Background: Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions.
Objective: This systematic review and meta-analysis aims to assess the impact of smartphone-delivered ABMT on attentional bias and symptoms of mental health problems. Specifically, we examined different design approaches and methods of administration, focusing on common mental health issues, such as anxiety and depression, and design elements, including gamification and stimulus types.
Methods: Our search spanned from 2014 to 2023 and encompassed 4 major databases: MEDLINE, PsycINFO, PubMed, and Scopus. Study selection, data extraction, and critical appraisal were performed independently by 3 authors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. When necessary, we pooled the standardized mean difference with a 95% CI. In addition, we conducted sensitivity, subgroup, and meta-regression analyses to explore moderator variables of active and placebo ABMT interventions on reducing symptoms of mental health problems and attentional bias.
Results: Our review included 12 papers, involving a total of 24,503 participants, and we were able to conduct a meta-analysis on 20 different study samples from 11 papers. Active ABMT exhibited an effect size (Hedges g) of -0.18 (P=.03) in reducing symptoms of mental health problems, while the overall effect remained significant. Similarly, placebo ABMT showed an effect size of -0.38 (P=.008) in reducing symptoms of mental health problems. In addition, active ABMT (Hedges g -0.17; P=.004) had significant effects on reducing attentional bias, while placebo ABMT did not significantly alter attentional bias (Hedges g -0.04; P=.66).
Conclusions: Our understanding of smartphone-delivered ABMT's potential highlights the value of both active and placebo interventions in mental health care. The insights from the moderator analysis also showed that tailoring smartphone-delivered ABMT interventions to specific threat stimuli and considering exposure duration are crucial for optimizing their efficacy. This research underscores the need for personalized approaches in ABMT to effectively reduce attentional bias and symptoms of mental health problems.
Trial registration: PROSPERO CRD42023460749; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=460749.
Background: Despite significant progress in our understanding of depression, prevalence rates have substantially increased in recent years. Thus, there is an imperative need for more cost-effective and scalable mental health treatment options, including digital interventions that minimize therapist burden.
Objective: This study focuses on a fully automated digital implementation of behavioral activation (BA)-a core behavioral component of cognitive behavioral therapy for depression. We examine the efficacy of a 1-month fully automated SMS text message-based BA intervention for reducing depressive symptoms and anhedonia.
Methods: To this end, adults reporting at least moderate current depressive symptoms (8-item Patient Health Questionnaire score ≥10) were recruited online across the United States and randomized to one of three conditions: enjoyable activities (ie, BA), healthy activities (ie, an active control condition), and passive control (ie, no contact). Participants randomized to enjoyable and healthy activities received daily SMS text messages prompting them to complete 2 activities per day; participants also provided a daily report on the number and enjoyment of activities completed the prior day.
Results: A total of 126 adults (mean age 32.46, SD 7.41 years) with current moderate depressive symptoms (mean score 16.53, SD 3.90) were recruited. Participants in the enjoyable activities condition (BA; n=39) experienced significantly greater reductions in depressive symptoms compared to participants in the passive condition (n=46). Participants in both active conditions-enjoyable activities and healthy activities (n=41)-reported reduced symptoms of anxiety compared to those in the control condition.
Conclusions: These findings provide preliminary evidence regarding the efficacy of a fully automated digital BA intervention for depression and anxiety symptoms. Moreover, reminders to complete healthy activities may be a promising intervention for reducing anxiety symptoms.
Background: Digital mental health technologies (DMHTs) have the potential to enhance mental health care delivery. However, there is little information on how DMHTs are evaluated and what factors influence their use.
Objective: A systematic literature review was conducted to understand how DMHTs are valued in the United States from user, payer, and employer perspectives.
Methods: Articles published after 2017 were identified from MEDLINE, Embase, PsycINFO, Cochrane Library, the Health Technology Assessment Database, and digital and mental health congresses. Each article was evaluated by 2 independent reviewers to identify US studies reporting on factors considered in the evaluation of DMHTs targeting mental health, Alzheimer disease, epilepsy, autism spectrum disorder, or attention-deficit/hyperactivity disorder. Study quality was assessed using the Critical Appraisal Skills Program Qualitative and Cohort Studies Checklists. Studies were coded and indexed using the American Psychiatric Association's Mental Health App Evaluation Framework to extract and synthesize relevant information, and novel themes were added iteratively as identified.
