Experience sampling-based personalized feedback and positive affect: a randomized controlled trial in depressed patients

Use of the experience sampling method in adolescents with Duchenne muscular dystrophy: a feasibility study

Experience sampling methods (ESM) using mobile health (mHealth) technology with a smartphone application are increasingly used in clinical practice and research. Still, recommendations are limited in young people, and adaptations may be necessary. Patients with Duchenne muscular dystrophy (DMD) are chronically treated with steroids from a young age. However, the impact of intermittent treatment schedules on fluctuations in somatic, cognitive and behavioural symptoms is poorly investigated. Existing studies are often cross-sectional and occur in controlled clinical settings, which do not provide sufficiently detailed insights into possible correlations. ESM might alleviate these problems. ESM innovates data collection with a smartphone application, which repeatedly assesses specific symptoms and contextual factors at random moments in daily life. We aimed to evaluate its feasibility in adolescents with DMD. In three (without/with/without steroids) 4-day periods of ESM, that were nested in 10/10 or 11/9 day on/off-medication periods, we evaluated its user-friendliness and compliance, and explored its ability to objectify fluctuations in somatic, cognitive and behavioural symptom severity and their relationship with contextual factors in seven DMD patients (age range 12-18 years) using intermittent corticosteroid treatment (dosage range 0.3-0.6 mg/kg/day). Patients reported that ESM was convenient and user-friendly. We were able to capture extensive intra-individual symptom fluctuations during intermittent corticosteroid treatment that were not revealed by routine clinical assessment. Implementing ESM to evaluate symptom fluctuation patterns in relation to treatment effects shows promise in adolescents with DMD. Optimization in further research is needed.

Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

BACKGROUND

Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques.

OBJECTIVE

The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity.

METHODS

A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models.

RESULTS

Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01).

CONCLUSIONS

The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.

The DizzyQuest Combined with Accelerometry: Daily Physical Activities and Limitations among Patients with Bilateral Vestibulopathy Due to DFNA9

BACKGROUND

DFNA9 is a genetic disease of the inner ear, causing progressive bilateral sensorineural deafness and bilateral vestibulopathy (BV). In this study, DizzyQuest, a mobile vestibular diary, and the MOX accelerometer were combined to assess the daily life functional limitations and physical activity of patients with DFNA9 suffering from BV. These parameters might be appropriate as potential candidacy criteria and outcome measures for new therapeutic interventions for BV.

METHODS

Fifteen DFNA9 patients with BV and twelve age-matched healthy controls were included. The DizzyQuest was applied for six consecutive days, which assessed the participants' extent of functional limitations, tiredness, types of activities performed during the day, and type of activity during which the participant felt most limited. The MOX accelerometer was worn during the same six days of DizzyQuest use, measuring the participants intensity and type of physical activity. Mixed-effects linear and logistic regression analyses were performed to compare the DFNA9 patients and control group.

RESULTS

DFNA9 patients with BV felt significantly more limited in activities during the day compared to the age-matched controls, especially in social participation (p < 0.005). However, these reported limitations did not cause adjustment in the types of activities and did not reduce the intensity or type of physical activity measured with accelerometry. In addition, no relationships were found between self-reported functional limitations and physical activity.

CONCLUSIONS

This study demonstrated that self-reported functional limitations are significantly higher among DFNA9 patients with BV. As a result, these limitations might be considered as part of the candidacy criteria or outcome measures for therapeutic interventions. In addition, the intensity or type of physical activity performed during the day need to be addressed more specifically in future research.

