Increased Monitoring of Physical Activity and Calories With Technology (IMPACT)

Since severe obesity in youth has been steadily increasing. Specialized pediatric obesity clinics provide programs to aid in reducing obesity. Since the home environment and parental behavioral modeling are two of the strongest predictors of child weight loss during behavioral weight loss interventions, a family-based treatment approach is best. This strategy has been moderately successful in our existing, evidence-based pediatric weight management program, Brenner Families In Training (Brenner FIT). However, since programs such as Brenner Families in Training rely on face-to-face interactions and delivery, they are sometimes by the time constraints experienced by families. Therefore, the purpose of this study is to develop and pilot a tailored, mobile health component to potentially increase the benefits seen by Brenner FIT standard program components and similar pediatric weight management programs.

For this project, we will randomize 80 youth with obesity (13 - 18yrs) and a caregiver (dyads) to the Brenner Families in Training (FIT) group or the Brenner Families in Training Mobile (mFIT) group. All youth participants will receive a commercially available activity monitor. Caregivers will receive podcasts with a story about a caregiver supporting weight loss in a child by providing healthy foods/activities for his/her family, including healthy eating and physical activity information. Children will receive animated videos that contain healthy eating and physical activity messaging, with an engaging story of a child losing weight. All participants will have access to a website and mobile apps where they will track weight, diet, and physical activity for themselves (youth) or their child (parents). Based on their reports of weight, eating, and physical activity, the messaging received from clinical staff by the families will be individually tailored to promote healthy behaviors and overcome perceived barriers. The proposed research is innovative in that it explicitly incorporates theory into the intervention and evaluation components of the project and builds upon an existing literature on mobile health interventions that use mobile technology.

weight management, pediatric weight management, mHealth, mobile technology, parent/child relations, dyad

The control group will receive standard Brenner FIT care. The intervention group will receive standard Brenner FIT care in additional to mobile health components in the hopes of maximizing the benefits that are already seen with pediatric weight management programs like Brenner FIT.

Adolescents will participate in Brenner Families in Training along with their caregiver. They will receive all components of standard Brenner FIT treatments.

Adolescents will participate in Brenner Families in Training along with their caregiver. Brenner mFIT (Families in Training + mobile health) includes all components of the standard Brenner FIT

Families attend an orientation, in which they are then scheduled for an initial introductory 2-hour intake group session and cooking class; these occur within 2-4 weeks of the orientation. Monthly 1-hour long visits with the dietitian, counselor, and PA specialist are held for 6 months, in which the child and caregiver see the pediatrician. During the 6 months of treatment, they attend 4 group classes, choosing from topics such as meal planning, PA, and parenting. Specialized visits with the PA specialist or dietician are scheduled as pertinent issues arise. Motivational interviewing, modified by Brenner FIT for use with families, is the key to treatment; family counselors are trained in cognitive behavioral therapy, parenting support/mindfulness, and employ these approaches to assist families in developing healthy habits.

Brenner mFIT includes all components of the standard Brenner FIT program in addition to six mobile health components. The six mHealth components that will be used in addition to standard Brenner Families in Training program include- a mobile-enabled website, diet and physical activity tracking apps and physical activity tracker tailored self-monitoring feedback caregiver podcasts animated videos for adolescent patients social support via social media.

Weight status of caregivers and youth will be quantified through calculation of BMI derived from measurement of height and weight at the intake and follow-up visits. Both height (plus/ minus 0.1 cm) and weight (plus/minus 0.5 kg) will be recorded twice and values will be averaged to produce the final value using a Tanita(registered trademark) digital scale and a Seca(registered trademark) Height Rod (respectively). BMI will be calculated as kg /m2. BMI z-score will be calculated using CDC growth charts.

Weight status of caregivers and youth will be quantified through calculation of BMI derived from measurement of height and weight at the intake and follow-up visits. Both height (plus/ minus 0.1 cm) and weight (plus/minus 0.5 kg) will be recorded twice and values will be averaged to produce the final value using a Tanita(registered trademark) digital scale and a Seca(registered trademark) Height Rod (respectively). BMI will be calculated as kg /m2. BMI z-score will be calculated using CDC growth charts.

Weight status of caregivers and youth will be quantified through calculation of BMI derived from measurement of height and weight at the intake and follow-up visits. Both height (plus/ minus 0.1 cm) and weight (plus/minus 0.5 kg) will be recorded twice and values will be averaged to produce the final value using a Tanita(registered trademark) digital scale and a Seca(registered trademark) Height Rod (respectively). BMI will be calculated as kg /m2. BMI z-score will be calculated using CDC growth charts.

