Unobtrusive Recognition of Personality Traits with Health Impact: A Literature Review with a Focus on Conscientiousness and Neuroticism

Non communicable diseases (NCDs) impose the greatest burden on global health. Any technology that helps making treatment more effective or efficient can potentially benefit humanity at a grand scale. Health information technology (HIT) has been identified as offering such potential, and indeed, existing HIT-based interventions are targeting NCD-related specific diseases such as diabetes, asthma, or mental illness. Rather generic determinants impacting health outcomes are the personality traits conscientiousness and neuroticism. We argue in this article that HIT-based interventions can benefit from an unobtrusive recognition of conscientiousness and neuroticism, both for tailoring interventions and for the adaptation of these traits. We conducted a systematic literature review to identify relevant behavioral features representing conscientiousness and neuroticism. Overall, 17 out of 262 articles have been found to be relevant for this purpose. We found that for conscientiousness, features relating to media consumption (video) and creation (photo) were highly relevant, as well as features related to communication style (use of negations), and features relating to the behavior of social contacts and variety of balancedness of relationships. For neuroticism, we found that features relating to the use of words of certain classes (religion, hearing) were particularly relevant, as well as variation in message length across contacts, and communication style (use of exclamation marks). This work concludes with an outlook on future research

Leveraging The Potential Of Personality Traits For Digital Health Interventions : A Literature Review On Digital Markers For Conscientiousness And Neurotism

Digital health interventions (DHIs) are designed to help individuals manage their disease, such as asthma, diabetes, or major depression. While there is a broad body of literature on how to design evidence- based DHIs with respect to behavioral theories, behavior change techniques or various design features, targeting personality traits has been neglected so far in DHI designs, although there is evidence of their impact on health. In particular, conscientiousness, which is related to therapy adherence, and neuroticism, which impacts long-term health of chronic patients, are two personality traits with an impact on health. Sensing these traits via digital markers from online and smartphone data sources and providing corresponding personality change interventions, i.e. to increase conscientiousness and to reduce neuroticism, may be an important active and generic ingredient for various DHIs. As a first step towards this novel class of personality change DHIs, we conducted a systematic literature review on relevant digital markers related to conscientiousness and neuroticism. Overall, 344 articles were reviewed and 21 were selected for further analysis. We found various digital markers for conscientiousness and neuroticism and discuss them with respect to future work, i.e. the design and evaluation of personality change DHIs

The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots

How Is Variety in Daily Life Related to the Expression of Personality States? An Ambulatory Assessment Study

People differ in the way they live their daily lives. For some people, daily life is characterized by multiple and diverse experiences, while others have more stability and routine in their lives. However, little is known about how variety in daily life relates to the expression of personality states. The present study examined within-person associations between variety in social partners, places, and activities with state expression. Data came from an ambulatory assessment study (N = 962, Mage = 25.49) with four assessments per day over a period of six consecutive days. The results of the multilevel modeling analyses suggest that variety in daily life is associated with some, but not all, state expressions. For instance, on days when participants experienced a greater variety in activities, they reported being less neurotic and conscientious, but also more agreeable. In addition, the links between all social partners, places, and activities with the expression of the state were examined simultaneously to obtain more detailed information on the multifaceted nature of situation-state expression links. We conclude that variety in daily life has both theoretical and empirical relevance for the expression of personality states

Personality change through a digital-coaching intervention: Using measurement invariance testing to distinguish between trait domain, facet, and nuance change

Recent intervention research has shown that personality traits can be modified through psychological interventions. However, it is unclear whether reported effects represent changes in the trait domain or only some facets or items. Using data ( N = 552) from a recent intervention trial, the present study examined the effects of a digital-coaching intervention on self- and observer-reported personality facets and items. We focused on participants who wanted to decrease in Negative Emotionality, increase in Conscientiousness or increase in Extraversion. We used measurement invariance testing to examine which level of the trait domain hierarchy changed during the intervention. For the self-reports, we found some heterogeneity in the effects on all three trait domains, but most notably Extraversion and Conscientiousness. Specifically, participants reported to increase strongly on sociability (Extraversion), and moderately on productiveness and organization (Conscientiousness), but not on the other facets of these trait domains. Observers generally reported small but non-significant changes, with no scalar invariance violations except for Extraversion. Overall, this suggests considerable heterogeneity in intervention-related personality change that can be overlooked if only focusing on the trait domain level. We discuss the relevance of measurement invariance testing and measurement approaches for personality development and intervention research

