Introduction: A great number of weight loss interventions have been delivered through digital solutions. Analysis of the effectiveness in terms of weight loss is fundamental to understand the real potential of digital technologies as tools for delivery of weight loss interventions. For this, we need accurate and reliable anthropometric data. For reasons of convenience, self-reported weight and height often replace actual measurements in these interventions. This might lead to misclassification of BMI status during selection of participants and to bias in the assessment of the outcomes. Therefore, it is fundamental to have validation studies of self-reported web-based data.
Objectives: We aimed to validate online self-reported height, weight and BMI in a POEmaS trial subsample.
Methods: We included 12.5% of the POEmaS´ population (n=159). Anthropometric data reported on the web-platform were compared to measured data by paired T-tests. Agreement was assessed by Bland-Altman plots. Multinomial regression was used to investigate factors associated with self-reported weight validity.
Results: There was no significant difference between reported and measured weight (0.4 kg, SD 1.7; p=0.13) and BMI (0.03 kg/m2, SD 0.87; p=0.06). Reported height was on average 0.4 cm (SD 1.2) higher than the measured ones (p<0.001). For all anthropometric data, >=95% of the cases were within the limits of agreement. Higher measured BMI was the only factor associated with low accuracy of weight report. Each unit increase in BMI increased the odds that the reported weight was lower than the one measured (OR 1.13; 95%CI 1.01-1.26).
Discussion: Self-reported weight and BMI change showed good agreement with measured ones. Since these are the primary outcomes of the POEmaS trial, the findings of the validation study suggest that the outcomes' accuracy is high and that it does not vary across gender, age, study group. These findings are relevant to digital health researchers and assessors and suggest that digital health interventions for weight loss might rely on self-reported assessment of outcomes. This might be particularly useful when other modes of assessment, such as anthropometry and e-scales, are not feasible or not available. However, we acknowledge that these results might not be applicable to low educated populations.
|Title of host publication||Digital Health|
|Subtitle of host publication||Changing the Way Healthcare is Conceptualised and Delivered - Selected Papers from the 27th Australian National Health Informatics Conference, HIC 2019|
|Editors||Elizabeth Cummings, Mark Merolli, Louise K. Schaper|
|Number of pages||7|
|Publication status||Published - 8 Aug 2019|
|Event||27th Australian National Health Informatics Conference, HIC 2019 - Melbourne, Australia|
Duration: 12 Aug 2019 → 14 Aug 2019
|Name||Studies in Health Technology and Informatics|
|Conference||27th Australian National Health Informatics Conference, HIC 2019|
|Period||12/08/19 → 14/08/19|
Bibliographical note'This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).'
- Mobile health