Stay in treatment: Predicting dropout from pediatric weight management study protocol

Diane C Berry, Erinn T Rhodes, Sarah Hampl, Caroline Blackwell Young, Gail Cohen, Ihuoma Eneli, Amy Fleischman, Edward Ip, Brooke Sweeney, Timothy T Houle, Joseph Skelton, Diane C Berry, Erinn T Rhodes, Sarah Hampl, Caroline Blackwell Young, Gail Cohen, Ihuoma Eneli, Amy Fleischman, Edward Ip, Brooke Sweeney, Timothy T Houle, Joseph Skelton

Abstract

Introduction: Childhood obesity is a serious public health concern. Multidisciplinary pediatric weight management programs have been deemed effective. However, effectiveness of these programs is impacted by attrition, limiting health benefits to children, and inefficiently utilizing scarce resources.

Methods: We have developed a model (the Outcomes Forecasting System, OFS) that isolates variables associated with attrition from pediatric weight management, with the potential to forecast participant dropout. In Aim 1, we will increase the power and precision of the OFS and then validate the model through the consistent acquisition of key patient, family, and treatment data, from three different weight management sites. In Aim 2, external validity will be established through the application of the OFS at a fourth pediatric weight management program. Aim 3 will be a pilot clinical trial, incorporating an intervention built on the results of Aims 1 and 2 and utilizing the OFS to reduce attrition.

Discussion: A greater understanding of the patient, family, and disease-specific factors that predict dropout from pediatric weight management can be utilized to prevent attrition. The goal of the current study is to refine the OFS to a level of precision and efficiency to be a valuable tool to any weight management program. By identifying the most pertinent factors driving attrition across weight management sites, new avenues for treatment will be identified. This study will result in a valuable forecasting tool that will be applicable for diverse programs and populations, decrease program costs, and improve patient retention, adherence, and outcomes.

Clinicaltrialsgov identifier: NCT04364282.

Keywords: Attrition; Family; Patient; Pediatric obesity; Prediction.

Conflict of interest statement

Dr. Rhodes is the Site Principal Investigator for a clinical trial sponsored by Astra Zeneca. Dr. Fleischman is the Site Principal Investigator for clinical trials sponsored by Soleno therapeutics, 10.13039/100016494Millendo therapeutics, and is a Co-Investigator for a clinical trial sponsored by Levo therapeutics. Dr. Sweeney is a member of Paediatric Obesity Global Advisory Board for Novo Norkisk. Dr. Hampl's institution receives royalties from a book she co-edited published by McGraw-Hill Education. Dr. Eneli has research funding for a Registry from Rhythm Pharmaceuticals. Dr. Houle is a research and statistical consultant to GlaxoSmithKline and Eli Lilly. He is also the Chief Scientist at StatReviewer.

© 2021 Published by Elsevier Inc.

Figures

Fig. 1
Fig. 1
Conceptual model of attrition (adapted from Rapoff [29]).

