“You are not alone”: A big data and qualitative analysis of men's unintended fatherhood

Background: Becoming a father is a profound change in a man's life that is not always planned or wanted. Little is known about the subjective experiences of men who become fathers unintentionally or reluctantly. The aim of this research was to explore how men who did not intend to have children discuss their feelings about becoming a father in an online, anonymous environment. We sought insights into emotional responses, appraisals of family functioning, and relationships with infants. Method: Data were collected from two Reddit forums for new and expectant fathers, r/Daddit and r/Predaddit. Approximately 2600 posts and 21,000 comments were extracted from the period between January 2019 and March 2020. We employed a two-stage methodology, blending big data techniques and qualitative analyses. Stage One included extraction and data preparation for topic modelling Stage Two was an adapted approach to thematic qualitative analysis. Results: Topic modelling revealed 49 topics of which 6 were relevant thematically to unintended fatherhood. Men's communication in these were then classified within three domains: 1) Men's Concerns included their mental health, problems bonding with baby, their relationships with family and partner, and finances; 2) Men's Affective Experiences existed on a spectrum of complex emotions including regret, resignation, ambivalence, acceptance, and excitement; and 3) the Purpose of Communication included asking for and offering advice, normalisation, and perspective. Conclusions: Online forums like Reddit provide a unique opportunity for fathers who did not intend to have children to normalize their experience by expressing concerns and emotions in a pseudonymous environment. This study highlights the supportive environment that online discussions offer to fathers, and particularly unexpected fathers who may face stigma or barriers in other settings

School-Based Prevention Of Depressive Symptoms: A Randomized Controlled Study Of The Effectiveness And Specificity Of The Penn Resiliency Program

The authors investigated the effectiveness and specificity of the Penn Resiliency Program (PRP; J. E. Gillham, L. H. Jaycox, K. J. Reivich, M. E. P. Seligman, & T. Silver, 1990), a cognitive-behavioral depression prevention program. Children (N = 697) from 3 middle schools were randomly assigned to PRP, Control (CON), or the Penn Enhancement Program (PEP; K. J. Reivich, 1996; A. J. Shatté, 1997), an alternate intervention that controls for nonspecific intervention ingredients. Children\u27s depressive symptoms were assessed through 3 years of follow-up. There was no intervention effect on average levels of depressive symptoms in the full sample. Findings varied by school. In 2 schools, PRP significantly reduced depressive symptoms across the follow-up relative to both CON and PEP. In the 3rd school, PRP did not prevent depressive symptoms. The authors discuss the findings in relation to previous research on PRP and the dissemination of prevention programs. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract

Informing mHealth and Web-Based Eating Disorder Interventions: Combining Lived Experience Perspectives With Design Thinking Approaches

Background: App-based interventions designed to prevent and treat eating disorders have considerable potential to overcome known barriers to treatment seeking. Existing apps have shown efficacy in terms of symptom reduction; however, uptake and retention issues are common. To ensure that apps meet the needs and preferences of those for whom they were designed, it is critical to understand the lived experience of potential users and involve them in the process of design, development, and delivery. However, few app-based interventions are pretested on and co-designed with end users before randomized controlled trials. Objective: To address the issue, this study used a highly novel design thinking approach to provide the context and a lived experience perspective of the end user, thus allowing for a deeper level of understanding. Methods: In total, 7 young women (mean age 25.83, SD 5.34, range 21-33 years) who self-identified as having a history of body image issues or eating disorders were recruited. Participants were interviewed about their lived experience of body image and eating disorders and reported their needs and preferences for app-based eating disorder interventions. Traditional (thematic analysis) and novel (empathy mapping; visually depicting and empathizing with the user’s personal experience) analyses were performed, providing a lived experience perspective of eating disorders and identifying the needs and preferences of this population in relation to app-based interventions for eating disorders. Key challenges and opportunities for app-based eating disorder interventions were also identified. Results: Findings highlighted the importance of understanding and identifying problematic eating disorder symptoms for the user, helpful practices for recovery that identify personal values and goals, the role of social support in facilitating hope, and aspects of usability to promote continued engagement and recovery. Conclusions: Practical guidance and recommendations are described for those developing app-based eating disorder interventions. These findings have the potential to inform practices to enhance participant uptake and retention in the context of app-based interventions for this population

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Usability Evaluation of a Cognitive-Behavioral App-Based Intervention for Binge Eating and Related Psychopathology: A Qualitative Study

Despite their promise as a scalable intervention modality for binge eating and related problems, reviews show that engagement of app-based interventions is variable. Issues with usability may account for this. App developers should undertake usability testing so that any problems can be identified and fixed prior to dissemination. We conducted a qualitative usability evaluation of a newly-developed app for binge eating in 14 individuals with a diagnostic- or subthreshold-level binge eating symptoms. Participants completed a semi-structured interview and self-report measures. Qualitative data were organized into six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Qualitative and quantitative results indicated that the app demonstrated good usability. Key advantages reported were its flexible content-delivery formats, level of interactivity, easy-to-understand information, and ability to track progress. Concerns with visual aesthetics and lack of professional feedback were raised. Findings will inform the optimal design of app-based interventions for eating disorder symptoms. </jats:p

Adolescent depression and the family : a paradox

Discusses the role of the family in the development, treatment and prevention of adolescent depression. Studies have demonstrated that between 21–32% of adolescents report mild to severe symptoms of depression. The research points out the need for increased attention to adolescent depression because of its high prevalence, the risk factor for the development of other disorders and suicide, recurrence and tendency to endure into adulthood. Many studies have shown a strong relationship between depressive symptomatology and family factors. Therefore, family interventions should play an important role in the prevention and treatment of adolescent depression. However, there exists a paradox in that the research published to date fails to show that family-intervention programs add to the efficacy of treatments provided to the adolescents. Possible explanations for this paradox are discussed

Targeting dietary restraint to reduce binge eating: A randomised controlled trial of a blended internet- And smartphone app-based intervention

Abstract Background Existing internet-based prevention and treatment programmes for binge eating are composed of multiple distinct modules that are designed to target a broad range of risk or maintaining factors. Such multi-modular programmes (1) may be unnecessarily long for those who do not require a full course of intervention and (2) make it difficult to distinguish those techniques that are effective from those that are redundant. Since dietary restraint is a well-replicated risk and maintaining factor for binge eating, we developed an internet- and app-based intervention composed solely of cognitive-behavioural techniques designed to modify dietary restraint as a mechanism to target binge eating. We tested the efficacy of this combined selective and indicated prevention programme in 403 participants, most of whom were highly symptomatic (90% reported binge eating once per week). Method Participants were randomly assigned to the internet intervention (n = 201) or an informational control group (n = 202). The primary outcome was objective binge-eating frequency. Secondary outcomes were indices of dietary restraint, shape, weight, and eating concerns, subjective binge eating, disinhibition, and psychological distress. Analyses were intention-to-treat. Results Intervention participants reported greater reductions in objective binge-eating episodes compared to the control group at post-test (small effect size). Significant effects were also observed on each of the secondary outcomes (small to large effect sizes). Improvements were sustained at 8 week follow-up. Conclusions Highly focused digital interventions that target one central risk/maintaining factor may be sufficient to induce meaningful change in core eating disorder symptoms. </jats:sec