Web-Based Therapist-Guided Mindfulness-Based Cognitive Behavioral Therapy for Body Dysmorphic Disorder: Pilot Randomized Controlled Trial.

BACKGROUND: Internet-based cognitive behavioral therapy (CBT) and stand-alone mindfulness meditation interventions are gaining empirical support for a wide variety of mental health conditions. In this study, we test the efficacy of web-based therapist-guided mindfulness-based cognitive behavioral therapy (CBT-M) for body dysmorphic disorder (BDD), a psychiatric disorder characterized by preoccupations with perceived defects in appearance. OBJECTIVE: This study aims to determine whether CBT-M for BDD delivered on the web is feasible and acceptable and whether mindfulness meditation adds to CBT treatment effects for BDD. METHODS: In this 8-week, 2-arm, parallel pilot randomized controlled trial, n=28 adults (aged between 18 and 55 years) were randomly allocated to an experimental group (web-based therapist-guided CBT-M) or a control group (web-based therapist-guided CBT). Study retention, accrual, and intervention adherence were assessed, along with self-report measures for BDD, depression, anxiety, and pain intensity taken at baseline and postintervention. RESULTS: This study was feasible to implement and deemed acceptable by participants. After 8 weeks, significant improvements were found on all outcome measures for both treatment groups, and large between-group effect sizes favoring CBT-M were found for BDD symptom severity (d=-0.96), depression (d=-1.06), pain severity (d=-1.12), and pain interference (d=-1.28). However, linear mixed models demonstrated no significant differences between the groups over 8 weeks. CONCLUSIONS: The results suggest that mindfulness meditation may add to beneficial web-based CBT treatment effects for BDD. An adequately powered randomized control trial of web-based CBT-M is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05402475, http://clinicaltrials.gov/ct2/show/NCT05402475.

Clinical Assessment of Eye Movement Desensitization and Reprocessing in Memory Distress: Protocol for a Double-Blinded Randomized Controlled Trial.

BACKGROUND: Exposures to "traumatic" events are widespread and can cause posttraumatic stress disorder (PTSD). Cognitive behavioral therapy and eye movement desensitization and reprocessing (EMDR) are frequently used and validated behavioral PTSD treatments. Despite demonstrated effectiveness, highly upsetting memory reactions can be evoked, resulting in extensive distress and, sometimes, treatment dropout. In recent years, multiple treatment approaches have aimed at reducing such upsetting memory reactions to traumatic memories while therapeutic progress proceeds. One of these methods, the flash technique (FT), a modification of standard EMDR (S-EMDR), appears effective in distressing memory reduction. This study will examine FT-EMDR and S-EMDR efficacies when both methods are delivered via web-based video. OBJECTIVE: This study aims to assess the relative efficacy of (web-based) FT-EMDR versus S-EMDR in reducing the PTSD symptoms, anxieties, and depression associated with traumatic memories at postintervention and 1-month follow-up. METHODS: This double-blinded, web-based, 2-arm randomized controlled trial will employ self-report outcomes. A total of 90 participants will be identified from the web-based CloudResearch platform and randomly allocated to the experimental or comparison group. Inclusion criteria are as follows: (1) approved for engagement by the CloudResearch platform; (2) 25-60 years of age; (3) residing in Canada or the United States; (4) a recalled disturbing memory of an event >2 years ago that has not repeated and was moderately or more upsetting during occurrence; (5) memory moderately or more upsetting at baseline and not linked to an earlier memory that is equally or more than equally disturbing. Exclusion criteria are bipolar disorder, borderline personality disorder, obsessive-compulsive disorder, schizophrenia, substance abuse or addiction in the past 3 months, suicidal ideation, and suicide attempt in the past 6 months. Interventions include guided video instruction of full FT or guided video of EMDR. Outcome measures are as follows: Primary outcome is PTSD symptoms that are measured by the PTSD Checklist for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders-5) at 1-month follow-up. Secondary outcomes are State Anxiety subscale of State-Trait Anxiety Inventory at baseline, postintervention, and 1-month follow-up; Trait Anxiety subscale of State-Trait Anxiety Inventory; depression (Patient Health Questionnaire-9); and Positive and Negative Affect Schedule measured at 1-month follow-up. RESULTS: If, at 1-month follow-up, the web-based FT-EMDR intervention is more effective in reducing PTSD symptoms (as measured by the PTSD Checklist for DSM-5) than EMDR, it may help reduce traumatic memory distress in multiple contexts. CONCLUSIONS: This randomized controlled trial will advance current understandings of PTSD symptoms and interventions that target traumatic memory-related distress. TRIAL REGISTRATION: ClinicalTrials.gov NCT05262127; https://clinicaltrials.gov/ct2/show/NCT05262127.

