Comparing recruitment strategies for a digital smoking cessation intervention: Technology-assisted peer recruitment, social media, ResearchMatch, and smokefree.gov

Development and validation of an 8-item version of the Real Relationship Inventory-Client form

OBJECTIVE

To develop and validate a very brief version of the 24-item Real Relationship Inventory-Client (RRI-C) form.

METHOD

Two independent samples of individual psychotherapy patients (Nsample1 = 700, Nsample2 = 434) completed the RRI-C along with other measures. Psychometric scale shortening involved exploratory factor analysis, item response theory analysis, confirmatory factor analysis (CFA), and multigroup CFA. Reliability and convergent and discriminant validity of the scale and subscales were also assessed.

RESULTS

The 8-item RRI-C (RRI-C-SF) preserves the two-factor structure: Genuineness (k = 4, α = .86) and Realism (k = 4, α = .87), which were correlated at r = .74. CFA provided the following fit indices for the bifactor model: X2/df = 2.16, CFI = .99, TLI = .96, RMSEA = .07, and SRMR = .03. Multigroup CFA showed that the RRI-C-SF was invariant across in-person and remote session formats. The RRI-C-SF demonstrated high reliability (α = .91); high correlation with the full-length scale (r = .96); and excellent convergent and discriminant validity with measures of other elements of the therapeutic relationship, personality characteristics, current mental health state, and demographic-clinical variables. Clinical change benchmarks were calculated to serve as valuable tools for both research and clinical practice.

CONCLUSION

The RRI-C-SF is a reliable measure that can be used for both research and clinical purposes. It enables a nuanced assessment of the genuineness and the realism dimensions of the real relationship.

Recruiting families using social media versus pediatric obesity clinics: A secondary analysis of the Aim2Be RCT

BACKGROUND

Recruitment of participants continues to be a challenge that researchers must overcome to yield successful study results. Over the past decade, there has been a dramatic increase in the use of social media platforms to recruit research participants. We conducted a secondary analysis of the Aim2Be randomized controlled trial (RCT) to examine if there was variability between participants recruited via social media versus pediatric obesity clinics.

METHODS

Parents and their children living with overweight or obesity were recruited through social media (i.e., Facebook advertisements) (n = 119) or pediatric obesity management clinics (n = 95) to participate in the Aim2Be RCT. We compared recruitment costs, recruitment rate, participant retention, intervention engagement, obesity-related risk factors, and behavioral habits.

RESULTS

Facebook recruitment resulted in more participant contacts, but higher attrition during 'high effort' stages of the recruitment process. Group differences emerged regarding costs (Facebook: $407 versus clinics: $699). There were no group differences in participant retention or intervention engagement. Families recruited from Facebook were younger parents (42.6 versus 46.0 years; p < 0.001) and children (12.2 versus 13.9 years; p < 0.001), a higher percentage male children, and fewer had previously participated in a pediatric weight management program. Parents recruited from Facebook self-reported greater screen time for themselves, and their children reported lower physical activity levels and higher caloric and sugar intake.

CONCLUSIONS

Social media and clinical site recruitment are complementary strategies that appear to draw in families with different profiles, but regardless of how they were recruited, all families had the potential to benefit from pediatric obesity management.

Cost-effectiveness of social media advertising as a recruitment tool: A systematic review and meta-analysis

Background

Recruitment of study participants is challenging and can incur significant costs. Social media advertising is a promising method for recruiting clinical studies and may improve cost efficiency by targeting populations likely to match a study's qualifications. Prior systematic reviews of social media as a recruitment tool have been favourable, however, there are no meta-analyses of its cost-effectiveness.

Methods

Studies evaluating recruitment costs through social media and non-social media methods were identified on MEDLINE and EMBASE. Articles were screened through a two-step process in accordance with PRISMA guidelines. Cost data were extracted from selected articles and meta-analyzed using the Mantel-Haenszel method. The primary outcome was the relative cost-effectiveness of social media compared to non-social media recruitment, defined as the odds ratio of recruiting a participant per US dollar spent. The secondary outcome was the cost-effectiveness of social media recruitment compared to other online recruitment methods only.

Results

In total, 23 studies were included in the meta-analysis. The odds ratio of recruiting a participant through social media advertising compared to non-social media methods per dollar spent was 1.97 [95% CI 1.24-3.00, P = 0.004]. The odds ratio of recruiting a participant through social media compared to other online methods only was 1.66 [95% CI 1.02-2.72, P = 0.04].

Conclusions

Social media advertising may be more cost-effective than other methods of recruitment, however, the magnitude of cost-effectiveness is highly variable between studies. There are limited data on newer social media platforms and on difficult-to-reach populations such as non-English speakers or older individuals.

Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation: A Randomized Clinical Trial

IMPORTANCE

Novel data science and marketing methods of smoking-cessation intervention have not been adequately evaluated.

