Effect of Computer-Assisted Cognitive Behavior Therapy vs Usual Care on Depression Among Adults in Primary Care: A Randomized Clinical Trial

Are cognitive behavioural therapy, cognitive therapy, and behavioural activation for depression effective in primary care? A systematic review and meta-analysis

Cognitive behavioural therapy (CBT) is a recommended first-line treatment for depression. Evidence mainly derives from studies in secondary care, though most treatment occurs in primary care. This review examined efficacy of CBT, cognitive therapy (CT), or behavioural activation (BA) for depression within primary care. Databases were searched for trials up to 23rd July 2024. Risk of bias was assessed using the Cochrane risk-of-bias tool, version 2.0.44 studies were included. CBT, CT, and BA significantly reduced depression symptoms compared to inactive controls (k = 40, g = 0.44, p < .001), but not active comparators (other therapies, medication or exercise) (k = 9, g = -0.06, p = .24). Heterogeneity was significant in studies comparing CBT, CT, or BA to inactive controls, but not in studies using active comparators. Most studies were rated at high risk of bias (36 studies, 81.8 %), predominantly due to use of patient-rated outcome measures in non-blinded studies, lack of ITT analyses, and lack of pre-registering protocols, all of which may result in inflated effect sizes. Although CBT, CT, or BA appears effective for depression in primary care against usual care or waiting list controls, when compared to active comparators no significant difference is seen, likely a result of variability in the quality of the included studies. Large studies of improved quality (including use of blinded observer-rated outcome measures and ITT analyses) may be required to justify guideline recommendations for CBT over other interventions for depression specifically in primary care.

Exploring digital health tools for depression management in primary health care: Systematic review and meta-analysis

BACKGROUND

Digital health tools are transforming mental health care, particularly in treating depression, which affects 5 % of the global population and is projected to be the top disease burden by 2030. In primary care, these tools improve accessibility and efficacy, addressing rising mental health demands, especially post-COVID-19.

OBJECTIVE

The aim of this study is to assess the efficacy of digital health tools for the management of depression within primary care.

METHODS

A systematic review followed PRISMA guidelines, focusing on digital health tools for reducing depressive symptoms. Controlled trials were included, with RCTs assessed via the Cochrane risk-of-bias tool and non-RCTs evaluated using the JBI checklist for quasi-experimental studies. Depressive symptom reduction was the primary outcome.

RESULTS

A total of 29 controlled trials met the inclusion criteria, identifying were web-based platforms, mobile apps, phone calls, text messages, and decision algorithms. The meta-analysis revealed that digital health tools had a significant effect on depressive symptoms (g = -0.22, 95 % CI: -0.37; -0.06, I2 = 79.64 %). At 6 to 12-month follow-up, the random effects meta-analysis showed that digital health tools had a significant effect on depressive symptoms (g = -0.19, 95%CI: -0.29; -0.09, I2 = 53.42 %).

CONCLUSIONS

Digital health tools are effective in reducing the symptoms of repression. Symptom severity does not predict suitability for digital treatment, emphasizing the need for gender-sensitive approaches and strategies for older adults. Integrating digital interventions into clinical guidelines requires studies like this to support their adoption in real-world practice.

Next-Generation Cognitive-Behavioral Therapy for Depression: Integrating Digital Tools, Teletherapy, and Personalization for Enhanced Mental Health Outcomes

Background and Objectives: This systematic review aims to present the latest developments in next-generation CBT interventions of digital support tools, teletherapies, and personalized treatment modules in enhancing accessibility, improving treatment adherence, and optimizing therapeutic outcomes for depression. Materials and Methods: This review analyzed 81 PRISMA-guided studies on the efficacy, feasibility, and applicability of NG-CBT approaches. Other important innovations include web-based interventions, AI-operated chatbots, and teletherapy platforms, each of which serves as a critical challenge in delivering mental health care. Key messages have emerged regarding technological readiness, patient engagement, and the changing role of therapists within the digital context of care. Results: Findings indicate that NG-CBT interventions improve treatment accessibility and engagement while maintaining clinical effectiveness. Personalized digital tools enhance adherence, and teletherapy platforms provide scalable and cost-effective alternatives to traditional therapy. Conclusions: Such developments promise great avenues for decreasing the global burden of depression and enhancing the quality of life through novel, accessible, and high-quality therapeutic approaches.

