N = 591) Variable



x

SD

Total Technology Use 852.24 44.09 General technology use 808.05 43.69 Health technology use 44.19 5.12 General Technology Use Networking total 258.08 26.89 Social networking 166.98 13.84 Twitter 91.10 13.05 Surfing 202.66 19.35 Gaming Total 93.22 18.19 Video gaming 59.77 10.40 Online gaming 33.45 8.15 Television 254.09 17.37 Health Technology Use Devices 7.35 3.17 Pedometer/accelerometer 2.39 1.51 Heart rate monitor 0.73 0.46 GPS device 4.23 1.20 Fitness gaming 11.34 2.81 Phone applications 5.32 1.22 Fitness tracker 3.39 1.05 Online activity tracker 1.07 0.42 Online nutrition tracker 2.32 0.73 Health surfing 10.89 1.56 Health networking 2.72 0.86 Health social networking 2.04 0.53 Health twitter 0.68 0.33 Health television 3.18 0.66

haviors (p < .05). Table 3 displays these associations. The hypothesis proposed by the researchers was supported, that lower technology usage was associated with better health related-behaviors or outcomes. BMI. Participants with varying BMI classifications significantly used 2 technologies differently: social networking and TV watching (p < .05). For both of these technologies, obese participants reported using twice the average minutes per week than the other BMI classifications. For social networking, obese participants spent an average of 411.27 minutes per week as compared to 117.66 for underweight 202.33 for normal weight and 163.86 for overweight participants. In regards to TV watching, obese participants reported 529.48 minutes per week, whereas underweight, normal weight and overweight participants respectively reported 257.70, 288.05, and 288.78 minutes of TV watching.  Physical activity. No significant associations were found among physical activity levels and technology usage. Within the category of video gaming, those in the low physical activity group

514

reported 174.29 minutes per week as compared to those in the moderate physical activity group with only 45.76 minutes per week. Additionally, those in the low physical activity group reported 105.24 minutes of multi-player online video gaming per week as compared with the moderate physical activity group with 25.26 minutes per week. However, those in the high physical activity group reported 183.44 minutes per week of online gaming. Sleep. Sleep behaviors revealed numerous significant differences by various technologies usages (p < .05). Those who reported less than 6 hours of sleep had significantly higher minutes per week of usage when compared to those who obtained an average of 9 or more hours of sleep in social networking (397.94 vs 119.25), Twitter (397.94 vs 63.86), Internet surfing (655.81 vs 99.47) and overall general technology use (1948.03 vs 978.98). Nutritional behaviors. In relation to nutritional behaviors, the only significant relationship found was TV watching. Those participants who reported eating no servings of fruit and vegetables per day only watched 141.72 minutes of TV where as those who ate 1-2 servings of fruits and vegetables

Melton et al

Table 3 Statistical Significance Differences among Health Behaviors and Technology Usage as Determined by ANOVAs Health Variable

Social Twitter Internet Video Net- Surfing Games working

BMI Underweight Normal Weight Overweight Obese

.039* .206 177.66 202.33 163.86 411.27

PA

103.47 129.94 101.49 285.34

.235 .803

.769

.883

.041*

43.32 191.99 68.18 106.11

257.70 288.05 288.78 529.48

888.52 1160.39 955.38 1938.3

.681

.851

.992

275.00 269.74 308.39

1120.48 1176.32 1200.26

214.29 279.98 319.47 477.36

92.10 71.11 73.61 128.75

.541

.303

386.00 384.71 282.55

174.29 45.76 94.81

105.24 25.26 183.44

.168

Low Moderate High

118.05 295.88 193.67

Sleep

.005** .002**

.379

.000**

.682

9 hours

397.94 166.08 119.25

665.81 222.47 99.47

65.58 85.09 137.25

77.76 178.76 125.64

398.23 259.75 433.57

1948.03 1000.90 978.98

.668

.676

.796

.032*

.384

229.47 360.00 258.41 278.05

84.93 66.05 93.48 42.60

69.04 60.59 93.48 42.60

141.72 369.16 320.01 185.15

789.89 1267.84 1063.77 853.72

Nutrition 0 Fruit/Veggies 1-2 Fruits/Veggies Fruits/Veggies 5+ Fruits/Veggies

61.90 154.98 137.42

.379

Multiplayer TV Total Online Watching Tech Games Time

397.94 88.75 63.86

.944 .495 197.13 231.04 247.25 186.03

67.60 181.00 108.53 138.40

.912

.052*

*p < .05; ** p < .01

watched 369.16 minutes, 3-4 servings watched 320.01 minutes, and 5+ servings watched 185.15 minutes per week.

