The association between BMI self-selection, self-reported BMI and objectively measured BMI

Emily Johnson; Seán R. Millar; Frances Shiely

doi:10.12688/hrbopenres.13252.1

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Research Article

The association between BMI self-selection, self-reported BMI and objectively measured BMI

[version 1; peer review: 1 approved with reservations]

Emily Johnson¹, Seán R. Millar², Frances Shiely ^2,3

PUBLISHED 19 Apr 2021

Author details Author details

¹ School of Medicine, University College Cork, Cork, T12 AK54, Ireland
² School of Public Health, University College Cork, Cork, T12 XF62, Ireland
³ Trials Research and Methodologies Unit (TRAMS), HRB Clinical Research Facility, University College Cork, Cork, T12 XF62, Ireland

Emily Johnson
Roles: Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Seán R. Millar
Roles: Formal Analysis, Validation, Writing – Review & Editing

Frances Shiely
Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background: It is challenging to measure BMI accurately in population studies because it is an expensive and time-consuming task. As a result, subjectively measured BMI, calculated from self-reported height and weight, which we know to be inaccurate, is often used. The purpose of this study is to determine whether using a different, subjective method of measuring BMI, BMI self-selection, would be more accurate. BMI self-selection is determined by asking participants to place themselves in one of the BMI categories, underweight, normal weight, overweight or obese.
Methods: A quantitative survey conducted in the outpatient departments of two hospitals in the South West of Ireland. Participants >18 years were included. Pregnant women were excluded. The final sample included 70 participants. Survey completion, including self-reported height and weight and BMI self-selection, preceded the objectively measured weight and height. Demographic and lifestyle factors were also recorded.
Results: 59% of the sample were overweight or obese –63% of females and 54% of males. The sensitivity for correct BMI self-selection for normal weight, overweight and obese categories was 90%, 36% and 33% respectively. The sensitivity for BMI categories calculated from self-reported height and weight was 83%, 57% and 59% respectively. In analysis, age, regular alcohol consumption, unhealthy diet and lower levels of physical activity were all associated with underestimation of BMI.
Conclusion: BMI self-selection is an accurate tool when used with normal weight individuals but shows poorer ability to demonstrate accurate measurement in overweight and obese BMI categories. It is inferior to the traditional self-reported BMI method for measuring BMI levels in the general population. Presently, we recommend continuing with self-reported BMI for BMI measurement in large population studies. However, further testing of BMI classification in larger studies is suggested and investigation into alternative objective methods of measurement is warranted.

Keywords

BMI, self-reported BMI, objectively measured BMI

Corresponding author: Frances Shiely

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2021 Johnson E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original data is properly cited.

How to cite: Johnson E, Millar SR and Shiely F. The association between BMI self-selection, self-reported BMI and objectively measured BMI [version 1; peer review: 1 approved with reservations]. HRB Open Res 2021, 4:37 (https://doi.org/10.12688/hrbopenres.13252.1) First published: 19 Apr 2021, 4:37 (https://doi.org/10.12688/hrbopenres.13252.1) Latest published: 19 Apr 2021, 4:37 (https://doi.org/10.12688/hrbopenres.13252.1)

Introduction

Ireland has one of the highest rates of overweight and obesity worldwide¹, with only 40% of the population recorded as normal weight in 2015². There have been numerous studies examining the role of self-reported body mass index (BMI), defined as using self-reported height and weight to calculate BMI, compared to objectively measured BMI (BMI calculated from measured height and weight). The results of these studies report that a significant proportion of overweight and obese adults are inaccurate in reporting their height and weight when compared to objectively measured BMI^3–14 . It has previously been stated that self-reported height is over estimated, regardless of gender, and self-reported weight is under estimated, by both men and women^4,10,12–14, resulting in lower BMI self-recordings. This is the case in Ireland also^6,8,9. Further studies report that overweight and obese individuals have a higher tendency to misperceive their weight^15–17 than normal weight individuals. In Ireland, it is reported that this bias in self-reporting height is stable over time while self-reporting weight bias has increased over time, supporting the notion of a continued increase in weight misperception and rising BMI among the Irish population⁹. The rising level of obesity in Ireland and the corresponding lack of ability of many overweight and obese individuals to recognise their weight status has the potential to cause a catastrophic economic effect on the health service in Ireland in terms of treatment for associated conditions^18–21. Obesity is associated with hypertension, diabetes mellitus, coronary heart disease and various cancers including breast and colon cancer, osteoarthritis and fertility complications^22,23. The psychosocial consequences of obesity such as depression, anxiety, eating disorders and substance abuse, can be observed worldwide and are a burden on society as well as individuals^24,25.

