Location-based Smartphone Technology to Guide College Students Healthy Choices Ph II

College students are in a newly independent phase of life; many of whom encounter unhealthy dietary decision- making, barriers to physical activity, and poor sleep behaviors. Healthy Detours is a location-based smartphone application tailored in real-time to a student's schedule and locale, and aims to guide students toward healthier eating, exercise, and lifestyle choices as a way to prevent the onset of damaging and costly health outcomes. Through a randomized control trial, this Phase II project will test the effectiveness of an evidence-based smartphone application that will provide students with on-demand, location-specific information about healthy lifestyle choices.

Poor eating habits, sedentary behavior, and deficient sleep quality are problematic for many college students. Surrounded by numerous unhealthy food choices and physical activity barriers, students are vulnerable to overweight and chronic diseases such as diabetes, heart disease, and cancer. Today's college students are more technologically connected than previous generations positioning mobile devices as an ideal method for reaching this population with healthy lifestyle information. In a Phase I Small Business Innovation Research (SBIR), our team developed Healthy Detours, an innovative mobile phone application (app) designed to assist college students make healthier choices about food and physical activity on and around their college campus. Specifically, we (a) developed a comprehensive database to provide user-specific information through the use of location-based services (LBS) focused on healthy lifestyle choices; (b) conducted an online survey with a national sample of undergraduates which examined eating and physical activity preferences, interest in smartphone technology to monitor healthy lifestyle choices, and use of "check-ins" for health information; (c) conducted focus groups which demonstrated the prototype app's ability to provide the user with rudimentary aspects of the LBS database to facilitate real-time decision making for healthier choices; (d) conducted lab and field usability testing of the app for its accuracy in identifying user location and functionality; and (e) developed a specifications document to outline the Phase II development plan. The results of the Phase I activities provided strong support for this project in which we propose to develop an interactive, mobile app that is guided by the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT). This project will produce a fully functional LBS smartphone app that will provide (1) immediate and relevant feedback at the point of decision-making to reinforce healthy lifestyle behaviors, (2) person- and location-specific tailored data, acquired through activity tracking, to provide users with necessary information to improve future decision-making, and (3) multiple health-tracking features that will aid in self-monitoring. Healthy Detours will be evaluated in a randomized controlled trial (n=300) with students at a large 4-year university. Primary outcomes include changes in dietary intake, physical activity behavior, sleep quality, and quality of life. Overall, the proposed project has the potential to significantly impact college students' health and quality of life by providing them with real-time healthy choices. Healthy Detours is one of the first comprehensive wellness apps offered to college students, and provides an opportunity via the SBIR funding mechanism to reach a large number of students as evidenced by the commercialization plan.

Participants will be encouraged to use the Healthy Detours app daily to track food, physical activity, and sleep.

Participants will be encouraged to use the FatSecret application daily to track food and physical activity.

The app features: Immediate and relevant feedback at the point of decision-making to reinforce healthy lifestyle behaviors within a population making crucial lifestyle choices. User-centered and location-specific tailored information to provide users with information to improve future decision-making through activity tracking. Multiple health-tracking features (i.e., diet and PA tracking, and accelerometry for PA and sleep) that will aid in self-monitoring.

Freely available app for weight loss and nutrition. Includes food and exercise diaries; calorie counting.

The National Cancer Institute's Automated Self-Administered 24-hour Recall (ASA24) will be administered at baseline (pre-test) to calculate the Healthy Eating Index (HEI). The ASA24 measures food intake for 24 hours and calculates the HEI. Scores for the HEI range from 0 to 100. A higher score represents a better outcome.

The National Cancer Institute's Automated Self-Administered 24-hour Recall (ASA24) will be administered at 6-weeks) to calculate the Healthy Eating Index (HEI). The ASA24 measures food intake for 24 hours and calculates the HEI. Scores for the HEI range from 0 to 100. A higher score represents a better outcome.

The National Cancer Institute's Automated Self-Administered 24-hour Recall (ASA24) will be administered at 12 weeks to calculate the Healthy Eating Index (HEI). The ASA24 measures food intake for 24 hours and calculates the HEI. Scores for the HEI range from 0 to 100. A higher score represents a better outcome.

A nine-item International Physical Activity Questionnaire Short-form (IPAQ-SF) will be administered at baseline. The IPAQ-SF calculates the MET-minutes per week expended. MET minutes represent the amount of energy expended carrying out physical activity. A higher score represents a better outcome. Scores can range from 0 to 10,080.