Personalized exposure and experience sampling method feedback versus exposure as usual for obsessive-compulsive disorder: a study protocol for a randomized controlled trial

BACKGROUND

Patients with obsessive-compulsive disorder (OCD) suffer from repetitive fearful intrusions which they try to neutralize by performing compulsions. OCD is considered to be the most resistant anxiety disorder with a remission rate of only 53% after a year of an evidence-based treatment. Therefore, it remains an obligation to develop and investigate more effective treatment interventions. This study aims to compare personalized exposure with response prevention (ERP) using experience sampling methodology-based feedback to ERP as usual in patients with OCD. Personalized exposure will be provided screen-to-screen in an ecologically valid (real time and real place) context by means of a smartphone application. This app will also be used to collect both objective and subjective data by means of experience sampling methodology (ESM). This ESM data will be used to identify triggers and protective factors for symptom severity, provide personalized feedback and optimize the effect of ERP. The primary goal of this RCT is to compare the effectiveness of personalized ERP to ERP as usual in the traditional context of a therapist's room in patients with OCD in OCD symptom severity, as well as differences in quality of life, depressive symptoms and anxiety states. Since both self-efficacy and experiential avoidance are known to influence symptom severity in OCS, a secondary goal is to examine if a possible treatment effect is mediated by self-efficacy or experiential avoidance.

METHODS

This study involves a randomized controlled trial with 20 weekly sessions by 2 groups (ERP as usual versus personalized ERP), repeated measurements at baseline (T0), 5 weeks of treatment (T1), 10 weeks of treatment (T2), 15 weeks of treatment (T3), posttest at 20 weeks (T4), 6 weeks follow-up (T5), 3 months follow-up (T6), 6 months follow-up (T7) and a year follow-up (T8). A hundred and sixty patients with an OCD diagnosis according to DSM-5 criteria will participate. Half of the group will receive exposure with response prevention as usual, the other half will receive personalized exposure with response prevention with a smartphone application and personalized feedback sessions based on experience sampling data. Multilevel mixed modelling analysis will be used to investigate differences in treatment effect, as well as differences in quality of life, depressive symptoms and anxiety states. We will use the macro of Preacher and Hayes and apply bootstrapping methods to assess the possible mediating effect of changes in self-efficacy and experiential avoidance on subsequent treatment effects.

DISCUSSION

This randomized controlled trial is the first to assess the influence of delivering ERP through video-calling and the use of an ESM intervention on the symptom severity of OCD. Since the global pandemic COVID-19, the use of video-calling to deliver psychological treatments has become more common, increasing the relevance of this study.

TRIAL REGISTRATION

ICTRP Trial NL8254. Registered on 2019-12-24.

Feedback based on experience sampling data: Examples of current approaches and considerations for future research

Methodologies such as the Experience Sampling Method (ESM) or Ecological Momentary Assessment allow the gathering of fine-graded, dynamic, personal data within a patient's daily life. Currently, it is studied whether feedback based on experience sampling data (ESM-based feedback) can be used as a clinical tool to inform shared decision-making in clinical practice. Although the potential of feedback is recognized, little is known on how to generate, use, and implement it. This article (i) presents n = 15 ongoing ESM projects within the Belgian-Dutch network for ESM research wherein ESM-based feedback is provided to various patient populations, and (ii) summarizes qualitative data on experiences with ESM-based feedback of researchers (n = 8) with extensive expertise with ESM (average of 10 years) involved in these ongoing studies. The following aspects appear to be of relevance when providing ESM-based feedback: training for healthcare professionals and researchers, the use of online interfaces and graphical visualizations to present data, and interacting with patients in a face-to-face setting when discussing the contextual relevance and potential implications. Prospectively, research may build on these aspects and create coherent consensus-based guidelines for the use of ESM-based feedback.

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

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data.In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems.In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings.Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

Discovering different profiles in the dynamics of depression based on real-time monitoring of mood: a first exploration

BACKGROUND

Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons.

METHODS

After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1-10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants.

RESULTS

Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively).

CONCLUSIONS

The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons.

A Tutorial on Estimating Time-Varying Vector Autoregressive Models

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.

Smartphone-Based Experience Sampling in People With Mild Cognitive Impairment: Feasibility and Usability Study

BACKGROUND

Daily functioning of people with cognitive disorders such as mild cognitive impairment (MCI) is usually depicted by retrospective questionnaires, which can be memory-biased and neglect fluctuations over time or contexts.

