Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-12-01T01:33:05.043Z Has data issue: false hasContentIssue false

Recruiting and retaining young adults: what can we learn from behavioural interventions targeting nutrition, physical activity and/or obesity? A systematic review of the literature

Published online by Cambridge University Press:  16 March 2021

Megan C Whatnall
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Melinda J Hutchesson
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Thomas Sharkey
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Rebecca L Haslam
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Aaron Bezzina
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Clare E Collins
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
Flora Tzelepis
Affiliation:
School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia Hunter Medical Research Institute, New Lambton Heights, NSW, Australia Hunter New England Population Health, Wallsend, NSW, Australia
Lee M Ashton*
Affiliation:
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan2308, Australia Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan2308, Australia
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Objective:

To describe strategies used to recruit and retain young adults in nutrition, physical activity and/or obesity intervention studies, and quantify the success and efficiency of these strategies.

Design:

A systematic review was conducted. The search included six electronic databases to identify randomised controlled trials (RCT) published up to 6 December 2019 that evaluated nutrition, physical activity and/or obesity interventions in young adults (17–35 years). Recruitment was considered successful if the pre-determined sample size goal was met. Retention was considered acceptable if ≥80 % retained for ≤6-month follow-up or ≥70 % for >6-month follow-up.

Results:

From 21 582 manuscripts identified, 107 RCT were included. Universities were the most common recruitment setting used in eighty-four studies (79 %). Less than half (46 %) of the studies provided sufficient information to evaluate whether individual recruitment strategies met sample size goals, with 77 % successfully achieving recruitment targets. Reporting for retention was slightly better with 69 % of studies providing sufficient information to determine whether individual retention strategies achieved adequate retention rates. Of these, 65 % had adequate retention.

Conclusions:

This review highlights poor reporting of recruitment and retention information across trials. Findings may not be applicable outside a university setting. Guidance on how to improve reporting practices to optimise recruitment and retention strategies within young adults could assist researchers in improving outcomes.

Type
Review Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

Young adults (aged 17–35 years) commonly develop poor lifestyle behaviours that track into later adulthood and increase the risk of chronic disease across the life course(Reference Liu, Daviglus and Loria1,Reference Zheng, Manson and Yuan2) . Rates of overweight and obesity have increased from 38 % to 46 % among those aged 18–24 years in the time periods from 2011–2012 to 2017–2018 in Australia(3,4) , while this has increased from 42 % to 60 % in 20–34-year-olds from 1994 to 2015 in the USA(5). The increase in overweight and obesity rates coincides with worsening dietary patterns and physical inactivity during this life stage. A systematic evaluation of dietary patterns in adults from 187 countries found that the unhealthiest dietary scores were in 20–29-year-olds compared with all other age ranges(Reference Imamura, Micha and Khatibzadeh6). Furthermore, a meta-analysis of physical activity change from adolescence to young adulthood demonstrated a 13–17 % (or 5·2–7·4 min/d) decline in moderate–vigorous physical activity from age 13 to 30 years(Reference Corder, Winpenny and Love7). Therefore, this represents a critical stage to establish healthy lifestyle behaviours.

Several reviews have provided insights into effective intervention approaches for improving health behaviour among young adults(Reference Ashton, Sharkey and Whatnall8Reference Poobalan, Aucott and Precious14); however, a more focused enquiry on recruitment and retention strategies is required. Recruitment refers to the process of selection of participants, from becoming aware of the health programme to enrolment of participants(Reference Gul and Ali15), while retention refers to keeping participants enrolled for the duration of the health programme(Reference Gul and Ali15). Effective recruitment and retention of young adults in health behaviour programmes are critical, yet present ongoing challenges. Both recruitment and retention may be impacted by transient living arrangements which are common during this life stage, while competing time demands may be prioritised over participation (e.g. study, work, socialising, relationships, family obligations and/or parenthood)(Reference Moe, Lytle and Nanney16,Reference Ashton, Hutchesson and Rollo17) . Furthermore, the perceptions around the imminence of health problems for someone in their life stage may affect overall recruitment of young adults to a health programme(Reference Ashton, Hutchesson and Rollo17,Reference Poobalan and Aucott18) .

Issues with recruitment or retention can have serious consequences for any research study. Failure to recruit the desired sample size can lead to underpowered studies, leading to a type II error (false-negative findings), while under recruiting can impact representativeness of population samples and therefore the external validity of results(Reference Bower, Brueton and Gamble19). Furthermore, prolonged recruitment can affect overall research expenditure(Reference Torgerson, Arlinger and Kappi20) and adversely affect the commitment of those already enrolled in the study(Reference Gul and Ali21). Failure to retain sufficient numbers may implicate the study power by affecting the composition between treatment and control groups(Reference Gul and Ali21). In addition, differential dropout may occur, whereby particular sub-groups of individuals may be more likely to dropout than others(Reference Gul and Ali21). These issues pose a threat to a study’s internal and external validity(Reference Bower, Brueton and Gamble19). Consequently, successful and timely recruitment and retention of participants are vital to the success of the overall health programme.

There is a lack of evidence to guide successful recruitment and retention strategies for young adults in health behaviour change interventions(Reference Tate, LaRose and Griffin22,Reference Crane, LaRose and Espeland23) . The few studies that have explored this have documented serious challenges. For instance, a systematic review of weight gain prevention interventions in young adults identified recruitment rates to be low (between 7·5 % and 48 % of their intended targeted population)(Reference Lam, Partridge and Allman-Farinelli24), while another review of health behaviour interventions in young adult men(Reference Ashton, Morgan and Hutchesson25) identified only 30 % of studies had achieved appropriate retention in line with the CONSORT requirements (≥80 % retained for ≤6-month follow-up or ≥70 % for >6-month follow-up) to prevent bias. Neither review looked at recruitment or retention rates by the various strategies used, while both confirmed that insufficient reporting limited the ability to determine which strategies were most successful.

Furthermore, these reviews searched for studies up until 2015(Reference Lam, Partridge and Allman-Farinelli24) and 2014(Reference Ashton, Morgan and Hutchesson25). However, health behaviour interventions among young adults have increased significantly in recent years, with the number of published interventions doubling between 2015 and 2019 for studies targeting adiposity (n 26)(Reference Ashton, Sharkey and Whatnall8) and between 2014 and 2018 for studies aiming to improve dietary intake among young adults (n 29)(Reference Ashton, Sharkey and Whatnall9). This presents an opportunity to conduct a more focused enquiry into recruitment and retention strategies among this group.

To inform strategies to reach and retain young adults for effective implementation of interventions, evidence-based guidance is needed. Therefore, the current review aims to describe the strategies used to recruit and retain young adults in interventions targeting nutrition, physical activity or overweight/obesity in young adults (aged 17–35 years), and the success and efficiency of these strategies. The following research questions are answered in this review:

  1. 1. What are the most common recruitment strategies for young adults?

  2. 2. What are the most successful recruitment strategies for young adults?

  3. 3. Is recruitment success affected by number of strategies used?

  4. 4. What are the most efficient recruitment strategies for young adults?

  5. 5. What are the costs per participant recruited?

  6. 6. What are the most common retention strategies for young adults?

  7. 7. Do studies adequately retain young adults?

  8. 8. What are the most successful retention strategies for young adults?

  9. 9. What are the costs per participant retained?

  10. 10. What reporting is needed to assess the success of recruitment strategies?

  11. 11. What reporting is needed to assess the success of retention strategies?

Methods

This is a secondary analysis of included studies in a systematic review exploring the effectiveness of nutrition, physical activity or overweight/obesity interventions in young adults. The review protocol was registered with PROSPERO (CRD42017075795), and the methods are consistent with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results for the effectiveness of interventions have been published previously(Reference Ashton, Sharkey and Whatnall8,Reference Ashton, Sharkey and Whatnall9,Reference Sharkey, Whatnall and Hutchesson26) . The current paper presents results for recruitment and retention data.

The eligibility criteria, literature search, study selection and risk of bias of individual studies in the systematic review have been previously described in detail(Reference Ashton, Sharkey and Whatnall8,Reference Ashton, Sharkey and Whatnall9) . Table 1 outlines the full eligibility criteria. In brief, included studies were randomised controlled trials (RCT) of behavioural interventions with the primary objective of improving nutrition or physical activity, or treating or preventing obesity. Participants were required to be healthy young adults (aged 17–35 years), and any comparator or control was considered for inclusion. The definition of young adults used in studies varies based on human development and sociological perspectives. For the current review, a broad age range was included to ensure a range of studies in healthy young adults across the age range of 17–35 years. Both the National Institute of Health(27) and the European commission of Men’s health(Reference White, De Sousa and De Visser28) have used 18–35 years to define young adults. The inclusion criteria for participants were shaped around this definition, and the rationale for including those aged 17 years was due to some countries enrolling those aged 17 years in tertiary education(29,30) . Articles were located by searching six databases (Medline, Embase, PsycINFO, Science Citation Index, Cinahl and Cochrane Library) for articles published in English from date of inception to 6 December 2019 (see online supplementary material, Supplementary Table 1), as well as searching reference lists of retrieved papers and key systematic reviews. In addition, citation searches of the final included papers were conducted in Scopus. Papers linked to relevant RCT, including published study protocol, recruitment or process evaluation papers, or those publishing outcomes at differing follow-up time points, were also considered. Two independent reviewers screened the title, abstract and keywords of articles, followed by assessment of full-text articles that appeared to meet the inclusion criteria and then selected studies for inclusion. A third reviewer was consulted if disagreement existed between the two reviewers. Reasons for exclusion were recorded for ineligible papers.

