A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers: study protocol

Medical Maximizing Preferences and Beliefs About Cancer Among US Adults

Importance

Medical overutilization contributes to significant health care expenditures and exposes patients to questionably beneficial surgery and unnecessary risk.

Objectives

To understand public attitudes toward medical utilization and the association of these attitudes with beliefs about cancer.

Design, Setting, and Participants

In this cross-sectional survey study conducted from August 26 to October 28, 2020, US-based, English-speaking adults were recruited from the general public using Prolific Academic, a research participant platform. Quota-filling was used to obtain a sample demographically representative of the US population. Adults with a personal history of cancer other than nonmelanoma skin cancer were excluded. Statistical analysis was completed in July 2022.

Main Outcome and Measures

Medical utilization preferences were characterized with the validated, single-item Maximizer-Minimizer Elicitation Question. Participants preferring to take action in medically ambiguous situations (hereafter referred to as "maximizers") were compared with those who leaned toward waiting and seeing (hereafter referred to as "nonmaximizers"). Beliefs and emotions about cancer incidence, survivability, and preventability were assessed using validated measures. Logistic regression modeled factors associated with preferring to maximize medical utilization.

Results

Of 1131 participants (mean [SD] age, 45 [16] years; 568 women [50.2%]), 287 (25.4%) were classified as maximizers, and 844 (74.6%) were classified as nonmaximizers. Logistic regression revealed that self-reporting very good or excellent health status (compared with good, fair, or poor; odds ratio [OR], 2.01 [95% CI, 1.52-2.65]), Black race (compared with White race; OR, 1.88 [95% CI, 1.22-2.89]), high levels of cancer worry (compared with low levels; OR, 1.62 [95% CI, 1.09-2.42]), and overestimating cancer incidence (compared with accurate estimation or underestimating; OR, 1.58 [95% CI, 1.09-2.28]) were significantly associated with maximizing preferences. Those who believed that they personally had a higher-than-average risk of developing cancer were more likely to be maximizers (23.6% [59 of 250] vs 17.4% [131 of 751]; P = .03); this factor was not significant in regression analyses.

Conclusions and Relevance

In this survey study of US adults, those with medical maximizing tendencies more often overestimated the incidence of cancer and had higher levels of cancer-related worry. Targeted and personalized education about cancer and its risk factors may help reduce overutilization of oncologic care.

Analysis of the components of cancer risk perception and links with intention and behaviour: A UK-based study

Risk perception refers to how individuals interpret their susceptibility to threats, and has been hypothesised as an important predictor of intentions and behaviour in many theories of health behaviour change. However, its components, optimal measurement, and effects are not yet fully understood. The TRIRISK model, developed in the US, conceptualises risk perception as deliberative, affective and experiential components. In this study, we aimed to assess the replicability of the TRIRISK model in a UK sample by confirmatory factor analysis (CFA), explore the inherent factor structure of risk perception in the UK sample by exploratory factor analysis (EFA), and assess the associations of EFA-based factors with intentions to change behaviour and subsequent behaviour change. Data were derived from an online randomised controlled trial assessing cancer risk perception using the TRIRISK instrument and intention and lifestyle measures before and after communication of cancer risk. In the CFA analysis, the TRIRISK model of risk perception did not provide a good fit for the UK data. A revised model developed using EFA consisted of two separate "numerical" and "self-reflective" factors of deliberative risk perception, and a third factor combining affective with a subset of experiential items. This model provided a better fit to the data when cross-validated. Using multivariable regression analysis, we found that the self-reflective and affective-experiential factors of the model identified in this study were reliable predictors of intentions to prevent cancer. There were no associations of any of the risk perception factors with behaviour change. This study confirms that risk perception is clearly a multidimensional construct, having identified self-reflective risk perception as a new distinct component with predictive validity for intention. Furthermore, we highlight the practical implications of our findings for the design of interventions incorporating risk perception aimed at behaviour change in the context of cancer prevention.

A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers

Few trial data are available concerning the impact of personalised cancer risk information on behaviour. This study assessed the short-term effects of providing personalised cancer risk information on cancer risk beliefs and self-reported behaviour. We randomised 1018 participants, recruited through the online platform Prolific, to either a control group receiving cancer-specific lifestyle advice or one of three intervention groups receiving their computed 10-year risk of developing one of the five most common preventable cancers either as a bar chart, a pictograph or a qualitative scale alongside the same lifestyle advice. The primary outcome was change from baseline in computed risk relative to an individual with a recommended lifestyle (RRI)1 at three months. Secondary outcomes included: health-related behaviours, risk perception, anxiety, worry, intention to change behaviour, and a newly defined concept, risk conviction. After three months there were no between-group differences in change in RRI (p = 0.71). At immediate follow-up, accuracy of absolute risk perception (p < 0.001), absolute and comparative risk conviction (p < 0.001) and intention to increase fruit and vegetables (p = 0.026) and decrease processed meat (p = 0.033) were higher in all intervention groups relative to the control group. The increases in accuracy and conviction were only seen in individuals with high numeracy and low baseline conviction, respectively. These findings suggest that personalised cancer risk information alongside lifestyle advice can increase short-term risk accuracy and conviction without increasing worry or anxiety but has little impact on health-related behaviour. Trial registration: ISRCTN17450583. Registered 30 January 2018.

Gorilla in our midst: An online behavioral experiment builder

Behavioral researchers are increasingly conducting their studies online, to gain access to large and diverse samples that would be difficult to get in a laboratory environment. However, there are technical access barriers to building experiments online, and web browsers can present problems for consistent timing-an important issue with reaction-time-sensitive measures. For example, to ensure accuracy and test-retest reliability in presentation and response recording, experimenters need a working knowledge of programming languages such as JavaScript. We review some of the previous and current tools for online behavioral research, as well as how well they address the issues of usability and timing. We then present the Gorilla Experiment Builder (gorilla.sc), a fully tooled experiment authoring and deployment platform, designed to resolve many timing issues and make reliable online experimentation open and accessible to a wider range of technical abilities. To demonstrate the platform's aptitude for accessible, reliable, and scalable research, we administered a task with a range of participant groups (primary school children and adults), settings (without supervision, at home, and under supervision, in both schools and public engagement events), equipment (participant's own computer, computer supplied by the researcher), and connection types (personal internet connection, mobile phone 3G/4G). We used a simplified flanker task taken from the attentional network task (Rueda, Posner, & Rothbart, 2004). We replicated the "conflict network" effect in all these populations, demonstrating the platform's capability to run reaction-time-sensitive experiments. Unresolved limitations of running experiments online are then discussed, along with potential solutions and some future features of the platform.