A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

Bos, Fionneke M.; Klipstein, Lino von; Emerencia, Ando C.; Veermans, Erwin; Verhage, Tom; Snippe, Evelien; Doornbos, Bennard; Hadders-Prins, Grietje; Wichers, Marieke; Riese, Harriëtte

doi:10.2196/36430

Cited by 29 publications

(18 citation statements)

References 103 publications

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“…Several tools are available in Belgium and the Netherlands to facilitate data collection, interpretation, and feedback processes. For example, at University Medical Center Groningen, The Netherlands, PETRA was developed in collaboration with patients and HCPs [ 30 ]. PETRA is a web-application that enables ESM monitoring and feedback in psychiatric care.…”

Section: Discussionmentioning

confidence: 99%

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

Bartels,

van Zelst,

Melo Moura

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

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

Bartels,

van Zelst,

Melo Moura

et al. 2023

Heliyon

Self Cite

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“…Thus, publicly available ambulatory assessment item repositories that contain validated items or scales for transdiagnostic constructs of interest would be a valuable tool to help advance applied ambulatory assessment. Item repositories are being developed for ecological momentary items used in research (Kirtley et al, 2022), and item repositories for clinical settings are also available (e.g., see supplementary material in Bos et al, 2022). However, these item repositories largely contain items used in ambulatory research as opposed to an item repository for self-report items (a) adapted from validated transdiagnostic instruments and (b) have evidence for their psychometric validity.…”

Section: Three Goals For Future Research On Applied Ambulatory Assess...mentioning

confidence: 99%

“…To give a few examples, this will require that software capable of providing meaningful summary statistics or other data (e.g., strength of relations between symptoms) with little to no analyses needing to be conducted by clinicians, ambulatory assessment protocols can be designed with ease and in a flexible manner (i.e., personalized), potentially be integrated with electronic health record systems, and ensure that ambulatory assessment software keeps up with routine operating system updates (Piot et al, 2022; Wasil et al, 2020). Despite these obstacles, promising evidence of how these obstacles can be addressed can be found in the web-based Personalized Treatment by Real-time Assessment application (PETRA; Bos et al, 2022). The PETRA application was developed using an iterative process over three phases that included the use of focus groups of clinicians and patients as well as testing prototypes of the software in usability sessions to improve ease-of-use.…”

Section: Three Goals For Future Research On Applied Ambulatory Assess...mentioning

confidence: 99%

Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances

Vize,

G. C. Wright

2023

Assessment

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Researchers and clinicians working within the Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition, Text Rev ( DSM-5-TR) framework face a difficult question: what does it mean to have an evidence-based assessment of a nonevidence-based diagnostic construct? Alternative nosological approaches conceptualize psychopathology as (a) hierarchical, allowing researchers to move between levels of description and (b) dimensional, eliminating artificial dichotomies between disorders and the dichotomy between mental illness and mental well-being. In this article, we provide an overview of ongoing efforts to develop validated measures of transdiagnostic nosologies (i.e., the Hierarchical Taxonomy of Psychopathology; HiTOP) with applications for measurement-based care. However, descriptive models like HiTOP, which summarize patterns of covariation among psychopathology symptoms, do not address dynamic processes underlying the problems associated with psychopathology. Ambulatory assessment, well-suited to examine such dynamic processes, has also developed rapidly in recent decades. Thus, the goal of the current article is twofold. First, we provide a brief overview of developments in constructing valid measures of the HiTOP model as well as developments in ambulatory assessment practices. Second, we outline how these parallel developments can be integrated to advance measurement-based treatment. We end with a discussion of some major challenges for future research to address to integrate advances more fully in transdiagnostic and ambulatory assessment practices.

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“…For instance, if an individual spends less time on messenger apps when stressed, this could suggest that they avoid social contact, whereas seeking social support might be beneficial. If individuals are not aware of this pattern, personalized prediction models could provide novel clinical insights and could potentially help to improve therapy outcomes (eg, within personalized treatment modules [40]). Finally, our exploratory findings provide important directions for confirmatory research.…”

Section: Principal Findingsmentioning

confidence: 99%

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

Aalbers¹,

Hendrickson²,

Abeele³

et al. 2023

JMIR Mhealth Uhealth

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Background Stress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied. Objective We aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people. Methods Student participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations. Results We identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models). Conclusions Our findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress.

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A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

Cited by 29 publications

References 103 publications

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

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

Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

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