Using Artificial Intelligence to Identify Effective Components of Computer-Assisted Cognitive Behavioural Therapy

Although clinician-supported computer-assisted cognitive-behaviour therapy (CCBT) is well established as an effective treatment for depression and anxiety, less is known about the specific interventions used during coaching sessions that contribute to outcomes. The current study used artificial intelligence (AI) to identify specific components of clinician-supported CCBT and correlated those scores with therapy outcomes. Data from a randomized clinical trial comparing clinician-supported CCBT with treatment as usual in a primary care setting were utilized. Participants (n = 95) engaged in CCBT with coaching sessions. The primary outcome was the Patient Health Questionnaire (PHQ-9), with Generalized Anxiety Disorder (GAD-7), Satisfaction with Life Scale (SWLS) and Automatic Thoughts Questionnaire (ATQ) ratings as secondary outcomes, which were assessed at 12 weeks (post), 3- and 6-month follow-up. The Lyssn system utilized AI technology to code CBT techniques and common general psychotherapeutic techniques. After controlling for initial ratings, 13 Lyssn-variables were observed to be significantly associated with reducing anxiety on the GAD-7 after 12 weeks of treatment. Among the most effective CBT interventions for anxiety included the use of guided discovery, understanding, interpersonal effectiveness and agenda setting. The most beneficial intervention was the proportion of open questions across all variables. Lyssn did not identify any CBT-specific interventions significantly associated with PHQ-9, SWLS or ATQ. Therapist use of CBT-specific techniques was significantly associated with reduction of anxiety symptoms after 12 weeks, but such gains were not observed at follow up. Therapist use of open questions was observed to be the most impactful technique contributing to treatment outcomes.

Digital Psychotherapies for Adults Experiencing Depressive Symptoms: Systematic Review and Meta-Analysis

BACKGROUND

Depression affects 5% of adults and it is a major cause of disability worldwide. Digital psychotherapies offer an accessible solution addressing this issue. This systematic review examines a spectrum of digital psychotherapies for depression, considering both their effectiveness and user perspectives.

OBJECTIVE

This review focuses on identifying (1) the most common types of digital psychotherapies, (2) clients' and practitioners' perspectives on helpful and unhelpful aspects, and (3) the effectiveness of digital psychotherapies for adults with depression.

METHODS

A mixed methods protocol was developed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search strategy used the Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) framework covering 2010 to 2024 and 7 databases were searched. Overall, 13 authors extracted data, and all aspects of the review were checked by >1 reviewer to minimize biases. Quality appraisal was conducted for all studies. The clients' and therapists' perceptions on helpful and unhelpful factors were identified using qualitative narrative synthesis. Meta-analyses of depression outcomes were conducted using the standardized mean difference (calculated as Hedges g) of the postintervention change between digital psychotherapy and control groups.

RESULTS

Of 3303 initial records, 186 records (5.63%; 160 studies) were included in the review. Quantitative studies (131/160, 81.8%) with a randomized controlled trial design (88/160, 55%) were most common. The overall sample size included 70,720 participants (female: n=51,677, 73.07%; male: n=16,779, 23.73%). Digital interventions included "stand-alone" or non-human contact interventions (58/160, 36.2%), "human contact" interventions (11/160, 6.8%), and "blended" including stand-alone and human contact interventions (91/160, 56.8%). What clients and practitioners perceived as helpful in digital interventions included support with motivation and accessibility, explanation of task reminders, resources, and learning skills to manage symptoms. What was perceived as unhelpful included problems with usability and a lack of direction or explanation. A total of 80 studies with 16,072 participants were included in the meta-analysis, revealing a moderate to large effect in favor of digital psychotherapies for depression (Hedges g=-0.61, 95% CI -0.75 to -0.47; Z=-8.58; P<.001). Subgroup analyses of the studies with different intervention delivery formats and session frequency did not have a statistically significant effect on the results (P=.48 and P=.97, respectively). However, blended approaches revealed a large effect size (Hedges g=-0.793), while interventions involving human contact (Hedges g=-0.42) or no human contact (Hedges g=-0.40) had slightly smaller effect sizes.