Physical activity data will be collected using ActiGraph (trademark) accelerometers worn continuously over 7 days except during bathing and sleeping.

Physical activity data will be collected using ActiGraph (trademark) accelerometers worn continuously over 7 days except during bathing and sleeping.

Physical activity data will be collected using ActiGraph (trademark) accelerometers worn continuously over 7 days except during bathing and sleeping.

To assess diet in participating youth, we will use NCI's automated, self-administered 24-hour dietary recall, the Automated Self-Administered 24-hour (ASA24 (registered trademark) dietary assessment tool (version: ASA24-2016) on three, non-consecutive days (including one weekend day). Caloric intake will be expressed in kilocalories in order to compare dietary behavior following the delivery of some program components. There are no specific ranges.

To assess diet in participating youth, we will use NCI's automated, self-administered 24-hour dietary recall, the Automated Self-Administered 24-hour (ASA24 (registered trademark) dietary assessment tool (version: ASA24-2016) on three, non-consecutive days (including one weekend day). Caloric intake will be expressed in kilocalories in order to compare dietary behavior following the delivery of some program components. There are no specific ranges.

To assess diet in participating youth, we will use NCI's automated, self-administered 24-hour dietary recall, the Automated Self-Administered 24-hour (ASA24 (registered trademark) dietary assessment tool (version: ASA24-2016) on three, non-consecutive days (including one weekend day). Caloric intake will be expressed in kilocalories in order to compare dietary behavior following the delivery of some program components. There are no specific ranges.

Inclusion Criteria: Youth with obesity, 13 - 18yrs, who are enrolled or eligible to enroll in Brenner Families in Training (FIT). Caregivers must live in the home with their youth participants. Obesity is defined a BMI (35.9 +/- 8.6). Participants must also have access to a smartphone or tablet Exclusion Criteria: Adolescents under the age of 13 will be excluded. If participants do not have access to a smartphone or tablet, they will not be able to participate.

Brown CL, Halvorson EE, Cohen GM, Lazorick S, Skelton JA. Addressing Childhood Obesity: Opportunities for Prevention. Pediatr Clin North Am. 2015 Oct;62(5):1241-61. doi: 10.1016/j.pcl.2015.05.013. Epub 2015 Jul 16.

Skelton JA, Beech BM. Attrition in paediatric weight management: a review of the literature and new directions. Obes Rev. 2011 May;12(5):e273-81. doi: 10.1111/j.1467-789X.2010.00803.x. Epub 2010 Sep 29.

Skelton JA, Goff DC Jr, Ip E, Beech BM. Attrition in a Multidisciplinary Pediatric Weight Management Clinic. Child Obes. 2011 Jun 20;7(3):185-193. doi: 10.1089/chi.2011.0010.

Irby MB, Kolbash S, Garner-Edwards D, Skelton JA. Pediatric Obesity Treatment in Children With Neurodevelopmental Disabilities: A Case Series and Review of the Literature. Infant Child Adolesc Nutr. 2012 Aug 1;4(4):215-221. doi: 10.1177/1941406412448527.

Irby M, Kaplan S, Garner-Edwards D, Kolbash S, Skelton JA. Motivational interviewing in a family-based pediatric obesity program: a case study. Fam Syst Health. 2010 Sep;28(3):236-46. doi: 10.1037/a0020101.

Skelton JA, Martin S, Irby MB. Satisfaction and attrition in paediatric weight management. Clin Obes. 2016 Apr;6(2):143-53. doi: 10.1111/cob.12138. Epub 2016 Jan 27.

Bishop J, Irby MB, Isom S, Blackwell CS, Vitolins MZ, Skelton JA. Diabetes prevention, weight loss, and social support: program participants' perceived influence on the health behaviors of their social support system. Fam Community Health. 2013 Apr-Jun;36(2):158-71. doi: 10.1097/FCH.0b013e318282b2d3.

Giannini C, Irby MB, Skelton JA. Caregiver Expectations of Family-based Pediatric Obesity Treatment. Am J Health Behav. 2015 Jul;39(4):451-60. doi: 10.5993/AJHB.39.4.1.

Skelton JA, Irby MB, Guzman MA, Beech BM. Children's Perceptions of Obesity and Health: A Focus Group Study With Hispanic Boys. Infant Child Adolesc Nutr. 2012 Oct 1;4(5):289-296. doi: 10.1177/1941406412446946.