Table 1 Eligibility criteria for participants, interventions, comparisons, outcomes and study design (PICOS)

Data extraction

Data relating to recruitment strategies (i.e. setting, method and duration), recruitment rates (i.e. number: invited, expressed interest/screened for eligibility, eligible, enrolled/randomised and commenced intervention), study characteristics (i.e. year of publication, country, intervention details and sample characteristics), retention strategies (i.e. any strategy used to keep participants enrolled for the study duration)(Reference Gul and Ali15), retention rates (i.e. number completing post-intervention and longest follow-up assessment) and cost of recruitment and retention strategies (total cost, cost per participant randomised and/or retained and breakdown of costs by each strategy) were extracted by one reviewer and checked by a second reviewer.

Synthesis of results and analytic strategy

Results are presented narratively to address all research questions. To evaluate the success of recruitment and retention strategies, the following metrics were used:

  • Recruitment rate (%) = total number of individuals randomised/total number who showed interest in the study

  • Participation rate (%) = total number of eligible participants randomised/total number eligible at baseline

  • Retention rate (%) = total number of participants who completed follow-up/total number who entered the study

Recruitment was considered successful if the pre-determined goal sample size was met(Reference Carroll, Yancey and Spring31). Recruitment efficiency was calculated as the number of participants randomised/recruitment duration in days. Retention was considered adequate if retention was ≥80 % for ≤6-month follow-up or ≥70 % for >6-month follow-up, as defined in several previous systematic reviews(Reference Ashton, Morgan and Hutchesson25,Reference van Sluijs, McMinn and Griffin32Reference Young, Morgan and Plotnikoff34) . Retention efficiency could not be calculated due to insufficient reporting of duration for follow-up data collection. The success of individual recruitment and retention strategies was calculated the same way, that is the number of studies which used a given strategy and had successful recruitment or retention divided by the number of studies which used that strategy. Efficiency of individual recruitment strategies was calculated as the mean and standard deviation across studies which used each strategy. Differences in recruitment and retention characteristics by intervention focus were assessed using Fisher’s exact test for categorical variables and ANOVA for continuous variables. An overall test of significance was carried out using a contingency table between numbers of recruitment strategies using Monte–Carlo exact χ 2 test (SPSS version 25). Costs relating to recruitment and retention strategies were reported in USD. To standardise, studies reporting cost information in a currency other than USD (n 10 studies) were converted to USD using xe.com (https://www.xe.com/currencytables/) from the year and month that the study was conducted. Cost per participant randomised and cost per participant retained at post-intervention and at longest follow-up were then calculated. Finally, any gaps in reporting of recruitment and retention information were collated, and a summary was provided for key information required when reporting recruitment and retention in trials.

Results

Description of included studies

A total of 21 582 manuscripts were identified and screened, and of these, 107 individual RCT were included(Reference Allman-Farinelli, Partridge and McGeechan35Reference Zhang and Cooke140) (Fig. 1). A summary of study characteristics is included in Table 2, with detailed characteristics presented in Supplemental Table 2. Fifty percentage of studies were published since 2015 (n 53), and around half were conducted in the USA (n 58, 54 %) and recruited participants from a University/College setting (n 84 studies, 79 %). There was a mean of 276 participants per study, and participants were mainly aged 17–25 years (n 60 studies, 56 %) and were of white/Caucasian ethnicity (n 62 studies, 58 %). Intervention focus was most commonly physical activity (n 31, 29 %) followed by nutrition (n 28, 26 %). The most common mode of intervention delivery was eHealth (e.g. email, mobile phone applications, websites and social media) (n 61 intervention arms, 35 %). The mean duration of interventions was 15·5 weeks, ranging from single session interventions to 3 years duration.

Fig. 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of included studies

Table 2 Summary of study characteristics in 107 studies of nutrition, physical activity and obesity interventions in young adults

* Reported by active intervention arms (n 173).

Recruitment

All recruitment details are summarised by intervention focus in Table 3. Responses to recruitment research questions are provided below.

Table 3 Summary of recruitment details of 107 studies of nutrition, physical activity and obesity interventions in young adults, by intervention focus

Results in bold were statistically significantly different between groups of studies with different intervention focus (P < 0·05).

* n 48 studies (14 physical activity, 9 nutrition, 9 weight gain prevention, 8 weight loss, 8 nutrition and physical activity).

n 21 studies (7 physical activity, 8 nutrition, 1 weight loss, 5 nutrition and physical activity).

n 78 studies (20 physical activity, 18 nutrition, 14 weight gain prevention, 14 weight loss, 12 nutrition and physical activity).

§ n 96 studies (26 physical activity, 26 nutrition, 14 weight gain prevention, 16 weight loss, 14 nutrition and physical activity).

|| Results available for only one weight loss study and are not reported.

Research question #1: What are the most common recruitment strategies for young adults?

The most common recruitment strategies were flyers (n 40, 37 %) and using an existing cohort or participant database (n 29, 27 %). Recruitment strategies are summarised in Table 4.

Table 4 Summary of recruitment strategies of 107 studies of nutrition, physical activity and obesity interventions in young adults

* n 1 study did not report the recruitment setting; therefore, the total includes all 107 studies, but the breakdown by recruitment setting includes 106 studies.

Research question #2: What are the most successful recruitment strategies for young adults?

The mean participation rate (% eligible who were randomised) could be calculated for ninety-six studies. The overall mean was 83 %. The mean recruitment rate (% interested who were randomised) was calculated for seventy-eight studies, with an overall mean of 55 %. The median recruitment rate (participants recruited per day) in the forty-eight studies which reported it was 1·9 participants per day.

Forty-six studies reported a power estimation (43 %), and seven pilot studies reported a goal sample size (7 %). Of these, forty-one studies (77 %) reported that participant recruitment reached the pre-determined sample size. Success of individual recruitment strategies was determined for forty-nine studies. To provide a meaningful representation of success, only recruitment strategies that were reported in at least three studies were considered, this included thirteen strategies. The top four strategies with the highest success rates were face-to-face (e.g. university/college fairs at start of semester open days) (n 13, 100 %), email (n 14, 88 %), newspaper advertisements (n 7, 88 %) and online advertisements (e.g. Google ads) (n 7, 88 %) (Fig. 2).

Fig. 2 Success* of recruitment strategies used across forty-nine studies of nutrition, physical activity and/or obesity interventions in young adults. *Recruitment was considered successful if the pre-determined goal sample size was met

Research question #3: Is recruitment success affected by number of strategies used?

The median number of recruitment strategies used across the forty-nine studies was 2 (range 1–9 strategies per study). Figure 3 presents the percentage ratio of recruitment success by number of recruitment strategies, highlighting that no relationship exists based on whether fewer or more strategies are utilised. The overall test of significance using a contingency table indicates that there was no significant relationship (Monte–Carlo exact χ 2 test, χ 2(7) = 4·8, P = 0·73).

Fig. 3 Recruitment success* (%) by number of recruitment strategies used. *Recruitment was considered successful if the pre-determined goal sample size was met

Research question #4: What are the most efficient recruitment strategies for young adults?

Efficiency of individual recruitment strategies was determined for forty-eight studies. As with recruitment success, to provide a meaningful representation of efficiency, only recruitment strategies that were reported in at least three studies were considered (n 13). The mean and standard deviation of recruitment efficiency was calculated for each recruitment strategy. The top four strategies which had the highest efficiency were advertising through letters (mean ± sd of 8·7 ± 14·1 participants recruited per day), using existing cohorts or participant databases (7·3 ± 10·8 participants recruited per day), face-to-face (6·4 ± 10·3 participants recruited per day) and advertising within university classrooms (6·1 ± 10·8 participants recruited per day) (Table 5).

Table 5 Efficiency of recruitment strategies used across forty-eight studies of nutrition, physical activity and/or obesity interventions in young adults

* Recruitment efficiency = the number of participants randomised/recruitment duration in days. The (n) refers to the number of studies which used each strategy.

Research question #5: What are the costs per participant recruited?

