Developing digital interventions for a post-Covid world: A smartphone-based approach-avoidance training to reduce alcohol craving.

Harmful alcohol use is a major public health issue. In-person treatment has been hindered by the restrictions necessary during the Covid-19 pandemic. This study examined the effects of an at-home smartphone-based cognitive bias modification training in heavy drinkers. Experiment 1 tested the effect of a short 20-30-min smartphone-based approach-avoidance training (AAT) on image-induced craving at a 1-day follow-up. Sixty-two participants consuming 14+ units of alcohol/week were allocated to either the training or waitlist group. Experiment 2 used an updated version of the same short AAT intervention with a sample of n = 107 participants who consumed 20+ units of alcohol/week. Training effects at 1-week follow-up were compared to an active control group. Experiment 1 showed a significant reduction in image-induced craving for the training group at 1-day follow-up. Experiment 2 found that AUDIT weekly scores were significantly reduced at 1-week follow-up for the training group, all the while craving for soft drinks remained unchanged. Experiment 1 served as a first proof of concept for the efficacy of the new smartphone-based AAT training, and experiment 2 suggested that training effects on problem alcohol use hold at 1-week follow-up.

The Case for the Patient-Centric Development of Novel Digital Sleep Assessment Tools in Major Depressive Disorder.

Background: Depression imposes a major burden on public health as the leading cause of disability worldwide. Sleep disturbance is a core symptom of depression that affects the vast majority of patients. Nonetheless, it is frequently not resolved by depression treatment and may even be worsened through some pharmaceutical interventions. Disturbed sleep negatively impact patients' quality of life, and persistent sleep disturbance increases the risk of recurrence, relapse, and even suicide. However, the development of novel treatments that might improve sleep problems is hindered by the lack of reliable low-burden objective measures that can adequately assess disturbed sleep in this population. Summary: Developing improved digital measurement tools that are fit for use in clinical trials for major depressive disorder could promote the inclusion of sleep as a focus for treatment, clinical drug development, and research. This perspective piece explores the path toward the development of novel digital measures, reviews the existing evidence on the meaningfulness of sleep in depression, and summarizes existing methods of sleep assessments, including the use of digital health technologies. Key Messages: Our objective was to make a clear call to action and path forward for the qualification of new digital outcome measures which would enable assessment of sleep disturbance as an aspect of health that truly matters to patients, promoting sleep as an important outcome for clinical development, and ultimately ensure that disturbed sleep will not remain the forgotten symptom of depression.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

The effects of mental health problems in childhood and adolescence in young adults: Results of the KiGGS cohort.

Mental health problems in childhood and adolescence may have effects into adulthood. With the KiGGS cohort, data are available for the first time that can be used to track the effects of internalising and externalising problems in childhood or adolescence into young adulthood on a national database. From the KiGGS baseline survey (2003-2006) to KiGGS Wave 2 (2014-2017), a total of 3,546 children and adolescents aged 11 to 17 years were tracked over a period of eleven years into young adulthood. Mental health problems in childhood or adolescence were variously associated with impaired mental health, lower life satisfaction and poorer quality of life and indicators of sexual and reproductive health in young adulthood. When psychosocial protective factors at the time of the KiGGS baseline survey were considered, the longitudinal correlations of internalising and externalising problems with indicators of mental health, life satisfaction and physical and psychological quality of life decreased, as did, to a lesser extent, the correlations with indicators of sexual and reproductive health and, for externalising disorders, also with low educational status (reference: medium). Implications for prevention and intervention are discussed.

Understanding the neural basis of cognitive bias modification as a clinical treatment for depression.

[Correction Notice: An Erratum for this article was reported in Vol 85(3) of Journal of Consulting and Clinical Psychology (see record 2017-07144-002). In the article, there was an error in the Discussion section's first paragraph for Implications and Future Work. The in-text reference citation for Penton-Voak et al. (2013) was incorrectly listed as "Blumenfeld, Preminger, Sagi, and Tsodyks (2006)". All versions of this article have been corrected.] Objective: Cognitive bias modification (CBM) eliminates cognitive biases toward negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions. METHOD: We investigated 2 new CBM methodologies using biologically plausible synaptic learning mechanisms-continuous transformation learning and trace learning-which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training. We first describe simulations with a simplified 1-layer neural network, and then we describe simulations in a biologically detailed multilayer neural network model of the ventral visual pathway. RESULTS: After training with either the continuous transformation learning rule or the trace learning rule, the 1-layer neural network eliminated biases in interpreting neutral stimuli as sad. The multilayer neural network trained with realistic face stimuli was also shown to be able to use continuous transformation learning or trace learning to reduce biases in the interpretation of neutral stimuli. CONCLUSIONS: The simulation results suggest 2 biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, that may subserve CBM. The results are highly informative for the development of experimental protocols to produce optimal CBM training methodologies with human participants. (PsycINFO Database Record