Repeated self-evaluations may involve familiarization: An exploratory study related to Ecological Momentary Assessment designs in patients with major depressive disorder

Ecological momentary assessment of affect in depression-prone and control samples: Survey compliance and affective yield

BACKGROUND

Ecological momentary assessment (EMA) is a high-frequency ambulatory data collection approach that has come to be widely used in emotion research. It therefore is timely to examine two features of EMA needed for a successful study: compliance with survey prompts and high affective yield (survey prompts that capture affect experience). We posit that compliance may be subject to temporal variation (time-of-day, days in study) and individual differences (depression history), and that affective yield may also differ by social context.

METHODS

We examined these issues in a sample of 318 young adults (Mage = 24.7 years, SD = 2.7), including those with current depression (n = 28), remitted depression (n = 168) and never-depressed controls (n = 122) who participated in a 7-day EMA protocol of negative and positive affect (NA and PA, respectively).

RESULTS

The overall compliance rate was 91% and remained stable across the survey week. However, subjects were significantly less likely to respond to the first daily prompt compared to those that followed. The likelihood of capturing NA and PA decreased with each EMA protocol day, and affective yield across social contexts differed by participants' depression status.

LIMITATIONS

The sample was largely comprised of White young adults. Relative to the remitted and control groups, the sample size for the currently depressed was unbalanced.

CONCLUSION

Researchers can optimize compliance and affective yield within EMA by considering depression, time-of-day, study duration, and social context. Clinicians using EMA to monitor affect may benefit from considering these parameters.

The effects of assessment intensity on participant burden, compliance, within-person variance, and within-person relationships in ambulatory assessment

Considering the very large number of studies that have applied ambulatory assessment (AA) in the last decade across diverse fields of research, knowledge about the effects that these design choices have on participants’ perceived burden, data quantity (i.e., compliance with the AA protocol), and data quality (e.g., within-person relationships between time-varying variables) is surprisingly restricted. The aim of the current research was to experimentally manipulate aspects of an AA study’s assessment intensity—sampling frequency (Study 1) and questionnaire length (Study 2)—and to investigate their impact on perceived burden, compliance, within-person variability, and within-person relationships between time-varying variables. In Study 1, students (n = 313) received either 3 or 9 questionnaires per day for the first 7 days of the study. In Study 2, students (n = 282) received either a 33- or 82-item questionnaire three times a day for 14 days. Within-person variability and within-person relationships were investigated with respect to momentary pleasant-unpleasant mood and state extraversion. The results of Study 1 showed that a higher sampling frequency increased perceived burden but did not affect the other aspects we investigated. In Study 2, longer questionnaire length did not affect perceived burden or compliance but yielded a smaller degree of within-person variability in momentary mood (but not in state extraversion) and a smaller within-person relationship between state extraversion and mood. Differences between Studies 1 and 2 with respect to the type of manipulation of assessment intensity are discussed.

Effects of ecological momentary assessment (EMA) induced monitoring on tinnitus experience: A multiple-baseline single-case experiment

INTRODUCTION

Ecological momentary assessment (EMA) is a method capable of assessing tinnitus experience throughout the day, enabling the exploration of daily dynamic changes of tinnitus expression. However, the effects on patients' tinnitus experience itself are still largely unknown. This study seeks to test the hypothesis that the use of EMA negatively influences tinnitus experience in participants with severe tinnitus.

METHOD

A multiple-baseline single-case experimental design included four severely affected tinnitus volunteers who were recruited online and randomized into different phasing schedules. Baseline phase (A) ranged from 11 to 24 days, followed by an EMA phase (B) for the remainder of the 33-day schedule. End-of-day diary assessments of tinnitus experience (e.g., annoyance, intrusiveness, mood) were visually inspected, and complemented with inferential statistics (randomization tests and Tau-U).

RESULTS

End-of-day diary data revealed no change in broadened median between phases. Nevertheless, tinnitus experience scores improved as variability decreased and a significant improvement in stress was observed through weighted Tau-U statistics.

CONCLUSION

Findings of this study corroborate that EMA assessment does not negatively affect tinnitus experience. On the contrary, participants may have improved. The underlying mechanism of improvements are still to be uncovered. Findings are limited to severely affected tinnitus sufferers at present.

The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population

Currently, little is known about the association between assessment intensity, burden, data quantity, and data quality in experience sampling method (ESM) studies. Researchers therefore have insufficient information to make informed decisions about the design of their ESM study. Our aim was to investigate the effects of different sampling frequencies and questionnaire lengths on burden, compliance, and careless responding. Students ( n = 163) received either a 30- or 60-item questionnaire three, six, or nine times per day for 14 days. Preregistered multilevel regression analyses and analyses of variance were used to analyze the effect of design condition on momentary outcomes, changes in those outcomes over time, and retrospective outcomes. Our findings offer support for increased burden and compromised data quantity and quality with longer questionnaires, but not with increased sampling frequency. We therefore advise against the use of long ESM questionnaires, while high-sampling frequencies do not seem to be associated with negative consequences.