OBJECTIVE

This study examines the feasibility and usability of applying the experience sampling method (ESM) in people with MCI to provide a detailed and dynamic picture of behavioral, emotional, and cognitive patterns in everyday life.

METHODS

For 6 consecutive days, 21 people with MCI used an ESM app on their smartphones. At 8 semi-random timepoints per day, participants filled in momentary questionnaires on mood, activities, social context, and subjective cognitive complaints. Feasibility was determined through self-reports and observable human-technology interactions. Usability was demonstrated on an individual and group level.

RESULTS

Of the 21 participants, 3 dropped out due to forgetting to carry their smartphones or forgetting the study instructions. In the remaining 18 individuals, the compliance rate was high, at 78.7%. Participants reported that momentary questions reflected their daily experiences well. Of the 18 participants, 13 (72%) experienced the increase in awareness of their own memory functions as pleasant or neutral.

CONCLUSIONS

Support was found for the general feasibility of smartphone-based experience sampling in people with MCI. However, many older adults with MCI are currently not in possession of smartphones, and study adherence seems challenging for a minority of individuals. Momentary data can increase the insights into daily patterns and may guide the person-tailored development of self-management strategies in clinical settings.

Introducing the DizzyQuest: an app-based diary for vestibular disorders

Background

Most questionnaires currently used for assessing symptomatology of vestibular disorders are retrospective, inducing recall bias and lowering ecological validity. An app-based diary, administered multiple times in daily life, could increase the accuracy and ecological validity of symptom measurement. The objective of this study was to introduce a new experience sampling method (ESM) based vestibular diary app (DizzyQuest), evaluate response rates, and to provide examples of DizzyQuest outcome measures which can be used in future research.

Methods

Sixty-three patients diagnosed with a vestibular disorder were included. The DizzyQuest consisted of four questionnaires. The morning- and evening-questionnaires were administered once each day, the within-day-questionnaire 10 times a day using a semi-random time schedule, and the attack questionnaire could be completed after the occurrence of a vertigo or dizziness attack. Data were collected for 4 weeks. Response rates and loss-to-follow-up were determined. Reported symptoms in the within-day-questionnaire were compared within and between patients and subgroups of patients with different vestibular disorders.

Results

Fifty-one patients completed the study period. Average response rates were significantly higher than the desired response rate of > 50% (p < 0.001). The attack-questionnaire was used 159 times. A variety of neuro-otological symptoms and different disease profiles were demonstrated between patients and subgroups of patients with different vestibular disorders.

Conclusion

The DizzyQuest is able to capture vestibular symptoms within their psychosocial context in daily life, with little recall bias and high ecological validity. The DizzyQuest reached the desired response rates and showed different disease profiles between subgroups of patients with different vestibular disorders. This is the first time ESM was used to assess daily symptoms and quality of life in vestibular disorders, showing that it might be a useful tool in this population.

Electronic supplementary material

The online version of this article (10.1007/s00415-020-10092-2) contains supplementary material, which is available to authorized users.

Usability of the Experience Sampling Method in Parkinson's Disease on a Group and Individual Level

BACKGROUND

Around 50% of PD patients experience motor fluctuations, which are often accompanied by mood fluctuations. The nature of the relationship between motor and mood fluctuations remains unknown. It is suggested that the experience sampling method can reveal such associations on both a group and individual level. Revealing group patterns may enhance our understanding of symptom interactions and lead to more general treatment recommendations, whereas analyses in individual patients can be used to establish a personalized treatment plan.

OBJECTIVES

To explore the usability of routinely collected experience sampling method data over a brief period of time to detect associations between motor fluctuations, affective state, and contextual factors in PD patients with motor fluctuations on a group level and on an individual level.

METHODS

Eleven patients with motor fluctuations collected data at 10 semirandom moments over the day for 5 consecutive days.

