Use of Ecological Momentary Assessment and Intervention in Treatment With Adults

Process mining in mHealth data analysis

This perspective article explores how process mining can extract clinical insights from mobile health data and complement data-driven techniques like machine learning. Despite technological advances, challenges such as selection bias and the complex dynamics of health data require advanced approaches. Process mining focuses on analyzing temporal process patterns and provides complementary insights into health condition variability. The article highlights the potential of process mining for analyzing mHealth data and beyond.

Digital Platform for the Prevention of Suicidal Behaviour and Non-Suicidal Self-Injuries in Adolescents: The SmartCrisis-Teen Study Protocol

Suicidal behavior and Non-Suicidal Self-Injuries (NSSIs) are a major health problem in the adolescent population. New technologies can contribute to the development of innovative interventions in suicide prevention. Here, we present the SmartCrisis-Teen study protocol. The study consists of a randomized clinical trial which aims to evaluate the effectiveness of a digital safety plan to prevent suicidal behavior and NSSIs in adolescents. This is a multicentric study which will be conducted among the adolescent population, both in clinical and student settings, with a target sample of 1080 participants. The intervention group will receive an Ecological Momentary Intervention (EMI) consisting of a digital safety plan on their mobile phone. All participants will receive their Treatment As Usual (TAU). Participants will be followed for six months, with weekly and monthly telephone visits and face-to-face visits at three and six months. Participants will be assessed using traditional questionnaires as well as Ecological Momentary Assessment (EMA) and Implicit Association Tests (IATs). With this intervention, we expect a reduction in NSSIs through the acquisition of coping strategies and a decrease in suicidal behavior over the course of follow-up. This study provides a novel, scalable digital intervention for preventing suicidal behavior and NSSIs in adolescents, which could contribute to improving adolescent mental health outcomes globally.

A socioemotional network perspective on momentary experiences of family conflict in young adults

Family conflict is an established predictor of psychopathology in youth. Traditional approaches focus on between-family differences in conflict. Daily fluctuations in conflict within families might also impact psychopathology, but more research is needed to understand how and why. Using 21 days of daily diary data and 6-times a day experience-sampling data (N = 77 participants; mean age = 21.18, SD = 1.75; 63 women, 14 men), we captured day-to-day and within-day fluctuations in family conflict, anger, anxiety, and sadness. Using multilevel models, we find that days of higher-than-usual anger are also days of higher-than-usual family conflict. Examining associations between family conflict and emotions within days, we find that moments of higher-than-usual anger predict higher-than-usual family conflict later in the day. We observe substantial between-family differences in these patterns with implications for psychopathology; youth showing the substantial interplay between family conflict and emotions across time had a more perseverative family conflict and greater trait anxiety. Overall, findings indicate the importance of increases in youth anger for experiences of family conflict during young adulthood and demonstrate how intensive repeated measures coupled with network analytic approaches can capture long-theorized notions of reciprocal processes in daily family life.

Measuring digital intervention user experience with a novel ecological momentary assessment (EMA) method, CORTO

Digital interventions often suffer from low usage, which may reflect insufficient attention to user experience. Moreover, the existing evaluation methods have limited applicability in the remote study of user experience of complex interventions that have expansive content and that are used over an extensive period of time. To alleviate these challenges, we describe here a novel qualitative Ecological Momentary Assessment (EMA) method: the CORTO method (Contextual, One-item, Repeated, Timely, Open-ended). We used it to gather digital intervention user experience data from Finnish adults (n = 184) who lived with interview-confirmed major depressive disorder (MDD) and took part in a randomized controlled trial (RCT) that studied the efficacy of a novel 12-week game-based digital intervention for depression. A second dataset on user experience was gathered with retrospective interviews (n = 22). We inductively coded the CORTO method and retrospective interview data, which led to four user experience categories: (1) contextual use, (2) interaction-elicited emotional experience, (3) usability, and (4) technical issues. Then, we used the created user experience categories and Template Analysis to analyze both datasets together, and reported the results qualitatively. Finally, we compared the two datasets with each other. We found that the data generated with the CORTO method offered more insights into usability and technical categories than the interview data that particularly illustrated the contextual use. The emotional valence of the interview data was more positive compared with the CORTO data. Both the CORTO and interview data detected 55 % of the micro-level categories; 20 % of micro-level categories were only detected by the CORTO data and 25 % only by the interview data. We found that the during-intervention user experience measurement with the CORTO method can provide intervention-specific insights, and thereby further the iterative user-centered intervention development. Overall, these findings highlight the impact of evaluation methods on the categories and qualities of insights acquired in intervention research.

Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model

Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.

Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults

The objective of this study was to predict the level of depressive symptoms in emerging adults by analyzing sociodemographic variables, affect, and emotion regulation strategies. Participants were 33 emerging adults (M = 24.43; SD = 2.80; 56.3 % women). They were asked to assess their current emotional state (positive or negative affect), recent events that may relate to that state, and emotion regulation strategies through ecological momentary assessment. Participants were prompted randomly by an app 6 times per day between 10 am and 10 pm for a seven-day period. They answered 1233 of the 2058 surveys (beeps), collectively. The analysis of observations, using Machine Learning (ML) techniques, showed that the Random Forest algorithm yields significantly better predictions than other models. The algorithm used 13 out of the 36 variables adopted in the study. Furthermore, the study revealed that age, emotion of worried and a specific emotion regulation strategy related to social exchange were the most accurate predictors of severe depressive symptoms. By carefully selecting predictors and utilizing appropriate sorting techniques, these findings may provide valuable supplementary information to traditional diagnostic methods and psychological assessments.

The Effects of the Processing of Positive Memories Technique on Posttrauma Affect and Cognitions Among Survivors of Trauma: Protocol for a Daily Diary Study

BACKGROUND

The Processing of Positive Memories Technique (PPMT) is a promising new treatment approach for posttraumatic stress disorder (PTSD), which involves detailed narration and processing of specific positive autobiographical memories. Indeed, preliminary case-series studies have found reductions in PTSD symptoms, negative affect, and negative cognitions among survivors of trauma who have received PPMT. However, PPMT's effects have not been investigated at the daily level. In this study, we describe the protocol for a study that will examine the daily-level impacts of PPMT in a trauma-exposed, nonclinical community sample.

OBJECTIVE

This study uses an innovative research protocol that combines case-series design and daily diary approaches to examine changes in daily affect, daily cognitions, and daily PTSD symptoms pre- and post-PPMT. We hypothesize that at the daily level, in comparison to their own pre-PPMT levels, following the PPMT intervention, participants will report (1) a lower count of endorsed daily PTSD symptoms, (2) increases in daily positive affect and decreases in daily negative affect, (3) increases in positive affect reactivity to daily positive events, and (4) decreases in daily posttrauma cognitions.

METHODS

We are currently recruiting participants (target n=70) from a metroplex in the southwest United States. Following a screening survey, eligible participants complete a preintervention baseline survey, followed by 21 daily surveys in their natural environments. Then, they receive 4 PPMT sessions on a weekly basis. After the conclusion of the PPMT intervention, participants complete a postintervention outcome survey and 21 daily surveys. To compare daily affect, daily cognitions, and daily PTSD symptoms before and after PPMT, we will use the daily diary report data and conduct multilevel random intercepts and slopes linear regression models.

RESULTS

Data collection was initiated in March 2022 and is expected to end by June 2024. As of November 28, 2023, a total of 515 participants had consented to the study in the screening phase. No analyses will be conducted until data collection has been completed.

CONCLUSIONS

Study findings could clarify whether deficits in positive autobiographical memory processes may also characterize PTSD alongside deficits in traumatic memory processes. Furthermore, PPMT could be an additional therapeutic tool for clinicians to help clients reduce posttraumatic distress in their everyday lives.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51838.

Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study

BACKGROUND

Increasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated.

OBJECTIVE

We aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states.

METHODS

Using EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach.

RESULTS

During stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination).

CONCLUSIONS

This study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual's own data.

m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice

In this paper, we present m-Path (www.m-Path.io), an online platform that provides an easy-to-use and highly tailorable framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people's everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., retrospective surveys or lab-based experiments), EMA and EMI have become popular in recent years. Although a surge in the use of these methods has led to a myriad of EMA and EMI applications, many existing platforms only focus on a single aspect of daily life data collection (e.g., assessment vs. intervention, active self-report vs. passive mobile sensing, research-dedicated vs. clinically-oriented tools). With m-Path, we aim to integrate all of these facets into a single platform, as it is exactly this all-in-one approach that fosters the clinical utility of accumulated scientific knowledge. To this end, we offer a comprehensive platform to set up complex and highly adjustable EMA and EMI designs with advanced functionalities, using an intuitive point-and click web interface that is accessible for researchers and clinicians with limited programming skills. We discuss the strengths of daily life data collection and intervention in general and m-Path in particular. We describe the regular workflow to set up an EMA or EMI design within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software.

A Mobile Application Adjunct to Augment Cognitive-Behavioral Group Therapy for Adolescents with Social Anxiety: Feasibility and Acceptability Results from the Wiring Adolescents with Social Anxiety via Behavioral Interventions Pilot Trial

Objective: Cognitive-Behavioral Group Therapy (CBGT) is an established treatment for Social Anxiety (SA). However, diagnostic recovery rate is only 20.5% in CBGT, and up to 50% of patients remain symptomatic posttreatment. Using videocalls to deliver digital CBGT (dCBGT) is feasible, cost-effective, and efficacious. Yet, the impact of dCBGT on social functioning remains limited, as dCBGT does not offer opportunities for monitoring cognition and behavior in social situations. Wiring Adolescents with Social Anxiety via Behavioral Interventions (WASABI), a clinician-assisted application that uses ecological momentary assessments (EMAs), cognitive bias tests, and clinical self-reports, was investigated as an adjunct to dCBGT. Methods: A prospective, parallel arm, double-blind randomized controlled trial was employed in 24 SA adolescents randomly assigned to dCBGT versus dCBGT plus WASABI. Results: Study completion rates (83%) and exit survey data indicated that WASABI is feasible and acceptable. Engagement with EMAs varied from four to 244 EMAs completed per person. Cognitive bias tests and clinical self-reports were completed at least weekly by 53% and 69% of participants, respectively. While standard tests did not reveal statistically significant differences between dCBGT plus WASABI and dCBGT alone, effect sizes were greater for dCBGT plus WASABI on symptom severity, social skills, and functioning. Conclusions: Despite the small sample, preliminary results suggest that WASABI is feasible, acceptable, and may be an effective augmentation tool for treating SA in teenagers.

Relations Between Posttraumatic Stress Disorder Symptoms and Positive Memory Characteristics Among Women Reporting Intimate Partner Violence: A Micro-Longitudinal Study

Evidence links posttraumatic stress disorder (PTSD) symptoms to positive autobiographical memory characteristics. To extend this research, we uniquely utilized micro-longitudinal data to examine (1) the trajectory of PTSD symptom count across 30 days; and (2) if more vividness and accessibility of retrieved positive memories at the daily level predicted decreases in the trajectory of PTSD symptom count across 30 days. The current study was a secondary data analysis of a larger study. The sample included 74 women who reported physical or sexual victimization in the past 30 days by their current male partner and reported the use of alcohol and/or drugs during that time (M_age = 39.68 years; 37.80% with diagnostic PTSD; 43.2% White; 37.8% Black or African American). They completed thrice daily measures of PTSD symptoms and positive memory characteristics (vividness and accessibility) across 30 days. Results of the random effects longitudinal multilevel model indicated that, on average, the relation between PTSD symptom count and positive memory vividness was positive and statistically significant (0.19, 95% Confidence Interval [CI] [0.2, 0.35]); and the relation between PTSD symptom count and positive memory accessibility was positive and statistically significant (0.31, 95% CI [0.15, 0.47]). The relationship between PTSD symptom count and positive memory vividness/accessibility (i.e., slopes) varied significantly across participants, with a wide range of positive and negative regression coefficients. Future research needs to investigate why and how positive memory vividness and accessibility may relate to trajectories of PTSD symptoms over time, with potential clinical implications for positive memory interventions addressing PTSD.

