Assessing the validity of self-reported weight, height, and body mass index among hispanic breast cancer survivors

Abstract

Self-reported measurements of body size are commonly used in cancer epidemiological studies; however, inaccuracies may lead to misclassification of body mass index (BMI) and biased estimates of associations with health outcomes. Despite their widespread use, the validity of these self-reported measures has not been well established among Hispanic breast cancer survivors, a growing and understudied population. This is particularly important, as prior research suggests that the accuracy of self-reported anthropometric measures may vary across racial and ethnic groups, highlighting the need for population-specific validation. To address this gap, we compared self-reported and measured weight, height, and BMI among 192 Hispanic women participating in the New Jersey Breast Cancer Survivors Study using intraclass correlation coefficients (ICCs), Bland–Altman analyses, and Cohen’s kappa, and examined predictors of reporting error using multivariable regression models. Data were collected during home visits, including body measurements taken by research staff and interviewer-administered questionnaires on body size and related factors. Self-reported and measured values were highly correlated (Intraclass correlation coefficients (95% Confidence Intervals (CI)): 0.99 (0.99, 0.99), 0.8 (0.64, 0.88), 0.95 (0.92, 0.97) for weight, height, and BMI, respectively). The agreement between self-reported and measured BMI categories (normal, overweight, and obese) was strong (kappa: 0.85; 95% CI: 0.79, 0.91). The mean difference observed between self-reported and measured BMI (-0.67 kg/m²) was primarily explained by slight over-reporting of height (mean difference: 1.94 cm). These findings support the use of self-reported weight, height, and BMI measures in large epidemiologic studies of Hispanic/ Latina cancer survivors when direct measurements are not feasible.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-60006-x.

Introduction

Body mass index (BMI), calculated using weight and height, is widely used in clinical and epidemiological settings to estimate body fatness and identify obesity. While BMI is an imperfect measure of body fat¹, it remains a practical and accessible tool for population-based health studies due to its simplicity and cost-effectiveness. In large-scale population-based epidemiological research, self-reported anthropometric data, including height and weight, are often used to derive BMI, offering substantial time and resource savings compared to researcher-measured values. However, previous studies evaluating the validity of self-reported anthropometric measurements in the general population suggest that weight tends to be underestimated, particularly in those with BMIs above 30 kg/m², and height tends to be overestimated^2,3. These reporting errors in height and weight may lead to underestimation of BMI, yielding systematic misclassification and potential errors in risk assessment in the general population. Importantly, even when correlations between self-reported and measured values are high, systematic biases can lead to BMI misclassification and may influence epidemiologic estimates of obesity and related health outcomes⁴. Moreover, these biases in self-reported measures may influence the results of studies focused on assessing the association of BMI with important health outcomes, such as mortality⁵, yielding inaccurate inferences.

Studies evaluating the accuracy of self-reported anthropometric measurements among cancer survivors, particularly those of diverse backgrounds, including Hispanic/Latina women, are limited⁶. Among cancer survivors, accurate BMI assessment is particularly important given the strong association of body weight with cancer prognosis and survivorship outcomes^7,8. For breast cancer survivors, elevated BMI and obesity are associated with adverse outcomes, including an increased risk of cancer recurrence and poorer overall and breast cancer-specific survival compared to women with a BMI < 25 kg/m^{2 7–9}. Cancer survivors may also experience specific challenges, such as cachexia or lymphedema, which may influence the accuracy of self-reported anthropometric measurements^10,11. In addition, cancer treatment can lead to fluctuations in body weight^12,13, further underscoring the importance of accurate BMI assessment.

These associations underscore the critical need for precise BMI classification to guide survivorship care and intervention strategies. One study suggests that self-reported height and weight are moderately accurate among African American breast cancer survivors, with older women (age ≥ 65 years) and those with higher BMIs tending to have a higher degree of misreporting⁶. Moreover, cultural, socioeconomic, and treatment-related factors unique to Hispanic/Latina populations, such as varying cultural perceptions of body weight and differential access to healthcare, may uniquely influence the accuracy of self-reported anthropometric data among Hispanic breast cancer survivors^14–17. Prior research has shown that the magnitude and direction of misreporting in self-reported height and weight can vary across racial and ethnic and socioeconomic groups, potentially leading to differential bias in BMI classification and related epidemiologic estimates^18,19. These findings further underscore the need for population-specific validation in this group.