Despite numerous government and public health initiatives aiming to reduce and draw attention to obesity and it’s causative factors in Ireland, the numbers continue to grow¹. There are two imperative factors in this public health emergency - firstly, the idea of weight perception and the accurate reporting of this, and secondly the normalisation of obesity in society^9,26–28. Obtaining an accurate figure of overweight and obese individuals is vital to predict and manage the public health services that will be required in the future. According to the World Health Organisation, Ireland is set to have the highest BMI per capita in Europe by 2030²⁹.

The aim of our study was to investigate a new means of measuring BMI for large epidemiological studies. BMI self-selection has not been evaluated previously. BMI self-selection is measured by asking individuals to self-select their BMI category, “underweight, normal weight, overweight, obese”. Our objectives were to (i) compare BMI self-selection with objectively measured BMI (calculated from measured height and weight) (ii) compare self-reported BMI (calculated from self-reported height and weight) versus objectively measured BMI (iii) determine if BMI self-selection is superior to self-reported BMI measurement at the population level (iv) establish the factors that influence an individual’s ability to correctly classify their BMI.

Methods

Study design

This was a quantitative, cross-sectional study using a self-completed questionnaire (including open ended questions) with additional measurements of weight and height. Opportunistic sampling was used at a range of outpatient clinics (ophthalmology, general surgery and gastroenterology) at Cork University and Mercy University Hospitals in the Southwest of Ireland from September 2019 to August 2020. Clinics were approached to participate while attempting to ensure a reasonable distribution of age and sex. Participants were required to be 18 years of age or older to participate in the study; pregnant individuals were excluded. Coronavirus disease 2019 (COVID-19) restrictions severely limited our opportunity to collect data in person. We availed of the limited window for data collection between wave 1 and wave 2. Clinics were not running at full capacity, so we were unable to reach our planned recruitment of 100 people before restrictions were again imposed, preventing the collection of data in person. However, this is a pilot study therefore no sample size was conducted. We adhered to the STROBE guidelines when reporting on the study.

Ethical considerations

Ethics committee approval conforming to the Declaration of Helsinki was obtained from the Clinical Research Ethics Committee of the Cork Teaching Hospitals (ECM 3(bbbb) 27/08/19). Permission to collect the data was sought, and granted, from the physicians in each clinic. All participants gave signed informed consent, including permission to use their data for research purposes.

Data collection

Participants were approached when they were seated waiting for their clinic appointment, or in between the appointment and waiting for a blood draw. They were given a questionnaire³⁰ and asked to complete the questions to the best of their ability. Demographic variables measured included sex, age, being in receipt of a medical card (proxy for socioeconomic status) and highest level of education completed. Educational levels were classified according to primary education, secondary education, some third level and completed third level, based on the education system in the Republic of Ireland. Subjects were asked to quantify their smoking status and whether they were regular users of alcohol. Participants were also asked to report their weekly exercise levels, quantified by on how many days a week they had completed at least 30 minutes of exercise, and whether they perceived their diet to be healthy, unhealthy or whether they did not know. Participants were asked to record their height and weight in the questionnaire, in whatever metric they were accustomed to, before their height and weight were measured. Subjective height and weight measurements were converted to metres and kilograms respectively. BMI self-selection was determined by asking study participants “Do you think that your BMI is “underweight, normal weight, overweight or obese?”

BMI measurement and classification

Weight and height measurements were collected by a trained researcher with reference to a standard operating procedures manual. Weight was measured using a portable electronic Tanita WB-100MA weighing scale (Tanita Corp, IL, USA) and height was measured with a portable Seca Leicester height/length stadiometer (Seca, Birmingham, UK). The weighing scale was placed on a firm flat surface and was calibrated weekly. Participants were also asked to record their perceived height and weight in the questionnaire, in whatever metric they were accustomed to. For both objectively and subjectively measured weight and height measurements, BMI was calculated as weight in kilograms divided by the square of height in metres. Objectively measured and self-reported BMI measurements were categorised as BMI levels 18.5–24.9 kg/m2 = ‘Normal-weight’, 25–29.9 kg/m2 = ‘Overweight’ and >30 kg/m2 = ‘Obese’.

Statistical methods

Descriptive characteristics were examined according to the full sample and by sex. Patterns of reporting bias were determined by cross-classifying objectively measured BMI categories with self-reported BMI categories (based on perceived weight and height) and BMI selection categories. BMI measurement bias was calculated as the difference between objectively measured BMI and self-reported BMI. This was recorded as a positive change, negative change or no change. Positive BMI bias implies that the participant had a higher BMI than they believed, while negative BMI bias indicates that the participant had a lower BMI than they believed, with higher positive values and lower negative values indicting greater misclassification. Independent sample t-tests or one-way ANOVA analyses were used to test mean BMI measurement bias according to sample characteristics.

