Cognitive behavioral therapy (CBT)-based mobile apps have been shown to improve CBT-based interventions effectiveness. Despite the proliferation of these apps, user-centered guidelines pertaining to their design remain limited. The study aims to identify design features of CBT-based apps using online app reviews. We used 4- and 5-star reviews, preprocessed the reviews, and represented the reviews using word-level bigrams. Then, we leveraged latent Dirichlet allocation (LDA) and visualization techniques using python library for interactive topic model visualization to analyze the review and identify design features that contribute to the success and effectiveness of the app. A total of 24,902 reviews were analyzed. LDA optimization resulted in 86 topics that were labeled by two independent researchers, with an interrater Cohen's kappa value of 0.86. The labeling and grouping process resulted in a total of six main design features for effective CBT-based mobile apps, namely, mental health management and support, credibility support, self-understanding and personality insights, therapeutic approaches and tools, beneficial rescue sessions, and personal growth and development. The high-level design features identified in this study could evidently serve as the backbone of successful CBT-based mobile apps for mental health.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1089/tmj.2024.0053 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Copy Number Variation in Asthma: An Integrative Review.
Clin Rev Allergy Immunol
January 2025
Postgraduate Program in Biochemistry, Federal University of Espírito Santo (UFES), Vitória, Espírito Santo, Brazil.
Asthma is a complex disease with varied clinical manifestations resulting from the interaction between environmental and genetic factors. While chronic airway inflammation and hyperresponsiveness are central features, the etiology of asthma is multifaceted, leading to a diversity of phenotypes and endotypes. Although most research into the genetics of asthma focused on the analysis of single nucleotide polymorphisms (SNPs), studies highlight the importance of structural variations, such as copy number variations (CNVs), in the inheritance of complex characteristics, but their role has not yet been fully elucidated in asthma.
View Article and Find Full Text PDFConceptual foundations of a REFRAME-based approach to discriminate across total knee implant designs based on the positions of functional centres of rotation.
Sci Rep
January 2025
Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, Munich, Germany.
In modern knee arthroplasty, surgeons increasingly aim for individualised implant selection based on data-driven decisions to improve patient satisfaction rates. The identification of an implant design that optimally fits to a patient's native kinematic patterns and functional requirements could provide a basis towards subject-specific phenotyping. The goal of this study was to achieve a first step towards identifying easily accessible and intuitive features that allow for discrimination between implant designs based on kinematic data.
View Article and Find Full Text PDFN2GNet tracks gait performance from subthalamic neural signals in Parkinson's disease.
NPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFDelivery of Prime editing in human stem cells using pseudoviral NanoScribes particles.
Nat Commun
January 2025
CIRI, Centre International de Recherche en Infectiologie Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, F-69007, Lyon, France.
Prime Editing can rewrite genes in living cells by allowing point mutations, deletions, or insertion of small DNA sequences with high precision. However, its safe and efficient delivery into human stem cells remains a technical challenge. In this report, we engineer Nanoscribes, virus-like particles that encapsidate ribonucleoprotein complexes of the Prime Editing system and allow their delivery into recipient cells.
View Article and Find Full Text PDFPrediction of pulmonary embolism by an explainable machine learning approach in the real world.
Sci Rep
January 2025
Department of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai, People's Republic of China.
In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!