RESULTS

On a group level, multilevel analyses showed significant associations between all motor symptoms and positive affect. Being at home was associated with increased balance problems and rigidity. Analyses on an individual level revealed much less significant associations that mostly, but not always, were in line with the results on a group level.

CONCLUSION

This exploratory study showed significant associations between affective state, motor symptoms, and contextual factors in a group of PD patients with motor fluctuations, but less so in individual patients. Given that the ultimate aim is to use the experience sampling method as an aid to personalize treatments, the sensitivity of the approach needs to be increased. © 2020 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Changes in the daily life experience of patients with obsessive-compulsive disorder following mindfulness-based cognitive therapy: Looking beyond symptom reduction using ecological momentary assessment

The current study examines changes in daily life experiences following mindfulness-based cognitive therapy (MBCT) in a sample of 38 patients with obsessive-compulsive disorder (OCD) and residual symptoms following cognitive behavioral therapy (CBT). Participants were randomized to either an MBCT group (n = 17), or to a psychoeducational group (OCD-EP; n = 21) as an active control condition. Both groups underwent ecological momentary assessment (EMA) six times a day for a period of six consecutive days pre- and posttreatment. Contrary to hypotheses, MBCT participation did not, when compared to OCD-EP, result in significant changes of positive affect, negative affect, acceptance of momentary emotions, or distress associated with obsessive-compulsive (OC) symptoms. In fact, insight into the unreasonableness of OC symptoms improved significantly more in the OCD-EP group compared to MBCT. However, in the MBCT group, the presence of momentary OC symptoms at post assessment predicted increased insight, which indicates an improved ability to detach from OC symptoms in the moments in which they occur. All in all, however, results do not indicate favorable changes in everyday life experiences of OCD patients following MBCT. Owing to the modest sample size, results must be considered preliminary. Implications and directions for future research are discussed.

An ecological momentary intervention incorporating personalised feedback to improve symptoms and social functioning in schizophrenia spectrum disorders

This study examined the feasibility and effectiveness of an interactive smartphone application that aimed to improve daily-life social functioning and symptoms in schizophrenia spectrum disorders (SZ) with Experience Sampling Method (ESM) derived personalised feedback.Two groups of outpatients with a diagnosis of SZ were included (one receiving ESM-derived personalised feedback (n = 27) and one without feedback (n = 23)) and used the interactive smartphone application for three weeks. Main outcomes were momentary symptoms and social functioning, as assessed by ESM questionnaires. Additionally, feasibility and user-friendliness of the application were assessed. The response rate was 64% for the ESM questionnaires. In the feedback group, participants indicated that on 49% of the ESM days they acted on at least one personalised feedback prompt per day. Momentary psychotic symptoms significantly decreased over time only in the feedback group. Momentary loneliness and questionnaire-assessed psychotic symptoms decreased over time, irrespective of feedback. Participants evaluated the app as user-friendly and understandable. Momentary personalised feedback may impact momentary psychosis in daily life. Feelings of loneliness and questionnaire-based measured psychotic symptoms may be more responsive to non-specific effects of daily-life self-monitoring, not requiring specific feedback. Ecological momentary interventions offer opportunities for accessible and effective interventions in SZ.

Experiences of a Web-Based Quality of Life Self-Monitoring Tool for Individuals With Bipolar Disorder: A Qualitative Exploration

BACKGROUND

Self-monitoring of symptoms is a cornerstone of psychological interventions in bipolar disorder (BD), but individuals with lived experience also value tracking holistic outcomes, such as quality of life (QoL). Importantly, self-monitoring is not always experienced positively by people with BD and may have lower than expected rates of engagement. Therefore, before progressing into QoL tracking tools, it is important to explore user perspectives to identify possible risks and benefits, optimal methods to support engagement, and possible avenues to integrate QoL self-monitoring practices into clinical work.

OBJECTIVE

This study aimed to conduct a qualitative exploration of how individuals with BD engaged with a Web-based version of a BD-specific QoL self-monitoring instrument, the QoL tool.