CONCLUSIONS

Digital interventions for depression were found to be effective regardless of format and frequency. Blended interventions have larger effect size than those involving human contact or no human contact. Digital interventions were helpful especially for diverse ethnic groups and young women. Future research should focus on understanding the sources of heterogeneity based on intervention and population characteristics.

TRIAL REGISTRATION

PROSPERO CRD42021238462; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=238462.

Reflections on Integrating a Therapist-guided CBT Website Into Routine Clinical Practice

Psychotherapy remains a scarce resource for patients, as problems such as provider shortages result in waitlists and lack of timely access. In their academic medical center outpatient clinic, the coauthors piloted use of a therapist-guided CBT website, Good Days Ahead, in routine clinical practice. They reflect on the benefits and challenges of this approach in this guest column.

Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.

App-Based Interventions for Moderate to Severe Depression: A Systematic Review and Meta-Analysis

Importance

Mobile mental health applications (apps) for moderate to severe depression are proliferating, likely owing to their capacity to overcome the limitations of conventional psychotherapy, but research on the potential moderators of treatment efficacy is lacking.

Objective

To examine the treatment efficacy associated with mobile app interventions for moderate to severe depression and identify the potential moderators associated with better treatment outcomes.

Data Sources

PubMed, Embase, and PsycINFO were searched from their inception to January 22, 2023.

Study Selection

Only randomized clinical trials evaluating mobile app treatments in adults with moderate to severe depression that published their results in English were included in the analysis.

Data Extraction and Synthesis

Three independent researchers extracted and assessed relevant studies, their risk of bias, the characteristics of the population and study design, and the components of the intervention program following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines. A fixed-effects model was used for data analysis, and exploratory post hoc meta-regression and subgroup analyses were also conducted. Data were analyzed from February 16 to March 25, 2023.

Main Outcomes and Measures

The main outcome was changes in depression symptom severity from before to after treatment, measured by standardized depression assessment instruments. Secondary outcomes included study-, intervention-, and patient-level factors associated with app efficacy.

Results

Of 2128 studies identified, 13 studies evaluating 16 intervention apps with 1470 participants with moderate to severe depression were included in the analysis. The overall pooled effect size of mobile app interventions vs both active and inactive control groups was 0.50 (95% CI, 0.40 to 0.61). Interventions with in-app notifications were associated with significantly lower treatment outcomes (standardized mean difference [SMD], 0.45; 95% CI, 0.29-0.60) than interventions without (SMD, 0.71; 95% CI, 0.54-0.87; P = .02). In addition, app interventions delivered for less than 8 weeks were associated with a significantly greater effect size (SMD, 0.77; 95% CI, 0.59-0.96) than interventions delivered for 8 weeks or longer (SMD, 0.43; 95% CI, 0.30-0.57; P = .004).

Conclusions and Relevance

In this systematic review and meta-analysis, the feasibility and efficacy of mobile app interventions were supported in treating moderate and severe depression, and practical implications were also provided for developing effective app-based interventions in clinical practice.

Assessing Patient Adherence to and Engagement With Digital Interventions for Depression in Clinical Trials: Systematic Literature Review

BACKGROUND

New approaches to the treatment of depression are necessary for patients who do not respond to current treatments or lack access to them because of barriers such as cost, stigma, and provider shortage. Digital interventions for depression are promising; however, low patient engagement could limit their effectiveness.