Total cost of recruitment was documented in three studies (Project Grad, TXT2BFIT and SNAP study)(Reference Allman-Farinelli, Partridge and McGeechan35,Reference Calfas, Sallis and Nichols46,Reference Wing, Tate and Espeland139,Reference Partridge, Balestracci and Wong141,Reference Tate, LaRose and Griffin142) . Costs in ascendency were: Project Grad which spent US$20 729 for recruiting participants from a University setting and implemented two recruitment strategies (mailed literature and telephone calls to students)(Reference Calfas, Sallis and Nichols46). Next, TXT2BFIT spent US$32 861·98 for recruiting participants from a community and University setting and implemented fourteen recruitment strategies (GP letter, Facebook advertisement, Google advertisement, Gumtree advertisement, Social media page, University e-newsletter, University web home page, University research volunteer page, poster, brochures, commuter newspaper advertisement, local newspaper advertisement, students’ magazine and word of mouth)(Reference Allman-Farinelli, Partridge and McGeechan35,Reference Partridge, Balestracci and Wong141) . Last, SNAP study spent US$139 543 to recruit participants from a community setting and implemented nine strategies (television, print media, radio, mass mailing, website recruitment, email, flyers and community events, study referral and word of mouth)(Reference Wing, Tate and Espeland139,Reference Tate, LaRose and Griffin142) . Two of these studies provided a breakdown of costs by strategy. Specifically, the SNAP study reported mass mailings (US$76 466·34) and television ($24 074·00) to be most expensive, while the cheapest paid strategies were flyers and community events (costed together at US$2713·27) and website recruitment (US$5222·23). There were no costs associated with some strategies including word of mouth and study referral. The most expensive strategies in the TXT2BFIT programme were brochures (US$12 922·20) and letters sent from general practitioners (US$9316·20), while the cheapest paid strategies were Gumtree advertisement (US$34·08) and University student magazines (US$701·52). There were several free strategies provided by University resources (i.e. University e-newsletter, University volunteer page, University web home page).

Cost per participant randomised was established in three studies. In the PROJECT GRAD study, this was US$45 for the passive recruitment method and $79 for the active recruitment method(Reference Calfas, Sallis and Nichols46). The TXT2BFIT and SNAP studies were more expensive with a total cost per participant randomised of US$131·77 and US$232·96, respectively(Reference Allman-Farinelli, Partridge and McGeechan35,Reference Wing, Tate and Espeland139,Reference Partridge, Balestracci and Wong141,Reference Tate, LaRose and Griffin142) .

Retention

Retention details are summarised by intervention focus in Table 6. Responses to retention research questions are provided below.

Table 6 Summary of retention details of 107 studies of nutrition, physical activity and obesity interventions in young adults, by intervention focus

* n 76 studies (21 physical activity, 16 nutrition, 16 weight gain prevention, 14 weight loss, 9 Nutrition and physical activity); 25 studies did not collect data at post-intervention, and 6 studies collected data but did not report retention.

n 49 studies (11 physical activity, 16 nutrition, 9 weight gain prevention, 5 weight loss, 8 nutrition and physical activity); 57 studies did not collect data beyond the end of the intervention.

Research question #6: What are the most common retention strategies for young adults?

Of the included studies, seventy-six studies (71 %) reported strategies to retain participants. Almost half of these studies (n 44, 41 %) used financial incentives such as gift cards, twenty-two studies (21 %) used reminders and increased contact with participants and eighteen studies (17 %) gave participants credit for university/college courses in return for participation (Table 6).

Research question #7: Do studies adequately retain young adults?

In the seventy-six studies which reported retention rates at post-intervention (range 1 week to 3 years, median: 12 weeks), there was a mean ± sd retention rate of 83 ± 14 %. Forty-nine studies measured retention rates at the longest follow-up and found that a mean ± sd of 76 ± 23 % of participants was retained. Longest follow-up ranged from 1 week to 2 years from baseline (mean 25·9 weeks, median 17·5 weeks) and ranged from 1 week to 1 year and 11 months from the end of the intervention (mean 17·3 weeks, median 11·5 weeks). Overall, seventy studies (65 %) had adequate retention (≥80 % retained for ≤6-month follow-up or ≥70 % for >6-month follow-up).

Research question #8: What are the most successful retention strategies for young adults?

Success of individual retention strategies was determined for seventy-four studies (69 %). To provide a meaningful representation of each strategy’s ability to meet the criteria for acceptable retention, only retention strategies that were reported in at least three studies were considered (n 6). The most successful retention strategies were providing participants with course credit for university/college courses (n 15, 88 %), prize/prize draw (n 9, 75 %) and financial incentives (n 31, 72 %). (Fig. 4).

Fig. 4 Adequacy* of retention strategies used across seventy-four studies of nutrition, physical activity and/or obesity interventions in young adults. *Retention was considered adequate if retention was ≥80% for ≤6-month follow-up or ≥70% for >6-month follow-up

Research question #9: What are the costs per participant retained?

Total cost of retention was documented in forty studies(Reference Allman-Farinelli, Partridge and McGeechan35,Reference Ashton, Morgan and Hutchesson38,Reference Brookie, Mainvil and Carr42Reference Calfas, Sallis and Nichols46,Reference Chang, Nitzke and Brown49,Reference Do, Kattelmann and Boeckner55,Reference Franko, Cousineau and Trant58Reference Gow, Trace and Mazzeo63,Reference Husband, Wharf-Higgins and Rhodes69Reference Jakicic, Davis and Rogers71,Reference Jung, Martin Ginis and Phillips74,Reference Katterman, Butryn and Hood76,Reference Katterman, Goldstein and Butryn77,Reference Kypri and McAnally86,Reference LaRose, Tate and Gorin88Reference LeCheminant, Smith and Covington91,Reference Meng, Peng and Shin101Reference Napolitano, Hayes and Bennett103,Reference Park, Nitzke and Kritsch109,Reference Pope and Harvey114,Reference Pope, Barr-Anderson and Lewis115,Reference Sandrick, Tracy and Eliasson119,Reference Schweitzer, Ross and Klein120,Reference Stice, Rohde and Shaw126,Reference Weinstock, Capizzi and Weber134,Reference Werch, Moore and Bian136,Reference Whatnall, Patterson and Chiu137,Reference Wing, Tate and Espeland139,Reference Kreausukon, Gellert and Lippke143) ranging from US$50 (n 1 strategy was used) to US$202 700 (n 1 strategy used), with a median cost of US$1935. The costs per participant retained at post-intervention were established in thirty-eight studies, ranging from US$0·50(Reference Kypri and McAnally86) to US$553·83(Reference Laska, Lytle and Nanney90), with a median cost of US$22·55. Costs per participant retained at longest follow-up were established in eighteen studies, ranging from US$1·15(Reference Kreausukon, Gellert and Lippke143) to US$97·20(Reference Gow, Trace and Mazzeo63), with a median cost of US$20.

Discussion

The current study provides a comprehensive review of the recruitment and retention data of 107 RCT targeting nutrition, physical activity or overweight/obesity in young adults. Notably, the review highlights the poor reporting of details related to recruitment information across the trials (i.e. only 46 % of studies provided sufficient information to determine the ability of individual recruitment strategies to meet pre-determined sample sizes), although reporting for retention was better (69 % of studies provided sufficient information to determine whether individual retention strategies achieved adequate retention rates). Guidance is required for researchers on how to improve reporting practices to help improve recruitment and retention strategies for use with young adult population samples.

Recruitment findings

In terms of recruitment, 77 % of studies had participant recruitment that met pre-determined sample sizes, with face-to-face recruitment most likely to achieve recruitment goals. This is higher when compared with another review (n 151 RCT) of recruitment data from trials that had no restrictions for age and found 56 % of studies achieved their target sample size(Reference Walters, dos Anjos Henriques-Cadby and Bortolami144). The most efficient strategy was advertising through letters. However, results from the current review should be interpreted with caution. Given that 79 % of studies used a university setting to recruit potential participants, findings may not be applicable to other settings.

When planning health behaviour interventions with young adults, researchers need to consider the efficiency of recruitment approaches in order to make the most of available resources and time. Despite the limitations relating to poor reporting of recruitment rates, the review provides some guidance for researchers working with young adults in regard to the number of participants required to respond to recruitment strategies to ensure a sufficient sample size is recruited. Our findings suggest just under one-third of young adults who express interest and/or are screened for eligibility then provide consent and participate in the study. Of those individuals who meet study inclusion criterion, almost two-thirds go on to participate in a study. Practically speaking if a sample of ˜100 young adults is required, this means that ˜300 young adults must be engaged by the recruitment strategies and screened for eligibility, of which ˜200 would meet inclusion criteria, and ˜100 would go on to participate in the study. Furthermore, studies focusing on weight gain prevention or weight loss took longer to recruit the target sample (mean of 375 d and 183 d) compared with studies focusing on nutrition and/or physical activity. They also screened more people and reported a much lower proportion of those expressing interest being randomised (47·6 % and 29·3 %). This is likely due to stricter inclusion criteria, but is an important consideration for studies focusing on weight with a need to allocate extra time and resources for recruitment.