Do positive memory characteristics influence daily-level trajectories of posttraumatic stress disorder symptoms? an exploratory daily diary study

BACKGROUND AND OBJECTIVES

Evidence links posttraumatic stress disorder (PTSD) with positive memory characteristics. To extend this research, we utilized daily diary data to examine (1) concurrent/lagged associations between daily PTSD symptom severity and positive memory vividness/accessibility; and (2) associations between baseline-assessed positive memory characteristics and changes in PTSD symptom severity over time.

DESIGN AND METHODS

A sample of 238 trauma-exposed participants (Mage = 21.19 years; 86% women) completed baseline and 10 daily measures of PTSD symptoms and positive memory characteristics. Multilevel models covaried for gender, number of trauma types, and number of completed surveys.

RESULTS

Days with greater PTSD symptom severity than an individual's average associated with less vividness (b = -0.02, p < .001) and accessibility (b = -0.02, p < .001) of the positive memory on the same day. Days with greater positive memory vividness (b = -1.06, p < .001) and accessibility (b = -0.93, p < .001) than an individual's average associated with less PTSD symptom severity on the same day. There were no significant lagged associations between these constructs. There were significant interactions between baseline-assessed psychological distance and time (b = -0.04, p  = .042) and between baseline-assessed visual perspective and time (b = 0.05, p = .023) on PTSD symptom severity across days.

CONCLUSIONS

Findings inform positive memory intervention targets for PTSD and provide impetus for longitudinal investigations on their inter-relations.

Enhancing Internet-based psychotherapy for adults with emotional disorders using ecological momentary assessments and interventions: Study protocol of a feasibility trial with "My EMI, Emotional Well-being" app

Introduction

Emotional disorders are the most frequent mental health problems globally. To ensure the dissemination of psychological treatments for these conditions, novel forms of delivery (e.g., Internet or mobile apps) and more scalable forms of psychotherapy (e.g., transdiagnostic interventions) have become increasingly popular. Research, however, shows that a significant number of patients, around 40 % according to some studies, do not respond to the interventions as expected (i.e., not-on-track patients). Ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) could simplify tailoring treatments to the patients' progress and rapidly respond to undesired outcomes during psychotherapy. Therefore, these would facilitate measurement-based care with little therapist involvement. This study aims to explore the feasibility of an app-based system called My EMI, Emotional Well-being for people with emotional disorders. According to daily EMAs, the app will provide personalized EMIs while participants receive a self-applied online transdiagnostic treatment. The app will be used as an add-tool to the online intervention to address emotion dysregulation, foster adherence, and reinforce contents. The current study describes the study protocol for this trial.

Method and analysis

A single-group, open trial design will be used. Participants will be 30 adults suffering from emotional disorders. Primary outcomes will be app usability, acceptability, and response rates. Secondary outcomes will be either evaluated in Qualtrics at pre-treatment, post-treatment, and 3-month follow-up (depression and anxiety severity, and transdiagnostic dimensions of emotional disorders) or daily throughout the study with the app (EMAs of mood and five transdiagnostic mechanisms of therapeutic change). EMIs will consist of brief, evidence-based transdiagnostic CBT digital content (images, infographics, or videos) delivered just-in-time. Only if problems persist, short phone calls or episodic videocalls will be conducted. The Ethics Committee of the Jaume I University approved the study and all its procedures (CD/111/2021) in December 2021.