OBJECTIVE

This systematic literature review (SLR) assessed how participant adherence to and engagement with digital interventions for depression have been measured in the published literature, what levels of adherence and engagement have been reported, and whether higher adherence and increased engagement are linked to increased efficacy.

METHODS

We focused on a participant population of adults (aged ≥18 years) with depression or major depressive disorder as the primary diagnosis and included clinical trials, feasibility studies, and pilot studies of digital interventions for treating depression, such as digital therapeutics. We screened 756 unique records from Ovid MEDLINE, Embase, and Cochrane published between January 1, 2000, and April 15, 2022; extracted data from and appraised the 94 studies meeting the inclusion criteria; and performed a primarily descriptive analysis. Otsuka Pharmaceutical Development & Commercialization, Inc (Princeton, New Jersey, United States) funded this study.

RESULTS

This SLR encompassed results from 20,111 participants in studies using 47 unique web-based interventions (an additional 10 web-based interventions were not described by name), 15 mobile app interventions, 5 app-based interventions that are also accessible via the web, and 1 CD-ROM. Adherence was most often measured as the percentage of participants who completed all available modules. Less than half (44.2%) of the participants completed all the modules; however, the average dose received was 60.7% of the available modules. Although engagement with digital interventions was measured differently in different studies, it was most commonly measured as the number of modules completed, the mean of which was 6.4 (means ranged from 1.0 to 19.7) modules. The mean amount of time participants engaged with the interventions was 3.9 (means ranged from 0.7 to 8.4) hours. Most studies of web-based (34/45, 76%) and app-based (8/9, 89%) interventions found that the intervention group had substantially greater improvement for at least 1 outcome than the control group (eg, care as usual, waitlist, or active control). Of the 14 studies that investigated the relationship between engagement and efficacy, 9 (64%) found that increased engagement with digital interventions was significantly associated with improved participant outcomes. The limitations of this SLR include publication bias, which may overstate engagement and efficacy, and low participant diversity, which reduces the generalizability.

CONCLUSIONS

Patient adherence to and engagement with digital interventions for depression have been reported in the literature using various metrics. Arriving at more standardized ways of reporting adherence and engagement would enable more effective comparisons across different digital interventions, studies, and populations.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.

Examining the description of the concept "treatment as usual" for patients with depression, anxiety and stress-related mental disorders in primary health care research - A systematic review

BACKGROUND

In randomized controlled trials (RCTs) within medical research, applied interventions are compared to treatment-as-usual (TAU) as the control condition. The aim of the current study was to examine how the concept of TAU is described when used as control condition in RCTs evaluating treatments for depression, anxiety syndromes, and stress-related mental disorders in primary care.

METHOD

A systematic review of RCTs utilizing TAU as control group in the RCT in accordance with PRISMA standards was conducted. We used one multidisciplinary database (Scopus), one database focused on nursing (Cinahl), and one medical database (PubMed). The searches were conducted in November 2021 and May 2022.

RESULTS

The included 32 studies comprised of 7803 participants. The content of TAU was classified as follows: 1) Basic descriptions of TAU lacking a detailed account as well as reference to local or national guidelines, 2) Moderate description of TAU including reference to national or local guidelines or a detailed description 3) Advanced description of TAU including references to national guidelines and a detailed description containing five key concepts: early assessment, accessibility, psychological treatment, medication, somatic examination. 18 studies had basic, 11 moderate, and 3 advanced descriptions of TAU.

LIMITATIONS

The limitations were that only studies published in English were included.

CONCLUSIONS

The current study provides an assessment tool with three classification levels for TAU. The description of TAU is still insufficient in RCT studies conducted in primary care, which may affect the interpretation of results. In future research a detailed description of TAU is recommended.

Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE)

BACKGROUND

Parents of children treated for cancer may experience mental health difficulties, such as depression and anxiety. There is a lack of evidence-based psychological interventions for parents, with psychological support needs unmet. An internet-administered, guided, low-intensity cognitive behavioral therapy-based (LICBT) self-help intervention may provide a solution.