References

    1. Skinner A.C., Perrin E.M., Skelton J.A. Prevalence of obesity and severe obesity in US children, 1999-2014. Obesity (Silver Spring) 2016;24(5):1116–1123.
    1. Association C.s.H. 2013. Survey Findings of Children's Hospitals Obesity Services.
    1. Whitlock E.P. Effectiveness of weight management interventions in children: a targeted systematic review for the USPSTF. Pediatrics. 2010;125(2):e396–418.
    1. Wilfley D.E. Improving access and systems of care for evidence-based childhood obesity treatment: conference key findings and next steps. Obesity (Silver Spring) 2017;25(1):16–29.
    1. Berkowitz R.I., Daniels S. Now is the time to improve access and healthcare systems for childhood obesity treatment. Obesity (Silver Spring) 2017;25(1):13–14. doi: 10.1002/oby.21740.
    1. Skelton J.A., Beech B.M. Attrition in paediatric weight management: a review of the literature and new directions. Obes. Rev. 2011;12(5):e273–e281.
    1. Skelton J.A. Attrition and family participation in obesity treatment programs: clinicians' perceptions. Acad Pediatr. 2012;12(5):420–428.
    1. Kitscha C.E. Reasons for non-return to a pediatric weight management program. Can. J. Diet Pract. Res. 2009;70(2):89–94.
    1. Grow H.M. Understanding family motivations and barriers to participation in community-based programs for overweight youth: one program model does not fit all. J. Publ. Health Manag. Pract. 2013;19(4):E1–E10.
    1. Dhaliwal J. Attrition and the management of pediatric obesity: an integrative review. Child. Obes. 2014;10(6):461–473.
    1. Skelton J.A., Bech B.M. Attrition in paediatric weight management: a review of the literature and new directions. Obes. Rev. 2011;12(5):e273–e281.
    1. Skelton J.A., Martin S., Irby M.B. Satisfaction and attrition in paediatric weight management. Clin Obes. 2016;6(2):143–153.
    1. Skelton J.A. Attrition in a multidisciplinary pediatric weight management clinic. Child. Obes. 2011;7(3):185–196.
    1. Skelton J.A., Irby M.B., Geiger A.M. A systematic review of satisfaction and pediatric obesity treatment: new avenues for addressing attrition. J. Healthc. Qual. 2014;36(4):5–22.
    1. Irby M.B. TeleFIT: adapting a multidisciplinary, tertiary-care pediatric obesity clinic to rural populations. Telemed. J. e Health. 2012
    1. Irby M.B. Pediatric obesity treatment in children with neurodevelopmental disabilities: a case series and review of the literature. Infant Child Adolesc Nutr. 2012;4(4):215–221.
    1. Skelton J.A., Irby M., Beech B.M. Bridging the gap between family-based treatment and family-based research in childhood obesity. Child. Obes. 2011;7(4):323–326.
    1. Hampl S. Patient engagement and attrition in pediatric obesity clinics and programs: results and recommendations. Pediatrics. 2011;128(Suppl 2):S59–S64.
    1. Hampl S. Parent perspectives on attrition from tertiary care pediatric weight management programs. Clin Pediatr (Phila) 2013;52(6):513–519.
    1. Sallinen B.J., Schaffer S., Woolford S.J. In their own words: learning from families attending a multidisciplinary pediatric weight management program at the YMCA. Child. Obes. 2013;9(3):200–207.
    1. Guzman A. Adapting a tertiary-care pediatric weight management clinic to better reach Spanish-speaking families. J. Immigr. Minority Health. 2012;14(3):512–515.
    1. Berlinski A. Redesigning care to meet national recommendation of four or more yearly clinic visits in patients with cystic fibrosis. BMJ Qual. Saf. 2014;23(Suppl 1):i42–i49.
    1. De Maeseneer J.M. Provider continuity in family medicine: does it make a difference for total health care costs? Ann. Fam. Med. 2003;1(3):144–148.
    1. Liu Q. Reminder systems and late patient tracers in the diagnosis and management of tuberculosis. Cochrane Database Syst. Rev. 2008;(4):CD006594.
    1. Abshire M. Participant retention practices in longitudinal clinical research studies with high retention rates. BMC Med. Res. Methodol. 2017;17(1):30.
    1. Lee J.S. Coverage of obesity treatment: a state-by-state analysis of Medicaid and state insurance laws. Publ. Health Rep. 2010;125(4):596–604.
    1. Altman D.G. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:b605.
    1. Sallinen Gaffka B.J. Parents and pediatric weight management attrition: experiences and recommendations. Child. Obes. 2013;9(5):409–417.
    1. Rapoff M.A. first ed. Springer; New York NY: 1999. Adherence to Pediatric Medical Regimens. 190.
    1. Children's Health Watch the Hunger Vital Sign. 2020.
    1. CDC National Center for Health Statistics . 2000. CDC Growth Charts: United States. Available from:
    1. Ihmels M.A. Development and preliminary validation of a family nutrition and physical activity (FNPA) screening tool. Int. J. Behav. Nutr. Phys. Activ. 2009;6:14.
    1. Ihmels M.A. Prediction of BMI change in young children with the family nutrition and physical activity (FNPA) screening tool. Ann. Behav. Med. 2009;38(1):60–68.
    1. Epstein N.B., Baldwin L.M., Bishop D.S. The McMaster family assessment device. J. Marital Fam. Ther. 1983;9(2):171–180.
    1. Mansfield A.K., Keitner G.I., Dealy J. The family assessment device: an update. Fam. Process. 2015;54(1):82–93.
    1. Schludermann S., Schludermann E. University of Manitoba; Winnipeg: 1988. Questionnaire for Children and Youth (CRPBI-30) Unpublished Manuscirpt.
    1. Matheny A.P. Bringing order out of chais: psychometric characteristics of the confusion, hubbub, and order scale. J. Appl. Dev. Psychol. 1995;16(3):429–444.
    1. Available from:
    1. Cohen S., Williamson G. In: Perceived Stress in a Probability Samle of the United States. The Social Psychology of Health. Spacapan S., Oskamp S., editors. California Sage; Newbury Park: 1988.
    1. Gunnarsdottir T. The role of parental motivation in family-based treatment for childhood obesity. Obesity (Silver Spring) 2011;19(8):1654–1662.
    1. Weiss B.D. Quick assessment of literacy in primary care : the newest vital sign. Ann. Fam. Med. 2005;3(6):514–522.
    1. Puhl R.M., Peterson J.L., Luedicke J. Weight-based victimization: bullying experiences of weight loss treatment-seeking youth. Pediatrics. 2013;131(1):e1–9.
    1. Kelly A.S. Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation. 2013;128(15):1689–1712.
    1. Finne E. Overweight children and adolescents--is there a subjective need for treatment? Int. J. Publ. Health. 2009;54(2):112–116.
    1. Edwards N.M., Pettingell S., Borowsky I.W. Where perception meets reality: self-perception of weight in overweight adolescents. Pediatrics. 2010;125(3):e452–e458.
    1. de Souza J.A. Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the COmprehensive Score for financial Toxicity (COST) Cancer. 2017;123(3):476–484.
    1. Marshall G.N., Hayes R.D. RAND Corporation; Santa Monica, CA: 1994. The Patient Satisfaction Questionnaire Short Form (PSQ-18)
    1. Marshall G.N., Hays R.D. RAND; Santa Monica, CA: 1994. The Patient Satisfaction Questionnaire Short-form (PSQ-18)
    1. Collins G.S. Transparent reporting of multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015 doi: 10.1136/bmj.g7594. 015 Jan 7;350: g7594, 2.
    1. Houle T.T. Forecasting individual headache attacks using perceived stress: development of a multivariable prediction model for persons with episodic migraine. Headache. 2017;57(7):1041–1050.
    1. Hemming K. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ. 2015;350:h391.
    1. Gurol-Urganci I. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst. Rev. 2013;(12):CD007458.
    1. Mbuagbaw L. Mobile phone text messaging interventions for HIV and other chronic diseases: an overview of systematic reviews and framework for evidence transfer. BMC Health Serv. Res. 2015;15:33.
    1. Nuti L. The impact of interventions on appointment and clinical outcomes for individuals with diabetes: a systematic review. BMC Health Serv. Res. 2015;15:355.
    1. Saultz J.W. Defining and measuring interpersonal continuity of care. Ann. Fam. Med. 2003;1(3):134–143.

Source: PubMed

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