Leveraging questionnaire-based physical activity levels (PAL) to identify energy intake misreporting using the Goldberg method: A doubly labeled water validation study

Leveraging questionnaire-based physical activity levels (PAL) to identify energy intake misreporting using the Goldberg method: A doubly labeled water validation study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Leveraging questionnaire-based physical activity levels (PAL) to identify energy intake misreporting using the Goldberg method: A doubly labeled water validation study View ORCID Profile Heather K Neilson , Shervin Asgari , View ORCID Profile Janet A. Tooze , Farah Khandwala , View ORCID Profile Anita Koushik , View ORCID Profile Rémi Rabasa-Lhoret , View ORCID Profile Karen A. Kopciuk , View ORCID Profile Ilona Csizmadi doi: https://doi.org/10.1101/2025.04.02.25325112 Heather K Neilson 1 Department of Cancer Epidemiology and Prevention Research, Cancer Care Alberta , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Heather K Neilson Shervin Asgari 1 Department of Cancer Epidemiology and Prevention Research, Cancer Care Alberta , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janet A. Tooze 2 Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Wake Forest University , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Janet A. Tooze Farah Khandwala 1 Department of Cancer Epidemiology and Prevention Research, Cancer Care Alberta , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anita Koushik 3 Department of Oncology, Faculty of Medicine and Health Sciences, McGill University , Montreal, Quebec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anita Koushik Rémi Rabasa-Lhoret 4 Departments of Medicine and Nutrition, Université de Montreal, and Montreal Clinical Research Institute , Montreal, Quebec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rémi Rabasa-Lhoret Karen A. Kopciuk 1 Department of Cancer Epidemiology and Prevention Research, Cancer Care Alberta , Calgary, Alberta, Canada 5 Department of Mathematics and Statistics, University of Calgary , Calgary, Alberta, Canada 6 Departments of Community Health Sciences, Cumming School of Medicine, University of Calgary , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karen A. Kopciuk Ilona Csizmadi 6 Departments of Community Health Sciences, Cumming School of Medicine, University of Calgary , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ilona Csizmadi For correspondence: icsizmad{at}ucalgary.ca Abstract Full Text Info/History Metrics Preview PDF ABSTRACT The Goldberg method has been suggested for identifying energy intake (EI) under-reporting in nutritional epidemiology. Its implementation, however, is limited by challenges associated with estimating physical activity levels (PAL). We quantified the accuracy of the Sedentary Time and Activity Reporting Questionnaire (STAR-Q) derived PAL (PAL STAR-Q ) combined with the Goldberg method (Goldberg-PAL STARQ ) to identify EI misreporting as compared with doubly labeled water (DLW) derived total energy expenditure (TEE DLW ). Between 2009 and 2011, 99 men and women completed a two-week DLW protocol, a food frequency questionnaire, and the STAR-Q. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of the Goldberg-PAL STAR-Q were determined. Fifty-eight percent of men and women were classified as under-reporters by Goldberg-PAL STAR-Q compared with 60% of men and 56% of women by TEE DLW . Among men, sensitivity, specificity, PPV, NPV and accuracy and 95% confidence intervals were 88% (68%-97%), 87% (61%-98%), 91% (72%-99%), 81% (56%-94%), and 87% (72%-95%), respectively; and among women 79% (62%-90%), 69% (50%-84%), 77% (60%-88%), 72% (52%-86%), and 75% (62%-84%), respectively. Validated individual level PALs used with the Goldberg method can be informative in sensitivity analyses to gain insight into EI misreporting in nutritional epidemiology studies lacking in objective EI measures. INTRODUCTION Under-reporting of energy intake (EI) on dietary questonnaires is a widely recognized problem that distorts estimates of dietary intake in nutrition surveys and etiologic studies – thus impeding progress in diet-related research ( 1 ). Various statistical approaches have been suggested to identify dietary intake misreporting, but none have satisfactorily addressed the problem ( 2 ). Regression calibration of dietary intake is a promising approach for nutrients and dietary patterns ( 3 – 5 ); however, the method relies on the knowledge of true dietary intake and an understanding of the underlying systematic errors associated with self-report which are generally unknown ( 6 ). Ideally, misreporting could be detected using ‘gold standard’ objective biomarkers of dietary intake, but, these methods are limited in number and usually too labor-intensive and costly to feasibly implement in large-scale epidemiologic studies ( 7 ). A practical alternative, specifically for EI is the classification of individuals according to the plausibility of their reported EI (rEI) compared with their energy requirements ( 8 ). The Goldberg method ( 9 ), originally proposed for identifying EI misreporting in community-dwelling populations, is based on the principle that under long-term, weight-stable conditions, EI is approximately equal to total energy expenditure (TEE). In addition, the method leverages the well-established relations between EI, TEE, basal metabolic rate (BMR) and physical activity level (PAL), where EI/BMR approximates TEE/BMR which in turn represents PAL ( 10 , 11 ). Further, the method proposes the estimation of Goldberg cut-point boundaries, or 95% confidence intervals (95% CI), against which ratios of rEI:BMR are compared to identify under and over-reporting. While conceptually tenable, the method is logistically challenged by difficulties associated with estimating PAL in free-living populations ( 11 , 12 ). In the absence of population-specific objective measures, a low-activity PAL of 1.55 based on World Health Organization energy requirement prediction equations ( 13 ) was applied in the original Goldberg paper ( 14 ). The Goldberg method combined with a globally applied PAL of 1.55 was subsequently compared with doubly labelled water (DLW), the ‘gold standard’ for estimating TEE ( 15 , 16 ), and reported to have acceptable accuracy in a study of middle-aged men and women; however, alternate PALs were not tested ( 17 ). More recently, contemporary DLW studies included in the report from the Dietary Reference Intake (DRI) for Energy ( 10 ), point to a median adult PAL of 1.68, suggesting that a global PAL of 1.55 may be unrealistically low for substantial segments of the population potentially resulting in very active under-reporters misclassified as acceptable. To our knowledge, the Goldberg method has not been evaluated for accuracy using PALs derived from comprehensive individual self-reports of activity despite their value in capturing habitual behaviors of relevance in nutritional epidemiology. With that in mind, we appraised the accuracy of the Goldberg method for classifying EI reporting status using individual-level PALs derived from the Sedentary Time and Activity Reporting Questionnaire (STAR-Q), a comprehensive all-inclusive activity questionnnaire previously validated against DLW ( 18 ). In our primary analysis, we determined the accuracy of the Goldberg method using STAR-Q-derived PALs, hereafter referred to as Goldberg-PAL STAR-Q , in comparison with DLW-derived TEE DLW for identifying EI misreporting on a food frequency questionnaire (FFQ). In secondary analyses we similarly tested the commonly used PAL of 1.55 and in sensitivity analyses we modified the assumption for FFQ variability in estimating of 95% CIs ( 17 ). METHODS Participants in this analysis were those enrolled in the original STAR-Q DLW validation study ( 18 ). The STAR-Q was conceptualized to capture 24-hour movement behaviours ( 19 ). It was therefore uniquely designed to ascertain self-reported time spent in sleep, sedentary behavior, and all types and intensities (e.g., light, moderate and vigorous) and domains of activity (e.g., occupation, leisure, household, and transportation) and took a median time of 34 minutes to complete ( 18 , 20 ). Subject Eligibility and Ethics Participant recruitment has previously been described ( 18 ). Briefly, urban residents living in Calgary, Alberta, Canada were recruited from the community or from the Alberta Tomorrow Project (ATP). The ATP is a large geographically dispersed cohort established in 2001 in Alberta, to study links between lifestyle behaviors and chronic disease risk ( 21 , 22 ). ATP participants who met eligibility