Effective and resource-efficient strategies for recruiting families in physical activity, sedentary behavior, nutrition, and obesity prevention research: A systematic review with expert opinion.
Justin M GuaglianoKatie L MortonClaire HughesEsther M F van SluijsPublished in: Obesity reviews : an official journal of the International Association for the Study of Obesity (2020)
We systematically identified effective and resource-efficient strategies for recruiting families into health promoting intervention research. Four databases were searched for reviews. Interventions were extracted from included reviews. Additionally, a Delphi study was conducted with 35 experts in family-based research. We assessed extracted data from our review and Delphi participants' opinions by collating responses into overarching themes based on recruitment setting then recruitment strategies to identify effective and resource-efficient strategies for recruiting families into intervention research. A total of 64 articles (n = 49 studies) were included. Data regarding recruitment duration (33%), target sample size (32%), reach (18%), expressions of interest (33%), and enrollment rate (22%) were scarcely reported. Recruitment settings (84%) and strategies (73%) used were available for most studies. However, the details were vague, particularly regarding who was responsible for recruitment or how recruitment strategies were implemented. The Delphi showed recruitment settings, and strategies fell under six themes: school-based, print/electronic media, community settings-based, primary care-based, employer-based, and referral-based strategies. Underrecruitment in family-based trials is a major issue. Reporting on recruitment can be improved by better adherence to existing guidelines. Our findings suggest a multifaceted recruitment approach targeting adults and children with multiple exposures to study information.
Keyphrases
- primary care
- physical activity
- randomized controlled trial
- healthcare
- mental health
- public health
- type diabetes
- systematic review
- metabolic syndrome
- young adults
- body mass index
- weight loss
- emergency department
- air pollution
- electronic health record
- adipose tissue
- skeletal muscle
- insulin resistance
- social media
- risk assessment
- case control
- deep learning
- artificial intelligence