TABLE J-1 Evidence on the Relationship Between Different Measurements of Physical Activity and Energy Expenditure: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Adamo et al., 2009 | 5 | 47 males and females 1–18 y; White European, U.S. African American, U.S. White | Indirect measures of physical activity included activity diaries or logs, questionnaires, surveys, and recall interviews | Mean difference from DLW in boys and girls combined |
| Adamo et al., 2009 | 13 | 110 males 1–18 y; White European, U.S. African American, U.S. White | Indirect measures of physical activity included activity diaries or logs, questionnaires, surveys, and recall interviews | Mean difference from DLW in boys |
| Adamo et al., 2009 | 13 | 93 females 1–18 y; White European, U.S. African American, U.S. White | Indirect measures of physical activity included activity diaries or logs, questionnaires, surveys, and recall interviews | Mean difference from DLW in girls |
| Dowd et al., 2018 | 27 | Males and females ≥ 19 y; high-income countries | Self-reported measures of PA included 7-day recall questionnaires, past year recall questionnaires, typical week questionnaires, and PA logs/diaries | Criterion validity of EE estimates compared to 8–15 days of DLW measurement |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Data from studies/substudies reporting on combined male and female data that compared an indirect measure to DLW indicated that indirect measures overestimated physical activity or energy expenditure with a mean percent difference of 22% and a range of –25% to 78%. | Overall, 19 of 24 studies unclearly reported or failed to report between one and five of the 16 components | — | Partially well done/reported |
| Results for male-only had mean percent differences of 0 (range: –33% to 56%). | Overall, 19 of 24 studies unclearly reported or failed to report between one and five of the 16 components | — | Partially well done/reported |
| Results for female-only had mean percent differences of –1.2 (range: –43% to 95%). | Overall, 19 of 24 studies unclearly reported or failed to report between one and five of the 16 components | — | Partially well done/reported |
| Mean percent differences for PA diaries ranged from –12.9% to 20.8%, self-reported PA energy expenditure recalled from the previous 7 days (or typical week) ranging from –59.5% to 62.1%, self-reported PA energy expenditure for the previous month ranged from –13.3% to 11.4%, self-reported PA from the previous 12 months ranged from –77.6% to 112.5%. | Mean AMSTAR score was 5.4 (out of 11) | — | Well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Dowd et al., 2018 | 24 | Males and females ≥ 19 y; high-income countries | Activity monitor determined energy expenditure | DLW |
| Dowd et al., 2018 | 9 | Males and females ≥ 19 y; high-income countries | Activity monitor determined PA intensity | Indirect calorimetry and whole-room calorimetry PA intensity |
| Dowd et al., 2018 | 31 | Males and females ≥19 y; high-income countries | Activity monitor determined energy expenditure | Indirect calorimetry EE |
| Dowd et al., 2018 | 3 | Males and females ≥ 19y; high-income countries | Pedometer determined EE | DLW |
| Helmerhorst et al., 2012 | 2 | 111 males and females < 18 y; high-income countries | Physical activity questionnaires | DLW |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| The range of MPD observed in studies that examined the criterion validity of activity monitor–determined energy expenditure ranged from –56.59% to 96.84%. However, a trend was apparent for activity monitor–determined energy expenditure to underestimate the criterion measure. | Mean AMSTAR score was 5.4 (out of 11) | — | Well done/reported |
| For LIPA, the MPD ranged from –79.8% to 429.1%. For MPA, MPD ranged from –50.4% to 454.1%, while estimates for VPA ranged from –100% to 163.6%. Energy expenditure estimates from activity monitoring devices for total PA were compared against indirect calorimetry estimates, where MPD ranged from –41.4% to 115.7%. The MPD range for activity monitor-determined total energy expenditure compared with whole room calorimetry were narrower (–16.7% to –15.7%). | Mean AMSTAR score was 5.4 (out of 11) | — | Well done/reported |
| Estimated energy expenditure was compared between activity monitors and indirect calorimetry (kcal over specified durations; [–68.5% to 81.1%]); (METs over specified durations; [–67.3% to 48.4%]). A single study compared the estimated energy expenditure from 5 different activity monitors and indirect calorimetry at incremental speeds (54, 80, 107, 134, 161, 188, and 214 m.min–1) in both men and women (MPD ranged from –60.4% to 90.8%). | Mean AMSTAR score was 5.4 (out of 11) | — | Well done/reported |
| In free-living studies that examined the criterion validity of pedometer determined energy expenditure, pedometers were worn for 2 to 8 days (–62.3% to 0.8%). | Mean AMSTAR score was 5.4 (out of 11) | — | Well done/reported |
| For PA EE, Spearman r ranged from 0.09 to 0.45 and MD was 0.46 to 0.76 kg/kg/d. For TEE, Spearman r ranged from 0.49 to 0.65; MD 2,800 kJ/day. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Helmerhorst et al., 2012 | 6 | 239 males and females 18–65 y; high-income countries | Physical activity questionnaires | DLW |
| Helmerhorst et al., 2012 | 2 | 86 males and females > 65 y; high-income countries | Physical activity questionnaires | DLW |