Personality change through a digital-coaching intervention: Using measurement invariance testing to distinguish between trait domain, facet, and nuance change

Recent intervention research has shown that personality traits can be modified through psychological interventions. However, it is unclear whether reported effects represent changes in the trait domain or only some facets or items. Using data ( N = 552) from a recent intervention trial, the present study examined the effects of a digital-coaching intervention on self- and observer-reported personality facets and items. We focused on participants who wanted to decrease in Negative Emotionality, increase in Conscientiousness or increase in Extraversion. We used measurement invariance testing to examine which level of the trait domain hierarchy changed during the intervention. For the self-reports, we found some heterogeneity in the effects on all three trait domains, but most notably Extraversion and Conscientiousness. Specifically, participants reported to increase strongly on sociability (Extraversion), and moderately on productiveness and organization (Conscientiousness), but not on the other facets of these trait domains. Observers generally reported small but non-significant changes, with no scalar invariance violations except for Extraversion. Overall, this suggests considerable heterogeneity in intervention-related personality change that can be overlooked if only focusing on the trait domain level. We discuss the relevance of measurement invariance testing and measurement approaches for personality development and intervention research

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals\u27 health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations

Corrigendum: Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol (Front. Public Health, (2021), 9, (625640), 10.3389/fpubh.2021.625640)

In the published article, there were errors regarding the affiliations of several authors. For “Joseph Ollier”, instead of having affiliation “1,2”, they should have “1”. For “Olivia Clare Keller”, instead of having affiliations “1,2,15”, they should have “1,15”. For “Lorainne Tudor Car”, instead of having affiliations “3,27”, they should have “4,27”. For “Alicia Salamanca-Sanabria” instead of having affiliation “3”, they should have “4”. For “Jacqueline Louise Mair”, instead of having affiliation “3”, they should have “4”. For “Tobias Kowatsch”, instead of having affiliation(s) “1,2,15,28”, they should have “1,4,15”. In the published article, there was also an error in affiliation “29”. Instead of “Center for Digital Health, Berlin Institute of Health and Charité, Berlin, Germany”, it should be “Center for Digital Health, Berlin Institute of Health at Charité, Berlin, Germany”. There was also an error in affiliation “4”. Instead of “Future Health Technologies Programme, Singapore-Eidgenössische Technische Hochschule (ETH) Centre at Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore”, it should be “Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore”. Additionally, there was an error in affiliation “23” instead of “Swiss Research Institute for Public Health and Addiction, Zurich University, Zurich, Switzerland” it should be “Swiss Research Institute for Public Health and Addiction, University of Zurich, Zurich, Switzerland”. The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

Studying Personality Change with Smartphone Data

Studies on personality change interventions report changes in self- and observer-reported personality within periods of a few months, or even weeks. This suggests a potential for valuable psychological interventions, because (1) many people want to change their personality traits, and (2) personality has a significant impact on important life outcomes like health and success in the workplace. However, it is not clear whether these changes in perceived personality are also reflected in actual and observable changes in behavior. As smartphones can unobtrusively collect data about many behaviors of daily life, they may be valuable tools for studying personality change. Associations between personality traits and unobtrusively collected data from smartphones have already been demonstrated, and suggest the possibility for behaviorally assessing personality change using smartphone data. However, this possibility has so far not been explored. In addition, personality states, which describe how personality traits are manifested in daily life situations, may be relevant to assessing personality change using smartphone data, but their relations to smartphone data have so far remained unexamined. In this thesis, we therefore address the following research questions related to behavioral indicators based on smartphone data: (1) Which behavioral indicators are relevant for an unobtrusive assessment of Big Five personality traits? (2) How are Big Five personality states associated with behavioral indicators? (3) Are the changes in self-assessed personality traits within a personality change study also reflected in behavioral indicators? We first introduce the research background, which includes topics from psychology, computer science, and from their relevant intersection (personality computing). We also describe the PEACH study, which is the source of the data that underlies this thesis. We then present a systematic literature review of studies on prediction of personality traits from smartphone data. The subsequent chapter investigates associations between personality states and smartphone data. Next, building on the findings from previous analyses, we use the collected smartphone data to assess personality change based on behavioral measures. Our results offer some confirmation for the potential of using smartphone data for studying personality change, but also suggest that it is difficult to realize this potential