OBJECTIVE

To compare machine learning recommender (ML recommender) computer tailoring of motivational text messages vs a standard motivational text-based intervention (standard messaging) and a viral peer-recruitment tool kit (viral tool kit) for recruiting friends and family vs no tool kit in a smoking-cessation intervention.

DESIGN, SETTING, AND PARTICIPANTS

This 2 ×2 factorial randomized clinical trial with partial allocation, conducted between July 2017 and September 2019 within an online tobacco intervention, recruited current smokers aged 18 years and older who spoke English from the US via the internet and peer referral. Data were analyzed from March through May 2022.

INTERVENTIONS

Participants registering for the online intervention were randomly assigned to the ML recommender or standard messaging groups followed by partially random allocation to access to viral tool kit or no viral tool kit groups. The ML recommender provided ongoing refinement of message selection based on user feedback and comparison with a growing database of other users, while the standard system selected messages based on participant baseline readiness to quit.

MAIN OUTCOMES AND MEASURES

Our primary outcome was self-reported 7-day point prevalence smoking cessation at 6 months.

RESULTS

Of 1487 participants who smoked (444 aged 19-34 years [29.9%], 508 aged 35-54 years [34.1%], 535 aged ≥55 years [36.0%]; 1101 [74.0%] females; 189 Black [12.7%] and 1101 White [78.5%]; 106 Hispanic [7.1%]), 741 individuals were randomly assigned to the ML recommender group and 746 individuals to the standard messaging group; viral tool kit access was provided to 745 participants, and 742 participants received no such access. There was no significant difference in 6-month smoking cessation between ML recommender (146 of 412 participants [35.4%] with outcome data) and standard messaging (156 of 389 participants [40.1%] with outcome data) groups (adjusted odds ratio, 0.81; 95% CI, 0.61-1.08). Smoking cessation was significantly higher in viral tool kit (177 of 395 participants [44.8%] with outcome data) vs no viral tool kit (125 of 406 participants [30.8%] with outcome data) groups (adjusted odds ratio, 1.48; 95% CI, 1.11-1.98).

CONCLUSIONS AND RELEVANCE

In this study, machine learning-based selection did not improve performance compared with standard message selection, while viral marketing did improve cessation outcomes. These results suggest that in addition to increasing dissemination, viral recruitment may have important implications for improving effectiveness of smoking-cessation interventions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03224520.

General Practice and Digital Methods to Recruit Stroke Survivors to a Clinical Mobility Study: Comparative Analysis

BACKGROUND

Participant recruitment remains a barrier to conducting clinical research. The disabling nature of a stroke, which often includes functional and cognitive impairments, and the acute stage of illness at which patients are appropriate for many trials make recruiting patients particularly complex and challenging. In addition, people aged 65 years and older, which includes most stroke survivors, have been identified as a group that is difficult to reach and is commonly underrepresented in health research, particularly clinical trials. Digital media may provide effective tools to support enrollment efforts of stroke survivors in clinical trials.

OBJECTIVE

The objective of this study was to compare the effectiveness of general practice (traditional) and digital (online) methods of recruiting stroke survivors to a clinical mobility study.

METHODS

Recruitment for a clinical mobility study began in July 2018. Eligible study participants included individuals 18 years and older who had a single stroke and were currently ambulatory in the community. General recruiting practice included calling individuals listed in a stroke registry, contacting local physical therapists, and placing study flyers throughout a university campus. Between May 21, 2019, and June 26, 2019, the study was also promoted digitally using the social network Facebook and the search engine marketing tool Google AdWords. The recruitment advertisements (ads) included a link to the study page to which users who clicked were referred. Primary outcomes of interest for both general practice and digital methods included recruitment speed (enrollment rate) and sample characteristics. The data were analyzed using the Lilliefors test, the Welch two-sample t test, and the Mann-Whitney test. Significance was set at P=.05. All statistical analyses were performed in MATLAB 2019b.

RESULTS

Our results indicate that digital recruitment methods can address recruitment challenges regarding stroke survivors. Digital recruitment methods allowed us to enroll study participants at a faster rate (1.8 participants/week) compared to using general practice methods (0.57 participants/week). Our findings also demonstrate that digital and general recruitment practices can achieve an equivalent level of sample representativeness. The characteristics of the enrolled stroke survivors did not differ significantly by age (P=.95) or clinical scores (P=.22; P=.82). Comparing the cost-effectiveness of Facebook and Google, we found that the use of Facebook resulted in a lower cost per click and cost per enrollee per ad.

CONCLUSIONS

Digital recruitment can be used to expedite participant recruitment of stroke survivors compared to more traditional recruitment practices, while also achieving equivalent sample representativeness. Both general practice and digital recruitment methods will be important to the successful recruitment of stroke survivors. Future studies could focus on testing the effectiveness of additional general practice and digital media approaches and include robust cost-effectiveness analyses. Examining the effectiveness of different messaging and visual approaches tailored to culturally diverse and underrepresented target subgroups could provide further data to move toward evidence-based recruitment strategies.