Online Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth With Major Depressive Disorders: Randomized Controlled Trial.

BACKGROUND: Approximately 70% of mental health disorders appear prior to 25 years of age and can become chronic when ineffectively treated. Individuals between 18 and 25 years old are significantly more likely to experience mental health disorders, substance dependencies, and suicidality. Treatment progress, capitalizing on the tendencies of youth to communicate online, can strategically address depressive disorders. OBJECTIVE: We performed a randomized controlled trial (RCT) that compared online mindfulness-based cognitive behavioral therapy (CBT-M) combined with standard psychiatric care to standard psychiatric care alone in youth (18-30 years old) diagnosed with major depressive disorder. METHODS: Forty-five participants were randomly assigned to CBT-M and standard care (n=22) or to standard psychiatric care alone (n=23). All participants were provided standard psychiatric care (ie, 1 session per month), while participants in the experimental group received an additional intervention consisting of the CBT-M online software program. Interaction with online workbooks was combined with navigation coaching delivered by phone and secure text messaging. RESULTS: In a two-level linear mixed-effects model intention-to-treat analysis, significant between-group differences were found for the Beck Depression Inventory-II score (difference -8.54, P=.01), Quick Inventory of Depressive Symptoms score (difference -4.94, P=.001), Beck Anxiety Inventory score (difference -11.29, P<.001), and Brief Pain Inventory score (difference -1.99, P=.03), while marginal differences were found for the Five Facet Mindfulness Questionnaire-Nonjudging subscale (difference -2.68, P=.05). CONCLUSIONS: These results confirm that youth depression can be effectively treated with online CBT-M that can be delivered with less geographic restriction. TRIAL REGISTRATION: Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052.

Correction: A Mindfulness-Based Intervention for Student Depression, Anxiety, and Stress: Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/23491.].

A Mindfulness-Based Intervention for Student Depression, Anxiety, and Stress: Randomized Controlled Trial.

BACKGROUND: University students are experiencing higher levels of distress and mental health disorders than before. In addressing mental health needs, web-based interventions have shown increasing promise in overcoming geographic distances and high student-to-counselor ratios, leading to the potential for wider implementation. The Mindfulness Virtual Community (MVC) program, a web-based program, guided by mindfulness and cognitive behavioral therapy principles, is among efforts aimed at effectively and efficiently reducing symptoms of depression, anxiety, and perceived stress in students. OBJECTIVE: This study's aim was to evaluate the efficacy of an 8-week MVC program in reducing depression, anxiety, and perceived stress (primary outcomes), and improving mindfulness (secondary outcome) in undergraduate students at a large Canadian university. Guided by two prior randomized controlled trials (RCTs) that each demonstrated efficacy when conducted during regular university operations, this study coincided with a university-wide labor strike. Nonetheless, the students' response to an online mental health program on a disrupted campus can provide useful information for anticipating the impact of other disruptions, including those related to the COVID-19 pandemic as well as future disruptions. METHODS: In this parallel-arm RCT, 154 students were randomly allocated to an 8-week MVC intervention (n=76) or a wait-list control (WLC) condition (n=78). The MVC intervention included the following: (1) educational and mindfulness video modules, (2) anonymous peer-to-peer discussions, and (3) anonymous, group-based, professionally guided, 20-minute videoconferences. Study outcomes were evaluated at baseline and at 8-week follow-up using the following: Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), the Perceived Stress Scale (PSS), and the Five Facets Mindfulness Questionnaire Short Form (FFMQ-SF). Generalized estimation equations with an AR (1) covariance structure were used to evaluate the impact of the intervention, with outcome evaluations performed on both an intention-to-treat (ITT) and per-protocol (PP) basis. RESULTS: Participants (n=154) included 35 males and 117 females with a mean age of 23.1 years. There were no statistically significant differences at baseline between the MVC and WLC groups on demographics and psychological characteristics, indicating similar demographic and psychological characteristics across the two groups. Results under both ITT and PP approaches indicated that there were no statistically significant between-group differences in PHQ-9 (ITT: ß=-0.44, P=.64; PP: ß=-0.62, P=.053), BAI (ITT: ß=-2.06, P=.31; PP: ß=-2.32, P=.27), and FFMQ-SF (ITT: ß=1.33, P=.43; PP: ß=1.44, P=.41) compared to WLC. There was a significant difference for the PSS (ITT: ß=-2.31, P=.03; PP: ß=-2.38, P=.03). CONCLUSIONS: During a university labor strike, the MVC program led to statistically significant reductions in PSS compared to the WLC group, but there were no other significant between-group differences. Comparisons with previous cycles of intervention testing, undertaken during nondisrupted university operations, when efficacy was demonstrated, are discussed. TRIAL REGISTRATION: ISRCTN Registry ISRCTN92827275; https://www.isrctn.com/ISRCTN92827275.