Data analysis was conducted using STATA SE Version 13 (Stata Corporation, College Station, TX, USA) for Windows. For all analyses, a P-value (two-tailed) of less than .05 was considered to indicate statistical significance.

Results

Descriptive characteristics

A total of 78 participants were recruited; six were excluded due to incomplete data; two subjects had an objectively measured BMI <18.5 kg/m2 and these participants were excluded from the analyses. Characteristics of the study population for the full sample and according to sex are presented in Table 1³⁰. Of this sample, 50% had received education beyond a secondary school level, with a higher percentage of male subjects indicating lower educational levels. A greater number of male participants reported smoking every day, lower levels of physical activity and an unhealthy diet. Regarding objectively measured BMI 41.4% of participants were normal weight, 20% and 38.6% of participants were either overweight or obese respectively, and a greater number of female subjects had higher BMI levels.

Table 1. Characteristics of the study population – full sample and stratified by sex.

Variable	Full sample (n=70)	Males (n=35)	Females (n=35)
Age (median, interquartile range)
Years	47.0 (28.0, 63.0)	47.0 (35.0, 62.0)	47.0 (25.0, 65.0)
Highest level of education completed (n, %)
Primary school	8 (11.4)	6 (17.1)	2 (5.7)
Secondary school	27 (38.6)	15 (42.9)	12 (34.3)
Post-leaving certificate	22 (31.4)	10 (28.6)	12 (34.3)
Completed third level	13 (18.6)	4 (11.4)	9 (25.7)
Medical card (n, %)
Yes	27 (38.6)	15 (42.9)	12 (34.3)
No	43 (61.4)	20 (57.1)	23 (64.4)
Smoking status (n, %)
Every day	11 (15.7)	8 (22.9)	3 (8.6)
Occasionally	13 (18.6)	7 (20.0)	6 (17.1)
Not at all	46 (65.7)	20 (57.1)	26 (74.3)
Regular alcohol consumption (n, %)
Yes	59 (84.3)	29 (82.9)	30 (85.7)
No	11 (15.7)	6 (17.1)	5 (14.3)
Days of exercise per week (n, %)
3 or less	44 (62.9)	24 (68.6)	20 (57.1)
4 or more	26 (37.1)	11 (31.4)	15 (42.9)
Healthy diet (n, %)
Yes	43 (61.4)	22 (62.9)	21 (60.0)
No	9 (12.9)	6 (17.1)	3 (8.6)
Do not know	18 (25.7)	7 (20.0)	11 (31.4)
Objectively measured BMI category* (n, %)
Normal weight	29 (41.4)	16 (45.7)	13 (37.1)
Overweight	14 (20.0)	5 (14.3)	9 (25.7)
Obese	27 (38.6)	14 (40.0)	13 (37.1)

*Based on objectively measured weight and height. BMI=body mass index.

BMI misclassification

Table 2 shows objectively measured BMI categories cross-classified with self-reported BMI categories (based on self-reported height and weight measurements) and BMI self-selection categories. Among normal weight subjects, 82.8% and 89.7% of participants correctly classified their BMI category according to self-reported BMI and BMI self-selection, respectively. Among overweight participants, the sensitivity for self-reported BMI was 57.1%, while the sensitivity for BMI self-selection was 35.7%. Subjects who were obese were also more likely to correctly classify their BMI category according to self-reported weight and height measurements when compared to BMI self-selection (59.3% vs. 33.3%).

Table 2. Cross-tabulation of self-reported BMI category and BMI self-selection category according to objectively measured BMI category.

Variable	Objectively measured BMI category*
	Normal weight	Overweight	Obese	Total
Self-reported BMI category**	n, %	n, %	n, %	n
Normal weight	24 (82.8)	4 (28.6)	0 (0.0)	28
Overweight	5 (17.2)	8 (57.1)	11 (40.7)	24
Obese	0 (0.0)	2 (14.3)	16 (59.3)	18
BMI self-selection category***	n, %	n, %	n, %
Normal weight	26 (89.7)	9 (64.3)	1 (3.7)	36
Overweight	3 (10.3)	5 (35.7)	17 (63.0)	25
Obese	0 (0.0)	0 (0.0)	9 (33.3)	9

*Based on objectively measured weight and height.

**Based on participants’ self-reported weight and height.

***Based on asking study participants “Do you think that your BMI is normal weight, overweight or obese?”

BMI=body mass index.

