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To test the effect of news media exposure to contradictory information about carbohydrates and dietary fats on levels of confusion, nutritional backlash and dietary intentions. We conducted an online survey experiment between 11 and 28 February 2018, randomizing participants to one of six experimental conditions. Two 'contradictory information' conditions asked participants to read one news article on the risks of a low-carbohydrate diet and one article on the risks of a low-fat diet. Two 'convergent information' conditions asked participants to read two articles with similar information on the risks of one of these two diets. A fifth 'established health recommendations' control condition asked participants to read two articles on the harms of smoking and sun exposure. A sixth 'no information' condition served as a second control group. We used general linear models to test hypotheses on the effects of exposure on confusion, nutritional backlash and dietary intentions. USA. Adults (n 901) registered with Amazon's Mechanical Turk (M-Turk). Exposure to contradictory information about carbohydrates and dietary fats increased confusion and nutritional backlash compared with exposure to established health recommendations for non-dietary behaviours and a no-exposure control. Exposure to contradictory information also increased confusion compared with exposure to consistent nutrition information regarding carbohydrates and dietary fats. Contradictory nutrition information in the news media can negatively affect consumers' attitudes, beliefs and behavioural intentions. Dietary debates that play out in the media may adversely influence both short-term dietary decisions and future efforts to communicate about unrelated nutrition issues.

Misinformation about vaccines is a significant barrier to public health, fueling hesitancy and resistance. Generative AI offers a scalable tool for assisting public health communicators in crafting targeted correction messages tailored to audience characteristics. This study investigates the effectiveness of AI-generated messages targeting extraversion and pseudoscientific beliefs compared to high-quality generic and non-vaccine-related messages. In a between-subjects experiment, 1435 U.S. adults were randomly assigned to one of four conditions: control, generic correction, extraversion-targeting correction, or pseudoscientific-belief-targeting correction. Participants rated their agreement with vaccine misbelief statements before and after exposure to a correction message. AI was used to generate the targeted correction messages, while the generic and control messages were sourced from real-world examples. Extraversion-targeting messages significantly reduced vaccine misbeliefs, performing comparably to high-quality generic messages, particularly among participants with higher extraversion levels. However, these effects did not extend to general vaccination attitudes. Pseudoscientific-belief-targeting messages were ineffective and, in some cases, backfired, reinforcing negative attitudes among individuals with strong pseudoscientific beliefs. This study demonstrates the potential of AI-assisted message generation for crafting effective correction messages, particularly when targeting personality traits like extraversion. However, the findings suggest that certain AI-generated messages may be less effective or even counterproductive when targeting entrenched beliefs, underscoring the need for human oversight in refining AI-generated messages. Future research should explore additional audience characteristics and optimize human-AI collaboration to enhance the effectiveness of AI-generated correction messages in public health communication. •AI-generated correction messages effectively reduce vaccine misbeliefs in targeted groups.•Extraversion-targeting messages perform well, matching high-quality generic corrections.•Pseudoscientific-belief targeting can backfire among those with strong beliefs.•Generative AI shows promise but requires careful design to avoid defensive reactions.

Despite extensive research on framing effects in public health communication, there is still a lack of knowledge on how gain frames v. loss frames can encourage healthy eating behaviour among children. Drawing on the Prospect Theory as well as on the Reactivity of Embedded Food Cues in Advertising Model, an experiment exposed children to an audio-visual cartoon movie with gain-framed nutritional messages about eating fruit (gain condition), loss-framed nutritional messages about eating fruit (loss condition) or a message without any food (control group). Children's fruit intake was measured as the dependent variable. Children's awareness of gain- and loss-framed arguments was treated as mediators, while children's age and parents' self-reported food-related mediation styles were modelled as moderators. Vienna, Austria, in 2018. Children aged 6-10 years (N 161). Children in the gain frame group were more aware of gain-framed arguments, and children in the loss frame group were more aware of loss-framed arguments than those in the control group. However, only the mediator awareness of gain-framed arguments increased fruit intake. Additionally, there was a direct effect of the gain-framed message on fruit intake compared to the control group. The loss condition did not reveal such an effect. Neither parent's food-related mediation styles nor children's age moderated those results. Gain-framing seems to be more effective in influencing children's healthy food choices compared to loss-framing. Implications for health communication strategies aimed at children are discussed.

Video played an important role in health communication throughout the COVID-19 pandemic. It was used to communicate pandemic information to the public, with a variety of formats, presenters, and topics. Evidence regarding the effectiveness of video features is available, while how individual characteristics of recipients influence communication comprehension is still limited. This study aimed to test 6 individual characteristics and assess their effect on the comprehension of pandemic video communication. Short health communication videos were presented to a large sample of subjects, receiving questionnaire responses from 1194 participants. Individual characteristics consisted of age, sex, living area, education level, income level, and belief in science. Communication comprehension consisted of both perceived and objective comprehension. The data were analyzed by multiple linear regression. Age had a negative effect on both perceived and objective comprehension-age was negatively associated with comprehension. There were sex differences, with higher perceived comprehension and lower objective comprehension among female than male individuals. Living in an urban or a rural area had no significant effect (all P>.05). The level of education and income had a positive effect on both subjective and objective comprehension. Finally, the belief in science had a positive effect on perceived comprehension (P<.001) but did not have a statistically significant effect on objective comprehension (P=.87). The main differences between those who think they understand pandemic communication and those who comprehend it better are sex (female individuals have a higher perception of having comprehended, while male individuals have higher levels of objective comprehension) and belief in science (higher belief in science leads to higher perceived comprehension, while it does not have any impact on objectively understanding the message conveyed).