Few studies reported the cost information related to recruitment (n 3 studies). In these studies, recruitment costs in ascendency were US$20 729 to enrol 338 participants, US$32 861·98 to enrol 250 participants and US$139 543 to enrol 609 participants. However, in-kind support from the University (i.e. University e-newsletter) was common; therefore, costs will likely be higher if recruiting outside of a University setting. The costs associated with recruiting young adults may be higher than other population groups due to extra challenges of recruiting this population group(Reference Lam, Partridge and Allman-Farinelli24). When compared with another systematic review of physical activity interventions among all adults (19 years of age and above), costs per participant recruited were approximately US$27·62 (reported as £20·32 but converted to USD for comparison)(Reference Cooke and Jones145). Although this was only reported in one study in the review, this appears lower than costs per participant recruited in this current review which ranged from US$45 to US$232·96.

The recruitment strategies predominantly used were more traditional, such as flyers and email, as opposed to ‘newer’ strategies, such as online advertising and social media. This is consistent with other reviews which have reported this information(Reference Lam, Partridge and Allman-Farinelli24,Reference Foster, Brennan and Matthews146) . There is potential to further explore the success of ‘newer’ strategies, given the high use and engagement with electronic and social media among young adults(147). The use of online platforms for intervention delivery (e.g. e-Health and mHealth) has advanced substantially in recent years among young adults and was the most common mode of intervention delivery identified in this review. However, there has not been the same rise in use of online platforms as a means of recruitment.

Comparably, other reviews of recruitment data among physical activity interventions(Reference Cooke and Jones145,Reference Foster, Brennan and Matthews146) , mental health trials(Reference Liu, Pencheon and Hunter148) and those using digital tools(Reference Frampton, Shepherd and Pickett149) to recruit have all highlighted inadequate reporting and evaluation, while accentuating the need for better guidance in reporting(Reference Kearney, Harman and Rosala-Hallas150). Recently, the ORRCA project (Online Resource for Recruitment research in Clinical triAls, www.orrca.org.uk) has created a database of all RCT on recruitment to help improve recruitment of participants into trials.

Research question #10: What reporting is needed to assess the success of recruitment strategies?

Gaps in reporting of key recruitment information mean that the success of recruitment strategies could not be evaluated sufficiently. Similarly, issues with reporting recruitment information were emphasised in the 2016 review on weight gain prevention interventions by Lam et al.(Reference Lam, Partridge and Allman-Farinelli24). It was hoped that the review by Lam et al. would illicit better reporting, but gaps still remain. As such, we have outlined the key recruitment information that should be reported in trials and the benefits of reporting this information (Table 7).

Table 7 Key reporting information for recruitment in trials

Retention findings

In terms of retention, 65 % of studies had adequate retention and the most successful retention strategy was course credit. The mean ± sd retention rate of 83 ± 14 % is slightly lower when compared with another review by Walters et al. (n 151 RCT) that had no restrictions for age and reported an average retention rate of 89 %(Reference Walters, dos Anjos Henriques-Cadby and Bortolami144). Of note, this current review included fewer longer-term trials with only 38 % having follow-up >6 months from baseline, compared with 64 % in the Walters review(Reference Walters, dos Anjos Henriques-Cadby and Bortolami144). Thus, young adults are likely to be harder to retain in the long term. Although reporting of retention was better than recruitment (69 % of studies provided sufficient information about retention strategies), results should be interpreted with caution. As was the case for the recruitment results, findings for retention (i.e. use of course credit) will unlikely be applicable outside of University or College settings.

Research question #11: What reporting is needed to assess the success of retention strategies?

Gaps in reporting retention information prevented detailed insights to inform successful retention strategies for use in this age group. Comparatively, a systematic review of smoking, nutrition, alcohol, physical activity and obesity RCT (n 10) also highlighted reporting issues about retention(Reference Ashton, Morgan and Hutchesson25). Therefore, we have outlined in Table 8 the key retention information to be reported in trials and the benefits of reporting this information.

Table 8 Key reporting information for retention in trials

Once more studies are available with detailed information on recruitment and retention, as outlined in Table 7 and Table 8, it will be worthwhile to update the current review. Providing greater detail on these aspects will assist with truly determining successful, cost-effective and efficient strategies to recruit and retain young adults. Furthermore, this information will help ensure statistical power is achieved and retained, leading to fewer false-negative findings. Finally, project co-ordinators will be better informed on ways to improve budgeting, planning and time management.

Strengths and limitations of the review

This review is the largest and most comprehensive review to evaluate recruitment and retention information from behavioural interventions targeting nutrition, physical activity or overweight/obesity in young adults. Furthermore, the quantification of recruitment and retention success, efficiency and cost is useful to inform future study planning. There are a number of limitations to acknowledge. Primarily, to be included, all participants were required to be within the age range of 17–35 years. As such, this risks excluding studies that included mainly young adults but also had some outside the age range. In addition, the calculations to determine the success of individual recruitment and retention strategies were basic (i.e. number of times the strategy was used in a study with recruitment or retention meeting criteria/the total number of times the strategy was used in a study). In implementing this approach, we were unable to determine if certain strategies had more ‘weighting’ with regard to recruitment or retention success. However, this approach was considered most appropriate as few studies provided recruitment and retention numbers by each strategy. Exploration of the combinations of recruitment and retention strategies was not feasible due to the large number of combinations with very few studies using the exact same combination of strategies and thus may result in false-positive or false-negative findings. For the recruitment rate calculation, a denominator of all those reached was not used because of the difficulty of measuring reach when passive/reactive recruitment strategies are used that require the potential participants to contact the research team. The definition of adequate retention is limited in that the cut-off of 6-months for follow-up does not consider additional cut-offs for studies with longer-term follow-up (e.g. >12 months). However, in this review, only 15 % of studies included follow-up >12 months from baseline. In addition, studies were limited to those published in English, which may have excluded relevant studies and could limit the generalisability of the findings.

Recommendations from this research

  • Future studies should report complete details of recruitment and retention as outlined in Table 7 and Table 8 within the main paper or in protocol papers or Supplementary information. Alternatively, journals and researchers should publish papers specifically focusing on recruitment and retention.

  • Greater focus on recruiting diverse samples of young adults and determining successful strategies to do so. In particular, different ethnicities and samples from a variety of socio-economic backgrounds.

  • Researchers should consider the number of young adults that need to be reached to recruit the pre-determined sample size. Based on the current review, if a sample recruitment target is 100 young adults, then at least 300 participants need to be engaged by recruitment strategies.

  • Researchers should consider sufficient costs when budgeting for recruitment and retention. Recruitment costs per participant randomised in the university setting ranged from US$45 to US$232·96. Furthermore, median retention costs were US$22·55 per participant retained (range: US$0·50 to US$553·83).

Conclusions

Overall, the current review emphasises poor reporting of recruitment, retention and related costs within behavioural interventions that include young adults. Due to the large proportion of studies that have used a university setting to recruit potential participants, findings may not be applicable to other settings. Guidance is required for researchers on how to improve reporting practices to help better establish recruitment and retention strategies for use with young adult populations. Essential recruitment and retention data are required in established checklists of reporting for trials (e.g. CONSORT) or as an extension. An update of this review will be required once a sufficient number of new studies publish essential recruitment and retention data.

Acknowledgements

Acknowledgements: Not applicable. Financial support: This research was supported by the School of Health Sciences strategic pilot grant (University of Newcastle). C.E.C. is supported by a National Health and Medical Research Council of Australia Senior Research Fellowship, and a Gladys M Brawn Senior Research Fellowship from the Faculty of Health and Medicine, the University of Newcastle, Australia. F.T. was supported by a National Health and Medical Research Council (NHMRC) Career Development Fellowship (APP1143269). The funding bodies had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. Conflict of interest: None of the authors had any financial support or relationships that may pose a conflict of interest. Authorship: CRediT Author statement contributions: M.C.W.: methodology, formal analysis, investigation and writing – original draft; M.J.H.: funding acquisition, conceptualisation, methodology, investigation, writing – original draft and supervision; T.S.: investigation and data curation; R.L.H.: investigation and writing – review & editing; A.B.: investigation and writing – review & editing; C.E.C.: funding acquisition, conceptualisation, methodology, investigation, writing – review & editing and supervision; F.T.: writing – original draft; L.M.A.: funding acquisition, methodology, conceptualisation, formal analysis, data curation, investigation, writing – original draft, project administration and supervision. Ethics of human subject participation: Not applicable.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980021001129

