Given the many statistical analysis options used for randomized controlled trials (RCTs) of behavioral interventions and the lack of clear guidance for analysis selection, the present study aimed to characterize the predominate statistical analyses utilized in RCTs in palliative care and behavioral research and to highlight the relative strengths and weaknesses of each of these methods as guidance for future researchers and reform.

All RCTs published between 2015 and 2021 were systematically extracted from 4 behavioral medicine journals and analyzed based on prespecified inclusion criteria. Two independent raters classified each of the manuscripts into 1 of 5 RCT analysis strategies.

There was wide variation in the methods used. The 2 most prevalent analyses for RCTs were longitudinal modeling and analysis of covariance. Application of method varied significantly by sample size.

Each statistical analysis presents its own unique strengths and weaknesses. The information resulting from this research may prove helpful for researchers in palliative care and behavioral medicine in navigating the variety of statistical methods available. Future discussion around best practices in RCT analyses is warranted to compare the relative impact of interventions in a more standardized way.

Previous randomized controlled trials (RCTs) suggest that attention control therapy (ACT), targeting aberrant fluctuations of attention toward and away from threats in patients with PTSD, may be effective in reducing symptoms. The current RCT examined whether the use of personalized-trauma stimuli enhances ACT efficacy in patients with PTSD. Additional moderators of treatment outcome were tested on an exploratory basis.

Sixty patients with PTSD were randomly assigned to either personalized ACT, non-personalized ACT, or a control condition. Changes in symptoms were examined across pre-treatment, post-treatment, and a 3-month follow-up. Attentional interference was examined pre- and post-treatment. Baseline clinical and cognitive indices as well as the time elapsed since the trauma were tested as potential moderators of treatment outcome.

A significant reduction in clinical symptoms was noted for all three conditions with no between-group differences. Attention bias variability decreased following ACT treatment. Personalized ACT was more effective relative to the control condition when less time had elapsed since the trauma. Baseline clinical and cognitive indices did not moderate treatment outcome.

In this RCT of patients with PTSD, ACT was no more effective in reducing PTSD symptoms than a control condition. The data also suggest a potential benefit of personalized ACT for patients who experienced their trauma more recently.

There is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.

A randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.

Those assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p = 0.001, 95% CI 0.26–0.70).

This trial demonstrates that a smartphone app can reduce depression symptoms and potentially prevent incident depression caseness and such interventions may have a role in improving working population mental health. Some caution in interpretation is needed regarding the clinical significance due to small effect size and trial attrition.

Trial Registration Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au/) ACTRN12617000548336

Concordant with an increased emphasis on consumer engagement, the Patient Global Impression Scale of Improvement (PGI-I) is commonly used as an outcome measure in studies evaluating the efficacy of treatments in medical and psychiatric conditions with subjective symptom domains. The current study evaluated the agreement between PGI-I and Clinician Global Impression Scale of Improvement (CGI-I) ratings and convergent validity of PGI-I among individuals with bipolar or major depressive disorders.

Data were derived from three double-blind, placebo-controlled, multicentre studies conducted from 2007 to 2015 among adult individuals (N = 472). Clinicians were asked to rate participants symptoms using the CGI-I as well as severity of depression by the Montgomery-Åsberg Depression (MADRS), quality of life (Q-LES-Q), social and occupational functioning (SOFAS), and functional impairment (LIFE–RIFT). Participants were asked to assess their symptom improvement with the PGI-I. Bland-Altman agreement plots and Intra-class correlations were used to evaluate agreement, and Spearman correlation coefficients were implemented to examine convergent validity. Sub-group analyses for disorder type (bipolar and major depression) were performed.

There was high agreement between the PGI-I and CGI-I ratings across follow-up time points (weeks 2, 4, 6, 8, 12, 16, 20, 24, and 28). Similar results were observed in male only and female only data and after adjustment for age and gender. Both PGI-I and CGI-I ratings were robustly positively correlated with MADRS, and LIFE-RIFT and negatively correlated with SOFAS and Q-LES-Q, supporting the convergent validity of the PGI-I. Sub-group analyses for bipolar and major depressive disorder showed similar findings.

Our findings support the utility of the PGI-I as a participant rated measure of global improvement among individuals with bipolar or major depressive disorders.

Severe health anxiety is frequent and costly, yet rarely diagnosed or treated. Earlier treatment studies show problems with recruitment, dropout and recovery. In the current study, the authors aimed to test the effect of acceptance and commitment group therapy (ACT-G) compared to waitlist in patients with severe health anxiety.

During March 2010 to April 2012, 126 consecutively referred patients meeting research criteria for severe health anxiety were block-randomized (1:1) to ACT-G or a 10 months’ waitlist (Clinicaltrials.gov, no. NCT01158430). Patients allocated to ACT-G were treated in seven groups of nine patients between December 2010 and October 2012 and received nine weekly 3-h group sessions and a booster session consisting of ACT techniques. The primary outcome was decided a priori as the mean change in self-reported illness worry on the Whiteley-7 Index (WI) from baseline to 10 months’ follow-up. Secondary outcomes were improvement in emotional distress and health-related quality of life at 10 months’ follow-up.

Intention-to-treat analysis showed a statistically significant mean difference of 20.5 points [95% confidence interval (CI) 11.7–29·4, p < 0.001] on the WI between the groups at 10 months, and the between-group effect sizes were large (Cohen's d = 0.89, 95% CI 0.50–1.29). The number needed to treat was 2.4 (95% CI 1.4–3.4, p < 0.001). Diagnosis and treatment were well accepted by the patients.

ACT-G seems feasible, acceptable and effective in treating severe health anxiety.

A multi-centre, four-arm trial (the PACE trial) found that rehabilitative cognitive behaviour therapy (CBT) and graded exercise therapy (GET) were more effective treatments for chronic fatigue syndrome (CFS) than specialist medical care (SMC) alone, when each was added to SMC, and more effective than adaptive pacing therapy (APT) when added to SMC. In this study we compared how many participants recovered after each treatment.

We defined recovery operationally using multiple criteria, and compared the proportions of participants meeting each individual criterion along with two composite criteria, defined as (a) recovery in the context of the trial and (b) clinical recovery from the current episode of the illness, however defined, 52 weeks after randomization. We used logistic regression modelling to compare treatments.

The percentages (number/total) meeting trial criteria for recovery were 22% (32/143) after CBT, 22% (32/143) after GET, 8% (12/149) after APT and 7% (11/150) after SMC. Similar proportions met criteria for clinical recovery. The odds ratio (OR) for trial recovery after CBT was 3.36 [95% confidence interval (CI) 1.64–6.88] and for GET 3.38 (95% CI 1.65–6.93), when compared to APT, and after CBT 3.69 (95% CI 1.77–7.69) and GET 3.71 (95% CI 1.78–7.74), when compared to SMC (p values ⩽0.001 for all comparisons). There was no significant difference between APT and SMC. Similar proportions recovered in trial subgroups meeting different definitions of the illness.

This study confirms that recovery from CFS is possible, and that CBT and GET are the therapies most likely to lead to recovery.