Factors associated with BMI bias

Table 3 shows mean BMI measurement bias according to sample characteristics. Males were more likely to underestimate their BMI levels compared to females (mean difference = 0.75, P = .009). A one-way ANOVA of mean BMI measurement bias and age revealed that those aged 45–54 years had the highest measurement bias, indicating this group were more likely to underestimate their self-reported BMI. Subjects who were regular users of alcohol (P = .006) and who indicated having an unhealthy diet (P = .009) were also more likely to underestimate their BMI, while participants who exercised 4 or more days per week had a greater likelihood to overestimate their BMI measurements compared to subjects with lower physical activity levels (-0.18 vs. 1.48, P for difference < .001). With regard to objectively measured BMI, normal weight subjects were more likely to overestimate their BMI, with subjects who were obese showing the highest measurement bias (P < .001), indicating a greater likelihood of underestimating BMI levels.

Table 3. Mean BMI measurement bias according to sample characteristics.

Variable	Mean BMI measurement bias	P-value
Sex		.009
Male	1.54 ± 2.1
Female	0.19 ± 2.1
Age range		.018
18–24 years	0.12 ± 2.3
25–34 years	-0.52 ± 2.3
35–44 years	1.71 ± 2.0
45–54 years	2.54 ± 2.0
55–64 years	1.24 ± 2.0
≥65 years	0.53 ± 2.2
Highest level of education completed		.084
Primary school	0.52 ± 1.8
Secondary school	1.67 ± 2.2
Post-leaving certificate	0.55 ± 2.3
Completed third level	-0.6 ± 1.8
Medical card holder		.584
Yes	1.05 ± 2.1
No	0.75 ± 2.3
Smoking status		.13
Every day	2.05 ± 1.9
Occasionally	0.92 ± 2.7
Not at all	0.57 ± 2.1
Regular alcohol consumption
Yes	1.01 ± 2.2	.006
No	0.09 ± 2.1
Days of exercise per week		<.001
3 or less	1.48 ± 2.1
4 or more	-0.18 ± 1.9
Healthy diet		.009
Yes	0.22 ± 1.9
No	2.81 ± 2.1
Do not know	1.43 ± 2.3
Objectively measured BMI category*		<.001
Normal weight	-0.51 ± 1.6
Overweight	0.94 ± 1.9
Obese	2.30 ± 1.9

Mean and ± one standard deviation for the difference between objectively measured BMI and self-reported

BMI is shown. P-values determined from independent sample t-tests or a one-way ANOVA. BMI=body mass index.

*Based on objectively measured weight and height.

Discussion

We examined a new way of measuring BMI, BMI self-selection. We theorised that if this was effective, BMI self-selection would change the way in which BMI measurement is reported at a population level. For a true comparison, it was necessary to collect data on objectively measured BMI (calculated from measured height and weight), self-reported BMI (calculated from self-reported height and weight) and BMI self-selection (participants self-select any of underweight, normal, overweight or obese BMI categories). Our findings show that BMI self-selection results in an underestimation of BMI in all three BMI categories, normal, overweight and obese with just 90%, 36%, and 33% agreement in the BMI categories, respectively. Comparatively, the level of agreement between objectively measured BMI and self-reported BMI, although slightly disimproved for the normal weight category at 83% is much increased for the overweight and obese categories (57% and 59% respectively). Therefore, our hypothesis that BMI self-selection would be more accurate than self-reported BMI, for measuring BMI in large population studies, is rejected.

Our results mirrored what has been previously reported^{6–8,10,12,14,16,31}, which is that individuals who are overweight or obese are poorer at recognising their overweight and obesity compared to individuals who are of normal weight. This was true for both BMI self-selection and self-reported BMI. Previous literature^{9,17,27,32,33} has cited possibilities as to why this occurs and we believe it holds true in this study also. Firstly, it may be that this group are afraid of the negative connotations that are associated with obesity and do not want to be associated with them. There is a low social desirability to be overweight and obese, which could be the reason why people do not willingly include themselves in those categories. It is also suggested that people are in denial about their weight status, associated again with the negative implications of obesity and the media portrayal of unhealthy weights. Lastly is the phenomenon of normalisation of obesity within our society. Due to the increasing rise in obesity in Ireland over time, we as a population are accustomed to seeing overweight and obese individuals in society. This has resulted in a shift in social norms – in addition to denial and negative connotations, it could be that individuals are oblivious to their own BMI status because they are increasingly seeing people with higher BMIs around them. This social norm of obesity is a topic that should be interrogated more in future studies.

