Cognitive Behavioral Therapy: Telehealth Effectiveness For Chronic Mental Disorders
Overview
Amidst the pandemic, the adoption of remote treatments, specifically telephone-delivered cognitive behavioral therapy (T-CBT), has witnessed a notable increase. This meta-analysis seeks to fill a gap in existing literature by evaluating the effectiveness of T-CBT in chronic and/or mental illnesses, comparing it with other interventions such as treatment as usual (TAU) or face-to-face CBT. The study encompasses 33 randomized controlled trials.
The analysis, employing Hedges’ g as the effect size (ES) metric, indicates a substantial positive impact of T-CBT compared to TAU across various psychological outcomes. Notably, a large ES was observed for depression (g = 0.84, p < 0.001), accompanied by moderate effects on anxiety (g = 0.57, p < 0.001), and smaller yet significant effects on mental quality of life (g = 0.33, p < 0.001), sleep disturbances (g = 0.37, p = 0.042), coping (g = 0.20, p = 0.016), and worry (g = 0.43, p = 0.001).
Furthermore, the meta-analysis comparing T-CBT with face-to-face CBT in the context of depression yielded a non-significant pooled ES (g = 0.06, p = 0.466). This suggests that, when treating depression, T-CBT is as efficient as traditional face-to-face CBT.
In conclusion, the results provide compelling evidence that T-CBT exhibits superior efficacy compared to TAU across multiple psychological dimensions. Additionally, its performance is on par with face-to-face CBT in addressing depression. This underscores the potential of T-CBT as a valuable and effective remote treatment option for diverse mental health concerns.
Introduction
Mental illnesses contribute significantly to the overall disease burden in affluent countries, surpassing the impact of heart disease, stroke, cancer, and other physical ailments. The societal and economic ramifications are substantial, with mental disorders accounting for a noteworthy percentage of missed workdays in comparison to other health conditions. Evidence-based psychotherapies, recommended by institutions like the National Institute for Health and Care Excellence (NICE), emphasize the importance of targeted interventions for conditions such as depression, anxiety disorders, irritable bowel syndrome (IBS), and more.
Psychotherapy, notably cognitive behavioral therapy (CBT), has evolved as a pivotal approach, demonstrating efficacy in alleviating symptoms across various mental health disorders. The success of CBT is underscored by randomized controlled trials (RCTs) that reveal its superiority over pharmacotherapy in certain conditions. As a “gold-standard” psychological treatment, CBT consistently exhibits positive effects on major depression, anxiety disorders, obsessive-compulsive disorder (OCD), and post-traumatic stress disorder (PTSD).
The advent of the pandemic prompted a shift toward remote treatments, including telephone-delivered cognitive behavioral therapy (T-CBT). These alternatives, especially relevant during extreme situations like the pandemic, offer significant advantages, particularly for individuals at higher risk or those facing physical challenges due to chronic diseases. Chronic illnesses, characterized by prolonged duration and the need for ongoing medical support, often coexist with mental health challenges. Depression and anxiety prevalence tends to be higher among individuals with chronic conditions, emphasizing the importance of addressing both physical and psychological aspects of health.
Studies investigating the efficacy of T-CBT in individuals with mental disorders, with or without chronic physical illnesses, indicate positive outcomes. T-CBT has been associated with symptom reduction and improved well-being, demonstrating effectiveness in promoting smoking cessation, reducing depression and anxiety symptoms, and addressing various addictions. However, comparisons between T-CBT and treatment as usual (TAU) have yielded mixed results, with some studies showing superiority and others reporting no significant difference.
Despite the growing popularity of T-CBT, there is a notable gap in the literature regarding its effectiveness compared to traditional face-to-face CBT, especially in individuals with mental and/or chronic illnesses. This meta-analysis aims to address this gap, evaluating whether T-CBT outperforms TAU in multiple psychological outcomes, including anxiety, coping strategies, depression, mental and physical quality of life, sleep disturbances, and worry. Additionally, the study aims to determine if T-CBT is as effective as traditional face-to-face CBT in treating these outcomes. This comprehensive analysis seeks to contribute valuable insights into the evolving landscape of psychotherapeutic interventions, especially considering the changing dynamics brought about by the COVID-19 pandemic.