References

Liu, K, Daviglus, ML, Loria, CM et al. (2012) Healthy lifestyle through young adulthood and the presence of low cardiovascular disease risk profile in middle age: the Coronary Artery Risk Development in (Young) Adults (CARDIA) study. Circulation 125, 9961004.CrossRefGoogle ScholarPubMed
Zheng, Y, Manson, JE, Yuan, C et al. (2017) Associations of weight gain from early to middle adulthood with major health outcomes later in life. JAMA 318, 255269.CrossRefGoogle ScholarPubMed
Australian Bureau of Statistics (2012) Australian Health Survey: First Results, 2011–12. Canberra: ABS.Google Scholar
Australian Bureau of Statistics (2018) National Health Survey: First Results, 2017–18. Canberra: ABS.Google Scholar
Centers for Disease Control and Prevention (2015) Normal weight, overweight and obesity among adults aged 20 and over, by selected characteristics: United States, selected years 1988–1994 through 2011–2014. http://www.cdc.gov/nchs/data/hus/2015/058.pdf Google Scholar
Imamura, F, Micha, R, Khatibzadeh, S et al. (2015) Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Health 3, e132e142.CrossRefGoogle ScholarPubMed
Corder, K, Winpenny, E, Love, R et al. (2019) Change in physical activity from adolescence to early adulthood: a systematic review and meta-analysis of longitudinal cohort studies. Br J Sports Med 53, 496503.CrossRefGoogle ScholarPubMed
Ashton, LM, Sharkey, T, Whatnall, MC et al. (2020) Which behaviour change techniques within interventions to prevent weight gain and/or initiate weight loss improve adiposity outcomes in young adults? A systematic review and meta-analysis of randomized controlled trials. Obes Rev 21, e13009.CrossRefGoogle ScholarPubMed
Ashton, LM, Sharkey, T, Whatnall, MC et al. (2019) Effectiveness of interventions and behaviour change techniques for improving dietary intake in young adults: a systematic review and meta-analysis of RCTs. Nutrients 11, 825.CrossRefGoogle ScholarPubMed
Willmott, TJ, Pang, B, Rundle-Thiele, S et al. (2019) Weight management in young adults: systematic review of electronic health intervention components and outcomes. J Med Internet Res 21, e10265.CrossRefGoogle ScholarPubMed
Nour, M, Chen, J & Allman-Farinelli, M (2016) Efficacy and external validity of electronic and mobile phone-based interventions promoting vegetable intake in young adults: systematic review and meta-analysis. J Med Internet Res 18, e58.CrossRefGoogle ScholarPubMed
Oosterveen, E, Tzelepis, F, Ashton, L et al. (2017) A systematic review of eHealth behavioral interventions targeting smoking, nutrition, alcohol, physical activity and/or obesity for young adults. Prev Med 99, 197206.CrossRefGoogle ScholarPubMed
Partridge, SR, Juan, SJ, McGeechan, K et al. (2015) Poor quality of external validity reporting limits generalizability of overweight and/or obesity lifestyle prevention interventions in young adults: a systematic review. Obes Rev 16, 1331.CrossRefGoogle ScholarPubMed
Poobalan, AS, Aucott, LS, Precious, E et al. (2010) Weight loss interventions in young people (18 to 25 year olds): a systematic review. Obes Rev 11, 580592.CrossRefGoogle ScholarPubMed
Gul, RB & Ali, PA (2010) Clinical trials: the challenge of recruitment and retention of participants. J Clin Nurs 19, 227233.CrossRefGoogle ScholarPubMed
Moe, SG, Lytle, LA, Nanney, MS et al. (2016) Recruiting and retaining young adults in a weight gain prevention trial: lessons learned from the CHOICES study. Clin Trials 13, 205213.CrossRefGoogle Scholar
Ashton, LM, Hutchesson, MJ, Rollo, ME et al. (2016) Motivators and barriers to engaging in healthy eating and physical activity: a cross-sectional survey in young adult men. Am J Men’s Health 11, 330343.CrossRefGoogle Scholar
Poobalan, A & Aucott, L (2016) Obesity among young adults in developing countries: a systematic overview. Curr Obes Rep 5, 213.CrossRefGoogle ScholarPubMed
Bower, P, Brueton, V, Gamble, C et al. (2014) Interventions to improve recruitment and retention in clinical trials: a survey and workshop to assess current practice and future priorities. Trials 15, 399.CrossRefGoogle ScholarPubMed
Torgerson, JS, Arlinger, K, Kappi, M et al. (2001) Principles for enhanced recruitment of subjects in a large clinical trial. the XENDOS (XENical in the prevention of Diabetes in Obese Subjects) study experience. Controlled Clin Trials 22, 515525.CrossRefGoogle Scholar
Gul, RB & Ali, PA (2010) Clinical trials: the challenge of recruitment and retention of participants. J Clin Nurs 19, 227233.CrossRefGoogle ScholarPubMed
Tate, DF, LaRose, JG, Griffin, LP et al. (2014) Recruitment of young adults into a randomized controlled trial of weight gain prevention: message development, methods, and cost. Trials 15, 326.CrossRefGoogle ScholarPubMed
Crane, MM, LaRose, JG, Espeland, MA et al. (2016) Recruitment of young adults for weight gain prevention: randomized comparison of direct mail strategies. Trials 17, 1.CrossRefGoogle ScholarPubMed
Lam, E, Partridge, SR & Allman-Farinelli, M (2016) Strategies for successful recruitment of young adults to healthy lifestyle programmes for the prevention of weight gain: a systematic review. Obes Rev 17, 178200.CrossRefGoogle ScholarPubMed
Ashton, LM, Morgan, PJ, Hutchesson, MJ et al. (2015) A systematic review of SNAPO (Smoking, Nutrition, Alcohol, Physical activity and Obesity) randomized controlled trials in young adult men. Prev Med 81, 221231.CrossRefGoogle ScholarPubMed
Sharkey, T, Whatnall, MC, Hutchesson, MJ et al. (2020) Effectiveness of gender-targeted versus gender-neutral interventions aimed at improving dietary intake, physical activity and/or overweight/obesity in young adults (aged 17–35 years): a systematic review and meta-analysis. Nutr J 19, 78.CrossRefGoogle ScholarPubMed
National Institute of Health (2010) Trials use technology to help young adults achieve healthy weights. http://www.nih.gov/news-events/news-releases/trials-use-technology-help-young-adults-achieve-healthy-weights (accessed July 2019).Google Scholar
White, A, De Sousa, B, De Visser, R et al. (2011) The State of Men’s Health in Europe: Extended Report. https://ec.europa.eu/health/sites/health/files/state/docs/men_health_extended_en.pdf (accessed July 2020).Google Scholar
Open Universities Australia (2021) Minimum age requirements. https://www.open.edu.au/your-studies/getting-started/minimum-age-requirements (accessed January 2021).Google Scholar
The Open University (2020) Policy for the admission of applications under the age of 18. https://help.open.ac.uk/documents/policies/admission-of-applicants-under-the-age-18/files/2/admission-under-18.pdf (accessed January 2021).Google Scholar
Carroll, JK, Yancey, AK, Spring, B et al. (2011) What are successful recruitment and retention strategies for underserved populations? Examining physical activity interventions in primary care and community settings. Translational Behav Med 1, 234251.CrossRefGoogle ScholarPubMed
van Sluijs, EM, McMinn, AM & Griffin, SJ (2007) Effectiveness of interventions to promote physical activity in children and adolescents: systematic review of controlled trials. BMJ 335, 703.CrossRefGoogle ScholarPubMed
Engbers, LH, van Poppel, MN, Paw, MJ et al. (2005) Worksite health promotion programs with environmental changes: a systematic review. Am J Prev Med 29, 6170.CrossRefGoogle ScholarPubMed
Young, MD, Morgan, PJ, Plotnikoff, RC et al. (2012) Effectiveness of male-only weight loss and weight loss maintenance interventions: a systematic review with meta-analysis. Obes Rev 13, 393408.CrossRefGoogle ScholarPubMed
Allman-Farinelli, M, Partridge, SR, McGeechan, K et al. (2016) A mobile health lifestyle program for prevention of weight gain in young adults (TXT2BFiT): nine-month outcomes of a randomized controlled trial. JMIR mHealth uHealth 4, e78.CrossRefGoogle ScholarPubMed
Amiot, CE, El Hajj Boutros, G, Sukhanova, K et al. (2018) Testing a novel multicomponent intervention to reduce meat consumption in young men. PLoS One 13, e0204590e0204590.CrossRefGoogle ScholarPubMed
Annesi, JJ, Howton, A, Johnson, PH et al. (2015) Pilot testing a cognitive-behavioral protocol on psychosocial predictors of exercise, nutrition, weight, and body satisfaction changes in a college-level health-related fitness course. J Am Coll Health 63, 268278.CrossRefGoogle Scholar
Ashton, LM, Morgan, PJ, Hutchesson, MJ et al. (2017) Feasibility and preliminary efficacy of the ‘HEYMAN’ healthy lifestyle program for young men: a pilot randomised controlled trial. Nutr J 16, 2.CrossRefGoogle ScholarPubMed
Bailey, BW, Bartholomew, CL, Summerhays, C et al. (2019) The impact of step recommendations on body composition and physical activity patterns in college freshman women: a randomized trial. J Obes 2019, 4036825.CrossRefGoogle ScholarPubMed
Bertz, F, Pacanowski, CR & Levitsky, DA (2015) Frequent self-weighing with electronic graphic feedback to prevent age-related weight gain in young adults. Obesity 23, 20092014.CrossRefGoogle ScholarPubMed
Bray, SR, Beauchamp, MR, Latimer, AE et al. (2011) Effects of a print-mediated intervention on physical activity during transition to the first year of university. Behav Med 37, 6069.CrossRefGoogle ScholarPubMed
Brookie, KL, Mainvil, LA, Carr, AC et al. (2017) The development and effectiveness of an ecological momentary intervention to increase daily fruit and vegetable consumption in low-consuming young adults. Appetite 108, 3241.CrossRefGoogle ScholarPubMed
Brown, ON, O’Connor, LE & Savaiano, D (2014) Mobile MyPlate: a pilot study using text messaging to provide nutrition education and promote better dietary choices in college students. J Am Coll Health 62, 320327.CrossRefGoogle ScholarPubMed
Buscemi, J, Yurasek, AM, Dennhardt, AA et al. (2011) A randomized trial of a brief intervention for obesity in college students. Clin Obes 1, 131140.CrossRefGoogle ScholarPubMed
Butryn, ML, Forman, E, Hoffman, K et al. (2011) A pilot study of acceptance and commitment therapy for promotion of physical activity. J Phys Activity Health 8, 516522.CrossRefGoogle ScholarPubMed
Calfas, KJ, Sallis, JF, Nichols, JF et al. (2000) Project GRAD: two-year outcomes of a randomized controlled physical activity intervention among young adults. Graduate ready for activity daily. Am J Prev Med 18, 2837.CrossRefGoogle ScholarPubMed
Cambien, F, Richard, JL, Ducimetiere, P et al. (1981) The Paris Cardiovascular Risk Factor Prevention Trial. Effects of two years of intervention in a population of young men. J Epidemiol Community Health 35, 9197.CrossRefGoogle Scholar
Carfora, V, Bertolotti, M & Catellani, P (2019) Informational and emotional daily messages to reduce red and processed meat consumption. Appetite 141, 104331.CrossRefGoogle ScholarPubMed
Chang, MW, Nitzke, S & Brown, R (2010) Design and outcomes of a Mothers in motion behavioral intervention pilot study. J Nutr Educ Behav 42, S1121.CrossRefGoogle ScholarPubMed