Cheng JK, Wen X, Coletti KD, Cox JE, Taveras EM. 2-Year BMI Changes of Children Referred for Multidisciplinary Weight Management. Int J Pediatr. 2014;2014:152586. doi: 10.1155/2014/152586. Epub 2014 Jan 30.

Kirk S, Zeller M, Claytor R, Santangelo M, Khoury PR, Daniels SR. The relationship of health outcomes to improvement in BMI in children and adolescents. Obes Res. 2005 May;13(5):876-82. doi: 10.1038/oby.2005.101.

Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013 May 1;20(3):513-8. doi: 10.1136/amiajnl-2012-001510. Epub 2013 Feb 21.

Turner-McGrievy G, Tate D. Tweets, Apps, and Pods: Results of the 6-month Mobile Pounds Off Digitally (Mobile POD) randomized weight-loss intervention among adults. J Med Internet Res. 2011 Dec 20;13(4):e120. doi: 10.2196/jmir.1841.

Turner-McGrievy GM, Campbell MK, Tate DF, Truesdale KP, Bowling JM, Crosby L. Pounds Off Digitally study: a randomized podcasting weight-loss intervention. Am J Prev Med. 2009 Oct;37(4):263-9. doi: 10.1016/j.amepre.2009.06.010.

Turner-McGrievy GM, Tate DF. Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention. Transl Behav Med. 2013 Sep;3(3):287-94. doi: 10.1007/s13142-012-0183-y.

Pagoto SL, Waring ME, Schneider KL, Oleski JL, Olendzki E, Hayes RB, Appelhans BM, Whited MC, Busch AM, Lemon SC. Twitter-Delivered Behavioral Weight-Loss Interventions: A Pilot Series. JMIR Res Protoc. 2015 Oct 23;4(4):e123. doi: 10.2196/resprot.4864.

Maher CA, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. Are health behavior change interventions that use online social networks effective? A systematic review. J Med Internet Res. 2014 Feb 14;16(2):e40. doi: 10.2196/jmir.2952.

Hales SB, Davidson C, Turner-McGrievy GM. Varying social media post types differentially impacts engagement in a behavioral weight loss intervention. Transl Behav Med. 2014 Dec;4(4):355-62. doi: 10.1007/s13142-014-0274-z.

Djafarian K, Speakman JR, Stewart J, Jackson DM. Comparison of activity levels measured by a wrist worn accelerometer and direct observation in young children. Open Journal of Pediatrics. 2013;03(04):422-427. 162.

Chandler JL, Brazendale K, Beets MW, Mealing BA. Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children. Pediatr Obes. 2016 Apr;11(2):120-7. doi: 10.1111/ijpo.12033. Epub 2015 Apr 20.

Crouter SE, Flynn JI, Bassett DR Jr. Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc. 2015 May;47(5):944-51. doi: 10.1249/MSS.0000000000000502.

Brazendale K, Beets MW, Bornstein DB, Moore JB, Pate RR, Weaver RG, Falck RS, Chandler JL, Andersen LB, Anderssen SA, Cardon G, Cooper A, Davey R, Froberg K, Hallal PC, Janz KF, Kordas K, Kriemler S, Puder JJ, Reilly JJ, Salmon J, Sardinha LB, Timperio A, van Sluijs EMF; International Children's Accelerometry Database (ICAD) Collaborators. Equating accelerometer estimates among youth: The Rosetta Stone 2. J Sci Med Sport. 2016 Mar;19(3):242-249. doi: 10.1016/j.jsams.2015.02.006. Epub 2015 Feb 23.

Chandler JL, Beets MW, Drenowatz C, et al. The Rosetta Stone for equating hip and wrist- based accelerometer derived estimates of physical activity among elementary aged youth. Under review.

Chandler JL, Beets MW, Drenowatz C, et al. Analysis of Accelerometer Counts during Sedentary Activities on Dominant and Non-Dominant Wrists in 5-11 year old Children. Under review.

Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. 2005 Nov;37(11 Suppl):S582-8. doi: 10.1249/01.mss.0000185292.71933.91.

Moore JB, Brinkley J, Crawford TW, Evenson KR, Brownson RC. Association of the built environment with physical activity and adiposity in rural and urban youth. Prev Med. 2013 Feb;56(2):145-8. doi: 10.1016/j.ypmed.2012.11.019. Epub 2012 Dec 3.

Moore JB, Brinkley J, Morris SF, Oniffrey TM, Kolbe MB. Effectiveness of Community-Based Minigrants to Increase Physical Activity and Decrease Sedentary Time in Youth. J Public Health Manag Pract. 2016 Jul-Aug;22(4):370-8. doi: 10.1097/PHH.0000000000000274.