Method
On June 29, 2022, a comprehensive literature search was conducted across Scopus, PubMed, and PsycINFO databases, employing a specific query related to telephone-based cognitive behavioral therapy (CBT). The meta-analytic study adhered to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement, ensuring a rigorous and systematic approach. The study protocol was registered with PROSPERO under the registration number CRD42021259516. Inclusion criteria for primary studies encompassed the requirement for studies to be in English, randomized controlled trials (RCTs) involving adults with mental disorders and/or chronic pathologies, with chronic diseases defined as long-lasting conditions necessitating ongoing medical assistance and impacting daily activities. Mental illnesses were limited to those outlined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders.
The data collection and coding process involved the selection of psychological variables as outcomes, including anxiety, coping, depression, mental and physical quality of life (QoL), sleep disturbances, and worry. Two authors independently executed the selection process, with consultation among other authors in cases of uncertainty. In instances where multiple measures were used for the same outcome, the most representative measure, commonly employed among primary studies, was prioritized. For primary studies reporting data at multiple follow-up points, the longest time point assessment was included in the meta-analytic study. Ethical committee approval was deemed unnecessary as primary studies had already obtained ethics approval.
The subsequent data extraction encompassed various elements, such as publication year, socio-demographic characteristics of the sample, treatment type (including session numbers and follow-up duration), control conditions, outcome measures, and data essential for computing effect sizes (ES). The Jadad Scale was employed to assess the quality and risk of bias in each primary study, with a score equal to or greater than 3 indicating high study quality. This meticulous and standardized methodology ensures the reliability and validity of the meta-analytic study, providing a robust foundation for the analysis and interpretation of the gathered data.
Statistical Analysis
Analyses were conducted using ProMeta3 software, employing a random-effect statistical model for a meticulous examination of the data. Each effect size (ES) was calculated using Hedges’ g, and a comprehensive pooling approach was employed to generate mean ES values for each outcome. Separate meta-analyses were conducted to derive individual ES values for specific outcomes, as well as an overall ES combining all outcomes. Statistical significance was set at a p-value < 0.05. In the interpretation of Hedges’ g, guidelines established by Cohen were followed, where values around 0.20 indicated small ES, approximately 0.50 denoted moderate ES, and values exceeding 0.80 signified large ES.
To assess interstudy heterogeneity, Cochran’s Q and I^2 statistics were computed. I^2 values below 50% indicated low heterogeneity, those between 50% and 75% suggested moderate heterogeneity, and values surpassing 75% indicated high interstudy heterogeneity. In instances of high heterogeneity, a sensitivity analysis was conducted to assess if the exclusion of individual studies could mitigate heterogeneity levels. Additionally, as a quality control measure, a publication bias analysis was performed to ensure the reliability and robustness of the findings. This systematic and statistical approach ensures the validity of the meta-analytic results, considering both individual outcomes and the overarching effectiveness of telephone-based cognitive behavioral therapy (T-CBT).
Result
The literature search initially yielded 445 primary studies, subsequently narrowed down to 321 after eliminating duplicates. Following a thorough assessment of titles and abstracts, 111 studies were excluded based on predefined criteria, resulting in the inclusion of 33 studies for the meta-analytic study. Among these, 29 studies were part of the analysis comparing the efficacy of telephone-based cognitive behavioral therapy (T-CBT) with treatment as usual (TAU), while an additional five studies were included in the comparison between T-CBT and face-to-face cognitive behavioral therapy (CBT). One study evaluated both T-CBT and TAU, and T-CBT and CBT, and was consequently included in both analyses.