METHODS

A total of 43 individuals with BD engaged with a self-management intervention with an optional Web-based QoL self-assessment tool as part of an overarching mixed method study. Individuals were later interviewed about personal experiences of engagement with the intervention, including experiences of gauging their own QoL. A thematic analysis was used to identify salient aspects of the experience of QoL self-monitoring in BD.

RESULTS

In total, 4 categories describing people's experiences of QoL self-monitoring were identified: (1) breadth of QoL monitoring, (2) highlighting the positive, (3) connecting self-monitoring to action, and (4) self-directed patterns of use.

CONCLUSIONS

The findings of this research generate novel insights into ways in which individuals with BD experience the Web-based QoL self-assessment tool. The value of tracking the breadth of domains was an overarching aspect, facilitating the identification of both areas of strength and life domains in need of intervention. Importantly, monitoring QoL appeared to have an inherently therapeutic quality, through validating flourishing areas and reinforcing self-management efforts. This contrasts the evidence suggesting that symptom tracking may be distressing because of its focus on negative experiences and positions QoL as a valuable adjunctive target of observation in BD. Flexibility and personalization of use of the QoL tool were key to engagement, informing considerations for health care providers wishing to support self-monitoring and future research into Web- or mobile phone-based apps.

Using Intensive Longitudinal Data to Study Treatment Effects in Patients with Major Depression: A Systematic Review

Depression is a disabling condition, causing suffering worldwide. The purpose of this paper is to review studies that have used Intensive Longitudinal Data (ILD) in research on depression treatment. Intensive longitudinal data in the form of daily diaries, experience sampling method (ESM), and ecological momentary assessment (EMA) are increasingly used in psychotherapy research to gather individualized data. A systematic search was conducted in the research databases PsycINFO, PubMed and Scopus on depression treatment assessed with ILD methods. Seven studies met inclusion criteria; two focused on the effects of cognitive therapy/cognitive behavioral therapy (CBT)/Self-System Therapy (SST); one focused on the effects of physical exercise; and four examined ESM as an add-on intervention to pharmacological treatment. ILD is useful when studying changes in complex patterns of patients’ functioning, such as daily affective reactivity, empowerment, daily dose-response effects of physical activity on PA, and associations between activity and depressive symptoms. Results also showed that ESM registration in itself can help patients with major depression (MDD) to engage more in physical and social activities and to spend less time alone or resting.

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modeling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression, and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy = 82% and sensitivity = 82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future.

Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review

Ecological momentary assessment (EMA) and ecological momentary intervention (EMI) are alternative approaches to retrospective self-reports and face-to-face treatments, and they make it possible to repeatedly assess patients in naturalistic settings and extend psychological support into real life. The increase in smartphone applications and the availability of low-cost wearable biosensors have further improved the potential of EMA and EMI, which, however, have not yet been applied in clinical practice. Here, we conducted a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to explore the state of the art of technology-based EMA and EMI for major depressive disorder (MDD). A total of 33 articles were included (EMA = 26; EMI = 7). First, we provide a detailed analysis of the included studies from technical (sampling methods, duration, prompts), clinical (fields of application, adherence rates, dropouts, intervention effectiveness), and technological (adopted devices) perspectives. Then, we identify the advantages of using information and communications technologies (ICTs) to extend the potential of these approaches to the understanding, assessment, and intervention in depression. Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances in machine learning for affective modelling.