To date, no study has specifically assessed the validity of self-reported weight, height, and BMI among Hispanic/Latina breast cancer survivors, a population with rising survivorship and unique cultural and clinical considerations. Although prior studies in the general population suggest that self-reported anthropometric measures are generally reliable^20–22, these findings may not be directly generalizable to Hispanic/Latina breast cancer survivors due to potential differences in cultural perceptions of body weight, comorbidity burden, and treatment-related changes in body composition. Assessing the validity of self-reported weight and height in this population is important, given that the population of Hispanic breast cancer survivors is rapidly increasing and that cancer is the leading cause of death among Hispanic individuals²³ calling for further research on cancer survivorship²⁴. Moreover, these measures are widely used in large epidemiologic and survivorship studies where direct measurements are not feasible, so inaccuracies in self-reported data may lead to misclassification of BMI and biased estimates in studies evaluating associations with cancer outcomes.

Given this important research gap, the primary objective of this study was to evaluate the accuracy of self-reported anthropometric measurements, including weight, height, and BMI, among Hispanic breast cancer survivors. A secondary aim was to identify demographic, clinical, and lifestyle factors that may predict reporting errors. To our knowledge, this is among the first studies validating self-reported height, weight, and BMI among Hispanic breast cancer survivors. By addressing this gap, this study aims to inform the appropriate use of self-reported anthropometric data in epidemiologic research and survivorship studies in this growing population.

Methods

Study Population

The New Jersey Breast Cancer Survivor Study (NJBCS) is an ongoing population-based survivorship cohort study with the long-term goal of assessing obesity-related factors that may be associated with components of survivorship, including health-related quality of life, among self-identified Hispanic women⁷ and has been expanded to also include Black breast cancer survivors. Participants in the NJBCS were identified in six counties in New Jersey through the New Jersey State Cancer Registry. Eligible participants included English- or Spanish-speaking women who self-identified as Hispanic or Black/African American, aged 20–75 years, with a recent, histologically confirmed diagnosis of breast cancer. Participants with a prior history of cancer other than non-melanoma skin cancer were excluded to reduce potential heterogeneity related to prior cancer diagnoses and treatments that could impact anthropometric measurements. After confirming eligibility, home visits were conducted an average of 22.5 months following breast cancer diagnosis. The timeframe post-diagnosis was selected to capture anthropometric data during a relatively stable survivorship period, when most participants had completed primary treatments (e.g., surgery, chemotherapy, and radiation therapy), although some may have remained on ongoing therapies such as endocrine treatment. This approach helps reduce variability from acute treatment-related weight fluctuations. During the visit, trained interviewers obtained informed consent, completed questionnaires on sociodemographic factors (e.g., education and marital status), lifestyle factors (e.g., smoking status, alcohol consumption, and physical activity), and medical history (e.g., history of comorbid conditions), and conducted anthropometric measurements. Interviewer-administered questionnaires were administered in English or Spanish, based on participant preference, by trained bilingual staff. All data were collected directly by the study investigators as part of the NJBCS under approved study protocols.

This study includes Hispanic participants in the NJBCS recruited between May 2019 and September 2023 (with a diagnosis between 2017 and 2022). The first participant was recruited on May 15, 2019. Of the 193 eligible participants, we excluded one participant from the present analyses who did not respond to the self-reported weight or height questions, leaving 192 participants in the final analysis.

The institutional review board at Rutgers University approved the study (protocol number: Pro2017000069, initial approval date for NJBCS: March 6, 2019), which occurred before recruitment began. The latest continuing approval date was March 17, 2026. The study was conducted in line with the principles of the Declaration of Helsinki, and informed consent was obtained from all participants included in the study.

Anthropometric data

During home visit interviews, participants reported their current weight and height. Self-reported and measured values were collected during the same home visit, minimizing time-related variation. Direct anthropometric measurements were taken after self-reported weight and height was collected during the home visit by trained research staff using standardized protocols and served as the reference (gold standard) for analyses. Participants were measured without shoes and wearing light clothing. Interviewers followed a standardized protocol to measure the weight and height of participants²⁵. Weight was measured to the nearest 0.1 kg using standardized digital scales (TBF-300 A; TANITA Corp., Tokyo, Japan), which were maintained and calibrated according to study procedures. Height was measured with a vertical ruler to the nearest 0.1 cm.