Body Mass Index Social Networking and TV watching were both found to be significantly different among BMI categories. Obese participants reported an average of almost 7 hours per week, or an hour per day, where as other groups collectively averaged 2 hours per week, or less than 20 minutes per day. Although previous research has revealed similar findings, establishing a relationship between obesity and social networking, the etiology is not fully understood.45 However, Puhl and Latner50 cautions us not to draw causality from these relations; obesity is not transmitted from affected to unaffected individuals along previously established social con-

nections. Explanation for this relationship is more likely to be rooted in that the Internet provides a convenient source of support for obese individuals through social networking51 Social networking might provide support for those who are struggling with their weight or looking for friendship without physical judgment of peers. Traditional social networking was founded in MySpace and Facebook, but new apps are using social networking (including health apps) to promote healthy behavior. Currently there is little knowledge on health apps and their potential impact on BMI. No matter the reason for the use of social networking, it is recommended to reduce and limit Internet use. Results from this study are congruent with the findings that Internet usage, including social networking, impacts obesity.52 In relation to TV watching, obese participants reported almost 9 hours per week, whereas underweight, normal weight and overweight participants reported an average about 4 hours of TV watching. This is consistent with previous research that supports sedentary behaviors increase the risk of obesity.13 Limiting television time may be a useful preventive measure to reduce overweightness and obesity.53 Traditional watching TV has been exclusively a sedentary behavior but now the younger

Am J Health Behav.™ 2014;38(4):510-518

DOI:

DISCUSSION This study examined associations between specific health behaviors and various technology usage among college students. In 3 of the 4 health behaviors evaluated, high technology usage was found to be significantly associated with BMI, sleep and nutrition behaviors. Technology usage was not significantly associated with physical activity.

http://dx.doi.org/10.5993/AJHB.38.4.4

515

Health-related Behaviors and Technology Usage among College Students generation is watching TV on mobile devices via tablets and phones. This beckons the question of are they actually moving while doing so (ie, walking on a treadmill, or walking to classes/work) or are they still in sedentary positions (the “couch potato”). Further investigation into how young adults are using new technology to view traditional TV watching is warranted. Physical Activity No significant differences were determined for self-reported levels of physical activity levels and technology usage. Those who were in the low physical activity group self-reported 174.29 minutes of video gaming as compared to those in the moderate physical activity group with only 45.76 minutes per week. Additionally, those in the low physical activity group reported 105.24 minutes of multiplayer online video gaming as compared with the moderate physical activity group with 25.26 minutes. However, those in the high physical activity group reported 183.44 minutes per week with online gaming. These findings, although not significant, support previous research in identifying a link between increased media usage and lower physical activity levels.53 However, the high use of technology in this population may serve as an avenue for physical activity promotion via smart phones and mobile technology.54,55 Further investigation is warranted to explore more differences that may exist between the different video game users, which could explain physical activity levels and the potential risk of obesity. Sleep Sleep behaviors revealed numerous significant differences among various technology outlets. In relation to social networking, Twitter, Internet surfing, and overall general technology use, those who reported less than 6 hours of sleep also reported significantly higher minutes per week of technology usage when compared to those who achieved an average of 9 or more hours of sleep. These results support previous findings suggesting that adolescents tend to use media and technology late into the night, causing disturbances in sleep patterns and daytime functioning.32,56,57 In general, however, newer technology app sleep trackers can help to turn around sleep patterns – positive health intervention. Although it appears that sleep and technology have a negative relationship, technology might be able to offer a solution to the lack of sleep. Promotion of healthy sleep habits should include the reduction of negative technology usage, particularly general Internet surfing. Nutrition On average, participants reported eating less than half the recommended servings of fruit and vegetables a day. Grimm, Foltz, Blanck, and Scanlon58 obtained similar findings, with 30.4% of young adults (18-24) eating ≤2 servings of fruit