Research demonstrates that adherence to healthy lifestyle behaviours is associated with a lower risk of heart disease³⁴, even among obese individuals. We also know that other negative lifestyle factors, e.g., not physically active at recommended levels, regular alcohol consumption, smoking and poor fruit and vegetable intake, influence our BMI levels³⁵. Our current study suggests that individuals who have healthier lifestyles are less likely to underestimate their BMI levels. In particular, diet plays a crucial role in weight control³⁶, and our results show that those who are poorer at perceiving their weight category are more likely to have an unhealthy diet or be unsure if their diet is healthy or not. Despite the aforementioned public health campaigns, this strongly suggests that these campaigns are not effectively reaching the individuals in the population who are most in need of the information and intervention. Often, these campaigns include images of morbidly obese people to whom the overweight and obese individuals in society can’t relate to and hence believe they are not at risk, a hypothesis supported in previous studies³⁷.

Strengths and weaknesses

Due to restrictions surrounding the COVID-19 pandemic, the collection of data was restricted for this study, resulting in a relatively small sample size. However, is a pilot study and our participants were opportunistically selected at random and therefore we believe that our results should be reasonably representative of the general population. In addition, overweight and obesity trends are consistent with the recent Healthy Ireland Study, suggesting a random sample of the population was achieved. The study excluded a proportion of the population – those that don’t accompany or attend medical appointments in hospitals. This could have resulted in skewed data because overweight and obese individuals have an increased risk of medical comorbidities that require medical care such as diabetes, rheumatoid arthritis, and cancer^38,39. However, our figures follow the Irish BMI trends, so we believe our study is a reasonable representation of the general population.

Conclusions

Though we have demonstrated that self-reported BMI is more reliable than BMI self-selection in population studies, it is still not accurate or reliable when compared to objectively measured BMI. As we can see here in Table 2, self-reported BMI is still poor for providing us with population figures in the overweight and obese categories, compared to objectively measured BMI. Researchers should still interrogate further methodologies for BMI measurement which would be more cost effective than measuring height and weight in large population studies.

Data availability

Underlying data

Zenodo: The Association between BMI self-selection, self-reported BMI and objectively measured BMI. https://doi.org/10.5281/zenodo.4644182³⁰.

This project contains the following underlying data:

- Archived data

Extended data

Zenodo: The Association between BMI self-selection, self-reported BMI and objectively measured BMI. https://doi.org/10.5281/zenodo.4644182³⁰.

This project contains the following extended data:

- Questionnaire

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Author contributions

EJ collected the data, drafted the manuscript and the revisions. SM guided the data analysis and oversaw the drafting of the methods section. FS conceptualised the study, contributed to the writing of the manuscript, reviewed the drafts.

Acknowledgements

We would like to acknowledge the participants in this survey who gave so generously of their time. We wish to acknowledge the Clinical Research Ethics Committee of the Cork Teaching Hospitals (CREC) for reviewing and granting ethical approval for this study.

Faculty Opinions recommended

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Author details Author details

¹ School of Medicine, University College Cork, Cork, T12 AK54, Ireland
² School of Public Health, University College Cork, Cork, T12 XF62, Ireland
³ Trials Research and Methodologies Unit (TRAMS), HRB Clinical Research Facility, University College Cork, Cork, T12 XF62, Ireland

Emily Johnson
Roles: Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Seán R. Millar
Roles: Formal Analysis, Validation, Writing – Review & Editing

Frances Shiely
Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 19 Apr 2021, 4:37

https://doi.org/10.12688/hrbopenres.13252.1

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© 2021 Johnson E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original data is properly cited.

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Johnson E, Millar SR and Shiely F. The association between BMI self-selection, self-reported BMI and objectively measured BMI [version 1; peer review: 1 approved with reservations]. HRB Open Res 2021, 4:37 (https://doi.org/10.12688/hrbopenres.13252.1)

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Reviewer Report 20 Jun 2024

Iliya Gutin, The University of Texas at Austin, Texas, USA

Approved with Reservations

https://doi.org/10.21956/hrbopenres.14418.r40712

I would like to thank the authors for an opportunity to review their work and hope my feedback proves helpful in revising the manuscript. I think that the study - comparing BMI based on self-reported height and weight and BMI ... Continue reading

I would like to thank the authors for an opportunity to review their work and hope my feedback proves helpful in revising the manuscript. I think that the study - comparing BMI based on self-reported height and weight and BMI based on self-selection of a BMI category to BMI based on measure height and weight - is timely and relevant, not only in the context of Ireland, but in the many countries and regions where obesity remains a public health challenge. I generally had no issues with the data collection or analysis, though - as I note in my comments below - the objective of the research as it pertains to "public health" was not always clear to me. At least part of this stems from a lack of acknowledgement of the limitations of "objective" BMI itself, which serves as the baseline for the comparisons. Also, the framing for the analysis does not provide the reader with much of a reason to think that the BMI self-selection measure would be very useful, given that it is an even more subjective measure than the already mis-reported self-reported BMI. Personally, I think the self-selection measure has utility and I would be interested to see some more discussion of this, but my primary recommendation is that the authors clarify what the end goal for these different measures actually is - especially in differentiating the accurate measurement of weight status (as defined by BMI) from the accurate measurement of population health as a function of weight status (as merely implied by BMI).