Discussion

Identifying personalized and scalable interventions is paramount to improve mental health care, especially its accessibility, and to reduce the psychological distress of people with mental health problems. Feasibility data of the app (EMA and EMI system) supported by a self-applied online transdiagnostic intervention will be important to explore whether this modern approach is a real option to move forward personalized psychological interventions for persons with emotional disorders.

Trial registration

ClinicalTrials.gov Identifier: NCT05109780. Registered 05 November 2021, https://clinicaltrials.gov/ct2/show/NCT05109780.

Posttraumatic stress disorder strengthens the momentary associations between emotion dysregulation and substance use: a micro-longitudinal study of community women experiencing intimate partner violence

BACKGROUND AND AIMS

A fast-growing body of literature linking emotion dysregulation to substance use has almost exclusively relied on cross-sectional designs and has generally failed to assess dysregulation stemming from positive emotions. The current study measured the momentary associations between both negative and positive emotion dysregulation and substance use, as well as the moderating role of post-traumatic stress disorder (PTSD).

DESIGN

Micro-longitudinal momentary data were collected three times per day for 30 days using phone-based interactive voice recording.

SETTING

Northeastern United States.

PARTICIPANTS

A total of 145 community women (M, age = 40.56, 40.3% white) experiencing intimate partner violence (IPV) and using substances were recruited from 2018 until 2020.

MEASUREMENTS

Momentary surveys assessed negative and positive emotion dysregulation and alcohol, drug, and cigarette use. PTSD diagnosis was derived from a structured diagnostic interview.

FINDINGS

Between-person relations that remained significant after covariate adjustment and correction for multiple testing were found for negative emotion dysregulation and both number of drinks (internal rate of return [IRR] = 1.45; 95% CI = 1.07, 1.93; β = 0.37) and drug use (OR = 3.78; 95% CI = 1.86, 7.69; β = 1.33), and positive emotion dysregulation and number of cigarettes (OR = 1.40; 95% CI = 1.07, 1.82; β = 0.34). Of six interactions tests, PTSD was only shown to moderate the within-person relation between positive emotion dysregulation and drug use, even after controlling for relevant covariates and adjusting for multiple testing (OR = 1.26; 95% CI = 1.04, 1.51; β = 0.23), such that women experiencing intimate partner violence with versus without PTSD were more likely to use drugs when experiencing periods of elevated positive emotion dysregulation.

CONCLUSIONS

Women experiencing intimate partner violence with higher levels of negative dysregulation relative to others may use more alcohol and be more likely to use drugs. Women experiencing intimate partner violence with higher levels of positive dysregulation relative to others may use more cigarettes. Women experiencing intimate partner violence who also have posttraumatic stress disorder may be more likely to use drugs when experiencing momentary elevated levels of positive emotion dysregulation than those who do not also have posttraumatic stress disorder.

Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V.2.0 randomised clinical trial

INTRODUCTION

Suicide is one of the leading public health issues worldwide. Mobile health can help us to combat suicide through monitoring and treatment. The SmartCrisis V.2.0 randomised clinical trial aims to evaluate the effectiveness of a smartphone-based Ecological Momentary Intervention to prevent suicidal thoughts and behaviour.

METHODS AND ANALYSIS

The SmartCrisis V.2.0 study is a randomised clinical trial with two parallel groups, conducted among patients with a history of suicidal behaviour treated at five sites in France and Spain. The intervention group will be monitored using Ecological Momentary Assessment (EMA) and will receive an Ecological Momentary Intervention called 'SmartSafe' in addition to their treatment as usual (TAU). TAU will consist of mental health follow-up of the patient (scheduled appointments with a psychiatrist) in an outpatient Suicide Prevention programme, with predetermined clinical appointments according to the Brief Intervention Contact recommendations (1, 2, 4, 7 and 11 weeks and 4, 6, 9 and 12 months). The control group would receive TAU and be monitored using EMA.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the University Hospital Fundación Jiménez Díaz. It is expected that, in the near future, our mobile health intervention and monitoring system can be implemented in routine clinical practice. Results will be disseminated through peer-reviewed journals and psychiatric congresses. Reference number EC005-21_FJD. Participants gave informed consent to participate in the study before taking part.