Covariates

Several additional factors were collected based on a priori hypotheses during interviewer-administered questionnaires to evaluate potential factors associated with BMI misreporting. Age, smoking status, time since diagnosis, diabetes, hypertension, education, marital status, and breast cancer treatments were obtained during home visit interviews. Furthermore, specific tumor characteristics, including histological subtype, stage, grade, and estrogen receptor (ER) status, were obtained from linkage with the New Jersey State Cancer Registry files.

Statistical analysis

BMI was calculated as weight (kg) divided by the square of height (m), using both measured and self-reported weight and height. Measured anthropometric values were collected directly in metric units, while self-reported values, which may have been provided in either metric or imperial units, were converted to kilograms and meters using standard conversion factors. We assessed the accuracy of self-reported height, weight, and BMI in several ways. First, we evaluated the degree and direction of misreporting by calculating the mean difference between self-reported and measured values. The mean difference was computed by subtracting the measured values from the self-reported values. Second, we analyzed agreement between self-reported and measured values using Pearson’s correlation coefficient and the intraclass correlation coefficient (ICC). ICCs were calculated using a two-way mixed-effects model with absolute agreement, appropriate for assessing agreement between self-reported and measured values. Pearson correlations were included to facilitate comparison with prior studies, whereas ICCs were the primary measure used to assess the absolute agreement between self-reported and measured anthropometric values. Using cross-tabulations and Cohen’s kappa, we further assessed agreement between self-reported and measured BMI values to evaluate BMI classification accuracy. We also constructed Bland-Altman plots²⁶ with corresponding 95% limits of agreement (LOA) to graphically assess the agreement between self-reported and measured height, weight, and BMI values. The LOAs, calculated as the mean difference ± 1.96 SD, indicate the range within which self-reported values may under- or overestimate the corresponding measured values.

Multivariable linear regression models were used to examine predictors of reporting errors for height, weight, and BMI. The initial full models included all factors listed in Table 1, and we used backward elimination as a secondary step to identify a parsimonious set of predictors, setting the statistical significance threshold at P < 0.10. This process was conducted iteratively and was not fully automated, with each model carefully evaluated to ensure epidemiologic and clinical plausibility. Covariates were selected based on a priori considerations informed by the literature and subject-matter knowledge. The differences between self-reported and measured weight, height, and BMI were used as outcomes for the regression models. Regression coefficients were estimated for each model, and adjusted mean differences were derived from the predicted values. All statistical analyses were performed using Stata software, version 16.1 (StataCorp) and SAS version 9.4 (Cary, NC: SAS Institute Inc.). This study is reported in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies²⁷.

Table 1.

Descriptive Characteristics of Women in the Study (n = 192).

			N	Percent e
Age at diagnosis (years)
< 55			103	53.6
55 - <65			55	28.6
≥ 65			34	17.7
Time since diagnosis
< 20 months			51	26.6
≥ 20 months			141	73.4
Baseline body mass index ( measured )
< 30 kg/m2			107	55.7
≥ 30 kg/m2			85	44.3
Education
≤ High School graduate			88	45.8
> High School graduate			104	54.2
Marital Status
Married/Living as married			101	52.6
Not Married			90	46.9
Unknown			1	0.5
Country of Birth
Born in one of the 50 U.S. States a			27	14.1
Not born in any of the 50 U.S. states b			165	85.9
Hispanic Origin
Central American			22	11.5
South American			67	34.9
Puerto Rican			37	19.3
Dominican			42	21.9
Cuban			16	8.3
Mexican and Other			8	4.2
Smoking status
Never-smoker			140	72.9
Ever-smoker c			51	26.6
Unknown			1	0.5
Alcohol Consumption
Non-drinker			95	49.5
< 3 drinks per week			68	35.4
≥ 3 drinks per week			29	15.1
Physical Activity Tertiles (MET Hours Per Week) d
Low			62	32.3
Medium			62	32.3
High			60	31.3
Unknown			8	4.1
History of Diabetes
No			151	78.6
Yes			41	21.4
History of Hypertension
No			119	62.0
Yes			73	38.0
Tumor Stage
0			11	5.7
I			103	53.6
II			38	19.8
III/IV			18	9.4
Unknown			22	11.5
Estrogen Receptor Status
Positive			149	77.6
Negative			34	17.7
Unknown			9	4.7
Surgery
No surgery			12	6.2
Lumpectomy			94	49.0
Mastectomy			79	41.1
Unknown			7	3.6
Chemotherapy
No			98	51.0
Yes			94	49.0
Radiation Therapy
No			50	26.0
Yes			142	74.0
Endocrine Therapy
No			37	19.3
Yes			151	78.6
Unknown			4	2.1