Morris SF, Kolbe MB, Moore JB. Lessons learned from a collaborative field-based collection of physical activity data using accelerometers. J Public Health Manag Pract. 2014 Mar-Apr;20(2):251-8. doi: 10.1097/PHH.0b013e3182893b9b.

Beets MW, Glenn Weaver R, Turner-McGrievy G, Huberty J, Ward DS, Freedman DA, Saunders R, Pate RR, Beighle A, Hutto B, Moore JB. Making healthy eating and physical activity policy practice: the design and overview of a group randomized controlled trial in afterschool programs. Contemp Clin Trials. 2014 Jul;38(2):291-303. doi: 10.1016/j.cct.2014.05.013. Epub 2014 Jun 2.

Thompson FE, Dixit-Joshi S, Potischman N, Dodd KW, Kirkpatrick SI, Kushi LH, Alexander GL, Coleman LA, Zimmerman TP, Sundaram ME, Clancy HA, Groesbeck M, Douglass D, George SM, Schap TE, Subar AF. Comparison of Interviewer-Administered and Automated Self-Administered 24-Hour Dietary Recalls in 3 Diverse Integrated Health Systems. Am J Epidemiol. 2015 Jun 15;181(12):970-8. doi: 10.1093/aje/kwu467. Epub 2015 May 10.

Kirkpatrick SI, Subar AF, Douglass D, Zimmerman TP, Thompson FE, Kahle LL, George SM, Dodd KW, Potischman N. Performance of the Automated Self-Administered 24-hour Recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr. 2014 Jul;100(1):233-40. doi: 10.3945/ajcn.114.083238. Epub 2014 Apr 30.

Skelton JA, Buehler C, Irby MB, Grzywacz JG. Where are family theories in family-based obesity treatment?: conceptualizing the study of families in pediatric weight management. Int J Obes (Lond). 2012 Jul;36(7):891-900. doi: 10.1038/ijo.2012.56. Epub 2012 Apr 24.

Kaplan SG, Arnold EM, Irby MB, Boles KA, Skelton JA. Family Systems Theory and Obesity Treatment: Applications for Clinicians. Infant Child Adolesc Nutr. 2014 Feb 1;6(1):24-29. doi: 10.1177/1941406413516001.

Haines J, Rifas-Shiman SL, Horton NJ, Kleinman K, Bauer KW, Davison KK, Walton K, Austin SB, Field AE, Gillman MW. Family functioning and quality of parent-adolescent relationship: cross-sectional associations with adolescent weight-related behaviors and weight status. Int J Behav Nutr Phys Act. 2016 Jun 14;13:68. doi: 10.1186/s12966-016-0393-7.

Sallis JF, Pinski RB, Grossman RM, Patterson TL, Nader PR. The development of self-efficacy scales for health related diet and exercise behaviors. Health Education Research. 1988;3(3):283-292.

Motl RW, Dishman RK, Dowda M, Pate RR. Factorial validity and invariance of a self-report measure of physical activity among adolescent girls. Res Q Exerc Sport. 2004 Sep;75(3):259-71. doi: 10.1080/02701367.2004.10609159.

Isasi CR, Wills TA. Behavioral Self-Regulation and Weight-Related Behaviors in Inner-City Adolescents: A Model of Direct and Indirect Effects. Child Obes. 2011 Aug 1;7(4):306-315. doi: 10.1089/chi.2011.0011.

Kendall P, Williams CL. Assessing the cognitive and behavioral components of children's selfmanagement. In: Karoly P, Kanfer F, eds. Self-Management and Behavior Change. New York: Pergamon Press; 1982.

Heiby EM. Assessment of Frequency of Self-Reinforcement. Journal of Personality and Social Psychology. 1983;44(6):1304-1307.

Golan M. Fifteen years of the Family Eating and Activity Habits Questionnaire (FEAHQ): an update and review. Pediatr Obes. 2014 Apr;9(2):92-101. doi: 10.1111/j.2047-6310.2013.00144.x. Epub 2013 Feb 28.

Ryan RM, Connell JP. Perceived locus of causality and internalization: examining reasons for acting in two domains. J Pers Soc Psychol. 1989 Nov;57(5):749-61. doi: 10.1037//0022-3514.57.5.749.

Deci EL, Schwartz AJ, Sheinman L, Ryan RM. An Instrument to Assess Adults Orientations toward Control Versus Autonomy with Children - Reflections on Intrinsic Motivation and Perceived Competence. Journal of Educational Psychology. 1981;73(5):642-650.