criteria for the DLW validation study – body mass index (BMI) ≤ 35, weight stable (± 2.5 kg for at least 3 months), not planning to gain or lose weight, not pregnant or breastfeeding, not diagnosed with metabolic disorders, and not taking medications that modify water balance – were actively recruited by mail. Non-ATP participants were recruited by community-based advertisements. The upper age limit for eligibility was 60 years, and the lower age limit was 30 and 35 years for urban Calgary and ATP participants, respectively. All participants provided written informed consent. Ethical approval was obtained from the Alberta Cancer Research Ethics Committee of Alberta Health Services and the Conjoint Health Research Ethics Board of the University of Calgary. All study procedures were in accordance with ethical standards outlined by the two governing bodies. Doubly labeled water study Details of the 14-day DLW protocol have been published ( 18 ). Briefly, at baseline (Day 0), following an overnight fast, participants provided urine and saliva samples to determine background isotope levels. Each subject was then orally dosed with 2.5g 10 atom % of 18 O per kg total body water (TBW) and 0.18 g 99 atom % deuterium ( 2 H) per kg estimated TBW (Rotem Inc., Topsfield, Massachusetts). Post-dose saliva samples were collected at 3 and 4 hours to estimate TBW from deuterium-isotope dilution, and second-void urine samples on Days 1, 8, and 14. On Day 14 after an overnight fast, participants received 0.18 g 99 atom % 2 H per kg TBW. Post-dose saliva sample collection was as described for Day 0. To estimate the decline in isotope enrichment, samples were batch-analyzed in duplicate using the Isoprime Stable Isotope Ratio Mass Spectrometer and the Multiflow-Bio module for Isoprime equipped with a Gilson 222XL Autosampler (GV Instruments, Manchester, United Kingdom). Data processing was performed using IonVantage (2012) software for Isoprime (Cheadle, United Kingdom). We calculated TEE DLW in kilocalories per day using a modified Weir equation ( 23 ) and an assumed respiratory quotient of 0.85. In the absence of objective measures, BMR was estimated using the Schofield Equation ( 24 ) to derive individual level PALs from DLW as: Health and lifestyle measurements Height and weight were measured (to the nearest 0.1 cm and 0.1 kg, respectively) on Days 0 and 14 of the DLW protocol using standard procedures ( 25 ). The TBF-310 Tanita Body Composition Analyzer and Scale (TheCompetitiveEdge.com, Preston, Washington) was used to measure body weight. BMI was determined as body weight divided by height-squared (kg/m 2 ). The Canadian Diet History Questionnaire I (C-DHQ I) was administered once on Day 0 of the DLW protocol to estimate rEI. The C-DHQ I is a comprehensive FFQ that assesses usual dietary intake over the past-year, modified from the U.S. National Cancer Institute’s DHQ for Canadian populations ( 26 , 27 ) and used in large epidemiologic studies including the ATP ( 21 , 22 , 28 , 29 ). The STAR-Q was completed on Day 14 of the DLW study to estimate average daily energy expenditure from all activities including sleep during the preceding month. STAR-Q-derived TEE and activity energy expenditure demonstrated high reproducibility over a three-month period, with intraclass correlation coefficients of 0.84 and 0.73, respectively ( 18 ). Compared with DLW, the STAR-Q demonstrated moderate validity for ranking individuals by TEE and activity energy expenditure (Spearman’s rho=0.53 and 0.40, respectively) ( 18 ). To derive TEE from the STAR-Q, activity code and metabolic equivalents of task (MET) ( 30 ) were first assigned to each reported activity. If the total time reported for physical activity, sedentary behavior, and sleep was less than 24 hours/day, a MET value of 1.2 was assigned for unaccounted time based on evidence that sedentary activity is more likely to be under-reported than other intensity levels of activity ( 18 , 31 ). Reported frequency, duration and assigned MET values were used to estimate energy expenditure for each activity in units of MET-hours/day, which were then summed to derive the total average MET-hours/day. The STAR-Q-derived TEE (TEE STAR-Q ) was then estimated as: where STAR-Q reported hours of sleep per day was multiplied by 0.9 METs ( 30 , 32 ) and then added to the