^a. Being born in one of the 50 United States, not including United States Territories such as Puerto Rico.

^b. Includes women born in Puerto Rico.

^c. Six women reported currently smoking at the time of a breast cancer diagnosis

^d. MET = Metabolic equivalents

^e. Percentages may not sum to 100% due to rounding and missing values.

Results

The study included 192 Hispanic breast cancer survivors. Table 1 summarizes the distribution of selected characteristics of the study population. Slightly more than half of the participants (53.6%) were aged 55 years or less at the time of diagnosis. Using measured BMI values, 44.3% of participants were classified as having obesity (BMI ≥ 30 kg/m²).

Concordance between self-reported and measured weight, height, and BMI

Table 2 presents the differences between self-reported and measured anthropometric values. On average, participants accurately reported their weight but slightly overreported their height, leading to an underestimation of BMI. The mean difference (SD) for self-reported and measured values was 1.94 cm (SD: 3.7) for height, 0.26 kg (SD: 2.1) for weight, and − 0.67 kg/m² (SD: 1.7) for BMI.

Table 2.

Self-reported and measured anthropometric values of participants in the study (N = 192).

	Self-Reported anthropometry (Mean ± SD)	Measured anthropometry (Mean ± SD)	Differences a (Mean ± SD)
Weight (kg)	74 ± 15.2	73.7 ± 15.2	0.26 ± 2.1
Height (cm)	159.8 ± 6.2	157.9 ± 6.6	1.94 ± 3.7
BMI (kg/m²)	28.9 ± 5.5	29.6 ± 5.9	−0.67 ± 1.7

BMI = body mass index; SD = standard deviation

a. Differences calculated as self-reported minus measured values.

Pearson’s correlation coefficients and ICCs were estimated to evaluate the agreement between self-reported and measured anthropometric data and are presented in Table 3. Overall, self-reported and measured weight (ICC: 0.99; 95% confidence interval (CI): 0.99, 0.99) and BMI (ICC: 0.95; 0.92, 0.97) demonstrated excellent agreement, while height (ICC: 0.8; 95% CI: 0.64, 0.88) showed moderate to excellent agreement. We also found that self-reported and measured values showed variable agreement across participant subgroups. Pearson’s correlations and ICCs for weight demonstrated agreement among all participant subgroups ranging from 0.97 to 0.99 (Table 3). Moderate agreement (0.68–0.93) is observed for most ICCs of height. There was more subgroup variability in the ICCs for height, with a value of 0.68 among participants with a history of diabetes. Pearson correlations and ICCs for BMI demonstrate good to excellent agreement in all subgroups, ranging from 0.89 to 0.99.

Table 3.

Pearson correlations and intraclass correlations between self-reported and measured anthropometric values (N = 192).