Wearable Technology and Physical Activity Behavior Change in Adults With Chronic Cardiometabolic Disease: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the effectiveness of wearable device interventions (eg, Fitbit) to improve physical activity (PA) outcomes (eg, steps/day, moderate to vigorous physical activity [MVPA]) in populations diagnosed with cardiometabolic chronic disease. DATA SOURCE: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses, an electronic search of 5 databases (Medline, PsychINFO, Scopus, Web of Science, and PubMed) was conducted. STUDY INCLUSION AND EXCLUSION CRITERIA: Randomized controlled trials (RCTs) published between January 2000 and May 2018 that used a wearable device for the full intervention in adults (18+) diagnosed with a cardiometabolic chronic disease were included. Excluded trials included studies that used devices at pre-post only, devices that administered medication, and interventions with no prospective control group comparison. DATA EXTRACTION: Thirty-five studies examining 4528 participants met the inclusion criteria. Study quality and RCT risk of bias were assessed using the Cochrane Collaboration Tool. DATA SYNTHESIS: Meta-analyses to compute PA (eg, steps/day) and selected physical dispersion and summary effects were conducted using the raw unstandardized pooled mean difference (MD). Sensitivity analyses were examined. RESULTS: Statistically significant increases in PA steps/day (MD = 2592 steps/day; 95% confidence interval [CI]: 1689-3496) and MVPA min/wk (MD = 36.31 min/wk; 95% CI: 18.33-54.29) were found for the intervention condition. CONCLUSION: Wearable devices positively impact physical health in clinical populations with cardiometabolic diseases. Future research using the most current technologies (eg, Fitbit) will serve to amplify these findings.

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.

BACKGROUND: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users' pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

Personal Health Coaching as a Type 2 Diabetes Mellitus Self-Management Strategy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: Personal health coaching (PHC) programs have become increasingly utilized as a type 2 diabetes mellitus (T2DM) self-management intervention strategy. This article evaluates the impact of PHC programs on glycemic management and related psychological outcomes. DATA SOURCES: Electronic databases (CINAHL, MEDLINE, PubMed, PsycINFO, and Web of Science). STUDY INCLUSION AND EXCLUSION CRITERIA: Randomized controlled trials (RCT) published between January 1990 and September 2017 and focused on the effectiveness of PHC interventions in adults with T2DM. DATA EXTRACTION: Using prespecified format guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. DATA SYNTHESIS: Quantitative synthesis for primary (ie, hemoglobin A1c [HbA1c]) and qualitative synthesis for selected psychological outcomes. RESULTS: Meta-analyses of 22 selected publications showed PHC interventions favorably impact HbA1c levels in studies with follow-ups at ≤3 months (-0.32% [95% confidence interval, CI = -0.55 to -0.09%]), 4 to 6 months (-0.50% [95% CI = -0.65 to -0.35%], 7 to 9 months (-0.66% [95% CI = -1.04 to -0.28%]), and 12 to 18 months (-0.24% [95% CI = -0.38 to -0.10%]). Subsequent subgroup analyses led to no conclusive patterns, except for greater magnitude of effect size in studies with conventional (2-arm) RCT design. CONCLUSIONS: The PHC appears effective in improving glycemic control. Further research is required to assess the effectiveness of specific program components, training, and supervision approaches and to determine the cost-effectiveness of PHC interventions.

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation.

BACKGROUND: Pain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. OBJECTIVE: The main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. METHODS: User engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). RESULTS: The clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). CONCLUSIONS: Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems.