DOI: 10.15125/bath-01588
title: Dataset for "Feasibility and acceptability of 7-day smartphone-based, activity-triggered Ecological Momentary Assessment among low-income older adults"

description: Smartphone-based Ecological Momentary Assessment (EMA) is increasingly used to collect real-time data on physical activity behaviour. The current study aimed to assess the feasibility and acceptability of activity-triggered EMA in low-income older adults. For 7 days, 39 older adults (76.4 ± 8.5 years; 76% earning below £25,000/year) received EMA surveys, delivered via the movisensXS application (version 1.5.23, movisens GmbH, Karlsruhe, Germany) for Android operating systems, when they surpassed a predefined activity/inactivity threshold, or when two hours elapsed between prompts. Participants wore a Move 4 activity sensor (movisens GmbH, Karlsruhe, Germany) to measure their steps. A post-study questionnaire assessed perceptions of acceptability.

The dataset includes all quantitative data needed to replicate analyses in the article "Feasibility and acceptability of 7-day smartphone-based, activity-triggered Ecological Momentary Assessment among low-income older adults." The "Descriptives" sheet contains a unique participant identifier, demographic information, and responses to the post-study questionnaire. The "EMA" sheet contains a unique participant identifier (Participant_ID), age (Age_years), biological sex (Biological_sex), time of day (Time_of_day), day of week (Weekday), and EMA compliance (EMA_compliance; whether participants completed the EMA prompt or missed the EMA prompt) variables needed to perform the multilevel logistic regression models. It also contains the data necessary to limit the sample to participants with valid activity sensor wear and run Model 2, including the length of time in minutes that participants were not wearing the activity sensor in the 15-minute window before (Nonwear_before) and after (Nonwear_after) the EMA survey, and concurrent physical activity (Concurrent_PA; the number of steps in the ± 15-minute window around the EMA prompt). 

Day of study (day number from 1 to 7), trigger type (whether participants received an activity-triggered, inactivity-triggered, or timeout EMA prompt), trigger time (absolute time of the auditory signal and/or vibration alerting participants that it was time to complete an EMA survey), EMA outcome (whether the EMA prompt was completed, not answered, or answered but incomplete), form start time (absolute time when the EMA survey was answered), form completion time (absolute time when the EMA survey was completed), observation number (variable that assigns the observation number to each row by participant ID), and observation counter (variable that assigns the number of total observations to each row of data for a given participant) variables are also provided to enable researchers to replicate all of the summary statistics presented in the article. A complete description of the variables, including the text of questionnaires (where relevant), is provided in the "Overview" sheet.
description: Full details of the data collection methods are provided in the published article. If an EMA survey was delivered at the end of the introductory appointment or during an interim appointment, participants had the option of completing it as a practice survey with the research team. These responses were discarded from analyses and have been omitted from the current dataset. The DataAnalyzer software (version 1.15.1; movisens GmbH, Karlsruhe, Germany) calculated steps with a 60-second resolution. The raw data was aggregated (using the "SUMIFS" function in Microsoft® Excel® for Microsoft 365 MSO, Version 2502 Build 16.0.18526.20416 64-bit) to obtain the length of time in minutes that participants were not wearing the activity sensor in the 15-minute window before (Nonwear_before) and after (Nonwear_after) the EMA survey, as well as the number of steps in the ± 15-minute window around the EMA prompt (Concurrent_PA). This aggregated data is presented in the current dataset.
description: Data were processed in Stata BE version 18.0 (StataCorp, College Station, TX), and multilevel models were performed in R version 4.4.1 with RStudio version 2024.04.2. Processing and analytic code (Stata and R) are available in the GitHub repository at https://github.com/OliviaMalkowski/EMA-feasibility.git. First, run the file "2025-08-19_Stata-do-file_v01" in Stata to replicate the summary statistics presented in the associated journal article and to prepare the datasets (i.e., limiting the sample to participants with valid activity sensor wear for Model 2) ahead of performing multilevel logistic regression modeling. Then, knit the file "2025-08-19_Feasibility_v01" in RStudio to run the multilevel logistic regression models (observations nested within persons) regressing EMA compliance on time-invariant (i.e., age, biological sex) and time-varying (i.e., time of day, day of week, concurrent physical activity) factors. The dataset is saved in XLSX format (given the multi-sheet capability of XLSX files) and can be opened with Microsoft Excel. The separate sheets (Overview, Descriptives, and EMA) are saved in CSV format and can be opened with any software that supports CSV files, including Microsoft Excel.
description: Rather than uploading a 'Readme' file or questionnaire templates, all information that would assist understanding and enable reuse of the data has been provided in the "Overview" sheet of the dataset.