I include comments following the sections of the manuscript they are in response to. I tried to be clear on which comments should be prioritized in a revision.

--

If space allows, I recommend briefly clarifying what it means to say "more accurate" in the abstract - as the two subjective measures facilitate different types of comparisons vis-a-vis accuracy. This is somewhat clearer in the full manuscript - though still an issue that I am not sure the authors fully grapple with - but the unqualified used of "accurate" in the abstract is a bit confusing.

--

This is a minor point, but, in the introduction, why do you report the statistic for "normal" weight rather than overweight and obesity (ideally, separately)?

--

What is meant by the "role of" self-reported BMI in the first paragraph? I think you just mean that prior studies have assessed the accuracy of this measure. If so, please say this more plainly.

--

Rather than BMI "self-recordings," pleasure use "self-reports."

--

Regarding the phrasing "lack of ability of many overweight and obese individuals to recognise their weight status": I imagine this is not your objective, but please be cautious about the use of language throughout the manuscript - especially in "individualizing" risk. I am not suggesting that individual responsibility - vis-a-vis beliefs and behaviors - have no role to play, but it doesn't take much to read a statement like this as implying that reducing obesity is as simple as making sure that overweight and obese individuals realize their weight is a problem... and that as perfectly "rational" actors, they would change their behavior as a consequence. Personally, I think it sufficient to note that obesity is - objectively speaking - a public health challenge and that we need reliable data to monitor it... wherein this misperception and reporting is an issue. However, I acknowledge not all share this perspective; thus, I offer this advice to the authors should it move them to make revisions.

--

From an organizational perspective, I would lead with the second paragraph in the introduction - or some version thereof - making it clear that obesity is an important public health concern in Ireland and that it is important to accurately monitor it. THEN, you could discuss the challenges therein - especially given the difficult of collecting measured height and weight at the population level (which is something you mention in the abstract, but not here).

One thing I would like the authors to think about carefully - and address in a revised version of the manuscript - is what the utility of the BMI "self-selection" measure actually is. That is, you emphasize the INACCURACY and MISPERCEPTION when it comes to measures of body size... so why would introducing an even more subjective measure help? To be clear, I think it is a very interesting measure... but it's public health utility - vis-a-vis the framing you use as motivation - is not clear to the reader.

--

Regarding Table 2 and any additional analyses: Why not also compare whether respondents' BMI self-selection accurately maps onto their self-reported BMI category? This not only seems relevant to the study, but would also provide some sense of "where" the bias is coming from. As in, discordance between the two would strongly suggest that individuals are less knowledgeable about BMI itself - and distinctions among categories - rather than "unaware" of their weight status.

--

Regarding the discussion (and the manuscript more generally): I was quite surprised to find no discussion of the limitations of objectively measured BMI itself in this analysis, as a somewhat tricky baseline "comparison" when it comes to these measures. There have been numerous papers on this topic (e.g., Gutin 2019), highlighting the relative arbitrariness of BMI categories and what they imply about categorical differences in weight status and, by extension, health. A comparison of self-reported to measured BMI - using a continuous operationalization - is a fairly clean measure of respondents' accuracy in REPORTING. Comparing the distributions of individuals in various BMI categories, in turn, allows us to understand potential misreporting bias at the population level. That's all well and good, especially when it comes to population health knowledge. The comparison becomes a bit trickier when moving to the BMI self-selection variable. Is this assessing individuals' knowledge of BMI categories? Or is this assessing individuals' self-assessment of their BMI relative to their self-assessment of their health? The former is more relevant to accurate public health reporting and measurement. I am not entirely sure what the latter means vis-a-vis "accuracy," in the sense that a respondent might be providing an entirely ACCURATE assessment of the extent to which BMI does or does not impact their health and/or quality of life - in a way, superior to what the blunt categories by objective BMI might provide. This is of course speculative - certainly without additional knowledge about what informs respondents' selections - but my overarching point is that it is important to be very clear on the overall objective in all this. If accuracy of assessment is purely a function of whether or not individuals know their BMI and the "appropriate" categorization, then that is fine. But this should be explicit. If accuracy of assessment is somehow supposed to capture accurate knowledge of one's health status - as I think this paper sometimes implies vis-a-vis behaviors and so forth - then there needs to be recognition of the limitations of BMI itself on that front (again, see Gutin 2019 as an example). I don't mean to steer the authors' work in a specific direction, but I think any study using BMI as a "standard" for comparison should be mindful of the large body of work challenging the premise of BMI as a standard in the first place.

