Egocentric social networks and smoking among adults with serious mental illness

Social networks of people with serious mental illness who smoke: potential role in a smoking cessation intervention

BACKGROUND

Smoking is a major contributor to morbidity and mortality among individuals with serious mental illness (SMI) and social networks may play an important role in smoking behaviors.

AIMS

Our objectives were to (1) describe the network characteristics of adults with SMI who smoke tobacco (2) explore whether network attributes were associated with nicotine dependence.

METHODS

We performed a secondary analysis of baseline data from a tobacco smoking cessation intervention trial among 192 participants with SMI. A subgroup (n = 75) completed questions on the characteristics of their social network members. The network characteristics included network composition (e.g. proportion who smoke) and network structure (e.g. density of connections between members). We used multilevel models to examine associations with nicotine dependence.

RESULTS

Participant characteristics included: a mean age 50 years, 49% women, 48% Black, and 41% primary diagnosis of schizophrenia/schizoaffective disorder. The median personal network proportion of active smokers was 22%, active quitters 0%, and non-smokers 53%. The density of ties between actively smoking network members was greater than between non-smoking members (55% vs 43%, p = .02). Proportion of network smokers was not associated with nicotine dependence.

CONCLUSIONS

We identified potential social network challenges and assets to smoking cessation and implications for network interventions among individuals with SMI.

A smoking cessation intervention for people with severe mental illness treated in ambulatory mental health care (KISMET): study protocol of a randomised controlled trial

BACKGROUND

Smoking among people with severe mental illness (SMI) is highly prevalent and strongly associated with poor physical health. Currently, evidence-based smoking cessation interventions are scarce and need to be integrated into current mental health care treatment guidelines and clinical practice. Therefore, the present study aims to evaluate the implementation and effectiveness of a smoking cessation intervention in comparison with usual care in people with SMI treated by Flexible Assertive Community Treatment (FACT) teams in the Netherlands.

METHODS

A pragmatic, cluster-randomised controlled trial with embedded process evaluation will be conducted. Randomisation will be performed at the level of FACT teams, which will be assigned to the KISMET intervention or a control group (care as usual). The intervention will include pharmacological treatment combined with behavioural counselling and peer support provided by trained mental health care professionals. The intervention was developed using a Delphi study, through which a consensus was reached on the core elements of the intervention. We aim to include a total of 318 people with SMI (aged 18-65 years) who smoke and desire to quit smoking. The primary outcome is smoking status, as verified by carbon monoxide measurements and self-report. The secondary outcomes are depression and anxiety, psychotic symptoms, physical fitness, cardiovascular risks, substance use, quality of life, and health-related self-efficacy at 12 months. Alongside the trial, a qualitative process evaluation will be conducted to evaluate the barriers to and facilitators of its implementation as well as the satisfaction and experiences of both patients and mental health care professionals.

DISCUSSION

The results of the KISMET trial will contribute to the evidence gap of effective smoking cessation interventions for people treated by FACT teams. Moreover, insights will be obtained regarding the implementation process of the intervention in current mental health care. The outcomes should advance the understanding of the interdependence of physical and mental health and the gradual integration of both within the mental health care system.

TRIAL REGISTRATION

Netherlands Trial Register, NTR9783. Registered on 18 October 2021.

An Ensemble Hybrid Feature Selection Method for Neuropsychiatric Disorder Classification

Magnetic resonance imagings (MRIs) are providing increased access to neuropsychiatric disorders that can be made available for advanced data analysis. However, the single type of data limits the ability of psychiatrists to distinguish the subclasses of this disease. In this paper, we propose an ensemble hybrid features selection method for the neuropsychiatric disorder classification. The method consists of a 3D DenseNet and a XGBoost, which are used to select the image features from structural MRI images and the phenotypic feature from phenotypic records, respectively. The hybrid feature is composed of image features and phenotypic features. The proposed method is validated in the Consortium for Neuropsychiatric Phenomics (CNP) dataset, where samples are classified into one of the four classes (healthy controls (HC), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), and schizophrenia (SD)). Experimental results show that the hybrid feature can improve the performance of classification methods. The best accuracy of binary and multi-class classification can reach 91.22 and 78.62 percent, respectively. We analyze the importance of phenotypic features and image features in different classification tasks. The importance of the structure MRI images is highlighted by incorporating phenotypic features with image features to generate hybrid features. We also visualize the features of three neuropsychiatric disorders and analyze their locations in the brain region.

Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study

Background

Online health communities (OHCs) have emerged as the leading venues for behavior change and health-related information seeking. The soul and success of these digital platforms lie in their ability to foster social togetherness and a sense of community by providing personalized support. However, we have a minimal understanding of how conversational posts in these settings lead to collaborative societies and ultimately result in positive health changes through social influence.

Objective

Our objective is to develop a content-specific and intent-sensitive methodological framework for analyzing peer interactions in OHCs.

Methods

We developed and applied a mixed-methods approach to understand the manifestation of expressions in peer interactions in OHCs. We applied our approach to describe online social dialogue in the context of two online communities, QuitNet (QN) and the American Diabetes Association (ADA) support community. A total of 3011 randomly selected peer interactions (n=2005 from QN, n=1006 from ADA) were analyzed. Specifically, we conducted thematic analysis to characterize communication content and linguistic expressions (speech acts) embedded within the two data sets. We also developed an empirical user persona based on their engagement levels and behavior profiles. Further, we examined the association between speech acts and communication themes across observed tiers of user engagement and self-reported behavior profiles using the chi-square test or the Fisher test.

