Quality assurance of data collection in the multi-site community randomized trial and prevalence survey of the children's healthy living program

Ashley Yamanaka, Marie Kainoa Fialkowski, Lynne Wilkens, Fenfang Li, Reynolette Ettienne, Travis Fleming, Julianne Power, Jonathan Deenik, Patricia Coleman, Rachael Leon Guerrero, Rachel Novotny, Ashley Yamanaka, Marie Kainoa Fialkowski, Lynne Wilkens, Fenfang Li, Reynolette Ettienne, Travis Fleming, Julianne Power, Jonathan Deenik, Patricia Coleman, Rachael Leon Guerrero, Rachel Novotny

Abstract

Background: Quality assurance plays an important role in research by assuring data integrity, and thus, valid study results. We aim to describe and share the results of the quality assurance process used to guide the data collection process in a multi-site childhood obesity prevalence study and intervention trial across the US Affiliated Pacific Region.

Methods: Quality assurance assessments following a standardized protocol were conducted by one assessor in every participating site. Results were summarized to examine and align the implementation of protocol procedures across diverse settings.

Results: Data collection protocols focused on food and physical activity were adhered to closely; however, protocols for handling completed forms and ensuring data security showed more variability.

Conclusions: Quality assurance protocols are common in the clinical literature but are limited in multi-site community-based studies, especially in underserved populations. The reduction in the number of QA problems found in the second as compared to the first data collection periods for the intervention study attest to the value of this assessment. This paper can serve as a reference for similar studies wishing to implement quality assurance protocols of the data collection process to preserve data integrity and enhance the validity of study findings.

Trial registration: NIH clinical trial #NCT01881373.

Keywords: Childhood; Multi-site; Obesity; Pacific; Prevalence; Quality assurance.