	Pearson Correlation			Intraclass Correlation Coefficient
	Weight	Height	BMI	Weight		Height		BMI
				ICC	95% CI	ICC	95% CI	ICC	95% CI
Overall	0.94	0.84	0.96	0.99	0.99, 0.99	0.8	0.64, 0.88	0.95	0.92, 0.97
Age at diagnosis (years)
< 55	0.99	0.86	0.95	0.99	0.99, 0.99	0.83	0.70, 0.90	0.94	0.91, 0.96
55 - <65	0.99	0.79	0.94	0.99	0.98, 0.99	0.73	0.45, 0.86	0.93	0.85, 0.97
≥ 65	0.99	0.86	0.97	0.99	0.99, 1.00	0.82	0.60, 0.91	0.97	0.93, 0.98
Time since diagnosis
< 20 months	0.99	0.84	0.92	0.99	0.99, 1.00	0.81	0.65, 0.90	0.91	0.85, 0.95
≥ 20 months	0.99	0.84	0.97	0.99	0.99, 0.99	0.8	0.61, 0.88	0.96	0.93, 0.97
BMI ( measured )
< 30 kg/m2	0.98	0.82	0.89	0.98	0.97, 0.99	0.79	0.66, 0.86	0.88	0.82, 0.92
≥ 30 kg/m2	0.98	0.87	0.91	0.98	0.97, 0.99	0.81	0.53, 0.91	0.89	0.76, 0.94
Education
≤ High School graduate	0.99	0.79	0.96	0.99	0.98, 0.99	0.75	0.57, 0.85	0.95	0.93, 0.97
> High School graduate	0.99	0.88	0.95	0.99	0.99, 1.00	0.84	0.67, 0.91	0.94	0.89, 0.96
Marital Status
Married/Living as married	0.99	0.86	0.96	0.99	0.99, 1.00	0.82	0.69, 0.89	0.95	0.92, 0.97
Not Married	0.99	0.83	0.95	0.99	0.98, 0.99	0.78	0.57, 0.88	0.94	0.90, 0.97
Country of Birth
Born in one of the 50 U.S. States a	0.99	0.89	0.97	0.99	0.99, 1.00	0.87	0.69, 0.94	0.96	0.90, 0.98
Not born in any of the 50 U.S. states b	0.99	0.83	0.95	0.99	0.98, 0.99	0.79	0.62, 0.88	0.94	0.90, 0.96
Smoking status
Never-smoker	0.99	0.82	0.96	0.99	0.99, 0.99	0.78	0.62, 0.87	0.95	0.92, 0.97
Ever-smoker	0.99	0.87	0.95	0.99	0.98, 0.99	0.84	0.63, 0.92	0.94	0.88, 0.96
Alcohol Consumption
Non-drinker	0.99	0.81	0.96	0.99	0.99, 1.00	0.78	0.62, 0.86	0.95	0.92, 0.97
< 3 drinks per week	0.99	0.86	0.94	0.99	0.98, 0.99	0.8	0.43, 0.91	0.93	0.87, 0.96
≥ 3 drinks per week	0.99	0.89	0.97	0.99	0.98, 1.00	0.88	0.75, 0.94	0.97	0.93, 0.99
Physical Activity (MET hours/week)
Low	0.99	0.88	0.97	0.99	0.99, 1.00	0.86	0.77, 0.92	0.97	0.94, 0.98
Medium	0.99	0.85	0.91	0.99	0.98, 0.99	0.81	0.59, 0.90	0.89	0.80, 0.94
High	0.99	0.78	0.96	0.98	0.97, 0.99	0.72	0.47, 0.85	0.95	0.91, 0.97
History of Diabetes
Yes	0.99	0.77	0.93	0.99	0.97, 0.99	0.68	0.26, 0.85	0.9	0.78, 0.95
No	0.99	0.87	0.96	0.99	0.99, 0.99	0.84	0.73, 0.90	0.95	0.93, 0.97
History of Hypertension
Yes	0.99	0.8	0.93	0.99	0.98, 0.99	0.75	0.50, 0.86	0.91	0.82, 0.96
No	0.99	0.87	0.96	0.99	0.99, 0.99	0.84	0.71, 0.90	0.96	0.93, 0.97
Tumor Stage
0	0.98	0.87	0.94	0.98	0.92, 0.99	0.85	0.56, 0.96	0.93	0.77, 0.98
I	0.99	0.86	0.94	0.99	0.98, 0.99	0.82	0.65, 0.90	0.93	0.89, 0.96
II	0.99	0.8	0.95	0.99	0.97, 0.99	0.74	0.51, 0.87	0.94	0.88, 0.97
III/IV	0.99	0.74	0.96	0.99	0.99, 1.00	0.69	0.31, 0.87	0.95	0.87, 0.98
Unknown	0.99	0.81	0.99	0.99	0.99, 1.00	0.76	0.44, 0.90	0.98	0.93, 0.99
Estrogen Receptor Status
Positive	0.98	0.81	0.93	0.98	0.96, 0.99	0.8	0.64, 0.88	0.94	0.91, 0.96
Negative	0.99	0.83	0.95	0.99	0.99, 0.99	0.76	0.47, 0.89	0.91	0.82, 0.96
Unknown	0.99	0.93	0.99	0.99	0.99, 1.00	0.91	0.66, 0.98	0.99	0.96, 1.00
Surgery
No surgery	0.97	0.93	0.95	0.97	0.91, 0.99	0.93	0.78, 0.98	0.95	0.85, 0.99
Lumpectomy	0.99	0.79	0.92	0.99	0.98, 0.99	0.73	0.47, 0.85	0.91	0.85, 0.94
Mastectomy	0.99	0.87	0.97	0.99	0.99, 1.00	0.84	0.72, 0.91	0.96	0.93, 0.98
Chemotherapy
Yes	0.99	0.8	0.97	0.99	0.99, 1.00	0.74	0.48, 0.86	0.96	0.91, 0.98
No	0.99	0.87	0.94	0.99	0.98, 0.99	0.85	0.74, 0.91	0.93	0.89, 0.95
Radiation Therapy
Yes	0.99	0.83	0.95	0.99	0.99, 0.99	0.78	0.59, 0.87	0.94	0.90, 0.96
No	0.99	0.87	0.97	0.99	0.99, 1.00	0.85	0.72, 0.92	0.97	0.94, 0.98
Endocrine Therapy
Yes	0.99	0.85	0.95	0.99	0.98, 0.99	0.81	0.67, 0.89	0.94	0.91, 0.96
No	0.99	0.83	0.97	0.99	0.99, 1.00	0.79	0.58, 0.90	0.96	0.92, 0.98