A nine-item International Physical Activity Questionnaire Short-form (IPAQ-SF) will be administered at 6 weeks. The IPAQ-SF calculates the MET minutes per week expended. MET minutes represent the amount of energy expended carrying out physical activity. A higher score represents a better outcome. Scores can range from 0 to 10,080.

A nine-item International Physical Activity Questionnaire Short-form (IPAQ-SF) will be administered at 12 weeks. The IPAQ-SF calculates the MET minutes per week expended. MET minutes represent the amount of energy expended carrying out physical activity. A higher score represents a better outcome. Scores can range from 0 to 10,080.

The Pittsburgh Sleep Quality Index (PSQI) will be administered at baseline (pre-test). The measure consists of 19 individual items that are used to calculate 7 component score and one composite score. Each item is weighted on a 0-3 interval scale. The composite score is calculated by totaling the seven component scores. The composite score ranges 0 to 21. A lower means healthier sleep quality.

The The Pittsburgh Sleep Quality Index (PSQI) will be administered at 6 weeks. The measure consists of 19 individual items that are used to calculate 7 component score and one composite score. Each item is weighted on a 0-3 interval scale. The composite score is calculated by totaling the seven component scores. The composite score ranges 0 to 21. A lower means healthier sleep quality.

The The Pittsburgh Sleep Quality Index (PSQI) will be administered at 12 weeks (post-test). The measure consists of 19 individual items that are used to calculate 7 component score and one composite score. Each item is weighted on a 0-3 interval scale. The composite score is calculated by totaling the seven component scores. The composite score ranges 0 to 21. A lower means healthier sleep quality.

The 12-Item Short Form Health Survey (SF-12) will be administered at baseline (pre-test). The SF-12 survey contains categorical questions (e.g., yes/no) that assess limitations in role functioning. The survey also contains Likert response formats on a three-point scale (e.g., not at all, a little bit, moderately, quite a bit, and extremely) to assess pain, and a five-point scale to assesses overall health (excellent, very good, good, fair, and poor). A six-point scale (e.g., all of the time, most of the time, a good bit of the time, some of the time, a little of the time, and none of the time) assesses mental health, vitality, and social functioning. The SF-12 is scored using the recommended Medical Outcomes Study (MOS) SAS software program that creates two summary scores, mental health (MCS12), and physical health (PCS12). The scores are represented as t-scores that are linear transformations with a mean of 50 and a standard deviation of 10 in the general U.S. population.

The 12-Item Short Form Health Survey (SF-12) survey contains categorical questions (e.g., yes/no) that assess limitations in role functioning. The survey also contains Likert response formats on a three-point scale (e.g., not at all, a little bit, moderately, quite a bit, and extremely) to assess pain, and a five-point scale to assesses overall health (excellent, very good, good, fair, and poor). A six-point scale (e.g., all of the time, most of the time, a good bit of the time, some of the time, a little of the time, and none of the time) assesses mental health, vitality, and social functioning. The SF-12 is scored using the recommended Medical Outcomes Study (MOS) SAS software program that creates two summary scores, mental health (MCS12), and physical health (PCS12). The scores are represented as t-scores that are linear transformations with a mean of 50 and a standard deviation of 10 in the general U.S. population.

The 12-Item Short Form Health Survey (SF-12) survey contains categorical questions (e.g., yes/no) that assess limitations in role functioning. The survey also contains Likert response formats on a three-point scale (e.g., not at all, a little bit, moderately, quite a bit, and extremely) to assess pain, and a five-point scale to assesses overall health (excellent, very good, good, fair, and poor). A six-point scale (e.g., all of the time, most of the time, a good bit of the time, some of the time, a little of the time, and none of the time) assesses mental health, vitality, and social functioning. The SF-12 is scored using the recommended Medical Outcomes Study (MOS) SAS software program that creates two summary scores, mental health (MCS12), and physical health (PCS12). The scores are represented as t-scores that are linear transformations with a mean of 50 and a standard deviation of 10 in the general U.S. population.