Chapman, J, Armitage, CJ & Norman, P (2009) Comparing implementation intention interventions in relation to young adults’ intake of fruit and vegetables. Psychol Health 24, 317332.CrossRefGoogle ScholarPubMed
Chiang, T-L, Chen, C, Hsu, C-H et al. (2019) Is the goal of 12,000 steps per day sufficient for improving body composition and metabolic syndrome? The necessity of combining exercise intensity: a randomized controlled trial. BMC Public Health 19, 1215.CrossRefGoogle ScholarPubMed
Conner, M, Rhodes, RE, Morris, B et al. (2011) Changing exercise through targeting affective or cognitive attitudes. Psychol Health 26, 133149.CrossRefGoogle ScholarPubMed
Cooke, PA, Tully, MA, Cupples, ME et al. (2013) A randomised control trial of experiential learning to promote physical activity. Educ for Primary Care 24, 427435.CrossRefGoogle ScholarPubMed
Cooke, R, Trebaczyk, H, Harris, P et al. (2014) Self-affirmation promotes physical activity. J Sport Exercise Psychol 36, 217223.CrossRefGoogle ScholarPubMed
Do, M, Kattelmann, K, Boeckner, L et al. (2008) Low-income young adults report increased variety in fruit and vegetable intake after a stage-tailored intervention. Nutr Res 28, 517522.CrossRefGoogle ScholarPubMed
Eiben, G & Lissner, L (2006) Health Hunters – an intervention to prevent overweight and obesity in young high-risk women. Int J Obes 30, 691696.CrossRefGoogle ScholarPubMed
Eisenberg, MH, Phillips, L, Fowler, L et al. (2017) The impact of E-diaries and accelerometers on young adults’ perceived and objectively assessed physical activity. Psychol Sport Exerc 30, 5563.CrossRefGoogle Scholar
Franko, DL, Cousineau, TM, Trant, M et al. (2008) Motivation, self-efficacy, physical activity and nutrition in college students: randomized controlled trial of an internet-based education program. Prev Med: Int J Devoted Pract Theor 47, 369377.CrossRefGoogle Scholar
Franko, DL, Jenkins, A & Rodgers, RF (2012) Toward reducing risk for eating disorders and obesity in latina college women. J Couns Dev 90, 298307.CrossRefGoogle Scholar
Godino, JG, Merchant, G, Norman, GJ et al. (2016) Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial. Lancet Diabetes Endocrinol 4, 747755.CrossRefGoogle ScholarPubMed
Gokee-LaRose, J, Gorin, AA & Wing, RR (2009) Behavioral self-regulation for weight loss in young adults: a randomized controlled trial. Int J Behav Nutr Phys Activity 6, 19.CrossRefGoogle ScholarPubMed
Goodman, S, Morrongiello, B & Meckling, K (2016) A randomized, controlled trial evaluating the efficacy of an online intervention targeting vitamin D intake, knowledge and status among young adults. Int J Behav Nutr Phys Activity 13, 116.CrossRefGoogle ScholarPubMed
Gow, RW, Trace, SE & Mazzeo, SE (2010) Preventing weight gain in first year college students: an online intervention to prevent the “freshman fifteen.” Eating Behav 11, 3339.CrossRefGoogle ScholarPubMed
Greene, GW, White, AA, Hoerr, SL et al. (2012) Impact of an online healthful eating and physical activity program for college students. Am J Health Promotion 27, E47E58.CrossRefGoogle ScholarPubMed
Halperin, DT, Laux, J, LeFranc-García, C et al. (2019) Findings from a randomized trial of weight gain prevention among overweight Puerto Rican young adults. J Nutr Educ Behav 51, 205216.CrossRefGoogle ScholarPubMed
Hebden, L, Cook, A, van der Ploeg, HP et al. (2014) A mobile health intervention for weight management among young adults: a pilot randomised controlled trial. J Hum Nutr Diet 27, 322332.CrossRefGoogle ScholarPubMed
Heeren, GA, Jemmott, JB, Marange, CS et al. (2017) Health-promotion intervention increases self-reported physical activity in Sub-Saharan African university students: a randomized controlled pilot study. Behav Med 19.Google ScholarPubMed
Hivert, M, Langlois, M, Berard, P et al. (2007) Prevention of weight gain in young adults through a seminar-based intervention program. Int J Obes 31, 12621269.CrossRefGoogle ScholarPubMed
Husband, CJ, Wharf-Higgins, J & Rhodes, RE (2019) A feasibility randomized trial of an identity-based physical activity intervention among university students. Health Psychol Behav Med 7, 128146.CrossRefGoogle ScholarPubMed
Hutchesson, M, Callister, R, Morgan, P et al. (2018) A targeted and tailored eHealth weight loss program for young women: the be positive be healthy randomized controlled trial. Healthcare 6, 39.CrossRefGoogle Scholar
Jakicic, JM, Davis, KK, Rogers, RJ et al. (2016) Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial. JAMA-J Am Med Assoc 316, 11611171.CrossRefGoogle ScholarPubMed
Jauho, AM, Pyky, R, Ahola, R et al. (2015) Effect of wrist-worn activity monitor feedback on physical activity behavior: a randomized controlled trial in Finnish young men. Prev Med Rep 2, 628634.CrossRefGoogle ScholarPubMed
Johnson, KC, Thomas, F, Richey, P et al. (2017) The primary results of the Treating Adult Smokers at Risk for Weight Gain with Interactive Technology (TARGIT) study. Obesity 25, 16911698.CrossRefGoogle ScholarPubMed
Jung, ME, Martin Ginis, KA, Phillips, SM et al. (2011) Increasing calcium intake in young women through gain-framed, targeted messages: a randomised controlled trial. Psychol Health 26, 531547.CrossRefGoogle ScholarPubMed
Kattelmann, KK, Bredbenner, CB, White, AA et al. (2014) The effects of Young Adults Eating and Active for Health (YEAH): a theory-based web-delivered intervention. J Nutr Educ Behavior 46, S27S41.CrossRefGoogle ScholarPubMed
Katterman, SN, Butryn, ML, Hood, MM et al. (2016) Daily weight monitoring as a method of weight gain prevention in healthy weight and overweight young adult women. J Health Psychol 21, 29552965.CrossRefGoogle ScholarPubMed
Katterman, SN, Goldstein, SP, Butryn, ML et al. (2014) Efficacy of an acceptance-based behavioral intervention for weight gain prevention in young adult women. J Contextual Behav Sci 3, 4550.CrossRefGoogle Scholar
Kendzierski, D, Ritter, RL, Stump, TK et al. (2015) The effectiveness of an implementation intentions intervention for fruit and vegetable consumption as moderated by self-schema status. Appetite 95, 228238.CrossRefGoogle ScholarPubMed
Kerr, DA, Harray, AJ, Pollard, CM et al. (2016) The connecting health and technology study: a 6-month randomized controlled trial to improve nutrition behaviours using a mobile food record and text messaging support in young adults. Int J Behav Nutr Phys Activity 13, 14.CrossRefGoogle ScholarPubMed
Kim, Y, Lumpkin, A, Lochbaum, M et al. (2018) Promoting physical activity using a wearable activity tracker in college students: a cluster randomized controlled trial. J Sports Sci 36, 18891896.CrossRefGoogle ScholarPubMed
Klem, ML, Viteri, JE & Wing, RR (2000) Primary prevention of weight gain for women aged 25–34: the acceptability of treatment formats. Int J Obes 24, 219225.CrossRefGoogle ScholarPubMed
Knauper, B, McCollam, A, Rosen-Brown, A et al. (2011) Fruitful plans: adding targeted mental imagery to implementation intentions increases fruit consumption. Psychol Health 26, 601617.CrossRefGoogle ScholarPubMed
Kothe, EJ & Mullan, BA (2014) A randomised controlled trial of a theory of planned behaviour to increase fruit and vegetable consumption. Fresh Facts Appetite 78, 6875.CrossRefGoogle ScholarPubMed
Kothe, EJ, Mullan, BA & Butow, P (2012) Promoting fruit and vegetable consumption. Testing an intervention based on the theory of planned behaviour. Appetite 58, 9971004.CrossRefGoogle ScholarPubMed
Kreausukon, P, Gellert, P, Lippke, S et al. (2012) Planning and self-efficacy can increase fruit and vegetable consumption: a randomized controlled trial. J Behav Med 35, 443451.CrossRefGoogle ScholarPubMed
Kypri, K & McAnally, HM (2005) Randomized controlled trial of a web-based primary care intervention for multiple health risk behaviors. Prev Med 41, 761766.CrossRefGoogle ScholarPubMed
LaChausse, RG (2012) My student body: effects of an internet-based prevention program to decrease obesity among college students. J Am Coll Health 60, 324330.CrossRefGoogle ScholarPubMed
LaRose, JG, Tate, DF, Gorin, AA et al. (2010) Preventing weight gain in young adults: a randomized controlled pilot study. Am J Prev Med 39, 6368.CrossRefGoogle Scholar
LaRose, JG, Tate, DF, Lanoye, A et al. (2019) Adapting evidence-based behavioral weight loss programs for emerging adults: a pilot randomized controlled trial. J Health Psychol 24, 870887.CrossRefGoogle ScholarPubMed
Laska, MN, Lytle, LA, Nanney, MS et al. (2016) Results of a 2-year randomized, controlled obesity prevention trial: Effects on diet, activity and sleep behaviors in an at-risk young adult population. Prev Med: Int J Devoted Pract Theor 89, 230236.CrossRefGoogle Scholar
LeCheminant, JD, Smith, JD, Covington, NK et al. (2011) Pedometer use in university freshmen: a randomized controlled pilot study. Am J Health Behav 35, 777784.CrossRefGoogle ScholarPubMed
Leinonen, AM, Pyky, R, Ahola, R et al. (2017) Feasibility of gamified mobile service aimed at physical activation in young men: population-based randomized controlled study (MOPO). JMIR mHealth uHealth 5, e146.CrossRefGoogle Scholar
Lhakhang, P, Godinho, C, Knoll, N et al. (2014) A brief intervention increases fruit and vegetable intake. A comparison of two intervention sequences. Appetite 82, 103110.CrossRefGoogle ScholarPubMed
Lua, PL, Wan Dali, WPE & Shahril, MR (2013) Multimodal nutrition education intervention: a cluster randomised controlled trial study on weight gain and physical activity pattern among university students in Terengganu, Malaysia. Malays J Nutr 19, 339352.Google Scholar
Lyzwinski, LN, Caffery, L, Bambling, M et al. (2019) The mindfulness app trial for weight, weight-related behaviors, and stress in University students: randomized controlled trial. JMIR mHealth uHealth 7, e12210.CrossRefGoogle ScholarPubMed
Maher, JP & Conroy, DE (2015) Habit strength moderates the effects of daily action planning prompts on physical activity but not sedentary behavior. J Sport Exerc Psychol 37, 97107.CrossRefGoogle Scholar
Martens, MP, Buscemi, J, Smith, AE et al. (2012) The short-term efficacy of a brief motivational intervention designed to increase physical activity among college students. J Phys Activity Health 9, 525532.CrossRefGoogle ScholarPubMed
Maselli, M, Gobbi, E & Carraro, A (2019) Effectiveness of individual counseling and activity monitors to promote physical activity among university students. J Sports Med Phys Fitness 59, 132140.Google ScholarPubMed
Matvienko, O, Lewis, DS & Schafer, E (2001) A college nutrition science course as an intervention to prevent weight gain in female college freshmen. J Nutr Educ 33, 95101.CrossRefGoogle ScholarPubMed
Memon, AR, Masood, T, Awan, WA et al. (2018) The effectiveness of an incentivized physical activity programme (Active Student) among female medical students in Pakistan: a randomized controlled trial. J Pak Med Assoc 68, 14381445.Google ScholarPubMed
