Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field

[Developments in the digitalization of public health since 2020 : Examples from the Leibniz ScienceCampus Digital Public Health Bremen]

Digital public health has received a significant boost in recent years, especially due to the demands associated with the COVID-19 pandemic. In this report, we provide an overview of the developments in digitalization in the field of public health in Germany since 2020 and illustrate these with examples from the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH).The following topics are central: How do digital survey methods as well as digital biomarkers and artificial intelligence methods shape modern epidemiology and prevention research? What is the status of digitalization in public health offices? Which approaches to health economics evaluation of digital public health interventions have been utilized so far? What is the status of training and further education in digital public health?The first years of the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH) were also strongly influenced by the COVID-19 pandemic. Repeated population-based digital surveys of the LSC indicated an increase in use of health apps in the population, for example, in applications to support physical activity. The COVID-19-pandemic has also shown that the digitalization of public health enhances the risk of misinformation and disinformation.

Accessibility and feasibility of experience sampling methods for mental health research with people with intellectual disability: Scoping of research and stakeholder views

BACKGROUND

Experience sampling may be useful for mental health research with people with intellectual disability, and evidence of the potential benefits is starting to emerge. This multiple-method study identified potential avenues for tailoring this method to the needs of people with intellectual disability.

METHOD

A scoping review was conducted. Five databases were searched for experience sampling studies involving people with intellectual disability. In addition, seven adults with an intellectual disability tested experience sampling apps with standardised questions about mental health and were interviewed about their experiences in semi-structured interviews.

RESULTS

Seven studies were included in the scoping review. Two studies investigated acceptability and feasibility. In the interviews, participants reported on the acceptability, availability, and appropriateness of experience sampling applications.

CONCLUSIONS

There are still important gaps in knowledge about acceptability, availability, and appropriateness of experience sampling for this population. Researchers are recommended to tailor experience sampling applications to the needs and interests of individual users.

The Role of Childhood Trauma in Affective Stress Recovery in Early Psychosis: An Experience Sampling Study

BACKGROUND AND HYPOTHESES

Affective recovery, operationalized as the time needed for affect to return to baseline levels after daily stressors, may be a putative momentary representation of resilience. This study aimed to investigate affective recovery in positive and negative affect across subclinical and clinical stages of psychosis and whether this is associated with exposure to childhood trauma (sexual, physical, and emotional abuse).

STUDY DESIGN

We used survival analysis to predict the time-to-recovery from a daily event-related stressor in a pooled sample of 3 previously conducted experience sampling studies including 113 individuals with first-episode psychosis, 162 at-risk individuals, and 94 controls.

STUDY RESULTS

Negative affective recovery (ie, return to baseline following an increase in negative affect) was longer in individuals with first-episode psychosis compared with controls (hazard ratio [HR] = 1.71, 95% confidence interval [CI; 1.03, 2.61], P = .04) and in at-risk individuals exposed to high vs low levels of emotional abuse (HR = 1.31, 95% CI [1.06, 1.62], P = .01). Positive affective recovery (ie, return to baseline following a decrease in positive affect) did not differ between groups and was not associated with childhood trauma.

CONCLUSIONS

Our results give first indications that negative affective recovery may be a putative momentary representation of resilience across stages of psychosis and may be amplified in at-risk individuals with prior experiences of emotional abuse. Understanding how affective recovery contributes to the development of psychosis may help identify new targets for prevention and intervention to buffer risk or foster resilience in daily life.

What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies

Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.

Investigating Relationships Among Self-Efficacy, Mood, and Anxiety Using Digital Technologies: Randomized Controlled Trial

BACKGROUND

Digital tools assessing momentary parameters and offering interventions in people's daily lives play an increasingly important role in mental health research and treatment. Ecological momentary assessment (EMA) makes it possible to assess transient mental health states and their parameters. Ecological momentary interventions (EMIs) offer mental health interventions that fit well into individuals' daily lives and routines. Self-efficacy is a transdiagnostic construct that is commonly associated with positive mental health outcomes.

OBJECTIVE

The aim of our study assessing mood, specific self-efficacy, and other parameters using EMA was 2-fold. First, we wanted to determine the effects of daily assessed moods and dissatisfaction with social contacts as well as the effects of baseline variables, such as depression, on specific self-efficacy in the training group (TG). Second, we aimed to explore which variables influenced both groups' positive and negative moods during the 7-day study period.

METHODS

In this randomized controlled trial, we applied digital self-efficacy training (EMI) to 93 university students with elevated self-reported stress levels and daily collected different parameters, such as mood, dissatisfaction with social contacts, and specific self-efficacy, using EMA. Participants were randomized to either the TG, where they completed the self-efficacy training combined with EMA, or the control group, where they completed EMA only.

RESULTS

In total, 93 university students participated in the trial. Positive momentary mood was associated with higher specific self-efficacy in the evening of the same day (b=0.15, SE 0.05, P=.005). Higher self-efficacy at baseline was associated with reduced negative mood during study participation (b=-0.61, SE 0.30, P=.04), while we could not determine an effect on positive mood. Baseline depression severity was significantly associated with lower specific self-efficacy over the week of the training (b=-0.92, SE 0.35, P=.004). Associations between higher baseline anxiety with higher mean negative mood (state anxiety: b=0.78, SE 0.38, P=.04; trait anxiety: b=0.73, SE 0.33, P=.03) and lower mean positive mood (b=-0.64, SE 0.28, P=.02) during study participation were found. Emotional flexibility was significantly enhanced in the TG. Additionally, dissatisfaction with social contacts was associated with both a decreased positive mood (b=-0.56, SE 0.15, P<.001) and an increased negative mood (b=0.45, SE 0.12, P<.001).

CONCLUSIONS

This study showed several significant associations between mood and self-efficacy as well as those between mood and anxiety in students with elevated stress levels, for example, suggesting that improving mood in people with low mood could enhance the effects of digital self-efficacy training. In addition, engaging in 1-week self-efficacy training was associated with increased emotional flexibility. Future work is needed to replicate and investigate the training's effects in other groups and settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05617248; https://clinicaltrials.gov/study/NCT05617248.