Imagine your mood: Study design and protocol of a randomized controlled micro-trial using app-based experience sampling methodology to explore processes of change during relapse prevention interventions for recurrent depression

Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers

BACKGROUND

Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation.

OBJECTIVE

In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies.

METHODS

Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week.

RESULTS

We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process.

CONCLUSIONS

This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention.

[Symptom Monitoring and Detection of Early Warning Signs in Bipolar Episodes Via App - Views of Patients and Relatives on e-Health Need]

BACKGROUND

The onset and early warning signs of episodes of bipolar disorder are often realized late by those affected. The earlier an incipient episode is treated, the more prognostically favorable the course will be. Symptom monitoring via smartphone application (app) could be an innovative way to recognize and react to early warning signs more swiftly. The aim of this study was to find out whether patients and their relatives consider technical support through an app to be useful and practical in the early warning sign detection and treatment.

METHODS

In the present study, 51 patients with bipolar disorder and 28 relatives were interviewed. We gathered information on whether participants were able to perceive early warning signs in form of behavioral changes sufficiently and in a timely fashion and also whether they would use an app as treatment support tool.

RESULTS

Although 94.1% of the surveyed patients and 78.6% of their relatives felt that they were well informed about the disease, 13.7% and 35.7%, respectively were not fully satisfied with the current treatment options. Early warning signs of every depressive development were noticed by 25.5% of the patients (relatives 10.7%). Every (hypo)manic development was only noticed by 11.8% of the patients (relatives 7.1%); 88.2% of the patients and 85.7% of the relatives noticed the same symptoms recurrently at the beginning of a depression and 70.6% and 67.9%, respectively, at the beginning of a (hypo)manic episode (in particular changes in physical activity, communication behavior and the sleep-wake rhythm). 84.3% of the patients and 89.3% of the relatives stated that they considered technical support that draws attention to mood and activity changes as useful and that they would use such an app for the treatment.

DISCUSSION

The current options for perceiving early warning signs of a depressive or (hypo)manic episode in bipolar disorder are clinically inadequate. Those affected and their relatives desire innovative, technical support. Early detection of symptoms, which often manifest themselves in changes in behavior or activity patterns, is essentiell for managing the course of bipolar disorder. In the future, smartphone apps could be used for clinical treatment and research through objective, continuous and.

Mental Imagery in the Science and Practice of Cognitive Behaviour Therapy: Past, Present, and Future Perspectives

Mental imagery has a long history in the science and practice of cognitive behaviour therapy (CBT), stemming from both behavioural and cognitive traditions. The past decade or so has seen a marked increase in both scientific and clinical interest in mental imagery, from basic questions about the processes underpinning mental imagery and its roles in everyday healthy functioning, to clinical questions about how dysfunctions in mental imagery can cause distress and impairment, and how mental imagery can be used within CBT to effect therapeutic change. This article reflects on the current state of mental imagery in the science and practice of CBT, in the context of past developments and with a view to future challenges and opportunities. An ongoing interplay between the various strands of imagery research and the many clinical innovations in this area is recommended in order to realise the full therapeutic potential of mental imagery in CBT.

Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool

Background: Sleep disturbances are common early warning signs of an episode of bipolar disorder, and early recognition can favorably impact the illness course. Symptom monitoring via a smartphone app is an inexpensive and feasible method to detect an early indication of changes such as sleep. The study aims were (1) to assess the acceptance of apps and (2) to validate sleeping times measured by the smartphone app UP!. Methods:UP! was used by 22 individuals with bipolar disorder and 23 controls. Participants recorded their time of falling asleep and waking-up using UP! for 3 weeks. Results were compared to a validated accelerometer and the Pittsburgh Sleep Quality Index. Additionally, participants were interviewed regarding early warning signs and their feedback for apps as monitoring tools in bipolar disorder (NCT03275714). Results: With UP!, our study did not find strong reservations concerning data protection or continual smartphone usage. Correlation analysis demonstrates UP! to be a valid tool for measuring falling asleep and waking-up times. Discussion: Individuals with bipolar disorder assessed the measurement of sleep disturbances as an early warning sign with a smartphone as positive. The detection of early signs could change an individual's behavior and strengthen self-management. The study showed that UP! can be used to measure changes in sleep durations accurately. Further investigation of smartphone apps' impact to measure other early signs could significantly contribute to clinical treatment and research in the future through objective, continuous, and individual data collection.

Redefining Therapeutic Outcomes of Depression Treatment

Responses to evidence-based interventions for depression are divergent: Some patients benefit more than others during treatment and some do not benefit at all or even deteriorate. Tailoring interventions to the individual may improve outcomes. However, such personalization of evidence-based treatment in depression requires investigation of individual outcomes and the individual trajectories towards these outcomes. This theoretical paper provides a critical reflection on individual outcomes of depression treatment. First, it is argued that outcomes should be broadened, from a focus on mainly depressive symptomatology to recovery in different domains. It is acknowledged that recovery from depression reflects a personal journey that differs from person to person. Second, outcome measures should be lengthened beyond the acute treatment phase, taking a lifetime perspective on depression. The challenge then is to discover which trajectories of what measures during what interventions result in personalized sustainable recovery and for whom. Routine outcome monitoring systems may be used to inform this quest towards assessment of personalized sustainable therapeutic outcomes. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify predictors of personalized sustainable recovery. Routine outcome monitoring systems may eventually be used to implement personalized treatments for depression that result in personalized sustainable recovery.

Computerized positive mental imagery training versus cognitive control training versus treatment as usual in inpatient mental health settings: study protocol for a randomized controlled feasibility trial

Background

Enhancing the capacity to experience positive affect could help improve recovery across a range of areas of mental health. Experimental psychopathology research indicates that a computerized cognitive training paradigm involving generation of positive mental imagery can increase state positive affect, and more recent clinical studies have suggested that this training could be used as an adjunct treatment module to target symptoms related to positive affect deficits, specifically anhedonia. The current study investigates the feasibility of adding a positive mental imagery computerized training module to treatment for patients in inpatient mental health settings, with a focus on increasing positive affect and reducing anhedonia. The positive mental imagery training (PMIT) is added to treatment as usual (TAU) in the inpatient setting, and compared to TAU alone, or TAU plus an alternative cognitive training module not hypothesized to increase positive affect, cognitive control training (CCT).

Methods

The study is a feasibility randomized controlled trial with three parallel arms. Up to 90 patients admitted to inpatient mental health treatment clinics in Germany will be randomized to PMIT + TAU, CCT + TAU, or TAU on a 1:1:1 ratio. PMIT or CCT consist of an introductory session followed by up to 8 full training sessions over 2 weeks. All three arms (including TAU) include regular completion of mood measures over the 2-week period. Outcome measures are completed pre and post this 2-week training/monitoring period, and at 2-week follow-up. Data will be presented in the form of both raw means and standardized effect sizes, with 95% confidence intervals, for both intention-to-treat and per-protocol samples.

Discussion

The study will inform feasibility of conducting a fully powered randomized controlled trial investigating the addition of the positive mental imagery training as a treatment adjunct to inpatient treatments for mental health, including potential refinement of study procedures, inclusion/exclusion criteria, and preliminary indications of the likely range of effect sizes.

Trial registration

clinicaltrials.gov, NCT02958228 (date registered: 4 November 2016).