Gutin, I. (2018). In BMI we trust: reframing the body mass index as a measure of health. Social Theory & Health, 16, 256-271.

--

Regarding the discussion of healthier lifestyles and BMI perceptions in the paragraph preceding "Strengths' and weaknesses": These are fair points, though I would challenge the authors to be open to other - or alternative - explanations, especially as they pertain to the TYPES of people more likely to have healthier lifestyles, and the extent to which that might be confounded with weight (mis)perception. Namely, is likely to be highly socially patterned, in ways that accounting for education and access to a medical card (which I will freely admit to being less familiar with) do not account for. This is not your fault, as you can only work with the data you have, but I would urge the authors to think more critically about such sources of concurrent influence on healthy lifestyles and perceptions.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Obesity; Body Size; Measurement

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Iliya Gutin, The University of Texas at Austin, Texas, USA

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20 Jun 2024 | for Version 1

Iliya Gutin, The University of Texas at Austin, Texas, USA

21 Views Cite this report Responses(0)

Approved With Reservations

I would like to thank the authors for an opportunity to review their work and hope my feedback proves helpful in revising the manuscript. I think that the study - comparing BMI based on self-reported height and weight and BMI based on self-selection of a BMI category to BMI based on measure height and weight - is timely and relevant, not only in the context of Ireland, but in the many countries and regions where obesity remains a public health challenge. I generally had no issues with the data collection or analysis, though - as I note in my comments below - the objective of the research as it pertains to "public health" was not always clear to me. At least part of this stems from a lack of acknowledgement of the limitations of "objective" BMI itself, which serves as the baseline for the comparisons. Also, the framing for the analysis does not provide the reader with much of a reason to think that the BMI self-selection measure would be very useful, given that it is an even more subjective measure than the already mis-reported self-reported BMI. Personally, I think the self-selection measure has utility and I would be interested to see some more discussion of this, but my primary recommendation is that the authors clarify what the end goal for these different measures actually is - especially in differentiating the accurate measurement of weight status (as defined by BMI) from the accurate measurement of population health as a function of weight status (as merely implied by BMI).

I include comments following the sections of the manuscript they are in response to. I tried to be clear on which comments should be prioritized in a revision.

--

If space allows, I recommend briefly clarifying what it means to say "more accurate" in the abstract - as the two subjective measures facilitate different types of comparisons vis-a-vis accuracy. This is somewhat clearer in the full manuscript - though still an issue that I am not sure the authors fully grapple with - but the unqualified used of "accurate" in the abstract is a bit confusing.

--

This is a minor point, but, in the introduction, why do you report the statistic for "normal" weight rather than overweight and obesity (ideally, separately)?

--

What is meant by the "role of" self-reported BMI in the first paragraph? I think you just mean that prior studies have assessed the accuracy of this measure. If so, please say this more plainly.

--

Rather than BMI "self-recordings," pleasure use "self-reports."

--

Regarding the phrasing "lack of ability of many overweight and obese individuals to recognise their weight status": I imagine this is not your objective, but please be cautious about the use of language throughout the manuscript - especially in "individualizing" risk. I am not suggesting that individual responsibility - vis-a-vis beliefs and behaviors - have no role to play, but it doesn't take much to read a statement like this as implying that reducing obesity is as simple as making sure that overweight and obese individuals realize their weight is a problem... and that as perfectly "rational" actors, they would change their behavior as a consequence. Personally, I think it sufficient to note that obesity is - objectively speaking - a public health challenge and that we need reliable data to monitor it... wherein this misperception and reporting is an issue. However, I acknowledge not all share this perspective; thus, I offer this advice to the authors should it move them to make revisions.

--

From an organizational perspective, I would lead with the second paragraph in the introduction - or some version thereof - making it clear that obesity is an important public health concern in Ireland and that it is important to accurately monitor it. THEN, you could discuss the challenges therein - especially given the difficult of collecting measured height and weight at the population level (which is something you mention in the abstract, but not here).

One thing I would like the authors to think about carefully - and address in a revised version of the manuscript - is what the utility of the BMI "self-selection" measure actually is. That is, you emphasize the INACCURACY and MISPERCEPTION when it comes to measures of body size... so why would introducing an even more subjective measure help? To be clear, I think it is a very interesting measure... but it's public health utility - vis-a-vis the framing you use as motivation - is not clear to the reader.