BMI = body mass index; ICC = Intraclass Correlation Coefficient

^a. Being born in one of the 50 United States, not including United States Territories such as Puerto Rico.

^b. Includes women born in Puerto Rico.

Cross-tabulations and Cohen’s kappa were used to assess the accuracy of BMI classification. Overall, 87.5% (169/192) of participants were correctly categorized based on self-reported BMI. As presented in Supplemental Table 1, classification accuracy was highest for those categorized for women classified as normal weight (BMI < 25 kg/m², 94.9%), followed by 86.9% for those classified as having obesity (BMI ≥ 30 kg/m²), and 84.1% for those classified as overweight (BMI 25–29.9 kg/m²). Cohen’s kappa for BMI classification was 0.85 (95% CI: 0.79, 0.91), indicating excellent agreement between self-reported and measured BMI categories. Bland-Altman plots comparing the differences between self-reported and measured values against their mean demonstrate strong agreement (Supplemental Fig. 1). The LOA for women in our study ranged from − 3.8 kg to 4.4 kg for weight, −5.2 cm to 9.1 cm for height, and − 4.1 kg/m² to 2.7 kg/m² for BMI.

Predictors of self-reported errors

Factors associated with differences between self-reported and measured weight, height, and BMI are shown in Table 4. For BMI, baseline-measured BMI was the only statistically significant predictor of self-reported errors. Women with obesity (BMI ≥ 30 kg/m²) were more likely to underestimate their BMI compared to women without obesity (BMI mean difference: −1.11, p < 0.01). For weight, three factors, including baseline BMI, education level, and history of diabetes, were statistically significantly associated with reporting discrepancies. Participants with a BMI < 30 kg/m² (weight mean difference:0.92, p < 0.01), lower education levels (weight mean difference: 0.78, p: 0.04), or no history of diabetes (weight mean difference: 0.88, p: 0.03) were more likely to overestimate weight. Height overestimation was significantly associated with diabetes (height mean difference for women with no history of diabetes: 3.58, p < 0.01) and chemotherapy history (height mean difference for no chemotherapy: 3.09, p: 0.04). Other factors considered in the models that were not statistically significantly associated with self-reported errors in weight, height, or measured BMI include age at diagnosis, time since diagnosis, country of birth, smoking status, physical activity, alcohol consumption, marital status, tumor stage, ER-status, history of hypertension, surgery, and endocrine therapy.

Table 4.

Multivariable-adjusted mean differences (95% confidence intervals) between self-reported and measured body mass index (BMI), weight, and height (n = 192).

