Health-promotion interventions targeting multiple behaviors: A meta-analytic review of general and behavior-specific processes of change.

Although health-promotion interventions that recommend changes across multiple behavioral domains are a newer alternative to single-behavior interventions, their general efficacy and their mechanisms of change have not been fully ascertained. This comprehensive meta-analysis (6,878 effect sizes from 803 independent samples from 364 research reports, N = 186,729 participants) examined the association between the number of behavioral recommendations in multiple-behavior interventions and behavioral and clinical change across eight domains (i.e., diet, smoking, exercise, HIV [Human Immunodeficiency Virus] prevention, HIV testing, HIV treatment, alcohol use, and substance use). Results showed a positive, linear effect of the number of behavioral recommendations associated with behavioral and clinical change across all domains, although approximately 87% of the samples included between 0 and 4 behavioral recommendations. This linear relation was mediated by improvements in the psychological well-being of intervention recipients and, in several domains (i.e., HIV, alcohol use, and drug use), suggested behavioral cuing. However, changes in information, motivation, and behavioral skills did not mediate the impact of the number of recommendations on behavioral and clinical change. The implications of these findings for theory and future intervention design are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Priming behavior: A meta-analysis of the effects of behavioral and nonbehavioral primes on overt behavioral outcomes.

Past meta-analyses of the effects of priming on overt behavior have not examined whether the effects and processes of priming behavioral or nonbehavioral concepts (e.g., priming action through the word go and priming religion through the word church) differ, even though these possibilities are important to our understanding of concept accessibility and behavior. Hence, we meta-analyzed 351 studies (224 reports and 862 effect sizes) involving incidental presentation of behavioral or nonbehavioral primes, a neutral control group, and at least one behavioral outcome. Our random-effects analyses, which used the correlated and hierarchical effects model with robust variance estimation (Pustejovsky & Tipton, 2021; Tanner-Smith et al., 2016), revealed a moderate priming effect (d = 0.37) that remained stable across behavioral and nonbehavioral primes and across different methodological procedures and adjustments for possible inclusion/publication biases (e.g., sensitivity analyses from Mathur & VanderWeele, 2020; sensitivity analyses from Vevea & Woods, 2005). Although the findings suggest that associative processes explain both the effects of behavioral and nonbehavioral primes, lowering the value of a behavior weakened the effect only when the primes were behavioral. These findings support the possibility that even though both types of primes activate associations that promote behavior, behavioral (vs. nonbehavioral) primes may provide a greater opportunity for goals to control the effect of the primes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.

OBJECTIVES: Social media messages have been increasingly used in health campaigns about prevention, testing, and treatment of HIV. We identified factors leading to the retransmission of messages from expert social media accounts to create data-driven recommendations for online HIV messaging. DESIGN AND METHODS: We sampled 20 201 HIV-related tweets (posted between 2010 and 2017) from 37 HIV experts. Potential predictors of retransmission were identified based on prior literature and machine learning methods, and were subsequently analyzed using multilevel negative binomial models. RESULTS: Fear-related language, longer messages, and including images (e.g. photos, gif, or videos) were the strongest predictors of retweet counts. These findings were similar for messages authored by HIV experts, and also messages retransmitted by experts, but created by nonexperts (e.g. celebrities or politicians). CONCLUSIONS: Fear appeals affect how much HIV messages spread on Twitter, as do structural characteristics, like the length of the tweet and inclusion of images. A set of five data-driven recommendations for increasing message spread is derived and discussed in the context of current centers for disease control and prevention social media guidelines.