Possible Application of Ecological Momentary Assessment to Older Adults' Daily Depressive Mood: Integrative Literature Review

BACKGROUND

Ecological momentary assessment is a method of investigating individuals' real-time experiences, behaviors, and moods in their natural environment over time. Despite its general usability and clinical value for evaluating daily depressive mood, there are several methodological challenges when applying ecological momentary assessment to older adults.

OBJECTIVE

The aims of this integrative literature review were to examine possible uses of the ecological momentary assessment methodology with older adults and to suggest strategies to increase the feasibility of its application in geriatric depression research and practice.

METHODS

We searched 4 electronic databases (MEDLINE, CINAHL, PsycINFO, and EMBASE) and gray literature; we also hand searched the retrieved articles' references. We limited all database searches to articles published in peer-reviewed journals from 2009 to 2019. Search terms were "ecological momentary assessment," "smartphone assessment," "real time assessment," "electronic daily diary," "mHealth momentary assessment," "mobile-based app," and "experience sampling method," combined with the relevant terms of depression. We included any studies that enrolled older adults even as a subgroup and that reported depressive mood at least once a day for more than 2 days.

RESULTS

Of the 38 studies that met the inclusion criteria, only 1 study enrolled adults aged 65 years or older as the entire sample; the remainder of the reviewed studies used mixed samples of both younger and older adults. Most of the analyzed studies (18/38, 47%) were quantitative, exploratory (descriptive, correlational, and predictive), and cohort in design. Ecological momentary assessment was used to describe the fluctuating pattern of participants' depressive moods primarily and to examine the correlation between mood patterns and other health outcomes as a concurrent symptom. We found 3 key methodological issues: (1) heterogeneity in study design and protocol, (2) issues with definitions of dropout and adherence, and (3) variation in how depressive symptoms were measured with ecological momentary assessment. Some studies (8/38, 21%) examined the age difference of participants with respect to dropout or poor compliance rate. Detailed participant burden was reported, such as technical problems, aging-related health problems, or discomfort while using the device.

CONCLUSIONS

Ecological momentary assessment has been used for comprehensive assessment of multiple mental health indicators in relation to depressive mood. Our findings provide methodological considerations for further studies that may be implemented using ecological momentary assessment to assess daily depressive mood in older adults. Conducting more feasibility studies focusing on older adults with standardized data collection protocols and mixed-methods research is required to reflect users' experiences. Further telepsychiatric evaluation and diagnosis based on ecological momentary assessment data should involve standardized and sophisticated strategies to maximize the potential of ecological momentary assessment for older adults with depression in the community setting.

Compliance and Retention With the Experience Sampling Method Over the Continuum of Severe Mental Disorders: Meta-Analysis and Recommendations

Background

Despite the growing interest in the experience sampling method (ESM) as a data collection tool for mental health research, the absence of methodological guidelines related to its use has resulted in a large heterogeneity of designs. Concomitantly, the potential effects of the design on the response behavior of the participants remain largely unknown.

Objective

The objective of this meta-analysis was to investigate the associations between various sample and design characteristics and the compliance and retention rates of studies using ESM in mental health research.

Methods

ESM studies investigating major depressive disorder, bipolar disorder, and psychotic disorder were considered for inclusion. Besides the compliance and retention rates, a number of sample and design characteristics of the selected studies were collected to assess their potential relationships with the compliance and retention rates. Multilevel random/mixed effects models were used for the analyses.

Results

Compliance and retention rates were lower for studies with a higher proportion of male participants (P<.001) and individuals with a psychotic disorder (P<.001). Compliance was positively associated with the use of a fixed sampling scheme (P=.02), higher incentives (P=.03), higher time intervals between successive evaluations (P=.02), and fewer evaluations per day (P=.008), while no significant associations were observed with regard to the mean age of the sample, the study duration, or other design characteristics.

Conclusions

The findings demonstrate that ESM studies can be carried out in mental health research, but the quality of the data collection depends upon a number of factors related to the design of ESM studies and the samples under study that need to be considered when designing such protocols.

Using ambulatory assessment to measure dynamic risk processes in affective disorders

BACKGROUND

Rapid advances in the capability and affordability of digital technology have begun to allow for the intensive monitoring of psychological and physiological processes associated with affective disorders in daily life. This technology may enable researchers to overcome some limitations of traditional methods for studying risk in affective disorders, which often (implicitly) assume that risk factors are distal and static - that they do not change over time. In contrast, ambulatory assessment (AA) is particularly suited to measure dynamic "real-world" processes and to detect fluctuations in proximal risk for outcomes of interest.

METHOD

We highlight key questions about proximal and distal risk for affective disorders that AA methods (with multilevel modeling, or fully-idiographic methods) allow researchers to evaluate.