516

a day and 21% eating ≤3 servings of vegetables a day. The authors suggest lack of availability, lack of knowledge of the benefits of eating fruits and vegetables, and cost as potential reasons for lack of adequate intake. Other researchers, while identifying young adults as a population lacking in sufficient fruit and vegetable intake, have introduced the idea of developing a mobile device capable of nutrition advice and dietary feedback to enhance the nutrition status of young adults.60 This newly introduced, innovative research combining technology and health promotion suggests promising outcomes. Further research building on the use of mobile technology to educate and promote proper nutrition may yield results. Limitations There were several limitations to this study. First, the sample was from one geographical area and might not be representative of other regions. The sample focused on freshmen college students and is not representative of college students as a whole. Any generalizability of the study results should be geared towards college freshmen. Whereas honest responses were an expectation of the study, all measurements relied on self-report. Thus, the extent to which participants were inclined to provide socially desirable responses was unknown. Although providing estimation, participants may over- or under-estimate actual behavior, which would impact results. For example, high reports of physical activity are routinely discovered when asking people to estimate individual levels.41,60 Additionally, causality or temporal relationships cannot be established within cross-sectional research methodologies. Conclusion Technology usage was significantly associated with 3 of the 4 health behaviors explored in this study. These results indicate that the more technology is used, the less advantageous it is for one’s health, particularly as it pertains to sleep patterns, BMI, and healthy eating. However, the casualty or temporal relationships were not established and further investigations are required to improve understanding of the impact that technology use can have on health behaviors. This unique study revealed not just general technology usage among college students but it defined and quantified the different types of technology that are currently utilized by this population: social networking, Twitter, general Internet surfing, gaming, multiplayer online gaming and television watching. As technology usage continues to evolve, it is important for health professionals to understand its implications on health behaviors. Human Subjects Statement The researchers’ Institutional Review Board approved this study prior to data collection, tracking number H12413.

Melton et al Conflicts of Interest Statement There are no known conflicts of interesting in this study.  1. Zickuhr K. Generations and their gadgets. Pew Internet & American Life Project (on-line). Available at: http:// pewinternet.org/Reports/2011/Generations-and-gadgets.aspx. Accessed March 25, 2013.  2. Smith A, Rainie L, Zickuhr K. College students and technology. Pew Internet & American Life Project (online). Available at: http://www.pewinternet.org/Reports/2011/College-students-and-technology.aspx. Accessed October 10, 2013.  3. Lenhart A, Purcell K, Smith A, Zickuhr K. Social Media & Mobile Internet Use among Teens and Young Adults. Washington, DC: Pew Internet & American Life Project; 2010: 155-179.  4. Smith A. 17% of cell phone owners do most of their online browsing on their phone rather than a computer or other device. Pew Internet & American Life Project (online). Available at: http://pewinternet.org/~/media// Files/Reports/2012/PIP_Cell_Phone_Internet_Access. pdf. Accessed February 2, 2013.  5. Kennedy T, Smith A, Wells A, Wellman B. Parents and spouses are using the internet and cell phones to create a “new connectedness” that builds on remote connections. Pew Internet & American Life Project (on-line). Available at: http://pewinternet.org/~/media//Files/ Reports/2008/PIP_Networked_Family.pdf. Accessed February 2, 2013.  6. Madden M. Sharing sites shoots up as the audience for online video continues to grow, a leading edge of internet users are migrating their viewing from their computer screens to their TV screens. Pew Internet & American Life Project (on-line). Available at: http://pewinternet. org/~/media//Files/Reports/2009/The-Audience-forOnline-Video-Sharing-Sites-Shoots-Up. Accessed February 2, 2013.  7. United States Department of Labor, Bureau of Labor Statistics. American Time Use Survey 2011 (on-line). Available at: http://www.bls.gov/tus/. Accessed October 10, 2013.  8. Salaway G, Caruso J, Nelson MR. The ECAR study of undergraduate students and information technology. Educause Center for Applied Research. 2008;8:9-16.  9. Junco R, Mastrodicasa J. Connecting to the Net Generation: What Higher Education Professionals Need to Know about Today’s Students. Washington, DC: National Association of Student Personnel Administrators; 2007. 10. Lau P, Lau E, Wong D, Ransdell L. A systematic review of information and communication technology-based interventions for promoting physical activity behavior change in children and adolescents. J Med Internet Res. 2011;13(3):e48. 11. Derbyshire K, Lust K, Grant J, et al. Problematic internet use and associated risks in a college sample. Compr Psychiatry. July 2013;54(5):415-422. 12. Rosenberg D, Norman G, Wagner N, et al. Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. J Phys Act Health. 2010;7:697-705. 13. Vandelanotte C, Sugiyama T, Gardiner P, Owen N. Associations of leisure-time internet and computer use with overweight and obesity, physical activity and sedentary behaviors: cross-sectional study. J Med Internet Res. July 27, 2009;11(3):e28. 14. Thorp A, Healy G, Dunstan D, et al. Deleterious associations of sitting time and television viewing time with cardiometabolic risk biomarkers: Australian Diabetes, Obesity and Lifestyle (AusDiab) study 2004-2005. Diabetes Care. February 2010;33(2):327-334.