Inclusion Criteria: age 18-29 enrolled in a university or college on the Denver Auraria campus or Louisiana State University (LSU) campus own a smartphone able to speak and read English; and consent to participate Exclusion Criteria: under age of 18 years not a current student at any Denver Auraria campus or at LSU

Cluskey M, Grobe D. College weight gain and behavior transitions: male and female differences. J Am Diet Assoc. 2009 Feb;109(2):325-9. doi: 10.1016/j.jada.2008.10.045.

Ferrara CM. The College Experience: Physical Activity, Nutrition, and Implications for Intervention and Future Research. Journal of Exercise Physiology online. 2009;12(1):23-35.

Hanson TL, Drumheller K, Mallard J, McKee C, Schlegel P. Cell phones, text messaging, and Facebook: competing time demands of today's college students. College Teaching. 2010;59(1):23-30.

Gaultney JF. The prevalence of sleep disorders in college students: impact on academic performance. J Am Coll Health. 2010;59(2):91-7. doi: 10.1080/07448481.2010.483708.

Laska MN, Pelletier JE, Larson NI, Story M. Interventions for weight gain prevention during the transition to young adulthood: a review of the literature. J Adolesc Health. 2012 Apr;50(4):324-33. doi: 10.1016/j.jadohealth.2012.01.016.

Li KK, Concepcion RY, Lee H, Cardinal BJ, Ebbeck V, Woekel E, Readdy RT. An examination of sex differences in relation to the eating habits and nutrient intakes of university students. J Nutr Educ Behav. 2012 May-Jun;44(3):246-50. doi: 10.1016/j.jneb.2010.10.002. Epub 2011 Jul 18.

Lloyd-Richardson EE, Bailey S, Fava JL, Wing R; Tobacco Etiology Research Network (TERN). A prospective study of weight gain during the college freshman and sophomore years. Prev Med. 2009 Mar;48(3):256-61. doi: 10.1016/j.ypmed.2008.12.009. Epub 2008 Dec 24.

Strong KA, Parks SL, Anderson E, Winett R, Davy BM. Weight gain prevention: identifying theory-based targets for health behavior change in young adults. J Am Diet Assoc. 2008 Oct;108(10):1708-1715. doi: 10.1016/j.jada.2008.07.007.

Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. J Am Coll Health. 2005 May-Jun;53(6):245-51. doi: 10.3200/JACH.53.6.245-251.

Zick CD, Smith KR, Brown BB, Fan JX, Kowaleski-Jones L. Physical activity during the transition from adolescence to adulthood. J Phys Act Health. 2007 Apr;4(2):125-37. doi: 10.1123/jpah.4.2.125.

Yeh MC, Matsumori B, Obenchain J, Viladrich A, Das D, Navder K. Validity of a competing food choice construct regarding fruit and vegetable consumption among urban college freshmen. J Nutr Educ Behav. 2010 Sep-Oct;42(5):321-7. doi: 10.1016/j.jneb.2009.08.004. Epub 2010 Jul 22.

Salcedo Aguilar F, Rodriguez Almonacid FM, Monterde Aznar ML, Garcia Jimenez MA, Redondo Martinez P, Marcos Navarro AI. [Sleeping habits and sleep disorders during adolescence: relation to school performance]. Aten Primaria. 2005 May 15;35(8):408-14. doi: 10.1157/13074792. Spanish.

Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006 Oct;10(5):323-37. doi: 10.1016/j.smrv.2005.11.001. Epub 2006 Mar 24.

Trockel MT, Barnes MD, Egget DL. Health-related variables and academic performance among first-year college students: implications for sleep and other behaviors. J Am Coll Health. 2000 Nov;49(3):125-31. doi: 10.1080/07448480009596294.

Kwan M, Faulkner G. Perceptions and barriers to physical activity during the transition to university. American Journal of Health Studies. 2011;26:87-96.

Wengreen HJ, Moncur C. Change in diet, physical activity, and body weight among young-adults during the transition from high school to college. Nutr J. 2009 Jul 22;8:32. doi: 10.1186/1475-2891-8-32.

Hoffman DJ, Policastro P, Quick V, Lee SK. Changes in body weight and fat mass of men and women in the first year of college: A study of the "freshman 15". J Am Coll Health. 2006 Jul-Aug;55(1):41-5. doi: 10.3200/JACH.55.1.41-46.

Kopelman P. Health risks associated with overweight and obesity. Obes Rev. 2007 Mar;8 Suppl 1:13-7. doi: 10.1111/j.1467-789X.2007.00311.x. No abstract available.