	BMI (kg/m2) Mean Difference (95% CI)a	P-value	Weight (kg) Mean Difference (95% CI)a	P-value	Height (cm) Mean Difference (95% CI)a	P-value
Baseline body mass index (BMI) (measured)
< 30 kg/m2	−0.32 (−0.64, 0.01)	Ref	0.92 (0.44, 1.39)	Ref	-
≥ 30 kg/m2	−1.11 (−1.48, −0.75)	< 0.01	0.02 (−0.43, 0.48)	< 0.01	-
Education
≤ High School graduate	-		0.78 (0.33, 1.24)	Ref	-
> High School graduate	-		0.16 (−0.31, 0.62)	0.04	-
History of Diabetes
No	-		0.88 (0.24, 1.52)	Ref	3.58 (2.49, 4.68)	Ref
Yes	-		0.06 (−0.28, 0.4)	0.03	1.51 (0.94, 2.08)	< 0.01
Chemotherapy
No	-		-		3.09 (2.26, 3.91)	Ref
Yes	-		-		2.01 (1.24, 2.78)	0.04
a. Estimates represent adjusted mean differences (self-reported minus measured values) derived from regression model predicted values.

Discussion

In this population-based study of Hispanic breast cancer survivors, self-reported weight, height, and BMI demonstrated strong agreement with measured values, with only modest systematic differences. Our findings indicate that while most participants accurately reported their BMI category, relatively minor discrepancies between self-reported and measured anthropometric data were observed, particularly among individuals with higher BMIs. Nonetheless, the overall agreement between self-reported and measured BMI, as indicated by the kappa coefficient (κ = 0.85), reflects excellent concordance. These findings support the use of self-reported anthropometric data in large-scale epidemiological studies where direct measurements are not feasible. Results from prior research suggest that weight underestimation and height overestimation contribute to BMI misclassification, particularly in individuals with higher BMI categories^3,6,28–32. For example, findings from a meta-analysis of 21 studies among over 18,000 women of reproductive age found a slight underestimation of weight (−0.94 kg) and an overestimation of height (0.36 cm), but noted that the magnitude of discrepancies is negligible with respect to research use²⁸. This is consistent with our study in Hispanic breast cancer survivors, where we observed slight discrepancies between measured and self-reported BMI; over 84% of women in the overweight or obese category were correctly classified, and nearly 95% of women in the normal BMI category were correctly classified.

Our analysis identified measured BMI as a statistically significant predictor of reporting errors, with participants with obesity (BMI ≥ 30 kg/m²) more likely to underestimate their BMI compared to participants without obesity (mean difference: −1.11 kg/m², p < 0.01). However, the magnitude of this underestimation was relatively modest, suggesting that despite the systematic bias, it may not substantially affect BMI classification accuracy for most participants.

This finding aligns with prior studies evaluating self-reported BMI accuracy in the general population (Cancer Prevention Study II (CPS-II), National Health and Nutrition Examination Survey (NHANES), and Sister Study), which similarly reported slight underestimations of BMI, primarily driven by height overestimation among individuals with higher BMI categories^3,31,32. Additionally, in a study among Black/African American breast cancer survivors, our team previously reported comparable modest discrepancies, reinforcing the overall reliability of self-reported BMI in cancer survivor populations⁶. Moreover, these findings are consistent with prior research demonstrating that the magnitude of misreporting varies across racial/ethnic and socioeconomic groups, which may contribute to differential bias in epidemiologic analyses¹⁸. Our study extends this evidence by providing population-specific validation among Hispanic breast cancer survivors, a group for whom such data have been limited. Importantly, our results further suggest that, despite modest systematic biases, self-reported anthropometric data can provide reasonably accurate BMI classification in this population, supporting their use in large-scale epidemiologic and survivorship studies where direct measurements are not feasible.

Furthermore, our results revealed greater discrepancies in self-reported height among women with diabetes (mean difference: 3.58 cm, p < 0.01) and among those who received chemotherapy (mean difference: 3.09 cm, p: 0.04). Although these differences were statistically significant, their magnitude was relatively modest and may reflect a combination of physiological and reporting-related factors. In particular, height overestimation may arise from participants reporting their previously measured or peak adult height rather than their current height. This is especially relevant given well-documented age- and disease-related declines in stature, which may be related to musculoskeletal changes, reductions in bone density, and intervertebral disc compression^33–36. These processes may be more pronounced among individuals with chronic conditions such as diabetes or among cancer survivors exposed to systemic therapy. While treatment-related factors, such as chemotherapy-induced peripheral neuropathy^37,38 and diabetic neuropathy³⁹ may contribute to functional changes (e.g., balance or posture), the observed discrepancies may also reflect true physiological changes in stature over time rather than inaccuracies in perception. While these modest discrepancies underscore the reliability of self-reported anthropometrics overall, they highlight the need for awareness of potential measurement errors among subgroups with significant medical comorbidities.

