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Changing personality traits with the help of a digital personality change intervention.

ABSTRACT: Personality traits predict important life outcomes, such as success in love and work life, well-being, health, and longevity. Given these positive relations to important outcomes, economists, policy makers, and scientists have proposed intervening to change personality traits to promote positive life outcomes. However, nonclinical interventions to change personality traits are lacking so far in large-scale naturalistic populations. This study (n = 1,523) examined the effects of a 3-mo digital personality change intervention using a randomized controlled trial and the smartphone application PEACH (PErsonality coACH). Participants who received the intervention showed greater self-reported changes compared to participants in the waitlist control group who had to wait 1 mo before receiving the intervention. Self-reported changes aligned with intended goals for change and were significant for those desiring to increase on a trait (d = 0.52) and for those desiring to decrease on a trait (d = -0.58). Observers such as friends, family members, or intimate partners also detected significant personality changes in the desired direction for those desiring to increase on a trait (d = 0.35). Observer-reported changes for those desiring to decrease on a trait were not significant (d = -0.22). Moreover, self- and observer-reported changes persisted until 3 mo after the end of the intervention. This work provides the strongest evidence to date that normal personality traits can be changed through intervention in nonclinical samples.

SUBMITTER: Stieger M

PROVIDER: S-EPMC7923371 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDigital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement.MethodsWe included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy.ResultsFrom the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success.DiscussionUsing log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.

Project description:Background: A key driver of antimicrobial resistance (AMR) is patient demand for unnecessary antibiotics, which is driven by patients' beliefs about antibiotics and AMR. Few interventions have targeted beliefs to reduce inappropriate demand. Objective: To examine whether a brief, online algorithm-based intervention can change beliefs that may lead to inappropriate antibiotic demand (i.e. perceptions of antibiotic necessity and lack of concern about antibiotic harm). Design: Pre- and post-intervention study. Participants: Participants were 18 years or older, and residing in the United Kingdom, who self-selected to participate via Amazon mTurk, an online survey plaform, and via research networks. Intervention: Participants were presented with a hypothetical situation of cold and flu symptoms, then exposed to the intervention. The online intervention comprised: 1) a profiling tool identifying individual beliefs (antibiotic necessity, concerns, and knowledge) driving inappropriate antibiotic demand; 2) messages designed to change beliefs and knowledge (i.e. reduce antibiotic necessity, and increase antibiotic concerns and knowledge), and 3) an algorithm linking specific messages to specific beliefs and knowledge. Main measures: The profiling tool was repeated immediately after the intervention and compared with baseline scores to assess change in beliefs. A paired samples t-test was used to determine intervention effect. Key Results: A total of 100 respondents completed the study. A significant change in beliefs relating to inappropriate demand was observed after the intervention, with a reduction in beliefs about antibiotic necessity (t = 7.254; p < 0.0001), an increase in antibiotic concerns (t = -7.214; p < 0.0001), and increases in antibiotic and AMR knowledge (t = -4.651; p < 0.0001). Conclusion: This study is the first to demonstrate that patient beliefs about antibiotics and AMR associated with inappropriate demand can be changed by a brief, tailored online intervention. This has implications for the design of future interventions to reduce unnecessary antimicrobial use.

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Changing personality traits with the help of a digital personality change intervention.

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