TRIAL REGISTRATION NUMBER

NCT04775160.

Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research

Suicide and suicide-related behaviors are prevalent yet notoriously difficult to predict. Specifically, short-term predictors and correlates of suicide risk remain largely unknown. Ecological momentary assessment (EMA) may be used to assess how suicidal thoughts and behaviors (STBs) unfold in real-world contexts. We conducted a systematic literature review of EMA studies in suicide research to assess (1) how EMA has been utilized in the study of STBs (i.e., methodology, findings), and (2) the feasibility, validity and safety of EMA in the study of STBs. We identified 45 articles, detailing 23 studies. Studies mainly focused on examining how known longitudinal predictors of suicidal ideation perform within shorter (hourly, daily) time frames. Recent studies have explored the prospects of digital phenotyping of individuals with suicidal ideation. The results indicate that suicidal ideation fluctuates substantially over time (hours, days), and that individuals with higher mean ideation also have more fluctuations. Higher suicidal ideation instability may represent a phenotypic indicator for increased suicide risk. Few studies succeeded in establishing prospective predictors of suicidal ideation beyond prior ideation itself. Some studies show negative affect, hopelessness and burdensomeness to predict increased ideation within-day, and sleep characteristics to impact next-day ideation. The feasibility of EMA is encouraging: agreement to participate in EMA research was moderate to high (median = 77%), and compliance rates similar to those in other clinical samples (median response rate = 70%). More individuals reported suicidal ideation through EMA than traditional (retrospective) self-report measures. Regarding safety, no evidence was found of systematic reactivity of mood or suicidal ideation to repeated assessments of STBs. In conclusion, suicidal ideation can fluctuate substantially over short periods of time, and EMA is a suitable method for capturing these fluctuations. Some specific predictors of subsequent ideation have been identified, but these findings warrant further replication. While repeated EMA assessments do not appear to result in systematic reactivity in STBs, participant burden and safety remains a consideration when studying high-risk populations. Considerations for designing and reporting on EMA studies in suicide research are discussed.

Examination of daily-level associations between posttraumatic stress disorder symptoms and COVID-19 worries

OBJECTIVE

Stressors and worries related to the COVID-19 pandemic have contributed to the onset and exacerbation of psychological symptoms such as posttraumatic stress disorder (PTSD). Using a microlongitudinal framework, we uniquely investigated bidirectional associations between daily-level PTSD symptoms and COVID-19 worries.

METHOD

Data from 42 trauma-exposed university students (Mage = 22.67 ± 5.02, 86.7% female) were collected between March and August 2020. Participants completed daily surveys for 10 days to assess PTSD symptom severity and COVID-19 worries. Multilevel regression was conducted to examine both lagged and simultaneous models of daily person-centered mean PTSD symptom severity predicting COVID-19 worries, and vice-versa.

RESULTS

Days with greater COVID-19 worries were associated with greater same-day (b = .53, SE = .19, p = .006) and next-day (b = .65, SE = .21, p = .003) PTSD symptom severity. Additionally, days with greater PTSD symptom severity were associated with greater same-day COVID-19 worries (b = .06, SE = .02, p = .006).

CONCLUSIONS

COVID-19 worries may influence same-day and next-day PTSD symptoms, and PTSD symptoms may influence same day COVID-19 worries. Findings substantiate the interplay between ongoing stress related to the COVID-19 pandemic and posttrauma symptoms and support therapeutically targeting COVID-19 stress in PTSD treatments to potentially impact posttrauma symptoms. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

What is the Current and Future Status of Digital Mental Health Interventions?