Capturing the risk of persisting depressive symptoms: A dynamic network investigation of patients' daily symptom experiences

What drives the large differences across patients in terms of treatment efficacy of major depressive disorder (MDD) is unclear. A network approach to psychopathology may help to reveal underlying mechanisms determining patients' capacity for recovery. We used daily diary MDD symptom data and six-month follow-up data on depression to examine how dynamic associations between symptoms relate to the future course of MDD. Daily experiences of depressive symptoms of 69 participants were assessed by means of the SCL-90-R depression subscale, three days a week for a period of six weeks, as part of a larger intervention study. Multilevel vector autoregressive modelling was used to estimate networks of dynamic symptom connections. Long-term outcome was determined by the percentage change in Hamilton Depression Rating Scale (HDRS) score between pre-intervention and six-month follow-up. For patients with more persisting symptoms, the symptom 'feeling everything is an effort' most strongly predicted other symptoms. The networks of the two groups did not significantly differ in overall connectivity. Findings suggest that future research should not solely focus on the presence or intensity of individual symptoms when predicting long-term outcomes, but should also examine the role of a specific symptom in the larger network of dynamic symptom-to-symptom interactions.

An Experience Sampling Method Intervention for Dementia Caregivers: Results of a Randomized Controlled Trial

OBJECTIVE

Ecological momentary interventions integrated with real-life assessments using the experience sampling method (ESM) could be promising to effectively support dementia caregivers in daily life. This study reports on the effectiveness of the ESM-based intervention "Partner in Sight."

DESIGN, SETTING, PARTICIPANTS

A randomized controlled trial with 76 dementia caregivers was performed. Participants were randomly assigned to the intervention group ("Partner in Sight": ESM self-monitoring and personalized feedback), the pseudo-intervention group (ESM self-monitoring without feedback), or the control group (usual care).

MEASUREMENTS

Effects were evaluated pre- and postintervention and at 2-month follow-up. Primary outcomes were retrospective measures of caregiver sense of competence and mastery. Secondary outcomes were retrospective measures of depression, anxiety, and perceived stress. Complementary ESM measures of positive and negative affect were collected pre- and postintervention.

RESULTS

Both the experimental and pseudo-experimental groups showed an increase in retrospective sense of competence and a decrease in perceived stress at 2-month follow-up. At postintervention, the experimental group showed a decrease in momentary negative affect compared with the pseudo-experimental and control groups. No effects were found for retrospective mastery, depression, anxiety, and momentary positive affect.

CONCLUSIONS

ESM interventions could be an important asset for increasing caregiver resources and could help caregivers to better adapt and manage difficult situations and to protect against negative emotions.

Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns

BACKGROUND

Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving.

METHODS

Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns.

RESULTS

The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values.

CONCLUSIONS

Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.

Economic evaluation of an experience sampling method intervention in depression compared with treatment as usual using data from a randomized controlled trial

BACKGROUND

Experience sampling, a method for real-time self-monitoring of affective experiences, holds opportunities for person-tailored treatment. By focussing on dynamic patterns of positive affect, experience sampling method interventions (ESM-I) accommodate strategies to enhance personalized treatment of depression-at potentially low-costs. This study aimed to investigate the cost-effectiveness of an experience sampling method intervention in patients with depression, from a societal perspective.

METHODS

Participants were recruited between January 2010 and February 2012 from out-patient mental health care facilities in or near the Dutch cities of Eindhoven and Maastricht, and through local advertisements. Out-patients diagnosed with major depression (n = 101) receiving pharmacotherapy were randomized into: (i) ESM-I consisting of six weeks of ESM combined with weekly feedback regarding the individual's positive affective experiences, (ii) six weeks of ESM without feedback, or (iii) treatment as usual only. Alongside this randomised controlled trial, an economic evaluation was conducted consisting of a cost-effectiveness and a cost-utility analysis, using Hamilton Depression Rating Scale (HDRS) and quality adjusted life years (QALYs) as outcome, with willingness-to-pay threshold for a QALY set at €50,000 (based on Dutch guidelines for moderate severe to severe illnesses).

RESULTS

The economic evaluation showed that ESM-I is an optimal strategy only when willingness to pay is around €3000 per unit HDRS and around €40,500 per QALY. ESM-I was the least favourable treatment when willingness to pay was lower than €30,000 per QALY. However, at the €50,000 willingness-to-pay threshold, ESM-I was, with a 46% probability, the most favourable treatment (base-case analysis). Sensitivity analyses confirmed the robustness of these results.