Results: Of the 4353 articles screened, data from 26 unique studies from patient, caregiver, and health care provider perspectives were included. Engagement style was the most reported theme (23/26, 88%), with users valuing DMHT usability, particularly alignment with therapeutic goals through features including anxiety management tools. Key barriers to DMHT use included limited internet access, poor technical literacy, and privacy concerns. Novel findings included the discreetness of DMHTs to avoid stigma.
Conclusions: Usability, cost, accessibility, technical considerations, and alignment with therapeutic goals are important to users, although DMHT valuation varies across individuals. DMHT apps should be developed and selected with specific user needs in mind.
Background: In this digital age, children typically start using the internet in early childhood. Studies highlighted that young children are vulnerable to internet addiction due to personal limitations and social influence (eg, family and school). Internet addiction can have long-term harmful effects on children's health and well-being. The high risk of internet addiction for vulnerable populations like young children has raised questions about how best to prevent the problem.
Objective: This review study aimed to investigate the existing interventions and explore future directions to prevent or reduce internet addiction risks in children younger than 12 years.
Methods: The systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant literature from 4 research databases (Scopus, Web of Science, PubMed, and PsycINFO). We included 14 primary studies discussing the interventions to prevent or reduce internet addiction risks in young children and their efficacy outcomes.
Results: The preventive interventions identified were categorized into four approaches as follows: (1) children's education, (2) parenting strategy, (3) strategic physical activity, and (4) counseling. Ten interventions showed promising efficacy in preventing or reducing internet addiction risks with small-to-medium effect sizes. Interventions that enhance children's competencies in having appropriate online behaviors and literacy were more likely to show better efficacy than interventions that force children to reduce screen time. Interventions that shift children's focus from online activities to real-world activities also showed promising efficacy in reducing engagement with the internet, thereby preventing addictive behaviors. We also identified the limitations of each approach (eg, temporariness, accessibility, and implementation) as valuable considerations in developing future interventions.
Conclusions: The findings suggest the need to develop more sustainable and accessible interventions to encourage healthy online behaviors through education, appropriate parenting strategies, and substitutive activities to prevent children's overdependence on the internet. Developing digital tools and social support systems can be beneficial to improve the capability, efficiency, and accessibility of the interventions. Future interventions also need to consider their appropriateness within familial context or culture and provide adequate implementation training. Last, policy makers and experts can also contribute by making design guidelines to prevent digital product developers from making products that can encourage overuse in children.
Background: Insomnia is a prevalent condition with significant health, societal, and economic impacts. Cognitive behavioral therapy for insomnia (CBTI) is recommended as the first-line treatment. With limited accessibility to in-person-delivered CBTI (ipCBTI), electronically delivered eHealth CBTI (eCBTI), ranging from telephone- and videoconference-delivered interventions to fully automated web-based programs and mobile apps, has emerged as an alternative. However, the relative efficacy of eCBTI compared to ipCBTI has not been conclusively determined.
Objective: This study aims to test the comparability of eCBTI and ipCBTI through a systematic review and meta-analysis of equivalence based on randomized controlled trials directly comparing the 2 delivery formats.
Methods: A comprehensive search across multiple databases was conducted, leading to the identification and analysis of 15 unique randomized head-to-head comparisons of ipCBTI and eCBTI. Data on sleep and nonsleep outcomes were extracted and subjected to both conventional meta-analytical methods and equivalence testing based on predetermined equivalence margins derived from previously suggested minimal important differences. Supplementary Bayesian analyses were conducted to determine the strength of the available evidence.
Results: The meta-analysis included 15 studies with a total of 1083 participants. Conventional comparisons generally favored ipCBTI. However, the effect sizes were small, and the 2 delivery formats were statistically significantly equivalent (P<.05) for most sleep and nonsleep outcomes. Additional within-group analyses showed that both formats led to statistically significant improvements (P<.05) in insomnia severity; sleep quality; and secondary outcomes such as fatigue, anxiety, and depression. Heterogeneity analyses highlighted the role of treatment duration and dropout rates as potential moderators of the differences in treatment efficacy.
Conclusions: eCBTI and ipCBTI were found to be statistically significantly equivalent for treating insomnia for most examined outcomes, indicating eCBTI as a clinically relevant alternative to ipCBTI. This supports the expansion of eCBTI as a viable option to increase accessibility to effective insomnia treatment. Nonetheless, further research is needed to address the limitations noted, including the high risk of bias in some studies and the potential impact of treatment duration and dropout rates on efficacy.
Trial registration: PROSPERO CRD42023390811; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=390811.
Background: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.
Objective: This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.
Methods: Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.
Results: A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.
Conclusions: The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.
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