References

    1. Moreno LA, De Henauw S, Gonzalez-Gross M, Kersting M, Molnar D, Gottrand F, et al. Design and implementation of the healthy lifestyle in Europe by nutrition in adolescence cross-sectional study. Int J Obes. 2008;32:S4–S11. doi: 10.1038/ijo.2008.177.
    1. The office of Research Integrity. Responsible conduct in data management. 2015. []. Accessed July 2015.
    1. Knatterud GL, Rockhold FW, George SL, Barton FB, Davis CE, Fairweather WR, et al. Guidelines for quality assurance in multicenter trials: a position paper. Control Clin Trials. 1998;19(5):477–493. doi: 10.1016/S0197-2456(98)00033-6.
    1. Freedland KE, Carney RM. Data management and accountability in behavioral and biomedical research. Am Psychol. 1992;47:640–645. doi: 10.1037/0003-066X.47.5.640.
    1. Gassman JJ, Owen WW, Kuntz TE, et al. Data quality assur-ance, monitoring, and reporting. Control Clin Trials. 1995;16(Suppl 2):104S–136S. doi: 10.1016/0197-2456(94)00095-K.
    1. Prud’homme GJ, Canner PL, Cutler JA. Quality assurance and monitoring in the hypertension prevention trial. Hypertension prevention trial research group. Control Clin Trials. 1989;10(Suppl 3):84S–94S. doi: 10.1016/0197-2456(89)90044-5.
    1. Karrison T. Data editing in a clinical trial. Control Clin Trials. 1981;2:15–29. doi: 10.1016/0197-2456(81)90055-6.
    1. Marinez YN, Mahan CA, Barnwell GM, et al. Ensuring data quality in medical research through an integrated data management system. Stat Med. 1984;3:101–111. doi: 10.1002/sim.4780030204.
    1. Bagniewska A, Black D, Molvig K, et al. Data quality in a distributed data processing system: the SHEP pilot study. Control Clin Trials. 1986;7:27–37. doi: 10.1016/0197-2456(86)90005-X.
    1. Severe JB, Schooler NR, Lee JH, et al. Ensuring data quality in a multicenter clinical trial: remote site data entry, central coordination and feedback. Psychopharmacol Bull. 1989;25:488–490.
    1. Sforza VA. Quality data: what are they? Ann Super Sanita. 1994;30:439–443.
    1. Hohnloser JH, Puerner F, Soltanian H. Improving coded data entry by an electronic patient record system. Methods Inf Med. 1996;35:108–111.
    1. Whitney CW, Lind BK, Wahl PW. Quality assurance and quality control in longitudinal studies. Epidemiol Rev. 1997;20(1):71–80. doi: 10.1093/oxfordjournals.epirev.a017973.
    1. Rosa C, Campbell A, Kleppinger C, Sampson R, Tyson C, Mamay-Gentilin S. Quality assurance of research protocols conducted in the community: the National Institute on Drug Abuse Clinical Trials Network experience. Clinical Trials. 2009;6(2):151–161. doi: 10.1177/1740774509102560.
    1. Fontana D, Matthys C, Engel P, Clevidence BA, Todd K, Ershow AG. Staffing needs for research diet studies. Well controlled diet studies in humans: a practical guide to design and management. Chicago: American Dietetic Association; 1999. pp. 299–322.
    1. Obarzanek E, Pratt CA. Girls health enrichment multi-site studies (GEMS): new approaches to obesity prevention among young African–American girls. Ethnicity Dis. 2002;13(Suppl 1):S1–S5.
    1. Klesges RC, Obarzanek E, Kumanyika S, Murray DM, Klesges LM, Relyea GE, et al. The Memphis girls’ health enrichment multi-site studies (GEMS): an evaluation of the efficacy of a 2-year obesity prevention program in African American girls. Arch Pediatr Adolesc Med. 2010;164(11):1007–1014. doi: 10.1001/archpediatrics.2010.196.
    1. Most MM, Craddick S, Crawford S, Redican S, Rhodes D, Rukenbrod F, et al. Dietary quality assurance processes of the DASH-Sodium controlled diet study. J Am Diet Assoc. 2003;103(10):1339–1346. doi: 10.1016/S0002-8223(03)01080-0.
    1. Ng M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2014;384(9945):766–781. doi: 10.1016/S0140-6736(14)60460-8.
    1. Murphy SP. Collection and analysis of intake data from the integrated survey. J Nutr. 2003;133(2):585S–589S.
    1. Novotny R, et al. The Pacific way to child wellness: the Children’s healthy living program for remote underserved minority populations of the Pacific region (CHL) Hawaii J Med Public Health. 2013;72(11):406–408.
    1. Hawley NL, McGarvey ST. Obesity and diabetes in Pacific Islanders: the current burden and the need for urgent action. Curr Diab Rep. 2015;15(5):29. doi: 10.1007/s11892-015-0594-5.
    1. McLennan AK, Ulijaszek SJ. Obesity emergence in the Pacific islands: why understanding colonial history and social change is important. Public Health Nutr. 2015;18(08):1499–1505. doi: 10.1017/S136898001400175X.
    1. Wilken LR, Novotny R, Fialkowski MK, Boushey CJ, Nigg C, Paulino Y, et al. Children’s Healthy Living (CHL) Program for remote underserved minority populations in the Pacific region: rationale and design of a community randomized trial to prevent early childhood obesity. BMC Public Health. 2013;13(1):944. doi: 10.1186/1471-2458-13-944.
    1. Li F, Wilkens L, Novotny R, Fialkowski M, Paulino Y, Nelson R, et al. Anthropometric standardization in the US Affiliated Pacific: the Children’S Healthy Living Program (1024.6) FASEB J. 2014;28(Suppl 1):1024–1026.
    1. Fialkowski MK, Yamanaka A, Wilkens LR, Braun KL, Butel J, Ettienne R, et al. Recruitment strategies and lessons learned from the Children’s Healthy Living Program Prevalence Survey. AIMS Public Health. 2016;3(1):140–157. doi: 10.3934/publichealth.2016.1.140.

Source: PubMed

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