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

Abstract

BackgroundQuality 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.MethodsQuality 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.ResultsData 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.ConclusionsQuality 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

Effect of the Children’s Healthy Living Program on Young Child Overweight, Obesity, and Acanthosis Nigricans in the US-Affiliated Pacific Region

“…To ensure consistency of protocol across sites, a quality assurance protocol was implemented during both data collection periods. 22 Data were collected on demographics, child body size, functional outcomes of obesity, and lifestyle behavior, which included sleep time, screen time, physical activity, and food intake. Measures included anthropometry, food and activity logs, and visual inspection of the neck for acanthosis nigricans.…”

Data quality in the context of longitudinal research studies

“…We detected expected data collection problems but also some less often discussed problems. Problems detected within the CRIBS study corresponding to problems discussed in other research papers (Yamanaka et al, 2016;Read et al, 2017;Young et al, 2007) were:…”

“…QA is a process used to prevent problems in the data collection process and to support subsequent data quality. It plays an important role in the conduct of a research study by helping to ensure findings and conclusions are correct and justifiable (Yamanaka et al, 2016). According to the Szklo and Nieto (2014), QA activities before data collection aim to prevent or at least minimize systematic or random errors in collecting and analysing data.…”

“…The development of manuals specifying quality control activities can also be considered as a quality assurance activity. QA therefore includes methods and procedures for preventing and correcting problems that may affect the quality of survey data (Biemer, Lyberg, 2003) The available literature on QA focuses mostly on standardizing the protocols and personnel training (Sáez et al, 2012;Chen et al, 2014;Szklo, Nieto, 2014;Yamanaka et al, 2016). QA steps mentioned in research papers can be summarized into three steps: (1) developing a procedure manual for data collection, (2) developing a detailed recruitment and training plan to enforce the value of collecting accurate data and (3) monitoring and evaluating the process in the field and identifying areas of improvement to strengthen the study's protocol.…”

A school-based epidemiological field survey: difficulties in collecting psychiatric outcome data in a middle-income country

“…Several explanations for the low number of epidemiological studies in psychiatry, particularly in CAP, in LMICs exist, including difficulties in recruitment and follow-up of subjects (often due deficient telephone, internet, and postal services), issues in obtaining valid and reliable data, mainly because of lack of trained personnel, as well as difficulties in standardizing procedures and systems for data imputation [ 14 ]. Therefore, the aim of this paper is to discuss the difficulties and feasibility of conducting a CAP longitudinal epidemiological survey in a pilot study of a school-based sample in the city of São Paulo, Brazil.…”