Risk communication tools based on epidemiological models can help inform decision-making, but must be responsive to health literacy needs to be effective. To facilitate informed choice about risks and benefits of COVID-19 vaccination, an epidemiological model called the COVID-19 Risk Calculator (CoRiCal) tool was developed by a multi-disciplinary team. This paper demonstrates how to use health literacy principles to improve consumer understanding of COVID-19 and vaccine effects, using a range of methods that could be applied to any health emergency. Stage 1: Health literacy optimisation and user testing to reduce improve understandability (n = 19). Stage 2: Experiments to explore the effect of risk communication formats on perceived understanding including probability, graphs, evaluative labels and comparison risks (n = 207). Stage 3: Randomised controlled trial (n = 2005) with 4 arms: 1) standard government information; 2) standard CoRiCal output based on bar graphs; 3) animation explaining bar graphs in “x per million” format; 4) animation explaining bar graphs in “1 in x chance” format. The primary outcome was knowledge about COVID-19 risk. Stage 1 reduced the complexity of the text and graphs. Stage 2 showed that different risk communication formats change perceived understanding, with a preference for evaluative labels across 2 experiments and some indication people with lower health literacy had a greater preference for bar graphs. Stage 3 showed both animations increased knowledge compared to standard government information. There was no difference between the probability formats, or by health literacy level. The results showed that simple explanations of complex epidemiological models improve knowledge about COVID-19 and vaccination. This demonstrates how health literacy design principles and short animations can be used to support informed decision making about health emergencies. •People with low health literacy have worse knowledge and health outcomes.•Risk communication tools can simplify complex vaccination evidence.•Health literacy design principles can be used to address consumer needs.•Short animations using multiple risk formats can improve vaccine knowledge.

Purpose Pictorial health warnings on cigarette packages are a prominent and effective means of communicating the risks of smoking; however, there is little research on effective types of message content and socio-demographic effects. This study tested message themes and content of pictorial warnings in Mexico. Methods Face-to-face surveys were conducted with 544 adult smokers and 528 youth in Mexico City. Participants were randomized to view 5-7 warnings for two of 15 different health effects. Warnings for each health effect included a text-only warning and pictorial warnings with various themes: \"graphic\" health effects, \"lived experience\", symbolic images, and testimonials. Results Pictorial health warnings were rated as more effective than text-only warnings. Pictorial warnings featuring \"graphic\" depictions of disease were significantly more effective than symbolic images or experiences of human suffering. Adding testimonial information to warnings increased perceived effectiveness. Adults who were female, older, had lower education, and intended to quit smoking rated warnings as more effective, although the magnitude of these differences was modest. Few interactions were observed between socio-demographics and message theme. Conclusions Graphic depictions of disease were perceived by youth and adults as the most effective warning theme. Perceptions of warnings were generally similar across socio-demographic groups.

Objectives. We examined the effects of messages describing consequences of childhood obesity on public attitudes about obesity prevention policy. Methods. We collected data from 2 nationally representative Internet-based surveys. First, respondents (n = 444) evaluated the strength of 11 messages about obesity’s consequences as reasons for government action. Second, we randomly assigned respondents (n = 2494) to a control group or to treatment groups shown messages about obesity consequences. We compared groups’ attitudes toward obesity prevention, stratified by political ideology. Results. Respondents perceived a message about the health consequences of childhood obesity as the strongest rationale for government action; messages about military readiness, bullying, and health care costs were rated particularly strong by conservatives, moderates, and liberals, respectively. A message identifying the consequences of obesity on military readiness increased conservatives’ perceptions of seriousness, endorsement of responsibility beyond the individual, and policy support, compared with a control condition. Conclusions. The public considers several consequences of childhood obesity as strong justification for obesity prevention policy. Activating new or unexpected values in framing a health message could raise the health issue’s salience for particular subgroups of the public.

Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machine learning systems (\"recommender systems\") are used to select messages by combining the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles. However, this approach has not been adequately tested for CTHC. Our aim was to compare, in a randomized experiment, a standard, evidence-based, rule-based CTHC (standard CTHC) to a novel machine learning CTHC: Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT). We hypothesized that PERSPeCT will select messages of higher influence than our standard CTHC system. This standard CTHC was proven effective in motivating smoking cessation in a prior randomized trial of 900 smokers (OR 1.70, 95% CI 1.03-2.81). PERSPeCT is an innovative hybrid machine learning recommender system that selects and sends motivational messages using algorithms that learn from message ratings from 846 previous participants (explicit feedback), and the prior explicit ratings of each individual participant. Current smokers (N=120) aged 18 years or older, English speaking, with Internet access were eligible to participate. These smokers were randomized to receive either PERSPeCT (intervention, n=74) or standard CTHC tailored messages (n=46). The study was conducted between October 2014 and January 2015. By randomization, we compared daily message ratings (mean of smoker ratings each day). At 30 days, we assessed the intervention's perceived influence, 30-day cessation, and changes in readiness to quit from baseline. The proportion of days when smokers agreed/strongly agreed (daily rating ≥4) that the messages influenced them to quit was significantly higher for PERSPeCT (73%, 23/30) than standard CTHC (44%, 14/30, P=.02). Among less educated smokers (n=49), this difference was even more pronounced for days strongly agree (intervention: 77%, 23/30; comparison: 23%, 7/30, P<.001). There was no significant difference in the frequency which PERSPeCT randomized smokers agreed or strongly agreed that the intervention influenced them to quit smoking (P=.07) and use nicotine replacement therapy (P=.09). Among those who completed follow-up, 36% (20/55) of PERSPeCT smokers and 32% (11/34) of the standard CTHC group stopped smoking for one day or longer (P=.70). Compared to standard CTHC with proven effectiveness, PERSPeCT outperformed in terms of influence ratings and resulted in similar cessation rates. Clinicaltrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 (Archived by WebCite at http://www.webcitation.org/6lEJY1KEd).