The prevalence of mental disorders continues to increase, especially with the advent of the COVID-19 pandemic. Although we have evidence-based psychological treatments to address these conditions, most people encounter some barriers to receiving this help (e.g., stigma, geographical or time limitations). Digital mental health interventions (e.g., Internet-based interventions, smartphone apps, mixed realities -virtual and augmented reality) provide an opportunity to improve accessibility to these treatments. This article summarizes the main contributions of the different types of digital mental health solutions. It analyzes their limitations (e.g., drop-out rates, lack of engagement, lack of personalization, lack of cultural adaptations) and showcases the latest sophisticated and innovative technological advances under the umbrella of precision medicine (e.g., digital phenotyping, chatbots, or conversational agents). Finally, future challenges related to the need for real world implementation of these interventions, the use of predictive methodology, and hybrid models of care in clinical practice, among others, are discussed.

Ecological momentary interventions for mental health: A scoping review

BACKGROUND

The development of mobile computing technology has enabled the delivery of psychological interventions while people go about their everyday lives. The original visions of the potential of these "ecological momentary interventions" were presented over a decade ago, and the widespread adoption of smartphones in the intervening years has led to a variety of research studies exploring the feasibility of these aspirations. However, there is a dearth of research describing the different dimensions, characteristics, and features of these interventions, as constructed.

OBJECTIVE

To provide an overview of the definitions given for "ecological momentary interventions" in the treatment of common mental health disorders, and describe the set of technological and interaction possibilities which have been used in the design of these interventions.

METHODS

A systematic search identified relevant literature published between 2009 and 2020 in the PubMed, PsycInfo, and ACM Guide to the Computing Literature databases. Following screening, data were extracted from eligible articles using a standardized extraction worksheet. Selected articles were then thematically categorized.

RESULTS

The search identified 583 articles of which 64 met the inclusion criteria. The interventions target a range of mental health problems, with diverse aims, intervention designs and evaluation approaches. The studies employed a variety of features for intervention delivery, but recent research is overwhelmingly comprised of studies based on smartphone apps (30 of 42 papers that described an intervention). Twenty two studies employed sensors for the collection of data in order to provide just-in-time support or predict psychological states.

CONCLUSIONS

With the shift towards smartphone apps, the vision for EMIs has begun to be realised. Recent years have seen increased exploration of the use of sensors and machine learning, but the role of humans in the delivery of EMI is also varied. The variety of capabilities exhibited by EMIs motivates development of a more precise vocabulary for capturing both automatic and human tailoring of these interventions.

Social Robots for Supporting Post-traumatic Stress Disorder Diagnosis and Treatment

Post-Traumatic Stress Disorder (PTSD) is a severe psychiatric disorder with profound public health impact due to its high prevalence, chronic nature, accompanying functional impairment, and frequently occurring comorbidities. Early PTSD symptoms, often observed shortly after trauma exposure, abate with time in the majority of those who initially express them, yet leave a significant minority with chronic PTSD. While the past several decades of PTSD research have produced substantial knowledge regarding the mechanisms and consequences of this debilitating disorder, the diagnosis of and available treatments for PTSD still face significant challenges. Here, we discuss how novel therapeutic interventions involving social robots can potentially offer meaningful opportunities for overcoming some of the present challenges. As the application of social robotics-based interventions in the treatment of mental disorders is only in its infancy, it is vital that careful, well-controlled research is conducted to evaluate their efficacy, safety, and ethics. Nevertheless, we are hopeful that robotics-based solutions could advance the quality, availability, specificity and scalability of care for PTSD.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation

Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

How Are Information and Communication Technologies Supporting Routine Outcome Monitoring and Measurement-Based Care in Psychotherapy? A Systematic Review