CONCLUSIONS

We may tentatively conclude that ESM-I is a cost-effective add-on intervention to pharmacotherapy in outpatients with major depression.

TRIAL REGISTRATION

Netherlands Trial register, NTR1974 .

Constructing a Reward-Related Quality of Life Statistic in Daily Life-a Proof of Concept Study Using Positive Affect

Background: Mental healthcare needs person-tailored interventions. Experience Sampling Method (ESM) can provide daily life monitoring of personal experiences. This study aims to operationalize and test a measure of momentary reward-related Quality of Life (rQoL). Intuitively, quality of life improves by spending more time on rewarding experiences. ESM clinical interventions can use this information to coach patients to find a realistic, optimal balance of positive experiences (maximize reward) in daily life. rQoL combines the frequency of engaging in a relevant context (a 'behavior setting') with concurrent (positive) affect. High rQoL occurs when the most frequent behavior settings are combined with positive affect or infrequent behavior settings co-occur with low positive affect. Methods: Resampling procedures (Monte Carlo experiments) were applied to assess the reliability of rQoL using various behavior setting definitions under different sampling circumstances, for real or virtual subjects with low-, average- and high contextual variability. Furthermore, resampling was used to assess whether rQoL is a distinct concept from positive affect. Virtual ESM beep datasets were extracted from 1,058 valid ESM observations for virtual and real subjects. Results: Behavior settings defined by Who-What contextual information were most informative. Simulations of at least 100 ESM observations are needed for reliable assessment. Virtual ESM beep datasets of a real subject can be defined by Who-What-Where behavior setting combinations. Large sample sizes are necessary for reliable rQoL assessments, except for subjects with low contextual variability. rQoL is distinct from positive affect. Conclusion: rQoL is a feasible concept. Monte Carlo experiments should be used to assess the reliable implementation of an ESM statistic. Future research in ESM should asses the behavior of summary statistics under different sampling situations. This exploration is especially relevant in clinical implementation, where often only small datasets are available.

A Complex Network Perspective on Clinical Science

Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, potentially making it possible to predict treatment change, relapse, and recovery. In this article, we discuss the complex network approach as an alternative to the latent disease model and its implications for classification, therapy, relapse, and recovery.

Interactive Voice Response with Feedback Intervention in Outpatient Treatment of Substance Use Problems in Adolescents and Young Adults: A Randomized Controlled Trial

PURPOSE

Substance use disorders and problematic substance use are common problems in adolescence and young adulthood. Brief personalized feedback has been suggested for treatment of alcohol and drug problems and poor mental health. This repeated measurement randomized controlled trial examines the effect of an interactive voice response (IVR) system for assessing stress, depression, anxiety and substance use.

METHODS

The IVR system was used twice weekly over 3 months after treatment initiation, with or without addition of a personalized feedback intervention on stress and mental health symptoms. Both IVR assessment only (control group) and IVR assessment including feedback (intervention group) were provided as an add-on to treatment-as-usual procedures (TAU) in outpatient treatment of substance use problems in adolescents and young adults (N = 73).

RESULTS

By using a mixed models approach, differences in change scores were analyzed over the three-month assessment period. Compared to the control group, the intervention group demonstrated significantly greater improvement in the Arnetz and Hasson stress score (AHSS, p = 0.019), the total Symptoms Checklist 8 score (SCL-8D, p = 0.037), the SCL-8D anxiety sub-score (p = 0.017), and on a summarized feedback score (p = 0.026), but not on the depression subscale. There were no differences in global substance use scores between the intervention group (feedback on mental health symptoms) and the control group.

CONCLUSION

In conclusion, IVR may be useful for follow-up and repeated interventions as an add-on to regular treatment, and personalized feedback could potentially improve mental health in adolescents and young adults with problematic substance use.

