Cognitive-Behavioral Therapy in the Digital Age: Presidential Address

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants' (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI[0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person's responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.

Remote CBT for Psychosis During the COVID-19 Pandemic: Challenges and Opportunities

The COVID pandemic is now leading to the emergence of a secondary mental health pandemic. Clients with psychosis are at increased risk of poorer medium- and long-term psychosocial and clinical outcomes. In response to the pressing need to flexibly deliver high-quality care to individuals with psychosis, this brief report proposes high yield cognitive behavioral techniques for psychosis (HY-CBt-p) facilitated by task sharing and digital enhancements. HY-CBt-p is delivered over fewer sessions than formulation-based Cognitive Behavioral Therapy for psychosis (CBTp), can be learned by a range of providers, and includes techniques such as developing a normalizing explanation; techniques to reduce anxiety, depression, and insomnia, which perpetuate psychotic symptoms; self-monitoring; reality testing; and wellness planning. Previous research suggests that effect sizes will be lower than that of 16-session formulation-driven CBTp, but additional research is needed to test the feasibility, acceptability, efficacy, and comparative effectiveness of different forms of remote-delivered CBTp.

A Mobile Phone-Based App for Use During Cognitive Behavioral Therapy for Adolescents With Anxiety (MindClimb): User-Centered Design and Usability Study

Background

Mobile device–based tools to help adolescents practice skills outside of cognitive behavioral therapy (CBT) sessions for treating an anxiety disorder may lead to greater treatment gains.

Objective

This study aimed to develop, design, and test the acceptability, learnability, heuristics, and usability of MindClimb, a smartphone-based app for adolescents with anxiety to use between CBT sessions to plan and complete exposure activities using skills (cognitive, relaxation, exposure practice, and reward) learned in treatment.

Methods

This 3-phase study took place from August 2015 to December 2018. In phase 1, the app was designed and developed in consultation with young people and CBT therapists to identify desired functions and content. Feedback was subjected to thematic analysis using a general inductive approach. In phase 2, we conducted 2 high-fidelity testing sessions using the think-aloud approach (acceptability, learnability, usability) and 10-item System Usability Scale with 10 adolescents receiving CBT. The high-fidelity MindClimb app was evaluated by 5 app developers based on Nielsen’s usability heuristics and 5-point severity ranking scale. In phase 3, a total of 8 adolescents and 3 therapists assessed the usability of MindClimb during CBT sessions by recording the frequency of skills practice, use of MindClimb features, satisfaction with the app, and barriers and facilitators to app use during treatment.

Results

Feedback from phase 1 consultations indicated that the app should (1) be responsive to user needs and preferences, (2) be easy to use and navigate, (3) have relevant content to the practice of CBT for anxiety, and (4) be aesthetically appealing. Using this feedback as a guide, a fully functional app prototype for usability testing and heuristic evaluation was developed. In phase 2, think-aloud and usability data resulted in minor revisions to the app, including refinement of exposure activities. The average system usability score was 77 in both testing cycles, indicating acceptable usability. The heuristic evaluation by app developers identified only minor errors (eg, loading speed of app content, with a score of 1 on the severity ranking scale). In phase 3, adolescents considered app features for completing exposure (6.2/10) and relaxation (6.4/10) modestly helpful. Both adolescents (average score 11.3/15, SD 1.6) and therapists (average score 10.0/12, 2.6 SD) reported being satisfied with the app.

Conclusions

The user-centered approach to developing and testing MindClimb resulted in a mobile health app that can be used by adolescents during CBT for anxiety. Evaluation of the use of this app in a clinical practice setting demonstrated that adolescents and therapists generally felt it was helpful for CBT practice outside of therapy sessions. Implementation studies with larger youth samples are necessary to evaluate how to optimize the use of technology in clinical care and examine the impact of the app plus CBT on clinical care processes and patient outcomes.

Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

The literature on affective determinants of physical activity (PA) is growing rapidly. The present paper aims to provide greater clarity regarding the definition and distinctions among the various affect-related constructs that have been examined in relation to PA. Affective constructs are organized according to the Affect and Health Behavior Framework (AHBF), including: (1) affective response (e.g., how one feels in response to PA behavior) to PA; (2) incidental affect (e.g., how one feels throughout the day, unrelated to the target behavior); (3) affect processing (e.g., affective associations, implicit attitudes, remembered affect, anticipated affective response, and affective judgments); and (4) affectively charged motivational states (e.g., intrinsic motivation, fear, and hedonic motivation). After defining each category of affective construct, we provide examples of relevant research showing how each construct may relate to PA behavior. We conclude each section with a discussion of future directions for research.