Results

Although social support, the most prevalent communication theme in both communities, was expressed in several subtle manners, the prevalence of emotions was higher in the tobacco cessation community and assertions were higher in the diabetes self-management (DSM) community. Specific communication theme-speech act relationships were revealed, such as the social support theme was significantly associated (P<.05) with 9 speech acts from a total of 10 speech acts (ie, assertion, commissive, declarative, desire, directive, expressive, question, stance, and statement) within the QN community. Only four speech acts (ie, commissive, emotion, expressive, and stance) were significantly associated (P<.05) with the social support theme in the ADA community. The speech acts were also significantly associated with the users’ abstinence status within the QN community and with the users’ lifestyle status within the ADA community (P<.05).

Conclusions

Such an overlay of communication intent implicit in online peer interactions alongside content-specific theory-linked characterizations of social media discourse can inform the development of effective digital health technologies in the field of health promotion and behavior change. Our analysis revealed a rich gradient of expressions across a standardized thematic vocabulary, with a distinct variation in emotional and informational needs, depending on the behavioral and disease management profiles within and across the communities. This signifies the need and opportunities for coupling pragmatic messaging in digital therapeutics and care management pathways for personalized support.

Cigarette Smoking and Dating App Use: Findings from a Survey in a Sample of Adults in Italy

Existing studies in the literature indicate an association between the use of dating apps and substance-related behaviours (i.e., alcohol consumption, drug consumption). However, to date, no studies investigated the relation between dating app use and smoking. This study aims to explore this association. A total of 1278 respondents completed an online ad hoc questionnaire assessing demographics, smoking habits, dating app use, motivations for using dating apps. Multiple logistic regression analyses were run to investigate the relation between demographics and dating apps use on tobacco consumption. Being active user was significantly associated with being smoker, light daily and moderate-to-heavy smoker. Among users, using apps with the motive of searching for friends accounted for lower odds of smoking, light daily smoking and moderate-to-heavy smoking. However, heavy dating app users were less likely to smoke, to be light daily smokers and to be moderate-to-heavy smokers. The study indicates an association between using the apps and smoking, suggesting that motives for using the apps and intensity of use may moderate this association.

Long-term antibiotic use during early life and risks to mental traits: an observational study and gene-environment-wide interaction study in UK Biobank cohort

The relationships between long-term antibiotic use during early life and mental traits remain elusive now. A total of 158,444 subjects from UK Biobank were used in this study. Linear regression analyses were first conducted to assess the correlations between long-term antibiotic use during early life and mental traits. Gene-environment-wide interaction study (GEWIS) was then performed by PLINK2.0 to detect the interaction effects between long-term antibiotic use during early life and genes on the risks of mental traits. Finally, DAVID tool was used to conduct gene ontology (GO) analysis of the identified genes interacting with long-term antibiotic use during early life. We found negative associations of long-term antibiotic use during early life with remembrance (p value=1.74 × 10^-6, b = -0.10) and intelligence (p value=2.64 × 10^-26, b = -0.13), and positive associations of long-term antibiotic use during early life with anxiety (p value = 2.75 × 10^-47, b = 0.12) and depression (p value=2.01 × 10^-195, b = 0.25). GEWIS identified multiple significant genes-long-term antibiotic use during early life interaction effects, such as ANK3 (rs773585997, p value = 1.78 × 10^-8) for anxiety and STRN (rs140049205, p value = 1.88 × 10^-8) for depression. GO enrichment analysis detected six GO terms enriched in the identified genes interacting with long-term antibiotic use during early life for anxiety, such as GO:0030425~dendrite (p value = 3.41 × 10^-2) and GO:0005886~plasma membrane (p value = 3.64 × 10^-3). Our study results suggest the impact of long-term antibiotic use during early life on the development of mental traits.

Assessing Ego-Centered Social Networks in formr: A Tutorial

In psychological science, ego-centered social networks are assessed to investigate the patterning and development of social relationships. In this approach, a focal individual is typically asked to report the people they interact with in specific contexts and to provide additional information on those interaction partners and the relationships with them. Although ego-centered social networks hold considerable promise for investigating various interesting questions from psychology and beyond, their implementation can be challenging. This tutorial provides researchers with detailed instructions on how to set up a study involving ego-centered social networks online using the open-source software formr. By including a fully functional study template for the assessment of social networks and extensions to this design, we hope to equip researchers from different backgrounds with the tools necessary to collect social-network data tailored to their research needs.

A 5-Factor Framework for Assessing Tobacco Use Disorder

Cigarette use is the leading cause of preventable death in the United States. Despite the well documented dangers of smoking, nearly 20% of adults report regular use of tobacco. A majority desire to discontinue but the long-term cessation success rate remains near 4%. One challenge to reducing the prevalence of tobacco use is an incomplete understanding of the individual correlates that reinforce continued use. Evidence from research on nicotine and tobacco suggests that Tobacco Use Disorder is a complex, and multifactorial condition. Personality traits, comorbidities, habits and lifestyle, genetics, socioeconomic status, and mental and physical health all contribute to the risk for dependence and to the likelihood of quitting. This perspective review provides an overview of some common factors that contribute to liability risk for Tobacco Use Disorder and a framework for assessing individual tobacco users. The framework includes 5 areas that research suggests contribute to continued tobacco use: nicotine addiction, psychological influences, behavioral dependencies, neurobiological factors, and social reinforcement. Nicotine addiction includes drug-seeking behavior and the role of withdrawal avoidance. Psychological and emotional states contribute to a perceived reliance on tobacco. Behavioral dependence is reinforced by associative and non-associative learning mechanisms. Neurobiological factors include genetic variables, variations in neurotransmitters and receptors, pharmacogenetics, and interaction between psychiatric illnesses and nicotine use and dependence. Finally, social reinforcement of smoking behavior is explained by a network phenomenon and consistent visual cues to smoke. A comprehensive assessment of individual tobacco users will help better determine appropriate treatment options to achieve improved efficacy and outcomes.