Description:
Objective: E-therapy shows promise as a solution to the barriers that stand in the way of people receiving eating disorder (ED) treatment. Despite the potential for e-therapy to reduce the well-known treatment gap, little is known about public views and perspectives on this mode of intervention delivery. This study explored attitudes toward, and preferences for, e-therapy among individuals spanning the spectrum of eating pathology. Method: Survey data assessing e-therapy attitudes and preferences were analyzed from 713 participants recruited from the public. Participants were categorized into one of five subgroups based on the type of self-reported ED symptoms and severity/risk level, ranging from high risk to a probable threshold or subthreshold ED. Results: Attitudes toward e-therapies appeared to be relatively positive; participants largely supported health care insurance coverage of costs for e-therapies, and were optimistic about the wide-ranging benefits of e-therapy. Although three-quarters of participants expressed a preference for face-to-face therapy, a significant percentage of participants (
Description:
Abstract Background Although effective treatments exist for diagnostic and subthreshold-level eating disorders (EDs), a significant proportion of affected individuals do not receive help. Interventions translated for delivery through smartphone apps may be one solution towards reducing this treatment gap. However, evidence for the efficacy of smartphones apps for EDs is lacking. We developed a smartphone app based on the principles and techniques of transdiagnostic cognitive-behavioral therapy for EDs and evaluated it through a pre-registered randomized controlled trial. Methods Symptomatic individuals (those who reported the presence of binge eating) were randomly assigned to the app ( n = 197) or waiting list ( n = 195). Of the total sample, 42 and 31% exhibited diagnostic-level bulimia nervosa and binge-eating disorder symptoms, respectively. Assessments took place at baseline, 4 weeks, and 8 weeks post-randomization. Analyses were intention-to-treat. The primary outcome was global levels of ED psychopathology. Secondary outcomes were other ED symptoms, impairment, and distress. Results Intervention participants reported greater reductions in global ED psychopathology than the control group at post-test ( d = −0.80). Significant effects were also observed for secondary outcomes ( d 's = −0.30 to −0.74), except compensatory behavior frequency. Symptom levels remained stable at follow-up. Participants were largely satisfied with the app, although the overall post-test attrition rate was 35%. Conclusion Findings highlight the potential for this app to serve as a cost-effective and easily accessible intervention for those who cannot receive standard treatment. The capacity for apps to be flexibly integrated within current models of mental health care delivery may prove vital for addressing the unmet needs of people with EDs.
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This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health, and (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
Description:
This paper describes the progress of our ongoing research into collaborative report writing for emergency management using web technologies to improve communication and shared knowledge in emergency situations. Specifically, the work presented in this paper focuses on the development of a user attribution framework that enhances the Differential Synchronisation (diffsync) technique by exploiting its diff operation. This technique improves real-time collaborative editing for emergency management by combining the benefits of user attribution with diffsync features such as convergence, scalability, and robustness to poor network environments. As a proof of concept, we implement a prototype collaborative system and report results of simulations to test scalability, efficiency, and correctness. Further, we consider the potential benefits of this framework for web-based collaborative report writing in the context of emergency management.