The quality assessment revealed that all included studies were randomized controlled trials (RCTs). While correct randomization was ensured across all studies, blinding procedures, especially regarding participants and therapists, were not consistently reported. Some studies implemented a masked procedure only for principal investigators and statisticians. Only two studies did not provide information on dropouts.
In the meta-analysis comparing the efficacy of T-CBT and TAU on psychological outcomes:
– Anxiety: Nine studies showed a significant and moderate effect size favoring T-CBT over TAU (Hedges’ g = 0.57, p < 0.001).
– Coping: Four studies indicated a small but significant effect in favor of T-CBT (Hedges’ g = 0.20, p = 0.016).
– Mental Quality of Life (QoL): Eight studies demonstrated a significant and low effect size favoring T-CBT (Hedges’ g = 0.33, p < 0.001).
– Worry: Three studies revealed a significant and low effect size in favor of T-CBT (Hedges’ g = 0.43, p = 0.001).
– Depression: A significant and large effect size favored T-CBT, but with high interstudy heterogeneity (Hedges’ g = 0.84, p < 0.001).
After a sensitivity analysis and removal of specific studies causing asymmetry, the overall effect size for depression became small, with moderate interstudy heterogeneity. The combined analysis across all outcomes indicated a significant and moderate effect size favoring T-CBT over TAU, with high interstudy heterogeneity and no significant publication bias.
For the meta-analysis comparing the efficacy of T-CBT and CBT on depression, five studies revealed a nonsignificant pooled effect size (Hedges’ g = 0.06, p = 0.466), with no significant interstudy heterogeneity or publication bias.
In summary, T-CBT demonstrated effectiveness in improving various psychological outcomes compared to TAU, with a notable emphasis on depression. However, when compared to traditional face-to-face CBT, T-CBT did not show a significant difference in treating depression.
Conclusion
This meta-analytic study aimed to assess the efficacy of Telephone-Based Cognitive Behavioral Therapy (T-CBT) in reducing psychological distress compared to other interventions (Treatment as Usual (TAU) or face-to-face CBT). The findings from the analysis of T-CBT versus TAU interventions indicated a large effect size (ES) for depression, a moderate ES for anxiety, and small ES for coping strategies, mental Quality of Life (QoL), sleep disturbances, and worry. No significant differences were observed between T-CBT and TAU for physical QoL. The combined analysis across all outcomes revealed a moderate overall ES, suggesting that T-CBT may be more effective than TAU in alleviating psychological symptomatology.
Specifically, T-CBT demonstrated superior efficacy in reducing depression, anxiety, worry, and improving sleep disturbances compared to TAU. Given the prevalence of depressive symptoms in individuals with chronic diseases, T-CBT’s effectiveness in addressing this condition, especially when accessibility to traditional therapy is limited, holds significant clinical relevance. The study suggested that T-CBT might offer successful strategies for managing uncertainty, thereby reducing anxiety and improving QoL.
The meta-analysis indicated that T-CBT interventions could enhance coping abilities, particularly crucial for individuals dealing with the stressors associated with chronic and mental diseases. T-CBT was found to increase mental QoL, emphasizing its potential in improving overall well-being among these populations. However, no significant differences were observed in physical QoL, warranting further investigation with larger samples and randomized controlled trial (RCT) designs.
In the analysis comparing T-CBT with face-to-face CBT on depression, no significant differences were found, supporting the notion that T-CBT could be a valid substitute for traditional CBT, particularly in exceptional circumstances where traditional delivery is challenging. Nevertheless, the study acknowledged the limitations of T-CBT, such as the absence of certain communication channels, urging careful consideration of therapeutic preferences and circumstances.
The study emphasized the need for more rigorous RCTs in psychotherapy research, acknowledging the challenge of blinding procedures. While the results were derived from peer-reviewed journals, the absence of gray literature might introduce a potential bias. Despite these limitations, the study concluded that T-CBT could be a suitable psychotherapeutic option for individuals with chronic and mental diseases, offering improved access to evidence-based treatments and potentially reducing the economic burden on national healthcare systems.
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