Designing daily-life research combining experience sampling method with parallel data

Abstract

Background Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. Methods Literature and expert opinions were integrated to formulate relevant guiding principles. Results Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. Conclusions The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.

Stress‐related fluctuations in personality functioning in daily life: Pilot data from an ambulatory monitoring study in outpatients diagnosed with borderline personality disorder

“…Third, momentary changes in PNS and SNS activity did not significantly predict PF within subjects. Indeed, the effects of ANS activity on people's behaviour are probably small, and monitoring stress physiology in daily life is complex (De Calheiros Velozo et al, 2022; Sinnaeve et al, 2021; Vaessen et al, 2021). We did not account for between‐subject variations in age, sex and overall health status, nor did we control for within‐subject variations in factors such as the time of day, movement artefacts, and ambient conditions or the use of substances between beeps.…”

Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study

“…One of the specific challenges in analyzing mobile sensing data alongside ESM data is that the 2 must be aligned, despite being collected at very different timescales and frequencies [ 37 ]. The function link in the mpathsenser package does exactly this.…”

It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

“…For example, passive smartphone measures are often measured almost continuously during the day, whereas ESM questionnaires are measured at only specific time points. Thus, to combine ESM and digital phenotyping, researchers have to choose a time window in which the passive measures are aggregated, for instance, before or after the ESM questionnaire was filled out (Velozo et al, 2022).…”

“…This is typically done for several days or even weeks (Myin-Germeys et al, 2018). Digital phenotyping and ESM are increasingly used together in psychological research because they provide a powerful tool for capturing the daily life of a participant by measuring feelings and behavior (Rehg et al, 2017;Velozo et al, 2022).…”

“…In recent years, digital phenotyping has become increasingly popular in psychological research and is expected to transform health care and clinical practice (Huckvale et al, 2019; Jongs, 2021; Velozo et al, 2022). Digital phenotyping involves the “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices” (Torous et al, 2016).…”