Digital privacy in mental healthcare: current issues and recommendations for technology use

Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on digital software and hardware. Emails, text messaging, telepsychology/telemental health therapy, electronic medical records, cloud-based storage, apps/applications, and assessments are now available within the provision of services. Of those mentioned, some are directly utilized for psychotherapy while others indirectly aid providers. Whereas professionals previously wrote notes locally, technology has empowered providers to work more efficiently with third-party services and solutions. However, the implementation of these advancements in mental healthcare involves consequences to digital privacy and might increase clients' risk of unintended breaches of confidentiality. This manuscript reviews common technologies, considers the vulnerabilities therein, and proposes suggestions to strengthen privacy.

Using digital technologies in clinical trials: Current and future applications

In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a methodological tool and as a mechanism to deliver interventions. At that time, there was limited guidance and limited use of digital technologies in clinical research. However, since then smartphones have become ubiquitous and digital health technologies have exploded. This paper provides an update to our earlier publication and an overview of how technology has been used in the past five years in clinical trials, providing examples with varying levels of technological integration and across different health conditions. Digital technology integration ranges from the incorporation of artificial intelligence in diagnostic devices to the use of real-world data (e.g., electronic health records) for study recruitment. Clinical trials can now be conducted entirely virtually, eliminating the need for in-person interaction. Much of the published research demonstrates how digital approaches can improve the design and implementation of clinical trials. While challenges remain, progress over the last five years is encouraging, and barriers can be overcome with careful planning.

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

BACKGROUND

Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.

OBJECTIVE

This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.

METHODS

In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.

RESULTS

The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.

CONCLUSIONS

These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

Stakeholder Perceptions of Internet-Delivered Cognitive Behavior Therapy as a Treatment Option for Alcohol Misuse: Qualitative Analysis

BACKGROUND

Internet-delivered cognitive behavior therapy (ICBT) has been found to be effective for treating alcohol misuse in research trials, but it is not available as part of routine care in Canada. Recent recommendations in the literature highlight the importance of integrating perspectives from both patient and health care stakeholders when ICBT is being implemented in routine practice settings.

OBJECTIVE

This study aimed to gain an understanding of how ICBT is perceived as a treatment option for alcohol misuse by interviewing diverse stakeholders. Specifically, the objectives were to (1) learn about the perceived advantages and disadvantages of ICBT for alcohol misuse and (2) elicit recommendations to inform implementation efforts in routine practice.

METHODS

A total of 30 participants representing six stakeholder groups (ie, patients, family members, academic experts, frontline managers, service providers, and health care decision makers) participated in semistructured interviews. To be included in the study, stakeholders had to reside in Saskatchewan, Canada, and have personal or professional experience with alcohol misuse. Interviews were transcribed verbatim, anonymized, and analyzed using thematic analysis.

RESULTS

Stakeholders identified numerous advantages of ICBT for alcohol misuse (eg, accessibility, convenience, privacy, relevance to technology-based culture, and fit with stepped care) and several disadvantages (eg, lack of internet access and technological literacy, isolation, less accountability, and unfamiliarity with ICBT). Stakeholders also provided valuable insight into factors to consider when implementing ICBT for alcohol misuse in routine practice. In terms of intervention design, stakeholders recommended a 6- to 8-week guided program that uses Web-based advertising, point-of-sale marketing, and large-scale captive audiences to recruit participants. With regard to treatment content, stakeholders recommended that the program focus on harm reduction rather than abstinence; be evidence based; appeal to the diverse residents of Saskatchewan; and use language that is simple, encouraging, and nonjudgmental. Finally, in terms of population characteristics, stakeholders felt that several features of the alcohol misuse population, such as psychiatric comorbidity, readiness for change, and stigma, should be considered when developing an ICBT program for alcohol misuse.

CONCLUSIONS

Stakeholders' insights will help maximize the acceptability, appropriateness, and adoption of ICBT for alcohol misuse and in turn contribute to implementation success. The methodology and findings from this study could be of benefit to others who are seeking to implement ICBT in routine practice.