total average MET-hours/day in awake time multiplied by baseline body weight. The equivalence of 1 MET = 1 kcal/kg/hr was applied to estimate the energy cost of activities ( 33 ) and 10% was added for the thermic effect of food ( 10 ). Individual PALs were derived from self-report (TEE STAR-Q ) as: where BMR EST was estimated using the Schofield sex-stratified equations based on age, weight and height ( 24 ). Classification of misreporting EI reporting status was determined for each of the ratios rEI:MBR EST , rEI:TEE DLW , and rEI:TEE STARQ compared with respective estimated 95% CIs based on PAL STAR-Q , TEE DLW , and TEE STAR-Q (calculations below). Each participant was classified as an under-reporter (UR), acceptable reporter (AR), or over-reporter (OR) according to comparisons of individual rEI ratios with 95% CIs. Participants with ratios falling below their respective 95% CIs were deemed URs, those with ratios above the upper boundary were deemed ORs and those with ratios between the boundaries were deemed ARs. In the primary analysis, to evaluate the Goldberg-PAL STAR-Q for classifying individual misreporting, 95% CIs for comparison with rEI:BMR EST were calculated as: where CV EI was the within-subject coefficient of variation (CV) for rEI, assigned as 18.6% for men and 19.8% for women based on repeatability of the US DHQ ( 17 ), d =1 since the C-DHQ I was administered once, CV BMR was the CV for estimated BMR assumed to be 8.5% for men and women as previously suggested ( 11 ) and CV PAL was the CV for PAL STAR-Q , calculated to be 17.5% for men and 15.1% for women from our three-month STAR-Q repeatability study ( 18 ). For the gold standard rEI:TEE DLW the 95% CIs were calculated as: where CV EI ( d =1) was assumed to be the same as above and CV DLW was assigned 5.1% for both men and women as previously reported ( 17 ). To examine the magnitude of discrepancies relative to the gold standard ratio rEI:TEE DLW , graphical illustrations described by Tooze et al . ( 17 ) were used comparing individual-level rEI:TEE STAR-Q and rEI:TEE DLW ratios to their respective 95% CIs. The 95% CI for with which rEI/TEE STAR-Q was compared was calculated as: where CV EI and d were as described above. The CV STAR-Q was the within-subject CV for TEE STAR-Q calculated to be 17.7% for men and 15.2% for women based on the first and second administrations of the STAR-Q in the repeatability study ( 18 ). If an individual rEI:TEE STAR-Q ratio fell between the lower (or upper) 95% CI boundaries for rEI:TEE STAR-Q and rEI:TEE DLW ratios, the source of discrepancy was attributed to differences in the 95% CI calculations. Otherwise, it was attributed to differences in TEE estimation (TEE STAR-Q vs TEE DLW ). Furthermore, the contribution of these discrepancies to sensitivity and specificity of Goldberg-PAL STAR-Q was determined. In secondary analyses, a constant PAL 1.55 was assumed for participants (PAL 1.55 ) and 95% CIs calculated as described above for PAL STAR-Q , except CV PAL was assumed to be 15% for both genders as previously reported ( 11 ). Finally in sensitivity analyses, for comparison with a previous study ( 17 ), d =∞ was substituted for d =1, in each of the cut-point equations (above) eliminating the CV 2 EI term. This alternate estimate of ‘ d ’ assumes that the C-DHQ I ascertains habitual diet over an infinite time period. Statistics All analyses were stratified by gender. Means (SD) were determined for continuous sociodemographic characteristics, frequencies and percentages for categorical characteristics, and medians (Q1, Q3) for rEI and energy expenditure. To appraise the validity of Goldberg-PAL STAR-Q and PAL 1.55 classifications, the estimated proportions of participants correctly classified as URs, ARs and ORs were compared with DLW classifications assumed to be true classifications. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the accuracy of UR and AR classifications were estimated ( 34 ), excluding ORs identified by TEE DLW,d=1 (n=1) and TEE DLW,d=∞ (n=7). The sensitivity was the proportion of true URs correctly classified by the Goldberg method (PAL STAR-Q or PAL 1.55 ) and specificity was the proportion of true ARs correctly classified. PPV was the proportion of Goldberg method URs that were true URs, interpreted as the probability of being a true UR if classified as UR by Goldberg. NPV was the proportion of Goldberg ARs that were true ARs, interpreted as the probability of being a true AR if classified as AR by the Goldberg method. Accuracy was determined as the proportion of true URs plus true ARs classified correctly with the Goldberg method. To appraise the validity of TEE STAR-Q for estimating the magnitude of misreporting (UR and OR) in kilocalories per day, the within-person differences of TEE STAR-Q - rEI were compared with TEE DLW - rEI using Pearson correlation coefficients and Wilcoxon signed-rank tests. Agresti Coull 95% CIs were calculated for diagnostic metrics using Python (Version 3.12.12; Statsmodels Version 0.14.5). All other analyses were performed using SAS Institute Software, Gary, NC (Version 9.4). A P -value < 0.05 was considered statistically significant. RESULTS Participants aged 30-60 years (n=106) were recruited to the original DLW study between July 2009 and July 2010 ( 18 ). Seven participants were excluded from the present analysis for reasons outlined in Figure 1 , leaving 99 participants of whom 60% were women. Compared with men, women were somewhat younger (mean: 46.2 vs 50.6 years of age), had a lower BMI (mean: 23.5 vs 25.9 kg/m 2 ), and were less likely to have completed post-secondary education (45% vs 51%) ( Table 1 ) . Most of the participants were Caucasian and had paid or volunteer work. Median EI, TEE DLW , TEE STAR-Q , and BMR EST were lower for women (1676 kcal, 2604 kcal, 2889 kcal and 1362 kcal, respectively) compared with men (2036 kcal, 3306 kcal, 3620 kcal, and 1866 kcal, respectively). Median values for PAL DLW and PAL STAR-Q , were 1.8 and 2.0 for men and, 1.9 and 2.1 for women ( Table 1 ) , consistent with ‘active’ and ‘very active’ PALs ( 10 ). Download figure Open in new tab Figure 1. Participant recruitment in the STAR-Q validation study (2009-2011), Alberta, Canada. View this table: View inline View popup Download powerpoint Table 1. Sociodemographic characteristics, energy expenditure and energy intake for 99 participants in the STAR-Q validation study ( 2009-2011 ) , Alberta, Canada. Results from the primary, secondary and sensitivity analyses are presented in Table 2 . In the primary analysis, 60% of men and 56% of women were classified as URs by TEE DLW , and 58% of men and women by the Goldberg-PAL STAR-Q . The Goldberg-PAL STAR-Q misclassified fewer men (15%) than women (25%), resulting in sensitivity, specificity, PPV, NPV and accuracy >80% among men, whereas for women these estimates were <80%. View this table: View inline View popup Table 2. Validity of the Goldberg method compared with the ‘gold standard’ doubly labeled water method for classifying energy intake misreporting, for 40 men and 59 women 1 in the STAR-Q validation study (2009-2011), Alberta, Canada. In secondary analyses, Goldberg-PAL 1.55 resulted in fewer participants classified as URs (35% and 19% for men and women, respectively) than with TEE DLW and Goldberg-PAL STAR-Q which led to high specificity and PPVs (both 93% for men and 100% for women), but low sensitivity and NPVs (all <60% for men and women). In sensitivity analyses, where d =∞ was assumed for the FFQ, a larger proportion of participants were classified as URs with both TEE DLW and Goldberg-PAL STAR-Q than with the assumption that d =1. Seven were deemed to be true ORs in contrast to one, when d= 1, attributable to a narrower 95% CI with d =∞ following elimination of the variability term. As such, less favorable results were seen for the identification of URs with the Goldberg-PAL STAR-Q except for specificity and PPV, which were 100% among men. However, the assumption that the FFQ captures habitual intake representing an infinite time period may be an overestimate of this attribute ( 35 ). Figures 2A and 2B illustrate individual-level agreement and magnitude of misreporting in kilocalories per day (i.e., TEE STAR-Q - rEI relative to TEE DLW - rEI). Pearson correlation coefficients were higher for men (r=0.76, P <0.0001; classification agreement in 34 of 40 men) than for women (r=0.37, P =0.0042; classification agreement in 44 of 59 women). Nonetheless, the Wilcoxon signed-rank test indicated statistically significant within-person differences between the two methods (P = 0.0021 and P < 0.0001 for men and women, respectively). The magnitude of EI under-reporting tended to be larger for misclassified URs (e.g., URs classified as ARs, approximately 1,000 to 1,500 kcal/d; 3 men and 7 women) while the magnitude of EI under-reporting was typically smaller for misclassified ARs (e.g., ARs classified as URs, <1,000 kcal/d approximately; 2 men and 8 women). Download figure Open in new tab Download figure Open in new tab Figure 2. Correlation between the degree of energy intake misreporting based on Goldberg classifications (i.e., individual differences: TEE STAR-Q – rEI) and the degree of energy intake misreporting based on doubly labeled water classifications (i.e., individual differences: TEE DLW -rEI) in kilocalories per day. Figure 2A shows the correlation among 40 men and Figure 2B shows the correlation among 59 women in the STAR-Q validation study (2009-2011), Alberta, Canada. AR, acceptable reporters; DLW, doubly labeled water; UR, under-reporters. Figures 3A and 3B , show whether discrepant classifications arose from different 95% CI cut-points or from different TEE estimation. Among 40 men ( Figure 3A ) , five misclassifications were due to TEE estimation, and one was due to interval cut-point. Among 59 women ( Figure 3B ) , 11 misclassifications were due to differences in TEE estimation and 4 due to cut-point estimation. Among true URs misclassified as ARs using the PAL STAR-Q (reflecting sensitivity), 2 of 3 men and 3 of 7 women were misclassified owing to TEE estimation. Among true ARs misclassified as URs using the PAL STAR-Q method (reflecting specificity), all misclassifications (2 men and 8 women) were due to differences in TEE estimation. Download figure Open in new tab Download figure Open in new tab Figure 3. Sources of discrepancy between the Goldberg and doubly labeled water methods for classifying energy intake misreporting. Figure 3A illustrates sources of discrepancy in men and Figure 3B illustrates sources of discrepancy in women. The gold standard doubly labeled water classifications of reporting are represented with shaded circles rEI:TEE DLW . The Goldberg classifications of misreporting, based on the ratio rEI:TEE STAR-Q , are represented with open circles (agreement between methods), squares (discrepant because of TEE estimation) or triangles (discrepant because of different cut-offs). The gray solid line represents the lower cut-off for gold standard doubly labeled water classifications (0.68 for men; 0.66 for women). The black solid line represents the lower cut-off for Goldberg classifications (0.58 for men; 0.59 for women). DISCUSSION Using DLW, the ‘gold-standard’ biomarker for TEE and an unbiased proxy for habitual EI in weight-stable individuals ( 2 ), we determined the accuracy of the Goldberg method using STAR-Q-derived PALs to classify EI reporting status among community dwelling middle-aged men and women following completion of an FFQ. The PAL STAR-Q generally improved the sensitivity and overall accuracy of the Goldberg method for discriminating between URs and ARs compared with a global PAL of 1.55. We also identified gender-based differences in the performance of the Goldberg-PAL STAR-Q . In men, the method discriminated between UR and AR with an overall high level of accuracy at 87%, while in women it exhibited fair performance, with an overall classification accuracy of 75%. A key determinant of the accuracy of the Goldberg method is the degree to which PALs represent populations under study. Three previous DLW validation studies have explored approaches to identify the optimal PAL for estimating EI misreporting ( 17 , 36 , 37 ). Two studies examined EI misreporting on food records and/or diet histories and found under-reporting in 14% ( 37 ) and 34% ( 36 ) of adult participants. Consistent with our results, the studies reported high levels of specificity but relatively low sensitivity for identifying URs when a global PAL of 1.55 was assumed. Additionally, Black ( 36 ) leveraged the strength of a large database comprised of 21 DLW studies (n=429) to test the effect of various global mean activity PALs (from 1.55 to 1.95) and reported incremental increases in sensitivity, with increasing PALs; however, this improvement coincided with decreases in specificity. In a small sample of adults (n=14), Livingstone et al ( 37 ) tested the performance of gender-specific PALs to detect URs based on individual heart-rate monitors matched to low, medium and high activity categories of PAL ( 13 ). Compared with PAL 1.55 , sensitivity increased substantially from 50% to 100% with negligible decreases in specificity. Subsequently, Tooze et al ( 17 ) reported favorable results for the Goldberg method with a PAL 1.55 to detect DLW-classified URs on 24-hour dietary recalls (∼20%) and an FFQ (∼50% UR) in the Observing Protein and Energy Nutrition (OPEN) Study (n=451). Specificity was 88% for the FFQ in both genders. The sensitivity to detect URs on the FFQ was high at 96% when usual intake was assumed for the FFQ ( d=∞ ), but substantially lower when the OPEN study FFQ within-person variability was applied (72% sensitivity for men and 62% for women). The results reported by Livingstone et al ( 37 ) and Black ( 36 ) are consistent with our results and support the premise that a global PAL 1.55 may be underestimating the activity levels of some adult populations – and hence, more individualized PALs may improve the overall accuracy of the Goldberg method to identify EI under-reporting. While our findings contrast those of Tooze et al ( 17 ), we note that the prevalence of true UR (50%) was lower in the OPEN study than in our population which affects PPV and NPV values. In addition, participants were on average less active and slightly older than in the STAR-Q validation study. Median values for DLW-derived TEE and PAL were 2,813 kcal and 1.69 for men and 2,283 kcal and 1.75 for women in OPEN ( 38 , 39 ) and 3,306 kcal and 1.8 for men and 2604 kcal and 1.9 for women in the STAR-Q study, with ages ranging from 40 to 69 years in OPEN ( 38 , 39 ) versus 30 to 60 years in the STAR-Q study. Statistical approaches, such as the exclusion of misreporters, and stratification and adjustment for reporting status in regression analyses ( 40 – 45 ), have been explored to handle identified URs. Consensus exists only with respect to an agreement that exclusion of misreporters is undesirable given loss of statistical power ( 41 ) and valuable information inherent in excluded data ( 40 ). While the optimal approach may be study-specific (e.g., health outcomes of interest, study design, method of data collection and population characteristics), corrective methods cannot be expected to fully eliminate bias associated with measurement error. Nonetheless, the identification of implausible EI reporting using the Goldberg method has been demonstrated to significantly reduce, albeit not completely eliminate, misreporting-related bias for some EI-outcome associations ( 44 , 45 ). This suggests an important role for the method in sensitivity analyses aiming to better understand the potential impact of EI misreporting on study results. Of note, the aforementioned studies used group level PALs – potentially limiting the extent to which bias due to misreporting could be mitigated. While the STAR-Q was designed to capture a comprehensive profile of daily activities over 24-hours, the misclassification of 50% of true URs as ARs and all true ARs as URs, was likely due to errors in the estimation of TEE STAR-Q ( Figure 3a and 3b ) . Using updated MET values ( 33 , 46 ) for energy expenditure, could improve future TEE and PAL estimations. In addition, the reporting of activities during the previous month are not without cognitive challenges, though less so than past-year reporting ( 20 ). The Activities Completed over Time in 24 hours (ACT24), an internet-based recall of previous day activities is even less cognitively challenging and has been shown to estimate mean TEE and PAL values within 5% of DLW-based estimates ( 47 , 48 ). The accuracy of short-term recall combined with longer-term measures that reflect habitual lifestyle behaviors is an intriguing approach that has been suggested for improving dietary intake measures ( 49 ) and may also improve TEE and PAL estimations. Moreover, new wearable device-based technologies may in the foreseeable future accurately estimate PALs and TEE in large populations, to complement the contextual data collected by traditional questionnaires ( 50 ). Hence, it is anticipated that methodologies combining device-derived estimates of energy expenditure and body composition may be developed to validate self-reported EI ( 51 – 53 ); though these approaches are still viewed as ‘works in progress’ ( 54 ). A noteworthy strength of this study is the validation of the Goldberg-PAL STAR-Q method against DLW – and the unique use of individual-level PALs not previously studied. Furthermore, as noted in the recent DRIs for Energy report, of 128 DLW studies representing 25 countries in the International Atomic Energy Agency DLW Database, none were Canadian ( 10 ) highlighting a gap in this area of Canadian research that this study begins to address. Nonetheless, our study has limitations that merit consideration. First, we used prediction equations for BMR ( 24 ), which accounts for a substantial proportion of TEE ( 55 ) and could be a source of error in the estimation of PAL STAR-Q . Second, we cannot be certain that some of the under-reporting was not true undereating, though we aimed to minimize this occurrence by recruiting generally weight stable participants to the DLW study. Third, we caution that we cannot assume that EI misreporting is reflective of misreporting of other nutrients. Indeed, though still relatively under-studied, evidence is accumulating to suggest that foods and nutrients vary in the accuracy with which they are reported ( 6 , 56 – 59 ). On a final note, our population was largely Caucasian middle-aged adults; hence, our results may not be generalizable to older ( 60 , 61 ), racially diverse populations and those with other characteristics that may affect reporting and health status ( 62 – 65 ). Overall, our DLW-validated results lend credibility to the thesis that detailed comprehensive questionnaire-based activities may be effectively employed with the Goldberg method to describe and explore EI misreporting in large-scale diet studies. Importantly, such strategies could create opportunities to carry out sensitivity analyses that provide critical insights into the impact of EI misreporting on diet-disease associations in nutritional epidemiology. Ongoing effort to improve upon this approach with cohort validation sub-studies and innovative study designs integrated with new technologies need to remain as research priorities in nutritional epidemiology. STATEMENTS AND DECLARATIONS Data availability Data described in the manuscript, code book, and analytic code will be made available upon reasonable request to the corresponding author. The data are not publicly available due to privacy and/or ethical restrictions. Conflicts of Interest IC and KAK were awarded research funding paid to their institutions from the Canadian Institutes of Health Research (CIHR: Grant MOP-86632) and the Alberta Cancer Research Institute (Grant 24265) to support this investigator-initiated study. Support during past 36 months : IC has received honoraria from the American Institute of Cancer Research and Genome Quebec for her role as a grant reviewer. AK has been awarded research funding paid to her institution from CIHR to support investigator-initiated research unrelated to this study. AK has also received an honorarium from the Université Laval for her role as an epidemiology program evaluator. JAT discloses her role as Committee Member on the Dietary Reference Intakes for Energy , National Academies of Sciences, Engineering, and Medicine, 2023 (no remuneration received). All other co-authors have no conflicts or competing interests to declare. Author contributions Concept and design : HKN, KAK and IC; Acquisition of data and/or lab analysis : HKN, KAK, RRL and IC; Statistical analysis : SA, FK and IC; Interpretation of data : All authors; Drafting the manuscript : HKN and IC; Critical revision of manuscript and important intellectual contribution : All authors; Obtaining funding : IC and KAK; Administrative, technical or material support : HKN, KAK and IC; Supervision : IC; Primary responsibility for the final content : IC. Footnotes ↵ * Co-senior authors. Sources of Support: Funding for this study was provided to institutions by the Canadian Institutes of Health Research (Grant MOP-86632) and the Alberta Cancer Research Institute (Grant 24265). Role of funding agencies: The funders had no role in the design of this study, the analysis, interpretation of results or drafting of the manuscript. Confidence intervals for diagnostic metrics have been added to Table 2. using the Agresti Coull method. 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