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:10.31234/osf.io/jh49z

Cited by 4 publications

(4 citation statements)

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“…First, the collected data needs to be available for analysis shortly after it is obtained, and the data needs to be analyzed directly after becoming available. Although this is not necessarily very challenging as (a) several apps (e.g., PETRA and m-Path; Bos et al, 2022;Mestdagh et al, 2022) already upload data in real-time, and (b) there are many examples of analyzing regularly incoming data using the EWMA procedure (see Montgomery, 2009 for an overview of historical applications), researchers still need to keep this in mind when aiming to base an intervention on the EWMA procedure. Finally, changes can only be detected after they have occurred, and no form of analysis can change this.…”

Section: Arl0mentioning

confidence: 99%

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

2022

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Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

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Section: Arl0mentioning

confidence: 99%

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

2022

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“…A large-scale implementation of ESM and ESM feedback in clinical settings would have to compensate for the researcher's expertise by training therapists and developing ESM tools that are automatized. The presented feedback interface contributed to such an ESM tool, as it formed the basis for the feedback module in the later developed PETRA application [13], which aims to make personalized ESM accessible in regular psychiatric care. Second, patient, therapist, and researcher invested considerable time and effort into the ESM module, illustrating that ESM will likely involve a cost-benefit analysis in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…The researcher helps to translate identified aspects into personalized ESM items. Items can be newly formulated or chosen from an existing item repository [13]. When formulating new items, the researcher aims to use the wording of the patient as much as possible, while also following criteria for high-quality ESM items [9].…”

Section: Step 2: Development and Initialization Of The Personalized E...mentioning

confidence: 99%

“…To fulfill the potential of ESM, patients and clinicians call for ESM to be personalized and flexibly integrated into their care [12]. Technically this is already possible on a large scale, since some digital tools are available such as PETRA [13], PsyMate™ [14], and m-Path [15], that offer (some) flexible options for personalization of ESM questionnaire content and schedules. However, we are only at the start of using ESM as a clinical tool, and there is still much uncertainty about its application in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Integrating personalized experience sampling in psychotherapy: A case illustration of the Therap-i module

Klipstein¹,

Servaas²,

Schoevers³

et al. 2022

Preprint

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Background: The experience sampling methodology (ESM) is increasingly being suggested as a clinical tool in mental health care, as it offers ecologically valid, microlevel information on psychopathological processes. Patients and clinicians have recommended that applications of ESM should be personalized and integrated into the existing clinical process, but there is still much uncertainty about how implementation may look like. Objective: To provide an example of personalized ESM assessment and feedback being integrated into psychotherapy for depression, specifically looking at the collaborative use of ESM in case conceptualization.Methods: George, a 27-year-old man diagnosed with depression, and his therapist participated in the Therap-i randomized controlled trial, which investigates the efficacy of a personalized ESM module added to psychotherapy. Together, they created a personalized ESM questionnaire, aiming to capture their hypotheses and questions regarding George’s case conceptualization. George then filled out his ESM questionnaire five times per day, for 8 weeks. During this period, ESM data were discussed and interpreted by George, his therapist, and a researcher, in three feedback sessions. In these sessions, data were visualized in a flexible feedback interface that allowed for collaborative exploration of George’s data. Both patient and therapist evaluated the module through questionnaires and George also participated in a semi-structured evaluation interview.Results: George’s ESM questionnaire included personalized items on the topics of self-esteem and open versus withdrawn behavior. He completed 241 (89.3%) assessments. Discussions during the feedback sessions focused on two core themes: First, George’s low energy level, which was further explored with regard to his sleep, medication, and activity patterns. Second, his low sense of self-esteem, which led to an in-depth exploration of his thinking patterns and social interactions. The ESM module was seen as useful and insightful by both George and therapist. Conclusions: This case shows how ESM and ESM-based feedback can stimulate the collaborative exploration of the patient’s complaints, and how it can provide useful insights for treatment. We discuss how our personalized ESM module relates to current clinical principles and practices, and make suggestions for further implementation.

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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 4 publications

References 64 publications

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

Integrating personalized experience sampling in psychotherapy: A case illustration of the Therap-i module

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

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