METHODS

The feasibility and acceptability of such an intervention was examined using a single-arm feasibility trial (ENGAGE). Primary objectives examined: (1) estimates of recruitment and retention rates; (2) feasibility and acceptability of data collection instruments and procedures; and (3) intervention feasibility and acceptability. Clinical outcomes were collected at baseline, post-treatment (12 weeks), and follow-up (6 months).

RESULTS

The following progression criteria were met: sample size was exceeded within 5 months, with 11.0% enrolled of total population invited, study dropout rate was 24.0%, intervention dropout was 23.6%, missing data remained at ≤10% per measure, and no substantial negative consequences related to participation were reported. Intervention adherence was slightly lower than progression criteria (47.9%).

CONCLUSION

Findings suggest an internet-administered, guided, LICBT self-help intervention may represent a feasible and acceptable solution for parents of children treated for cancer. With minor study protocol and intervention modifications, progression to a pilot randomized controlled trial (RCT) and subsequent superiority RCT is warranted.

Internet-delivered psychological treatment as an add-on to treatment as usual for common mental disorders: A systematic review with meta-analysis of randomized trials

BACKGROUND

Psychological treatments for common mental disorders are increasingly being delivered remotely via the internet. Evidence suggests that internet-delivered cognitive behavioural therapy (iCBT) is superior to waitlist. However, the benefits are unclear of using this treatment modality as an add-on to treatment as usual (TAU) in regular healthcare.

METHODS

The literature was systematically searched up to August 2021 for randomized trials of internet-delivered psychological treatments using TAU as the comparator. Eligible participants were diagnosed with depressive, anxiety, obsessive-compulsive, or trauma- and stress-related disorders. Outcomes of interest were symptoms, functioning, quality of life, healthcare utilization, and negative effects. Results were synthesized using random-effects meta-analyses. Quality of evidence was assessed using GRADE.

RESULTS

The included studies evaluated iCBT for adults with depression (k = 9), depressive or anxiety disorders (k = 4), and post-traumatic stress disorder (k = 2) and were conducted in primary care or similar settings. For depression, low-certainty evidence suggested beneficial short-term effects on symptoms (g = -0.23; 95 % CI: = -0.37, -0.09), response rate (OR = 2.46; 1.31, 4.64), and remission (OR = 1.70; 1.19, 2.42;). The certainty of evidence was very low for long-term effects, other outcomes, and other disorders.

LIMITATIONS

TAU varied across studies and was often insufficiently described.

CONCLUSIONS

iCBT as a complement to usual care for adult with depression may result in a small incremental effect, which potentially could be clinically important. Studies are lacking for several common disorders and for children, adolescents, and the elderly. More robust studies of long-term effects are also needed, to better inform clinical decision-making.

Use of Telehealth to Address Depression and Anxiety in Low-income US Populations: A Narrative Review

Symptoms of anxiety and depressive disorders have been increasing substantially among adults in the United States (US) during the COVID-19 pandemic, particularly for low-income populations. Under-resourced communities have difficulties accessing optimal treatment for anxiety and depression due to costs as well as the result of limited access to health care providers. Telehealth has been growing as a digital strategy to treat anxiety and depression across the country but it is unclear how best to implement telehealth interventions to serve low-income populations. A narrative review was conducted to evaluate the role of telehealth in addressing anxiety and depression in low-income groups in the US. A PubMed database search identified a total of 14 studies published from 2012 to 2022 on telehealth interventions that focused on strengthening access to therapy, coordination of care, and medication and treatment adherence. Our findings suggest that telehealth increases patient engagement through virtual therapy and the use of primarily telephone communication to treat and monitor anxiety and depression. Telehealth seems to be a promising approach to improving anxiety and depressive symptoms but socioeconomic and technological barriers to accessing mental health services are substantial for low-income US populations.