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. 2021 Jul 5;23(7):e25850.
doi: 10.2196/25850.

Studying Microtemporal, Within-Person Processes of Diet, Physical Activity, and Related Factors Using the APPetite-Mobile-App: Feasibility, Usability, and Validation Study

Alea Ruf  1 , Elena Doris Koch  2 , Ulrich Ebner-Priemer  2   3 , Monika Knopf  4 , Andreas Reif  1 , Silke Matura  1
Affiliations

Studying Microtemporal, Within-Person Processes of Diet, Physical Activity, and Related Factors Using the APPetite-Mobile-App: Feasibility, Usability, and Validation Study

Alea Ruf et al. J Med Internet Res. .

Abstract

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.

Keywords: diet; dietary assessment; ecological momentary assessment; feasibility; mHealth; microtemporal processes; mobile phone; physical activity; smartphone-app; usability; validity; within-person factors.

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Conflict of interest statement

Conflicts of Interest: UEP reports consultancy for Boehringer-Ingelheim. All other authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Sampling strategy of the APPetite-mobile-app.
Figure 2
Figure 2
Screenshots of the 6-step process of the APPetite-food record.
Figure 3
Figure 3
Bland-Altman plot assessing agreement between habitual energy intake in kJ per day captured by the APPetite-food record and the 24-hour recall (red line: mean difference=app–24-hour recall; dark red lines: 95% CI of mean difference; blue lines: lower and upper limits of agreement; dark blue lines: 95% CI of lower and upper limits of agreement).
See this image and copyright information in PMC

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