Meng, J, Peng, W, Shin, SY et al. (2017) Online self-tracking groups to increase fruit and vegetable intake: a small-scale study on mechanisms of group effect on behavior change. J Med Internet Res 19, e63.CrossRefGoogle Scholar
Middleton, KR & Perri, MG (2014) A randomized trial investigating the effect of a brief lifestyle intervention on freshman-year weight gain. J Am Coll Health 62, 101109.CrossRefGoogle ScholarPubMed
Napolitano, MA, Hayes, S, Bennett, GG et al. (2013) Using Facebook and text messaging to deliver a weight loss program to college students. Obesity 21, 2531.CrossRefGoogle ScholarPubMed
Nix, E & Wengreen, HJ (2017) Social approval bias in self-reported fruit and vegetable intake after presentation of a normative message in college students. Appetite 116, 552558.CrossRefGoogle ScholarPubMed
O’Brien, LM & Palfai, TP (2016) Efficacy of a brief web-based intervention with and without SMS to enhance healthy eating behaviors among university students. Eating Behav 23, 104109.CrossRefGoogle ScholarPubMed
Ohtsuki, M, Shibata, K, Fukuwatari, T et al. (2018) Randomized controlled trial of educational intervention to increase consumption of vegetables by Japanese university students. Health Educ 118, 290303.CrossRefGoogle Scholar
Ornes, L & Ransdell, LB (2007) Web-based physical activity intervention for college-aged women. Int Electron J Health Edu 10, 126137.Google Scholar
Ortega, RM, Rodriguez-Rodriguez, E, Aparicio, A et al. (2006) Responses to two weight-loss programs based on approximating the diet to the ideal: differences associated with increased cereal or vegetable consumption. Int J Vitamin Nutr Res 76, 367376.CrossRefGoogle ScholarPubMed
Park, A, Nitzke, S, Kritsch, K et al. (2008) Internet-based interventions have potential to affect short-term mediators and indicators of dietary behavior of young adults. J Nutr Educ Behav 40, 288297.CrossRefGoogle ScholarPubMed
Pearson, ES, Irwin, JD, Morrow, D et al. (2013) The CHANGE program: comparing an interactive vs. prescriptive approach to self-management among university students with obesity. Can J Diabetes 37, 411.CrossRefGoogle ScholarPubMed
Pellitteri, K, Huberty, J, Ehlers, D et al. (2017) Fit minded college edition pilot study: can a magazine-based discussion group improve physical activity in female college freshmen? J Public Health Manage Pract 23, e10e19.CrossRefGoogle ScholarPubMed
Pfeffer, I & Strobach, T (2019) Effects of a planning intervention on physical activity behavior in an RCT: intention strength as moderator and action planning, coping planning, and coping self-efficacy as mediators. Sport Exerc Perform Psychol 8, 192209.Google Scholar
Phimarn, W, Paktipat, P, Pansiri, K et al. (2017) Effect of weight control counselling in overweight and obese young adults. Indian J Pharm Sci 79, 3541.CrossRefGoogle Scholar
Pope, L & Harvey, J (2014) The efficacy of incentives to motivate continued fitness-center attendance in college first-year students: a randomized controlled trial. J Am Coll Health 62, 8190.CrossRefGoogle ScholarPubMed
Pope, ZC, Barr-Anderson, DJ, Lewis, BA et al. (2019) Use of wearable technology and social media to improve physical activity and dietary behaviors among college students: a 12-Week randomized pilot study. Int J Environ Res Public Health 16, 3579.CrossRefGoogle ScholarPubMed
Richards, A, Kattelmann, KK & Ren, C (2006) Motivating 18- to 24-year-olds to increase their fruit and vegetable consumption. J Am Dietetic Assoc 106, 14051411.CrossRefGoogle ScholarPubMed
Rompotis, CJ, Grove, JR & Byrne, SM (2014) Benefits of habit-based informational interventions: a randomised controlled trial of fruit and vegetable consumption. Aust NZ J Public Health 38, 247252.CrossRefGoogle ScholarPubMed
Rote, AE, Klos, LA, Brondino, MJ et al. (2015) The efficacy of a walking intervention using social media to increase physical activity: a randomized trial. J Phys Activity Health 12, S18S25.CrossRefGoogle ScholarPubMed
Sandrick, J, Tracy, D, Eliasson, A et al. (2017) Effect of a counseling session bolstered by text messaging on self-selected health behaviors in college students: a preliminary randomized controlled trial. JMIR mHealth uHealth 5, e67.CrossRefGoogle ScholarPubMed
Schweitzer, AL, Ross, JT, Klein, CJ et al. (2016) An electronic wellness program to improve diet and exercise in college students: a pilot study. Jmir Res Protocols 5, e4855.CrossRefGoogle ScholarPubMed
Share, BL, Naughton, GA, Obert, P et al. (2015) Effects of a multi-disciplinary lifestyle intervention on cardiometabolic risk factors in young women with abdominal obesity: a randomised controlled trial. PLoS ONE [Electronic Resource] 10, e0130270.CrossRefGoogle ScholarPubMed
Sharp, P & Caperchione, C (2016) The effects of a pedometer-based intervention on first-year university students: a randomized control trial. J Am Coll Health 64, 630638.CrossRefGoogle ScholarPubMed
Simons, D, De Bourdeaudhuij, I, Clarys, P et al. (2018) Effect and process evaluation of a smartphone app to promote an active lifestyle in lower educated working young adults: cluster randomized controlled trial. JMIR mHealth uHealth 6, e10003.CrossRefGoogle ScholarPubMed
Sriramatr, S, Berry, TR & Spence, JC (2014) An Internet-based intervention for promoting and maintaining physical activity: a randomized controlled trial. Am J Health Behav 38, 430439.CrossRefGoogle ScholarPubMed
Stephens, JD, Yager, AM & Allen, J (2017) Smartphone technology and text messaging for weight loss in young adults: a randomized controlled trial. J Cardiovasc Nurs 32, 3946.CrossRefGoogle ScholarPubMed
Stice, E, Rohde, P, Shaw, H et al. (2013) Efficacy trial of a selective prevention program targeting both eating disorders and obesity among female college students: 1- and 2-year follow-up effects. J Consult Clin Psychol 81, 183189.CrossRefGoogle ScholarPubMed
Strohacker, K, Galarraga, O, Emerson, J et al. (2015) Impact of small monetary incentives on exercise in university students. Am J Health Behav 39, 779786.CrossRefGoogle ScholarPubMed
Svetkey, LP, Batch, BC, Lin, PH et al. (2015) Cell phone intervention for you (CITY): a randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology. Obesity 23, 21332141.CrossRefGoogle ScholarPubMed
Tavakoli, HR, Dini-Talatappeh, H, Rahmati-Najarkolaei, F et al. (2016) Efficacy of HBM-based dietary education intervention on knowledge, attitude, and behavior in medical students. Iranian Red Crescent Med J 18, e23584.CrossRefGoogle ScholarPubMed
Tobias, LL & MacDonald, ML (1977) Internal locus of control and weight loss: an insufficient condition. J Consult Clin Psychol 45, 647653.CrossRefGoogle Scholar
Uglem, S, Stea, TH, Kjollesdal, MKR et al. (2013) A nutrition intervention with a main focus on vegetables and bread consumption among young men in the Norwegian National Guard. Food Nutr Res 57, 21036.CrossRefGoogle ScholarPubMed
Valve, P, Lehtinen-Jacks, S, Eriksson, T et al. (2013) LINDA: a solution-focused low-intensity intervention aimed at improving health behaviors of young females: a cluster-randomized controlled trial. BMC Public Health 13, 1044.CrossRefGoogle ScholarPubMed
Walsh, JC, Corbett, T, Hogan, M et al. (2016) An mHealth intervention using a smartphone app to increase walking behavior in young adults: a pilot study. JMIR mHealth uHealth 4, e109.CrossRefGoogle ScholarPubMed
Weinstock, J, Capizzi, J, Weber, SM et al. (2014) Exercise as an intervention for sedentary hazardous drinking college students: a pilot study. Ment Health Phys Activity 7, 5562.CrossRefGoogle ScholarPubMed
Weinstock, J, Petry, NM, Pescatello, LS et al. (2016) Sedentary college student drinkers can start exercising and reduce drinking after intervention. Psychol Addict Behav 30, 791801.CrossRefGoogle ScholarPubMed
Werch, CE, Moore, MJ, Bian, H et al. (2010) Are effects from a brief multiple behavior intervention for college students sustained over time? Prev Med 50, 3034.CrossRefGoogle ScholarPubMed
Whatnall, MC, Patterson, AJ, Chiu, S et al. (2019) Feasibility and preliminary efficacy of the Eating Advice to Students (EATS) brief web-based nutrition intervention for young adult university students: a pilot randomized controlled trial. Nutrients 11, 905.CrossRefGoogle ScholarPubMed
Williams, DR & Lewis, NM (2002) Effectiveness of nutrition counseling in young adult males. Nutr Res 22, 911917.CrossRefGoogle Scholar
Wing, RR, Tate, DF, Espeland, MA et al. (2016) Innovative self-regulation strategies to reduce weight gain in young adults: the Study of Novel Approaches to Weight Gain Prevention (SNAP) randomized clinical trial. JAMA Internal Med 176, 755762.CrossRefGoogle Scholar
Zhang, Y & Cooke, R (2012) Using a combined motivational and volitional intervention to promote exercise and healthy dietary behaviour among undergraduates. Diabetes Res Clin Pract 95, 215223.CrossRefGoogle ScholarPubMed
Partridge, SR, Balestracci, K, Wong, ATY et al. (2015) Effective strategies to recruit young adults into the TXT2BFiT mHealth randomized controlled trial for weight gain prevention. Jmir Res Protocols 4, e4268.CrossRefGoogle ScholarPubMed
Tate, DF, LaRose, JG, Griffin, LP et al. (2014) Recruitment of young adults into a randomized controlled trial of weight gain prevention: message development, methods, and cost. Trials [Electronic Resource] 15, 326.Google ScholarPubMed
Kreausukon, P, Gellert, P, Lippke, S et al. (2012) Planning and self-efficacy can increase fruit and vegetable consumption: a randomized controlled trial. J Behav Med 35, 443451.CrossRefGoogle ScholarPubMed
Walters, SJ, dos Anjos Henriques-Cadby, IB, Bortolami, O et al. (2017) Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme. BMJ Open 7, e015276.CrossRefGoogle ScholarPubMed
Cooke, R & Jones, A (2017) Recruiting adult participants to physical activity intervention studies using sport: a systematic review. BMJ Open Sport & Amp;Amp; Exerc Med 3, e000231.Google ScholarPubMed
Foster, CE, Brennan, G, Matthews, A et al. (2011) Recruiting participants to walking intervention studies: a systematic review. Int J Behav Nutr Phys Act 8, 137.CrossRefGoogle ScholarPubMed
Pew Research Center (2019) Internet/Broadband Fact Sheet. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/ (accessed January 2021).Google Scholar
Liu, Y, Pencheon, E, Hunter, RM et al. (2018) Recruitment and retention strategies in mental health trials: a systematic review. PLoS One 13, e0203127.CrossRefGoogle ScholarPubMed
Frampton, GK, Shepherd, J, Pickett, K et al. (2020) Digital tools for the recruitment and retention of participants in randomised controlled trials: a systematic map. Trials 21, 478.CrossRefGoogle ScholarPubMed
Kearney, A, Harman, NL, Rosala-Hallas, A et al. (2018) Development of an online resource for recruitment research in clinical trials to organise and map current literature. Clin Trials 15, 533542.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Eligibility criteria for participants, interventions, comparisons, outcomes and study design (PICOS)