RESULTS

Key questions include between-subject questions to understand who is at risk (e.g., are people with more affective instability at greater risk than others?) and within-subject questions to understand when risk is most acute among those who are at risk (e.g., does suicidal ideation increase when people show more sympathetic activation than usual?). We discuss practical study design and analytic strategy considerations for evaluating questions of risk in context, and the benefits and limitations of self-reported vs. passively-collected AA.

LIMITATIONS

Measurements may only be as accurate as the observation period is representative of individuals' usual life contexts. Active measurement techniques are limited by the ability and willingness to self-report.

CONCLUSIONS

We conclude by discussing how monitoring proximal risk with AA may be leveraged for translation into personalized, real-time interventions to reduce risk.

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

BACKGROUND

Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups.

OBJECTIVE

The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression.

METHODS

The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models).

RESULTS

On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960).

CONCLUSIONS

This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Does repeatedly reporting positive or negative emotions in daily life have an impact on the level of emotional experiences and depressive symptoms over time?

The extent to which people are aware of their emotional experiences, label them and communicate them to the outside world are considered to impact emotional experience itself and potentially people’s depressive symptom levels. All of these aspects are important elements of one of the most common methods to study and measure emotions in the context of daily life, the so-called experience sampling method (ESM). A straightforward question that arises when using this method is whether participating in ESM may bring about changes in the momentary emotional self-reports of the people engaging in it, thereby effectively influencing that what it intends to measure; emotional experience over time, and whether this would relate to average levels of depressive symptoms. To examine these questions, we conducted a 7-day ESM study in which 90 participants were randomly assigned to repeatedly report either positive emotions only, negative emotions only or non-emotional internal states only, course using smartphones. Participants also completed pre-, post- and follow-up measurements of levels of depressive symptoms. Results showed no significant impact on self-reported momentary emotions, respective to their condition, over time nor on average levels of depressive symptoms across groups. These findings suggest that the repeated assessment of emotions in daily life, over the course of seven days, does not impact their emotional experience over time nor levels of depressive symptoms.

Persuasive technology to support active and healthy ageing: An exploration of past, present, and future

The age of the population worldwide is rapidly increasing, bringing social and economic challenges. Persuasive technology can alleviate the burden on traditional healthcare services when used to support healthy behaviors, for instance in the prevention and treatment of chronic diseases. Additionally, healthy behaviors are key factors for active and healthy ageing by delaying or even reversing functional decline. In this manuscript, we present a multi-perspective analysis of technologies that can be used in the support of active and healthy ageing in the daily life. First, we take the perspective of physical and mental health, by focusing on the promotion of physical activity and emotional wellbeing. From a temporal perspective, we look at how technology evolved from past, present and future. The overview of the literature is structured in four main sections: (1) measurement of current behavior (monitoring), (2) analysis of the data gathered to derive meaningful information (analyzing & reasoning), (3) support the individual in the adoption or maintenance of a behavior (coaching), and (4) tools or interfaces that provide the information to the individual to stimulate the desired behavior (applications). Finally, we provide recommendations for the design, development and implementation of future technological innovations to support Active and Healthy Ageing in daily life.

Data quality and feasibility of the Experience Sampling Method across the spectrum of severe psychiatric disorders: a protocol for a systematic review and meta-analysis

BACKGROUND

Due to a number of methodological advantages and theoretical considerations, more and more studies in clinical psychology research employ the Experience Sampling Method (ESM) as a data collection technique. Despite this growing interest, the absence of methodological guidelines related to the use of ESM has resulted in a large heterogeneity of designs while the potential effects of the design itself on the response behavior of the participants remain unknown. The objectives of this systematic review are to investigate the associations between the design characteristics and the data quality and feasibility of studies relying on ESM in severe psychiatric disorders.

METHODS

We will search for all published studies using ambulatory assessment with patients suffering from major depressive disorder, bipolar disorder, and psychotic disorder or individuals at high risk for these disorders. Electronic database searches will be performed in PubMed and Web of Science with no restriction on the publication date. Two reviewers will independently screen original studies in a title/abstract phase and a full-text phase based on the inclusion criteria. The information related to the design and sample characteristics, data quality, and feasibility will be extracted. We will provide results in terms of a descriptive synthesis, and when applicable, a meta-analysis of the findings will be conducted.

DISCUSSION

Our results will attempt to highlight how the feasibility and data quality of ambulatory assessment might be related to the methodological characteristics of the study designs in severe psychiatric disorders. We will discuss these associations in different subsamples if sufficient data are available and will examine limitations in the reporting of the methods of ambulatory studies in the current literature.

SYSTEMATIC REVIEW REGISTRATION

The protocol for this systematic review was registered on PROSPERO (PROSPERO 2017: CRD42017060322 ) and is available in full on the University of York website ( http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42017060322 ).