Psychotherapy has proven to be effective for a wide range of mental health problems. However, not all patients respond to the treatment as expected (not-on-track patients). Routine outcome monitoring (ROM) and measurement-based care (MBC), which consist of monitoring patients between appointments and using this data to guide the intervention, have been shown to be particularly useful for these not-on-track patients. Traditionally, though, ROM and MBC have been challenging, due to the difficulties associated with repeated monitoring of patients and providing real-time feedback to therapists. The use of information and communication technologies (ICTs) might help reduce these challenges. Therefore, we systematically reviewed evidence regarding the use of ICTs for ROM and MBC in face-to-face psychological interventions for mental health problems. The search included published and unpublished studies indexed in the electronic databases PubMed, PsycINFO, and SCOPUS. Main search terms were variations of the terms “psychological treatment”, “progress monitoring or measurement-based care”, and “technology”. Eighteen studies met eligibility criteria. In these, ICTs were frequently handheld technologies, such as smartphone apps, tablets, or laptops, which were involved in the whole process (assessment and feedback). Overall, the use of technology for ROM and MBC during psychological interventions was feasible and acceptable. In addition, the use of ICTs was found to be effective, particularly for not-on-track patients, which is consistent with similar non-ICT research. Given the heterogeneity of reviewed studies, more research and replication is needed to obtain robust findings with different technological solutions and to facilitate the generalization of findings to different mental health populations.

Pilot randomised controlled trial of a brief coping-focused intervention for hearing voices blended with smartphone-based ecological momentary assessment and intervention (SAVVy): Feasibility, acceptability and preliminary clinical outcomes

BACKGROUND

Voice-hearing experiences can be distressing and impairing, and existing psychological treatments show modest effectiveness. Ecological momentary assessment and intervention (EMA/I) are two promising approaches which may be used as digital tools to support and enhance existing psychological therapies. The aim of this study was to investigate the potential clinical utility of smartphone-based EMA/I in a blended, coping focused therapy for voice-hearing experiences.

METHOD

This pilot RCT focused on feasibility, acceptability and preliminary estimations of efficacy. Thirty-four participants with persisting and distressing voices were randomised to receive the four-session intervention along-side treatment-as-usual (TAU) or TAU-only.

RESULTS

Findings supported the feasibility and acceptability of the approach, with good engagement and satisfaction rates, and clinical outcomes showed the intervention holds promise for improving coping, overall severity of voices and to some degree their negative impact.

CONCLUSION

This is the first examination of the use of EMA/I in a blended therapy for psychotic experiences, with findings suggesting these technologies show promise as clinical tools.

Neighborhood crime, socioeconomic status, and suspiciousness in adolescents and young adults at Clinical High Risk (CHR) for psychosis

INTRODUCTION

Contextual factors representing chronic stressors, such as neighborhood crime characteristics, have been repeatedly linked to compromised mental and physical health, and may contribute to the pathologizing of normative/non-clinical experiences. However, the impact of such structural factors has seldom been incorporated in Clinical High Risk (CHR) for psychosis research. Understanding how context can influence the presence or severity of symptoms such as suspiciousness/paranoia may have important relevance for promoting valid and reliable assessment, as well as for understanding ways in which environment may be related to illness development and expression.

METHODS

A total of 126 adolescents and young adults (nCHR = 63, ncontrol = 63) underwent clinical interviews for Clinical High-Risk syndromes. Neighborhood crime indices and socioeconomic status were calculated through geocoding and extracting of publicly available Census and Federal Bureau of Investigation (FBI) data. Analyses examined presence of associations between neighborhood crime indices, socioeconomic status, suspiciousness and total symptoms.

RESULTS

Greater neighborhood crime was related to increased suspiciousness in CHR individuals, even after controlling for neighborhood socioeconomic status, r = 0.27, p = .03. Neighborhood crime was not related to total symptoms, and neither was neighborhood socioeconomic status.

DISCUSSION

Results suggest neighborhood crime uniquely related to suspiciousness symptoms in CHR individuals, while this was not the case for healthy volunteers (HV). Future work will be critical for determining the extent to which assessors are pathologizing experiences that are normative for a particular context, or rather, if a stressful context is serving as a sufficient environmental stressor to unmask emerging psychosis.