Figure 1

Fig. 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of included studies

Figure 2

Table 2 Summary of study characteristics in 107 studies of nutrition, physical activity and obesity interventions in young adults

Figure 3

Table 3 Summary of recruitment details of 107 studies of nutrition, physical activity and obesity interventions in young adults, by intervention focus

Figure 4

Table 4 Summary of recruitment strategies of 107 studies of nutrition, physical activity and obesity interventions in young adults

Figure 5

Fig. 2 Success* of recruitment strategies used across forty-nine studies of nutrition, physical activity and/or obesity interventions in young adults. *Recruitment was considered successful if the pre-determined goal sample size was met

Figure 6

Fig. 3 Recruitment success* (%) by number of recruitment strategies used. *Recruitment was considered successful if the pre-determined goal sample size was met

Figure 7

Table 5 Efficiency of recruitment strategies used across forty-eight studies of nutrition, physical activity and/or obesity interventions in young adults

Figure 8

Table 6 Summary of retention details of 107 studies of nutrition, physical activity and obesity interventions in young adults, by intervention focus

Figure 9

Fig. 4 Adequacy* of retention strategies used across seventy-four studies of nutrition, physical activity and/or obesity interventions in young adults. *Retention was considered adequate if retention was ≥80% for ≤6-month follow-up or ≥70% for >6-month follow-up

Figure 10

Table 7 Key reporting information for recruitment in trials

Figure 11

Table 8 Key reporting information for retention in trials

Supplementary material: File

Whatnall et al. supplementary material

Whatnall et al. supplementary material

Download Whatnall et al. supplementary material(File)
File 195 KB