An Exploratory Randomized Controlled Trial of Personalized Lifestyle Advice and Tandem Skydives as a Means to Reduce Anhedonia

Anhedonia is a major public health concern and has proven particularly difficult to counteract. It has been hypothesized that anhedonia can be deterred by engagement in rewarding social and physical events. The aims of the present study were to examine (1) the effects of personalized lifestyle advice based on observed individual patterns of lifestyle factors and experienced pleasure in anhedonic young adults; and (2) whether a tandem skydive can enhance the motivation to carry out the recommended lifestyle changes. Participants (N = 69; M_age = 21.5, SD = 2.0; 79.7% female) were selected through an online screening survey among young adults. Inclusion criteria were persistent anhedonia and willingness to perform a tandem skydive. Participants filled out questionnaires on their smartphones for 2 consecutive months (3 times per day). After the first month, they were randomly assigned to one of three groups: (1) no intervention, (2) lifestyle advice, and (3) lifestyle advice and tandem skydive. The momentary questionnaire data were analyzed using interrupted time series analyses (ITSA) in a multilevel model and monthly pleasure and depression questionnaires by repeated measures ANOVA. No group differences were found in monthly depression and pleasure scores, but the momentary data showed higher positive affect (PA) and pleasure ratings in the month following the intervention in the two intervention groups than in the control group. The tandem skydive did not have any effects above the effects of the lifestyle advice. Our results indicate that providing personalized lifestyle advice to anhedonic young adults can be an effective way to increase PA and pleasure.

Mental disorders as networks of problems: a review of recent insights

PURPOSE

The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.

METHODS

This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention.

RESULTS

Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality-a metric that measures how connected and clinically relevant a symptom is in a network-is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies.

CONCLUSIONS

We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients.

The Effects of Psychotherapy for Major Depressive Disorder on Daily Mood and Functioning: A Longitudinal Experience Sampling Study

Experience sampling methodology (ESM) was used in a randomized controlled trial of short-term therapy to examine changes in daily affect and reactivity to daily event appraisals among depressed patients. Fifty-five depressed adults (mean age 37 years, 80% female) were randomly assigned to one of two therapy conditions. Using an interactive voice response system, participants rated activities and emotional functioning eight times per day for seven days. Thirty-one participants completed treatment and repeated ESM post-treatment. Broad improvements in mood, cognition, and physical functioning were similar across treatment conditions, with the largest improvements for markers of positive affect. Participants demonstrated increased resilience, i.e., diminished reactivity to stressors at post-treatment. Changes in reactivity to positive daily situations were minimal. Findings underscore the utility of ESM in psychotherapy research and the importance of including measures of both positive and negative affect and experiences.

Unraveling the Relationship between Motor Symptoms, Affective States and Contextual Factors in Parkinson's Disease: A Feasibility Study of the Experience Sampling Method

Background

In Parkinson's disease (PD), the complex relationship between motor symptoms, affective states, and contextual factors remains to be elucidated. The Experience Sampling Method provides (ESM) a novel approach to this issue. Using a mobile device with a special purpose application (app), motor symptoms, affective states and contextual factors are assessed repeatedly at random moments in the flow of daily life, yielding an intensive time series of symptoms and experience. The aim of this study was to study the feasibility of this method.

Method

We studied the feasibility of a five-day period of ESM in PD and its ability to objectify diurnal fluctuations in motor symptom severity and their relation with affect and contextual factors in five PD patients with motor fluctuations.

Results

Participants achieved a high compliance, with 84% of assessment moments completed without disturbance of daily activities. The utility of the device was rated 8 on a 10-point scale. We were able to capture extensive diurnal fluctuations that were not revealed by routine clinical assessment. In addition, we were able to detect clinically relevant associations between motor symptoms, emotional fluctuations and contextual factors at an intra-individual level.

Conclusions

ESM represents a viable and novel approach to elucidate relationships between motor symptoms, affective states and contextual factors at the level of individual subjects. ESM holds promise for clinical practice and scientific research.