15. Yoo Y, Cho O, Cha K. Associations between overuse of the Internet and mental health in adolescents. Nurs Health Sci. August 29, 2013. 16. Matthews C, George S, Moore S, et al. Amount of time spent in sedentary behaviors and cause-specific mortality in US adults. Am J Clin Nutr. 2012;95(2):437-445. 17. Bélanger RE, Akre C, Berchtold A, Michaud PA. A Ushaped association between intensity of Internet use and adolescent health. Pediatrics. 2011;127(2):e330-e335. 18. Bener A, Al-Mahdi HS, Ali AI, et al. Obesity and low vision as a result of excessive Internet use and television viewing. Inter J Food Sci Nutr. 2011;62(1):60-62. 19. Lajunen HR, Keski-Rahkonen A, Pulkkinen L, et al. Are computer and cell phone use associated with body mass index and overweight? A population study among twin adolescents. BMC Public Health. 2007;7(1):24. 20. Ogden C, Carroll M, Kit B, Flegal K. Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief. January 2012;(82):1-8. 21. Collins A, Pakiz B, Rock C. Factors associated with obesity in Indonesian adolescents. Int J Pediatr Obes. 2008;3(1):58-64. 22. Carlson S, Fulton J, Schoenborn C, Loustalot F. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans. Am J Prev Med. 2010;39(4):305-313. 23. United States Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. Washington, DC: USDHHS; 2008. 24. Troiano R, Berrigan D, Dodd K, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181-188. 25. Bauer K, Friend S, Graham D, Neumark-Sztainer D. Beyond screen time: assessing recreational sedentary behavior among adolescent girls. J Obes. 2012:183-194. doi:10.1155/2012/183194 26. Leatherdale S. Factors associated with communicationbased sedentary behaviors among youth: are talking on the phone, texting, and instant messaging new sedentary behaviors to be concerned about? J Adolesc Health. 2010;47(3):315-318. 27. Singh G, Kogan M, Siahpush M, van Dyck P. Independent and joint effects of socioeconomic, behavioral, and neighborhood characteristics on physical inactivity and activity levels among US children and adolescents. J Community Health. 2008;33(4):206-216. 28. National Association for the Education of Young Children. Technology and interactive media as tools in early childhood programs serving children from birth through age 8 (on-line). Available at: http://www.naeyc.org/ files/naeyc/file/positions/PS_technology_WEB2.pdf. Accessed on February 3, 2013. 29. Lepp A, Barkley J, Sanders G, et al. The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. Int J Behav Nutr Phys Act. June 21, 2013;10:79. 30. Cain N, Gradisar M. Electronic media use and sleep in school-aged children and adolescents: a review. Sleep Med. 2010;11(8):735-742. 31. Archbold K, Vasquez M, Goodwin J, Quan S. Effects of sleep patterns and obesity on increases in blood pressure in a 5-year period: report from the Tucson Children’s Assessment of Sleep Apnea Study. J Pediatr. 2012;161(1):26-30. 32. Calamaro C, Mason T, Ratcliffe SJ. Adolescents living the 24/7 lifestyle: effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics. 2009;123(6):e1005-e1010. 33. Choi K, Son H, Park M, et al. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin Neurosci. 2009;63(4):455-462.

Am J Health Behav.™ 2014;38(4):510-518

DOI:

References

http://dx.doi.org/10.5993/AJHB.38.4.4

517

Health-related Behaviors and Technology Usage among College Students 34. Taylor A, Winefield H, Kettler L, et al. A population study of 5 to 15 year olds: full time maternal employment not associated with high BMI. The importance of screenbased activity, reading for pleasure and sleep duration in children’s BMI. Matern Child Health J. 2012;16(3):587599. 35. Atkinson N, Desmond S, Saperstein S, et al. Assets, challenges, and the potential of technology for nutrition education in rural communities. J Nutr Educ Behav. November 2010;42(6):410-416. 36. Neville L, O’Hara B, Milat A. Computer-tailored dietary behaviour change interventions: a systematic review. Health Educ Res. 2009;24(4):699-720. 37. American College Health Association. American College Health Association-National College Health Assessment II: Undergraduate Reference Group Executive Summary Spring 2012. Hanover, MD: American College Health Association; 2012. 38. Nollen N, Hutcheson T, Ellerbeck E, et al. Development and functionality of a handheld computer program to improve fruit and vegetable intake among low-income youth. Health Educ Res. April 2013;28(2):249-264. 39. Long J, Boswell C, Song H, et al. Effectiveness of cell phones and mypyramidtracker.gov to estimate fruit and vegetable intake. Appl Nurs Res. February 2013;26(1):1723. 40. Sira N, Pawlak R. Prevalence of overweight and obesity, and dieting attitudes among Caucasian and African American college students in Eastern North Caroline: a cross-sectional survey. Nutr Res Pract. 2010;4(1):36-42. 41. Marshall S, Gorely T, Biddle S. A descriptive epidemiology of screen-based media use in youth; a review and critique. J Adolesc. 2006;29:333-349. 42. Craig M, Marshall A, Sjöström M, et al. International Physical Activity Questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):13811395. 43. Maddison R, NiMhurchu C, Jiang Y, et al. International Physical Activity Questionnaire (IPAQ) and New Zealand Physical Activity Questionnaire (NZPAQ): a doubly labeled water validation. Int J Behav Nutr Phys Act. 2007;4:62. 44. Marsland A, Petersen K, Sathanur R, et al. Demographic and health characteristic measure. Psychosomatic Med. 2006;68:895-903. 45. Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9(6):755-762. 46. McDermott RJ, Sarvela PD. Health Education Evaluation and Measurement: a Practitioner’s Perspective. New York: McGraw-Hill 1999:132-140.

518

47. Centers for Disease Control and Prevention. Healthy Weight: It’s Not a Diet, It’s a Lifestyle (on-line). Available at: http://www.cdc.gov/healthyweight/assessing/bmi/ adult_bmi/index.html. Accessed February 3, 2013. 48. Agresti A, Franklin C. Statistics: The Art and Science of Learning from Data. New Jersey: Pearson Prentice Hall; 2007. 49. IBM Corporation. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corporation; 2010. 50. Puhl RM, Latner JD. Stigma, obesity, and the health of the nation’s children. Psych Bulletin. 2007;133(4):557580. 51. Lewis S, Thomas SL, Blood R, et al. ‘I’m searching for solutions’: why are obese individuals turning to the internet for help and support with ‘being fat’? Health Expectations. 2011;14(4):339-350. doi:10.1111/j.13697625.2010.00644.x 52. Garrison MM, Liekweg K, Christakis DA. Media use and child sleep: the impact of content, timing, and environment. Pediatrics. 2011;128(1):29-35. 53. Melkevik O, Torsheim T, Iannotti R, Wold B. Is spending time in screen-based sedentary behaviors associated with less physical activity: a cross national investigation. Int J Behav Nutr Phys Act. May 21, 2010;7:46. 54. Hurling R, Catt M, Sodhi J, et al. Using internet and mobile phone technology to deliver an automated physical activity program: randomized controlled trial. J Med Internet Res. April 27, 2007;9(2):e7. 55. Villiard H, Moreno M. Fitness on Facebook: advertisements generated in response to profile content. Cyberpsychol Behav Soc Netw. October 2012;15(10):564-568. 56. Garrison MM, Liekweg K, Christakis DA. Media use and child sleep: the impact of content, timing, and environment. Pediatrics. 2011;128(1):29-35. 57. Thomée S, Härenstam A, Hagberg M. Computer use and stress, sleep disturbances, and symptoms of depression among young adults–a prospective cohort study. BMC Psych. 2012;12(1):176. 58. Grimm KA, Foltz JL, Blanck HM, Scanlon KS. Household income disparities in fruit and vegetable consumption by state and territory: results of the 2009 Behavioral Risk Factor Surveillance System. J Acad Nutr Diet. 2012;112(12):2014-2021. 59. Kerr D, Pollard C, Howat P, et al. Connecting health and technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC Public Health. 2012;12(1):477. 60. Catenacci V, Grunwald G, Ingebrigtsen J, et al. Physical activity patterns using accelerometry in the National Weight Control Registry. Obesity. 2011;19(6):1163-1170.

Copyright of American Journal of Health Behavior is the property of PNG Publications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.