As mentioned, our results are consistent with findings from major national and international studies, including NHANES, which similarly report modest underestimations of self-reported BMI^4,20,31,32. A comprehensive review by Maukonen et al. similarly noted that while self-reporting tends to produce small systematic biases, typically underestimating BMI by approximately 0.5 to 1 kg/m², these differences rarely impact epidemiological classifications of BMI²⁹. Additionally, our finding that education level is significantly associated with reporting errors aligns with findings from the CPS-II, which reported that lower education levels were associated with greater discrepancies in self-reported anthropometric measures²⁰. Interestingly, while we found no significant association between age and reporting errors, age-related differences have been documented in NHANES, suggesting that the relationship may differ across diverse populations^31,32.

Unique to this study is its focus on Hispanic breast cancer survivors, a demographic underrepresented in research evaluating self-reported anthropometric data. The findings provide valuable insights into potential biases in self-reported data for this population and highlight the need for accurate BMI assessment to understand its implications for breast cancer outcomes. Accurate BMI measurement and classification are essential for cancer survivorship care, given the well-documented associations between obesity and poorer overall and cancer-specific survival, increased risk of recurrence, and reduced treatment efficacy^8,40–42. While the minor reporting biases observed may not drastically affect epidemiological studies, clinicians and researchers should remain aware of potential underestimations among survivors with obesity. Misclassification of BMI due to reporting errors can lead to inaccurate risk assessments, inaccurate medication dosing, and missed opportunities for intervention. For instance, underestimating BMI in survivors with overweight or obesity may delay timely recommendations for weight management strategies, which are critical for improving survivorship outcomes. From a research perspective, self-reported anthropometric data remain valuable for large-scale studies, offering time- and cost-efficiency. Our findings suggest that self-reported BMI remains a valuable tool for large-scale studies in Hispanic cancer survivors.

A key strength of this study is its focus on Hispanic breast cancer survivors, a rapidly growing and understudied population in cancer survivorship research. Additionally, using both self-reported and directly measured anthropometric data enables a robust evaluation of reporting accuracy. However, this study is not without limitations. The sample size, while sufficient to identify overall trends, may limit the generalizability of findings to other Hispanic populations or breast cancer survivors with different clinical and sociodemographic characteristics. Furthermore, self-reporting biases may be influenced by psychological factors, such as body image perceptions or stigma related to obesity, which were not directly measured. The cross-sectional design also prevents examination of longitudinal variations in reporting accuracy, particularly during different phases of cancer survivorship or treatment.

Future studies are warranted to investigate further the factors underlying misreporting among breast cancer survivors, such as cultural perceptions of body weight, education levels, or the physiological impact of cancer treatment. Longitudinal studies evaluating anthropometric reporting accuracy across treatment and survivorship trajectories could provide valuable insights into how accuracy evolves over time. Additionally, exploring the integration of technologies, such as smart scales or mobile health tracking apps for self-monitoring weight and height, could offer innovative solutions to improve data accuracy.

Conclusion

In conclusion, in this population-based study of Hispanic breast cancer survivors, self-reported weight, height, and BMI demonstrated strong agreement with measured values, with only modest systematic differences. While self-reported data provides practical benefits, potential inaccuracies, particularly among individuals with overweight and obesity, must be considered in research and clinical settings. Given the strong overall agreement observed, self-reported BMI measures are appropriate for large-scale epidemiological studies among Hispanic breast cancer survivors, particularly when direct measures are not feasible.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Study conception and design were performed by NZ, EVB, and BQ. NZ wrote the first and revised draft for the main manuscript text, tables, and figures. All authors commented and contributed to previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to participant confidentiality but are available from the corresponding author on reasonable request (contact: [nz171@rutgers.edu](mailto: nz171@rutgers.edu) ).

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The institutional review board at Rutgers University approved the study.

Consent to participate

Informed consent was obtained from all individual participants included in the study.