--

Regarding Table 2 and any additional analyses: Why not also compare whether respondents' BMI self-selection accurately maps onto their self-reported BMI category? This not only seems relevant to the study, but would also provide some sense of "where" the bias is coming from. As in, discordance between the two would strongly suggest that individuals are less knowledgeable about BMI itself - and distinctions among categories - rather than "unaware" of their weight status.

--

Regarding the discussion (and the manuscript more generally): I was quite surprised to find no discussion of the limitations of objectively measured BMI itself in this analysis, as a somewhat tricky baseline "comparison" when it comes to these measures. There have been numerous papers on this topic (e.g., Gutin 2019), highlighting the relative arbitrariness of BMI categories and what they imply about categorical differences in weight status and, by extension, health. A comparison of self-reported to measured BMI - using a continuous operationalization - is a fairly clean measure of respondents' accuracy in REPORTING. Comparing the distributions of individuals in various BMI categories, in turn, allows us to understand potential misreporting bias at the population level. That's all well and good, especially when it comes to population health knowledge. The comparison becomes a bit trickier when moving to the BMI self-selection variable. Is this assessing individuals' knowledge of BMI categories? Or is this assessing individuals' self-assessment of their BMI relative to their self-assessment of their health? The former is more relevant to accurate public health reporting and measurement. I am not entirely sure what the latter means vis-a-vis "accuracy," in the sense that a respondent might be providing an entirely ACCURATE assessment of the extent to which BMI does or does not impact their health and/or quality of life - in a way, superior to what the blunt categories by objective BMI might provide. This is of course speculative - certainly without additional knowledge about what informs respondents' selections - but my overarching point is that it is important to be very clear on the overall objective in all this. If accuracy of assessment is purely a function of whether or not individuals know their BMI and the "appropriate" categorization, then that is fine. But this should be explicit. If accuracy of assessment is somehow supposed to capture accurate knowledge of one's health status - as I think this paper sometimes implies vis-a-vis behaviors and so forth - then there needs to be recognition of the limitations of BMI itself on that front (again, see Gutin 2019 as an example). I don't mean to steer the authors' work in a specific direction, but I think any study using BMI as a "standard" for comparison should be mindful of the large body of work challenging the premise of BMI as a standard in the first place.

Gutin, I. (2018). In BMI we trust: reframing the body mass index as a measure of health. Social Theory & Health, 16, 256-271.

--

Regarding the discussion of healthier lifestyles and BMI perceptions in the paragraph preceding "Strengths' and weaknesses": These are fair points, though I would challenge the authors to be open to other - or alternative - explanations, especially as they pertain to the TYPES of people more likely to have healthier lifestyles, and the extent to which that might be confounded with weight (mis)perception. Namely, is likely to be highly socially patterned, in ways that accounting for education and access to a medical card (which I will freely admit to being less familiar with) do not account for. This is not your fault, as you can only work with the data you have, but I would urge the authors to think more critically about such sources of concurrent influence on healthy lifestyles and perceptions.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Obesity; Body Size; Measurement

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

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The association between BMI self-selection, self-reported BMI and objectively measured BMI

Abstract

Keywords

Introduction

Methods

Study design

Ethical considerations

Data collection

BMI measurement and classification

Statistical methods

Results

Descriptive characteristics

Table 1. Characteristics of the study population – full sample and stratified by sex.

BMI misclassification

Table 2. Cross-tabulation of self-reported BMI category and BMI self-selection category according to objectively measured BMI category.

Factors associated with BMI bias

Table 3. Mean BMI measurement bias according to sample characteristics.

Discussion

Strengths and weaknesses

Conclusions

Data availability

Underlying data

Extended data

Author contributions

Acknowledgements

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Are you a HRB-funded researcher?

Thank you!

The association between BMI self-selection, self-reported BMI and objectively measured BMI

Abstract

Keywords

Introduction

Methods

Study design

Ethical considerations

Data collection

BMI measurement and classification

Statistical methods

Results

Descriptive characteristics

Table 1. Characteristics of the study population – full sample and stratified by sex.

BMI misclassification

Table 2. Cross-tabulation of self-reported BMI category and BMI self-selection category according to objectively measured BMI category.

Factors associated with BMI bias

Table 3. Mean BMI measurement bias according to sample characteristics.

Discussion

Strengths and weaknesses

Conclusions

Data availability

Underlying data

Extended data

Author contributions

Acknowledgements

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Competing Interests Policy

Stay Updated

Are you a HRB-funded researcher?

Thank you!