Use and reporting of Bland-Altman analyses in studies of self-reported versus measured weight and height

Individual, family, and environmental correlates of fundamental motor skills among school-aged children: a cross-sectional study in China

OBJECTIVE

This cross-sectional study examined the socio-ecological factors influencing fundamental motor skills (FMS) in Chinese school-aged children.

METHODS

A total of 1012 parent-child pairs were randomly sampled between March-1st and April-15th, 2022. Based on the socio-ecological model of Children's FMS, three levels of factors: individual-level (e.g., demographic, physical, psychological, and behavioral characteristics of children), family-level (e.g., caregiver demographics, parental support, and socioeconomic status), and environmental factors (e.g., availability of physical activity equipment) were assessed using self-reported scales (e.g., the Self-perception Profile for Children, the Physical Activity Enjoyment Scale, and the 12-item Psychological Well-Being Scale for Children) and objective measures (e.g., ActiGraph GT3X, the Chinese National Student Physical Fitness Standard, and the Test of Gross Motor Development-Third Edition). Multi-level regression models were employed using SPSS.

RESULTS

The results demonstrated that children's age, sex, physical fitness, parental support, and the quality of home and community physical activity environments consistently influenced all three types of FMS, including locomotor, ball, and composite skills. Additionally, seven individual-level factors (children's age, sex, body mass index, light physical activity, sleep duration, perceived motor competence, and physical fitness) were associated with different types of FMS.

CONCLUSIONS

The findings underscore the multidimensional and complex nature of FMS development, with individual-level factors playing a particularly significant role. Future research should adopt rigorous longitudinal designs, comprehensive assessment tools covering various FMS skills, and objective measurement of parents' movement behaviors to better understand the strength and direction of the relationship between socio-ecological factors and children's FMS.

Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water

Accurate dietary assessment is crucial for nutrition and health research. Traditional methods, such as food records, food frequency questionnaires, and 24-hour dietary recalls (24HR), have limitations, such as the need for trained interviewers, time-consuming procedures, and inaccuracies in estimations. Novel technologies, such as image-based dietary assessment apps, have been developed to overcome these limitations. SNAQ is a novel image-based food-recognition app which, based on computer vision, assesses food type and volume, and provides nutritional information about dietary intake. This cross-sectional observational study aimed to investigate the validity of SNAQ as a dietary assessment tool for measuring energy and macronutrient intake in adult women with normal body weight (n = 30), compared to doubly labeled water (DLW), a reference method for total daily energy expenditure (TDEE). Energy intake was also estimated using a one-day 24HR for direct comparison. Bland-Altman plots, paired difference tests, and Pearson's correlation coefficient were used to assess agreement and relationships between the methods. SNAQ showed a slightly higher agreement (bias = -329.6 kcal/day) with DLW for total daily energy intake (TDEI) compared to 24HR (bias = -543.0 kcal/day). While both SNAQ and 24HR tended to underestimate TDEI, only 24HR significantly differed from DLW in this regard (p < 0.001). There was no significant relationship between estimated TDEI and TDEE using SNAQ (R2 = 27%, p = 0.50) or 24HR (R2 = 34%, p = 0.20) and there were no significant differences in energy and macronutrient intake estimates between SNAQ and 24HR (Δ = 213.4 kcal/day). In conclusion, these results indicate that SNAQ provides a closer representation of energy intake in adult women with normal body weight than 24HR when compared to DLW, but no relationship was found between the energy estimates of DLW and of the two dietary assessment tools. Further research is needed to determine the clinical relevance and support the implementation of SNAQ in research and clinical settings. Clinical trial registration: This study is registered on ClinicalTrials.gov with the unique identifier NCT04600596 (https://clinicaltrials.gov/ct2/show/NCT04600596).

Evaluation of concordance in estimation of excess mortality due to COVID-19 pandemic

BACKGROUND

The World Health Organization (WHO) kept track of COVID-19 data at country level daily during the pandemic that included the number of tests, infected cases and fatalities. This daily record was susceptible to change depending on the time and place and impacted by underreporting. In addition to reporting cases of excess COVID-19-related deaths, the WHO also provided estimates of excess mortality based on mathematical models.

OBJECTIVE

To evaluate the WHO reported and model-based estimate of excess deaths to determine the degree of agreement and universality.

METHODOLOGY

Epidemiological data gathered from nine different countries between April 2020 and December 2021 are used in this study. These countries are India, Indonesia, Italy, Russia, United Kingdom, Mexico, the United States, Brazil and Peru and each of them recorded more than 1.5 million deaths from COVID-19 during these months. Statistical tools including correlation, linear regression, intraclass correlation and Bland-Altman plots are used to assess the degree of agreement between reported and model-based estimates of excess deaths.

RESULTS

The WHO-derived mathematical model for estimating excess deaths due to COVID-19 was found to be appropriate for only four of the nine chosen countries, namely Italy, United Kingdom, the United States and Brazil. The other countries showed proportional biases and significantly high regression coefficients.

CONCLUSION

The study revealed that, for some of the chosen nations, the mathematical model proposed by the WHO is practical and capable of estimating the number of excess deaths brought on by COVID-19. However, the derived approach cannot be applied globally.

How is online self-reported weight compared with image-captured weight? A comparative study using data from an online longitudinal study of young adults

BACKGROUND

Accurate anthropometric measurement is important within epidemiological studies and clinical practice. Traditionally, self-reported weight is validated against in-person weight measurement.

OBJECTIVES

This study aimed to 1) determine the comparison of online self-reported weight against images of weight captured on scales in a young adult sample, 2) compare this across body mass index (BMI), gender, country, and age groups, and 3) explore demographics of those who did/did not provide a weight image.

METHODS

Cross-sectional analysis of baseline data from a 12-mo longitudinal study of young adults in Australia and the UK was conducted. Data were collected by online survey via Prolific research recruitment platform. Self-reported weight and sociodemographics (for example, age, gender) were collected for the whole sample (n = 512), and images of weight for a subset (n = 311). Tests included Wilcoxon signed-rank test to evaluate differences between measures, Pearson correlation to explore the strength of the linear relationship, and Bland-Altman plots to evaluate agreement.

RESULTS

Self-reported weight [median (interquartile range), 92.5 kg (76.7-112.0)] and image-captured weight [93.8 kg (78.8-112.8)] were significantly different (z = -6.76, P < 0.001), but strongly correlated (r = 0.983, P < 0.001). In the Bland-Altman plot [mean difference -0.99 kg (-10.83, 8.84)], most values were within limits of agreement (2 standard deviation). Correlations remained high across BMI, gender, country, and age groups (r > 0.870, P < 0.002). Participants with BMI in ranges 30-34.9 and 35-39.9 kg/m2 were less likely to provide an image.

CONCLUSIONS

This study demonstrates the method concordance of image-based collection methods with self-reported weight in online research.

Assessment of Plasma Tylosin Concentrations: A Comparative Study of Immunoassay, Microbiological Assay, and Liquid Chromatography/Mass Spectrometry

Employing affordable and uncomplicated sample preparation techniques to recommend the most efficient antibacterial therapy could help reduce antibiotic-resistant bacteria. This study evaluated the suitability of immunoassays and microbiological assays as alternatives for liquid chromatography/mass spectrometry (LC/MS) in determining plasma tylosin concentrations after intramuscular administration at a dose of 20 mg/kg to both healthy and diseased pigs in clinical veterinary practice. The diseased pigs were confirmed using the target genes Actinobacillus pleuropneumoniae (apxIVA) and Pasteurella multocida (kmt1). The methods showed good linearity, precision, and accuracy. In both healthy and diseased pigs, a significant correlation was observed between LC/MS and the microbiological assay (Pearson correlation coefficient: 0.930, p < 0.001 vs. Pearson correlation coefficient: 0.950, p < 0.001) and between LC/MS and the enzyme-linked immunosorbent assay (ELISA) (Pearson correlation coefficient: 0.933; p < 0.001 vs. Pearson correlation coefficient: 0.976, p < 0.001). A strong correlation was observed between the microbiological assay and the ELISA in both healthy and diseased pigs (Pearson correlation coefficient: 0.911; p < 0.001 vs. Pearson correlation coefficient: 0.908, p < 0.001). A Bland-Altman analysis revealed good agreement between the methods, i.e., 95% of the differences were within the limits of agreement. Therefore, the microbiological assay and the ELISA, which demonstrated sufficient precision and accuracy, can be viable alternatives to LC/MS when it is unavailable.

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

BACKGROUND

In many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that self-reported instead of objective data lead to an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia. This study aimed to assess the agreement between self-reported and measured height, weight, hypertension and hypercholesterolemia and to identify an adequate approach for valid measurement error correction.

METHODS

Nine thousand four hundred thirty-nine participants of the 2018 Belgian health interview survey (BHIS) older than 18 years, of which 1184 participated in the 2018 Belgian health examination survey (BELHES), were included in the analysis. Regression calibration was compared with multiple imputation by chained equations based on parametric and non-parametric techniques.

RESULTS

This study confirmed the underestimation of risk factor prevalence based on self-reported data. With both regression calibration and multiple imputation, adjusted estimation of these variables in the BHIS allowed to generate national prevalence estimates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data. However, for hypercholesterolemia, for which the regression model's accuracy was poor, multiple imputation was the only approach which provided smaller standard errors than those based on clinical data.

CONCLUSIONS

The random-forest multiple imputation proves to be the method of choice to correct the bias related to self-reported data in the BHIS. This method is particularly useful to enable improved secondary analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from HIS and objective measurements should be used in risk factor monitoring.

Does the use of prediction equations to correct self-reported height and weight improve obesity prevalence estimates? A pooled cross-sectional analysis of Health Survey for England data

OBJECTIVE

Adults typically overestimate height and underestimate weight compared with directly measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report.

DESIGN

Population-based cross-sectional study.

PARTICIPANTS

38 940 participants aged 16+ (Health Survey for England 2011-2016) with non-missing self-reported and interviewer-measured height and weight.

MAIN OUTCOME MEASURES

Comparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI: kg/m²) including (1) difference between means and obesity prevalence and (2) measures of agreement for BMI classification.

RESULTS

On average, men overestimated height more than women (1.6 cm and 1.0 cm, respectively; p<0.001), while women underestimated weight more than men (2.1 kg and 1.5 kg, respectively; p<0.001). Underestimation of BMI was slightly larger for women than for men (1.1 kg/m² and 1.0 kg/m², respectively; p<0.001). Obesity prevalence based on BMI from self-report was 6.8 and 6.0 percentage points (pp) lower than that estimated using measured BMI for men and women, respectively. Corrected BMI (based on models containing all significant predictors of misreporting of height and weight) lowered underestimation of obesity to 0.8pp in both sexes and improved the sensitivity of obesity over self-reported BMI by 15.0pp for men and 12.2pp for women. Results based on simpler models using age alone as a predictor of misreporting were similar.

CONCLUSIONS

Compared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables did not improve obesity classification enough to justify the added complexity of including them in prediction equations.

Reliability of Self-Reported Height and Weight in Children: A School-Based Cross-Sectional Study and a Review

Since anthropometric measurements are not always feasible in large surveys, self-reported values are an alternative. Our objective was to assess the reliability of self-reported weight and height values compared to measured values in children with (1) a cross-sectional study in Switzerland and (2) a comprehensive review with a meta-analysis. We conducted a secondary analysis of data from a school-based study in Switzerland of 2616 children and a review of 63 published studies including 122,629 children. In the cross-sectional study, self-reported and measured values were highly correlated (weight: r = 0.96; height: r = 0.92; body mass index (BMI) r = 0.88), although self-reported values tended to underestimate measured values (weight: -1.4 kg; height: -0.9 cm; BMI: -0.4 kg/m²). Prevalence of underweight was overestimated and prevalence of overweight was underestimated using self-reported values. In the meta-analysis, high correlations were found between self-reported and measured values (weight: r = 0.94; height: r = 0.87; BMI: r = 0.88). Weight (-1.4 kg) and BMI (-0.7 kg/m²) were underestimated, and height was slightly overestimated (+0.1 cm) with self-reported values. Self-reported values tended to be more reliable in children above 11 years old. Self-reported weight and height in children can be a reliable alternative to measurements, but should be used with caution to estimate over- or underweight prevalence.

Dietary Isorhamnetin Intake Is Inversely Associated with Coronary Artery Disease Occurrence in Polish Adults

The role of antioxidative agents in coronary artery disease (CAD) has been investigated, but the analysis of specific flavonols intake in Polish adults requires validated tools. The aim of this study was to estimate the dietary intake of flavonols in CAD patients by creating a food frequency questionnaire (FFQ) dedicated for this purpose in Polish adults. The FFQ included 140 products from 12 food groups. The study involved 103 adult respondents (43 CAD patients and 60 healthy controls). Mean daily intakes of total flavonols, quercetin, kaempferol, myricetin and isorhamnetin were calculated as absolute values and quartiles. Mean daily intakes of 12 main food categories and 27 subcategories were calculated as portions and quartiles. The validity test revealed high correlation for total flavonols, kaempferol, myricetin and isorhamnetin and moderate for quercetin. In the reproducibility analysis, the correlation was high for total flavonols, quercetin, kaempferol and myricetin, moderate for isorhamnetin and high for all 12 categories and 25 out of 27 subcategories of the tested food groups. The application of the FFQ in healthy adults and CAD patients revealed that dietary intakes of total flavonols and proportional intakes of kaempferol and isorhamnetin in Polish adults and CAD patients are higher than in most other European countries, while the proportional intakes of quercetin and myricetin are lower than in most European countries. The comparison between CAD patients and the healthy controls revealed significant differences in dietary isorhamnetin intake (p = 0.002). The results suggest that dietary isorhamnetin could have a potential role in CAD prevention.

Agreement Between Clinically Measured Weight and Self-reported Weight Among Patients With Type 2 Diabetes Through an mHealth Lifestyle Coaching Program in Denmark: Secondary Analysis of a Randomized Controlled Trial

Background

Digital health interventions are increasingly used to handle and promote positive health behaviors. Clinical measures are often used, and a certain precision is essential for digital health interventions to have an effect. Only few studies have compared clinically measured weights with self-reported weights. No study has examined the validity of self-reported weight from a mobile app used in a tailored weight loss intervention.

Objective

The aim of this study was to analyze the agreement between clinically measured weight and self-reported weight collected from a mobile health lifestyle coaching program during a 12-month weight loss intervention for obese patients with and without type 2 diabetes. The secondary aim was to investigate the determinants for possible discrepancies between clinically measured and self-reported weights of these patients with different demographic and lifestyle characteristics and achievements of weight loss goals.

Methods

Weight registrations were collected from participants (N=104) in a Danish randomized controlled trial examining the effect of a digital lifestyle intervention on weight loss among obese patients with and without type 2 diabetes. Data were collected at baseline and after 6 and 12 months. Self-reported weight was measured at home and registered in the app.

Results

Self-reported body weight was lower than the weight measured in the clinic after 6 months by 1.03 kg (95% CI 1.01-1.05; P<.001) and after 12 months also by 1.03 kg (95% CI 0.99-1.04; P<.001). After 6 months, baseline weight and BMI were associated with a discrepancy of 0.03 kg (95% CI 0.01-0.04; P=.01) and 0.09 kg (95% CI 0.02-0.17; P=.02) per increment of 1 kg and 1 kg/m2, respectively, between clinically measured weight and self-reported weight. Weight change during the first 6 months was also associated with a difference of 0.1 kg (95% CI 0.04-0.01; P<.001) per kilogram of difference in weight between clinically measured weight and self-reported weight. Participants who did not achieve the 5% weight loss goal underestimated their weight by 0.79 kg (95% CI 0.34-1.23) at 6 months. After 12 months, only baseline weight was associated with a discrepancy of 0.03 kg (95% CI 0.01-0.05; P=.02) per increment of kilogram between clinically measured weight and self-reported weight. None of the other factors showed any significant discrepancy after 12 months.

Conclusions

Self-reported weight obtained from mobile health is a valid method for collecting anthropometric measurements.

Trial Registration

ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915

Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting

OBJECTIVE

Fat, carbohydrates (mainly lactose) and protein in breast milk all provide indispensable benefits for the growth of newborns. The only source of nutrition in early infancy is breast milk, so the energy of breast milk is also crucial to the growth of infants. Some macronutrients composition in human breast milk varies greatly, which could affect its nutritional fulfillment to preterm infant needs. Therefore, rapid analysis of macronutrients (including lactose, fat and protein) and milk energy in breast milk is of clinical importance. This study compared the macronutrients results of a mid-infrared (MIR) analyzer and an ultrasound-based breast milk analyzer and unified the results by machine learning.

METHODS

This cross-sectional study included breastfeeding mothers aged 22-40 enrolled between November 2019 and February 2021. Breast milk samples (n = 546) were collected from 244 mothers (from Day 1 to Day 1086 postpartum). A MIR milk analyzer (BETTERREN Co., HMIR-05, SH, CHINA) and an ultrasonic milk analyzer (Honɡyanɡ Co,. HMA 3000, Hebei, CHINA) were used to determine the human milk macronutrient composition. A total of 465 samples completed the tests in both analyzers. The results of the ultrasonic method were mathematically converted using machine learning, while the Bland-Altman method was used to determine the limits of agreement (LOA) between the adjusted results of the ultrasonic method and MIR results.

RESULTS

The MIR and ultrasonic milk analyzer results were significantly different. The protein, fat, and energy determined using the MIR method were higher than those determined by the ultrasonic method, while lactose determined by the MIR method were lower (all p < 0.05). The consistency between the measured MIR and the adjusted ultrasound values was evaluated using the Bland-Altman analysis and the scatter diagram was generated to calculate the 95% LOA. After adjustments, 93.96% protein points (436 out of 465), 94.41% fat points (439 out of 465), 95.91% lactose points (446 out of 465) and 94.62% energy points (440 out of 465) were within the LOA range. The 95% LOA of protein, fat, lactose and energy were - 0.6 to 0.6 g/dl, -0.92 to 0.92 g/dl, -0.88 to 0.88 g/dl and - 40.2 to 40.4 kj/dl, respectively and clinically acceptable. The adjusted ultrasonic results were consistent with the MIR results, and LOA results were high (close to 95%).

CONCLUSIONS

While the results of the breast milk rapid analyzers using the two methods varied significantly, they could still be considered comparable after data adjustments using linear regression algorithm in machine learning. Machine learning methods can play a role in data fitting using different analyzers.

Design of Growth Trend Map of Children and Adolescents Based on Bone Age

Accurate height prediction has important reference significance for the development of children and adolescents and the selection of athletes. The current mainstream height prediction methods include the B-P (Bayley–Pinneau) method and the TW2 (Tanner–Whitehouse) method. A large number of documents show that the B-P method and the TW2 method have relatively large deviations in the lifelong height prediction results of Chinese children and adolescents. Based on the data collected by the Chinese Adolescent Students' Physical Fitness and Growth and Development Health Project in Zhejiang's primary and secondary schools, this paper proposes a graph of height growth trends based on bone age. The height map of age has more reference value. Aiming at the feasibility of the height data in the statistical results, the interpolation prediction method is used to verify the data, and the height growth trend graph is drawn through the method of fitting. Validation results with actual data show that the average error of the lifetime height prediction of the height growth trend map proposed in this paper is 2.1 cm, which is 1.4 cm lower than the 3.5 cm error predicted by the B-P method and 0.4 cm lower than the 2.5 cm error predicted by the TW2 method.

Assessing the Utility of a Quality-of-Care Assessment Tool Used in Assessing Comprehensive Care Services Provided by Community Health Workers in South Africa

Background

Few studies exist on the tools for assessing quality-of-care of community health worker (CHW) who provide comprehensive care, and for available tools, evidence on the utility is scanty. We aimed to assess the utility components of a previously-reported quality-of-care assessment tool developed for summative assessment in South Africa.

Methods

In two provinces, we used ratings by 21 CHWs and three team leaders in two primary health care facilities per province regarding whether the tool covered everything that happens during their household visits and whether they were happy to be assessed using the tool (acceptability and face validity), to derive agreement index (≥85%, otherwise the tool had to be revised). A panel of six experts quantitatively validated 11 items of the tool (content validity). Content validity index (CVI), of individual items (I-CVI) or entire scale (S-CVI), should be >80% (excellent). For the inter-rater reliability (IRR), we determined agreement between paired observers' assigned quality-of-care messages and communication scores during 18 CHW household visits (nine households per site). Bland and Altman plots and multilevel model analysis, for clustered data, were used to assess IRR.

Results

In all four CHW and team leader sites, agreement index was ≥85%, except for whether they were happy to be assessed using the tool, where it was <85% in one facility. The I-CVI of the 11 items in the tool ranged between 0.83 and 1.00. For the S-CVI, all six experts agreed on relevancy (universal agreement) in eight of 11 items (0.72) whereas the average of I-CVIs, was 0.95. The Bland-Altman plot limit of agreements between paired observes were −0.18 to 0.44 and −0.30 to 0.44 (messages score); and −0.22 to 0.45 and −0.28 to 0.40 (communication score). Multilevel modeling revealed an estimated reliability of 0.77 (messages score) and 0.14 (communication score).

Conclusion

The quality-of-care assessment tool has a high face and content validity. IRR was substantial for quality-of-care messages but not for communication score. This suggests that the tool may only be useful in the formative assessment of CHWs. Such assessment can provide the basis for reflection and discussion on CHW performance and lead to change.

Prediction of body fat in adolescents: validity of the methods relative fat mass, body adiposity index and body fat index

OBJECTIVE

To verify the validity of anthropometric methods body adiposity index (BAI), relative fat mass (RFM) and body fat index (BFI) to estimate body fat percentage (%BF) in adolescents.

METHODS

A cross-sectional study was carried out with 420 Brazilian adolescents aged 15-19 years, stratified by age (< 18 years, n = 356; ≥ 18 years, n = 64) and sex (boys, n = 216; girls, n = 204). The Anthropometric measurements height, body weight, hip circumference and waist circumference were collected to calculate the %BF by BAI, RFM, BFI methods. Subsequently, %BF was measured by dual emission X-ray absorptiometry (DXA), adopted as a reference method. In the statistical analysis of the data, the Pearson correlation test and the paired t test between %BF obtained by the equations and by the DXA were performed. The method validation criterion was that 68% of individuals should be within an acceptable error range of ± 3.5% of BF and Cohen's Kappa index ≥ 0.61. Additionally, the Bland-Altman graphical analysis was performed.

RESULTS

All methods showed a high correlation with DXA. For the Kappa index, only the RFM reached the criterion in the total sample (0.67) and in the sample < 18 years (0.68). None of the methods reached the criterion of 68% of the sample within the error range of ± 3.5% of BF.

CONCLUSION

The BAI, RFM and BFI equations were not valid for predicting BF in the studied sample according to the criteria adopted regardless of sex or age.

LEVEL OF EVIDENCE

Level V, cross-sectional descriptive study.

Does Self-Perception Equal the Truth When Judging Own Body Weight and Height?

Background: Data from the research project “Fitness clubs—a venue for public health?” provided an opportunity to evaluate the accuracy of self-reported body weight and height, and subsequent Body Mass Index (BMI), as well as the “trueness” of novice exercisers perception of weight status category, which has not been examined in this population. The aims were to examine self-reported body weight, height, and calculated BMI data from an online survey compared with measured data at fitness club start-up, investigate how accurately novice exercisers place themselves within self-classified weight group (underweight, normal weight, overweight, and obese), and compare this with fitness club attendance at three months follow-up. Methods: Prior to anthropometric measurements, 62 men and 63 women responded to an online questionnaire, including body weight (kilogram, kg) and height (centimeters, cm), and self-classified weight group (“I think I am … underweight, normal weight, overweight, obese”). We used the following statistical analysis: Paired sample t-tests, a Bland–Altman plot kappa statistics, chi-squared tests, and a logistic regression. Results: Mean difference of BMI calculated from self-reported and measured data was 0.06 (95% CI −0.29 to 0.17, p = 0.593) in men, and 0.16 (95% CI −0.40 to 0.09, p = 0.224) in women, with four participants being outliers of the 95% limits of agreement (Bland-Altman plot). Allowing a difference of 0.5 kg between self-reported and measured weight, we found that 16% reported their weight correctly, 31.2% underreported (−1.89 ± 1.59 kg), and 52.8% overreported (1.85 ± 1.23 kg), with no sex differences (p = 0.870). Further, our results suggest that both sexes may have difficulty recognizing overweight/obesity in themselves, and particularly men are likely to underreport their perceived weight group compared with women. More than half (53.3%) of the overweight men perceived themselves to be normal weight (women: 14%), and only 33.3% of obese men and women correctly classified themselves as being obese. We did not find any difference between participants correctly or incorrectly classifying weight group and fitness club attendance (≥2 times a week) at three months follow-up. Conclusion: Both sexes reported body weight and height reasonably accurately, and BMI based on self-report appears to be valid measure. Still, a large proportion of novice exercisers do not recognise their own overweight or obesity status, which may in part explain why public health campaigns do not reach risk populations.

Skeletal muscle mass at C3 may not be a strong predictor for skeletal muscle mass at L3 in sarcopenic patients with head and neck cancer

PURPOSE

To evaluate the feasibility of using skeletal muscle mass (SMM) at C3 (C3 SMM) as a diagnostic marker for sarcopenia in head and neck cancer (HNC) patients.

METHODS

We evaluated 165 HNC patients and 42 healthy adults who underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomography scans. The paravertebral muscle area at C3 and skeletal muscle area at L3 were measured by CT. Pearson's correlation was used to assess the relationship between L3 and C3 SMMs. The prediction model for L3 SMM was developed by multiple linear regression. Then the correlation and the agreement between actual and predicted L3 SMMs were assessed. To evaluate the diagnostic value of C3 SMM for sarcopenia, the receiver operating characteristics (ROC) curves were analyzed.

RESULTS

Of the 165 HNC patients, 61 (37.0%) were sarcopenic and 104 (63.0%) were non-sarcopenic. A very strong correlation was found between L3 SMM and C3 SMM in both healthy adults (r = 0.864) and non-sarcopenic patients (r = 0.876), while a fair association was found in sarcopenic patients (r = 0.381). Prediction model showed a very strong correlation between actual SMM and predicted L3 SMM in both non-sarcopenic patients and healthy adults (r > 0.9), whereas the relationship was moderate in sarcopenic patients (r = 0.7633). The agreement between two measurements was good for healthy subjects and non-sarcopenic patients, while it was poor for sarcopenic patients. On ROC analysis, predicted L3 SMM showed poor diagnostic accuracy for sarcopenia.

CONCLUSIONS

A correlation between L3 and C3 SMMs was weak in sarcopenic patients. A prediction model also showed a poor diagnostic accuracy. Therefore, C3 SMM may not be a strong predictor for L3 SMM in sarcopenic patients with HNC.

An analysis of weight loss efforts and expectations in a Canadian Cohort: A retrospective medical chart review

The weight loss history and weight loss goals were examined in 4108 patients referred to a publicly funded evidence-based clinical weight management program using a retrospective chart review. The majority of patients were white females, aged of 50.1 ± 13.7 years and a BMI of 39.0 ± 7.5 kg/m² . Pregnancy was the most common reason for weight gain in women (17.7%) and while the reasons in men are more varied (injury, stress and medication: all ~5%). Over 50% of patients cited no specific reason for their weight gain. Self-directed diet and exercise were the most commonly used self-reported weight loss methods. Women were more likely to report using medication and bariatric surgical weight loss methods than men (P < .05), and only 5% of women and 12.8% men report never having tried to lose weight in the past. Patients had an ideal weight loss goal of 28.9% to 34.4%, which was similar to the patient's lowest reported adult weight. Approximately 70% of patients enrolling at the clinic had previously lost at least 4.5 kg (10 lb) one or more times and 16.7% lost weight at least 5 times in their life, but patient weight at enrolment was similar to their highest adult reported weight. Thus, most patients referred to this clinical weight management program have previously attempted self-directed diet and exercise weight loss. Patients were close to their highest adult weight and had very large weight loss goals, similar to what is typically achieved only by surgical intervention, despite having already had several bouts of weight loss.

An Extension of the Bland-Altman Plot for Analyzing the Agreement of More than Two Raters

The Bland–Altman plot is the most common method to analyze and visualize agreement between raters or methods of quantitative outcomes in health research. While very useful for studies with two raters, a limitation of the classical Bland–Altman plot is that it is specifically used for studies with two raters. We propose an extension of the Bland–Altman plot suitable for more than two raters and derive the approximate limits of agreement with 95% confidence intervals. We validated the suggested limit of agreement by a simulation study. Moreover, we offer suggestions on how to present bias, heterogeneity among raters, as well as the uncertainty of the limits of agreement. The resulting plot could be utilized to investigate and present agreement in studies with more than two raters.

Predictors of Weight Loss and Weight Gain in Weight Management Patients during the COVID-19 Pandemic

OBJECTIVE

To examine the associations between patient struggles, health, and weight management changes during the COVID-19 pandemic.

METHODS

585 patients attending a publicly funded clinical weight management program responded to an electronic survey.

RESULTS

Over half of the patients reported worsened overall health, mental health, physical activity, or diet during the pandemic. Approximately 30% of patients lost ≥3% of their body weight and 21% gained ≥3% of their body weight between March and July of the pandemic. Reports of social isolation was associated with increased odds for weight loss in women (OR = 2.0, 1.2-3.3), while low motivation (OR = 1.9, 1.0-3.7), depression (OR = 2.5, 1.0-6.3), and struggles with carbohydrate intake (OR = 2.1, 1.0-4.3) were associated with weight gain. Cooking more at home/eating less take out was associated with increased likelihood of weight loss (OR = 2.1, 1.1-3.9) and lower odds for weight gain (OR = 0.2, 0.1 to 0.97). Working from home was not associated with weight loss or weight gain (P > 0.6).

CONCLUSION

The COVID-19 pandemic is associated with certain factors that may facilitate weight loss and other factors that promote weight gain. Thus, depending on the patient experience during the pandemic, prevention of weight gain may be more appropriate than weight loss.

Exploring the Validity of the 14-Item Mediterranean Diet Adherence Screener (MEDAS): A Cross-National Study in Seven European Countries around the Mediterranean Region

This study provides comprehensive validation of the 14-item Mediterranean Diet Adherence Screener (14-MEDAS) in an adult population from Greece (GR), Portugal (PT), Italy (IT), Spain (SP), Cyprus (CY), Republic of North Macedonia (NMK), and Bulgaria (BG). A moderate association between the 14-MEDAS and the reference food diary was estimated for the entire population (Pearson r = 0.573, p-value < 0.001; Intraclass Correlation Coefficient (ICC) = 0.692, p-value < 0.001) with the strongest correlation found in GR, followed by PT, IT, SP, and CY. These results were supported by kappa statistics in GR, PT, IT, and SP with ≥50% of food items exhibiting a fair or better agreement. Bland-Altman analyses showed an overestimation of the 14-MEDAS score in the whole population (0.79 ± 1.81, 95%Confidence Interval (CI) 0.61, 0.96), but this value was variable across countries, with GR, NMK, and BG exhibiting the lowest bias. Taking all analyses together, the validation achieved slightly better results in the Mediterranean countries but a definitive validation ranking order was not evident. Considering growing evidence of the shift from Mediterranean Diet (MD) adherence and of the importance of culture in making food choices it is crucial that we further improve validation protocols with specific applications to compare MD adherence across countries.

Reporting Standards for a Bland-Altman Agreement Analysis: A Review of Methodological Reviews

The Bland-Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland-Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland-Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569-575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland-Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland-Altman agreement analyses.