Occurrence of and Reasons for “Missing Events” in Mobile Dietary Assessments: Results From Three Event-Based Ecological Momentary Assessment Studies

Abstract

Background Establishing a methodology for assessing nutritional behavior comprehensively and accurately poses a great challenge. Mobile technologies such as mobile image-based food recording apps enable eating events to be assessed in the moment in real time, thereby reducing memory biases inherent in retrospective food records. However, users might find it challenging to take images of the food they consume at every eating event over an extended period, which might lead to incomplete records of eating events (missing events). Objective Analyzing data from 3 studies that used mobile image-based food recording apps and varied in their technical enrichment, this study aims to assess how often eating events (meals and snacks) were missed over a period of 8 days in a naturalistic setting by comparing the number of recorded events with the number of normative expected events, over time, and with recollections of missing events. Methods Participants in 3 event-based Ecological Momentary Assessment (EMA) studies using mobile image-based dietary assessments were asked to record all eating events (study 1, N=38, 1070 eating events; study 2, N=35, 934 eating events; study 3, N=110, 3469 eating events). Study 1 used a basic app; study 2 included 1 fixed reminder and the possibility to add meals after the actual eating events occurred instead of in the moment (addendum); and study 3 included 2 fixed reminders, an addendum feature, and the option to record skipped meals. The number of recalled missed events and their reasons were assessed by semistructured interviews after the EMA period (studies 1 and 2) and daily questionnaires (study 3). Results Overall, 183 participants reported 5473 eating events. Although the momentary adherence rate as indexed by a comparison with normative expected events was generally high across all 3 studies, a differential pattern of results emerged with a higher rate of logged meals in the more technically intensive study 3. Multilevel models for the logging trajectories of reported meals in all 3 studies showed a significant, albeit small, decline over time (b=−.11 to −.14, Ps<.001, pseudo-R²=0.04-0.06), mainly because of a drop in reported snacks between days 1 and 2. Intraclass coefficients indicated that 38% or less of the observed variance was because of individual differences. The most common reasons for missing events were competing activities and technical issues, whereas situational barriers were less important. Conclusions Three different indicators (normative, time stability, and recalled missing events) consistently indicated missing events. However, given the intensive nature of diet EMA protocols, the effect sizes were rather small and the logging trajectories over time were remarkably stable. Moreover, the individual’s actual state and context seemed to exert a greater influence on adherence rates than stable individual differences, which emphasizes the need for a more nuanced understanding of the factors that affect momentary adherence.

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

“…There could be several reasons for missed EMA surveys, which likely contributed to fewer lapse or meal recordings than we would expect. To limit common obstacles in completing EMA, such as device-related problems (e.g., low battery) and situational barriers (e.g., driving), 72 the main trial now includes more participant training in using the LifeData app to report on eating occasions (e.g., a Frequently Asked Questions document, periodic brief refreshers on using LifeData and reporting on lapses at treatment visits, specific instruction for using the ‘user-initiated surveys’ function to report lapses and meals if EMA notifications are missed due to situational barriers). In addition to procedures to limit barriers for completing EMA, monetary compensation has been used in prior work to enhance participants’ motivation to respond to surveys.…”

Memorable meals: The memory-experience gap in day-to-day experiences

“…All participants gave written informed consent prior to participation. While the present publication focused on in-the-moment experienced and remembered eating happiness, logging behavior and reasons for missing events are reported in Ziesemer et al ([ 26 ]; Study 3), and further data is presented elsewhere [ 41 ].…”

“…To support data collection and ensure high compliance, the participants were asked to choose individually timed reminders in the morning and evening before the study started. The morning reminder encouraged participants to use the app and record all eating occasions during the day, while the evening reminder prompted them to record any meals or snacks they had missed during the day (see also Ziesemer et al [ 26 ]).…”

“…There has not yet been a systematic investigation of bias in the remembrance of day-to-day experiences, of which eating, including multiple eating occasions over the course of a day [ 26 ], is one of the most common [ 27 , 28 ]. Although some studies have already investigated the memory-experience gap in this area, results are mixed regarding the influence of distinct in-the-moment characteristics [ 29 – 31 ].…”

Evidenzbasierung in Primärprävention und Gesundheitsförderung: Methoden und Vorgehensweisen in 5 Forschungsverbünden

“…Die Anwendungen wurden in Bezug auf die Nutzerfreundlichkeit, Funktionalität und Wirksamkeit optimiert, was nur durch eine enge Zusammenarbeit zwischen den potenziellen Anwender*innen und den Expert*innen aus den verschiedenen Teilprojekten und Fachdisziplinen möglich war. Hier konnte mithilfe der SMARTFOOD -App, die u. a. eine fotobasierte Erfassung des Essverhaltens umfasst, eine sehr hohe Teilnahme- und Verhaltenserfassungsrate realisiert werden [ 62 ].…”