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Background: Diet and physical activity (PA) have a major impact on physical and mental health. However, there is a lack of effective strategies for sustaining these health-protective behaviors. A shift to a microtemporal, within-person approach is needed to capture dynamic processes underlying eating behavior and PA, as they change rapidly across minutes or hours and differ among individuals. However, a tool that captures these microtemporal, within-person processes in daily life is currently not present.
Objective: The APPetite-mobile-app is developed for the ecological momentary assessment of microtemporal, within-person processes of complex dietary intake, objectively recorded PA, and related factors. This study aims to evaluate the feasibility and usability of the APPetite-mobile-app and the validity of the incorporated APPetite-food record.
Methods: The APPetite-mobile-app captures dietary intake event-contingently through a food record, captures PA continuously through accelerometers, and captures related factors (eg, stress) signal-contingently through 8 prompts per day. Empirical data on feasibility (n=157), usability (n=84), and validity (n=44) were collected within the Eat2beNICE-APPetite-study. Feasibility and usability were examined in healthy participants and psychiatric patients. The relative validity of the APPetite-food record was assessed with a subgroup of healthy participants by using a counterbalanced crossover design. The reference method was a 24-hour recall. In addition, the energy intake was compared with the total energy expenditure estimated from accelerometry.
Results: Good feasibility, with compliance rates above 80% for prompts and the accelerometer, as well as reasonable average response and recording durations (prompt: 2.04 min; food record per day: 17.66 min) and latencies (prompts: 3.16 min; food record: 58.35 min) were found. Usability was rated as moderate, with a score of 61.9 of 100 on the System Usability Scale. The evaluation of validity identified large differences in energy and macronutrient intake between the two methods at the group and individual levels. The APPetite-food record captured higher dietary intakes, indicating a lower level of underreporting, compared with the 24-hour recall. Energy intake was assessed fairly accurately by the APPetite-food record at the group level on 2 of 3 days when compared with total energy expenditure. The comparison with mean total energy expenditure (2417.8 kcal, SD 410) showed that the 24-hour recall (1909.2 kcal, SD 478.8) underestimated habitual energy intake to a larger degree than the APPetite-food record (2146.4 kcal, SD 574.5).
Conclusions: The APPetite-mobile-app is a promising tool for capturing microtemporal, within-person processes of diet, PA, and related factors in real time or near real time and is, to the best of our knowledge, the first of its kind. First evidence supports the good feasibility and moderate usability of the APPetite-mobile-app and the validity of the APPetite-food record. Future findings in this context will build the foundation for the development of personalized lifestyle modification interventions, such as just-in-time adaptive interventions.
Background: Understanding which factors influence dietary intake, particularly in daily life, is crucial given the impact diet has on physical as well as mental health. However, a factor might influence whether but not how much an individual eats and vice versa or a factor’s importance may differ across these two facets. Distinguishing between these two facets, hence, studying dietary intake as a dual process is conceptually promising and not only allows further insights, but also solves a statistical issue. When assessing the association between a predictor (e.g. momentary affect) and subsequent dietary intake in daily life through ecological momentary assessment (EMA), the outcome variable (e.g. energy intake within a predefined time-interval) is semicontinuous. That is, one part is equal to zero (i.e. no dietary intake occurred) and the other contains right-skewed positive values (i.e. dietary intake occurred, but often only small amounts are consumed). However, linear multilevel modelling which is commonly used for EMA data to account for repeated measures within individuals cannot be applied to semicontinuous outcomes. A highly informative statistical approach for semicontinuous outcomes is multilevel two-part modelling which treats the outcome as generated by a dual process, combining a multilevel logistic/probit regression for zeros and a multilevel (generalized) linear regression for nonzero values. Methods: A multilevel two-part model combining a multilevel logistic regression to predict whether an individual eats and a multilevel gamma regression to predict how much is eaten, if an individual eats, is proposed. Its general implementation in R, a widely used and freely available statistical software, using the R-package brms is described. To illustrate its practical application, the analytical approach is applied exemplary to data from the Eat2beNICE-APPetite-study. Results: Results highlight that the proposed multilevel two-part model reveals process-specific associations which cannot be detected through traditional multilevel modelling. Conclusions: This paper is the first to introduce multilevel two-part modelling as a novel analytical approach to study dietary intake in daily life. Studying dietary intake through multilevel two-part modelling is conceptually as well as methodologically promising. Findings can be translated to tailored nutritional interventions targeting either the occurrence or the amount of dietary intake.
Introduction: The influence of our diet on mental health is of increasing importance in current research. Study results on the gut-brain axis suggest that the gut microbiome can influence mental processes via neuronal, hormonal and immune signaling pathways [1]. The gut microbiome is largely influenced by our diet. Some studies provide evidence that a "Western diet" rich in saturated fat and sugar may promote mental disorders [2]. There is evidence, that dietary behaviour in individuals with Attention Deficit Hyperactivity Disorder (ADHD) is characterized by an increased intake of sugar and saturated fat [3]. So far, it is unclear whether this dietary pattern contributes to ADHD symptoms such as impulsivity. The aim of this study is to investigate the influence of certain macronutrients such as fats and mono/disaccharides on impulsivity in individuals with ADHD. Using our APPetite-mobile-app [4] enabled us to study dietary behaviour and momentary impulsiveness in everyday life of our participants.
Methods: 43 participants with ADHD (mean age 36.0 ± 12.3 years, 21 females) and 186 healthy controls (mean age 28.5 ± 7.7 years, 133 females) without any psychiatric condition were included into the study. Food intake was recorded over a period of three days using the APPetite-mobile-app via a 6 step process: (1) Selection of meal type, (2) Entry of time of meal, (3) Selection of consumed foods and drinks, (4) Specification of consumed amounts, (5) Presentation of reminder for commonly forgotten foods, and (6) Indication of predominant reason for eating. In addition to entering consumed foods in the APPetite-mobile-app, subjects completed an online food log for the last 24 hours (myfood 24) at the beginning of the study. After the data collection period, a detailed analysis of the ingested nutrients was performed for each subject. Trait impulsivity was assessed using the UPPS-P, a self-assessment questionnaire. Momentary impulsiveness was assessed via the mHealth APP by means of the Momentary Impulsiveness scale (MIS). The MIS consists of 4 questions capturing different aspects of impulsivity. The participants were prompted to answer these questions at 8 semi-random times per day between 8 AM and 10 PM. The minimum time between 2 prompts was 1 hour. Thereby participants could not predict the exact time of the next prompt and the assessed situations are a better reflection of the participant’s real life.
Results: ANOVA revealed higher levels of both, trait and momentary impulsivity in individuals with ADHD compared to controls (p < 0,01). After preprocessing of data that was sampled via the mHealth APP is completed, a regression analysis with different macronutrients as predictors and impulsivity as dependent variable will be computed. To assess the association between momentary impulsiveness and dietary intake, generalized linear multilevel modelling will be used. Results of these analyses will be presented.