| Jeran et al., 2016 | 24 | 1,148 males and females ≥ 19 y; mix of general population, soldiers, and patients (COPD and cancer); high-income countries | Assess whether study or accelerometer device characteristics influence the association between accelerometer-derived physical activity output and DLW-derived AEE | Crude R2 accelerometer output vs. AEE or AEE per kg |
| O’Driscoll et al., 2020 | 60 | 1,946 males and females ≥ 19 y; high-income countries | EE estimate of wrist-worn or arm devices (40 different devices; 33 wrist-worn) | — |
| O’Driscoll et al., 2020 | 60 | 1,946 males and females ≥ 19 y; high-income countries | TEE estimate of wrist-worn or arm devices (10 different devices) | — |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| For PA EE, Spearman r = 0.39 and MD was −12.9 kJ/day from one study. Four studies reported TEE and Spearman r ranging from 0.15 to 0.67; Pearson r ranged from 0.12 to 0.58; MD ranged from –3,451.9 to 7,455 kJ/day. One study reported PAL, and the Pearson r ranged from 0.34 to 0.69. | — | — | Not well done/reported |
| For TEE, Spearman r ranged from 0.10 to 0.64; Pearson r ranged from 0.11 to 0.65; MD ranged from 435 to 3,146 (men) and 37 to 2,037 (women) kJ/day. | — | — | Not well done/reported |
| Crude R2 ranged from 0.043 to 0.80 with a median of 0.26. Crude R2 did not significantly differ by accelerometer recording period (≤ 1 week vs. 41 week), body position (trunk vs. limbs), wear time (waking hours vs. 24 hours), accelerometer output type (uniaxial vs. triaxial outputs) or accelerometer output metrics (counts vs. steps vs. other) (all p-values of Mann–Whitney U-test and Kruskal–Wallis test, 40.05). There was a significant inverse association between crude R2 and sample size (r = –0.45, p = .03). There was no significant correlation between crude R2 and mean age of participants (r = 0.16, p = .44). | — | — | Not well done/reported |
| Overall, devices underestimated EE (ES, –0.23, 95% CI, –0.44 to –0.03; n = 104; p = .03) and showed significant heterogeneity between devices (I2, 92.18%; p ≤ .001). | |||
| The pooled effect for TEE showed a significant underestimation of EE (ES: –0.68; 95% CI, –1.15 to –0.21; n = 16; p = .005), and significant heterogeneity was observed between devices (I2, 92.71%; p < .01). The SWA p3 did not differ significantly from criterion measures and showed significant heterogeneity (I2, 94.20%; p = .001). | Median score of 13; 1 low-quality, 48 moderate-quality, and 11 high-quality | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Pisanu et al., 2020 | 5 | 734 males and females ≥ 19 y with overweight and obesity; high-income countries | REE estimated from wearable accelerometer-based devices | — |
| Pisanu et al., 2020 | 9 | 339 males and females ≥ 19 y with overweight and obesity; high-income countries | PA EE estimated from wearable accelerometer–based devices during different structured physical activities | — |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| One study obtained an underestimation of REE SWA, although the statistical significance was not specified. However, a significant overestimation of SWA was observed in all four other studies. Pearson’s correlation coefficient was reported in three studies, in which it ranged between 0.58 (obtained in women) and 0.88 (obtained in the whole population). Results of Bland–Altman analysis revealed the tendency of the bias to increase as the REE increased across participants. Authors did not find any relationship between the bias and age, BMI, fat-free mass, total body water, and extracellular water of individuals. Bland–Altman plots indicated that SWA systematically overestimated REE in women displaying low REE values and underestimated REE in women displaying high REE values. |
Risk of bias was judged as low | — | Well done/reported (or partially well done/reported if heterogeneity issue is important) |
| A general trend toward overestimation can be noticed. However, the study protocol differs greatly among the included studies. | Risk of bias was judged as low | — | Well done/reported (or partially well done/reported if heterogeneity issue is important) |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Pisanu et al., 2020 | 5 | 185 males and females ≥ 19 y with overweight and obesity; high-income countries | TEE or PA EE free-living from wearable accelerometer-based devices | — |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| The accuracy of the Caltrac uniaxial accelerometer in the measurement of TEE was evaluated: even if the accuracy of the instrument was good at a group level, at the individual level, differences were large. An underestimation of EE in free-living conditions was obtained in one study. RT3 limits of agreement were smaller than TriTrac-R3D, but presented limitations at individual levels. Bland–Altman plots showed that SWA and IDEEA accurately estimated TEE, and the IDEEA accelerometer accurately measured AEE. On the other hand, the performance of Actical was low. Accuracy of TEE and AEE estimates of the SWA, using software versions 6.1 and 5.1 in a sample of older participants (78–89 years old), who were overweight as a group. Both versions showed high Pearson’s correlation coefficients (r > 0.75) for TEE. On the other hand, AEE was underestimated by both versions 6.1 and 5.1. Nevertheless, Bland–Altman plots revealed no systematic bias when considering both TEE and AEE. |
Risk of bias was judged as low | — | Well done/reported (or partially well done/reported if heterogeneity issue is important) |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Plasqui et al., 2013 | 25 | 944 males and females; high-income countries | Validity of wearable PA monitor estimates of EE | — |
| Sharifzadeh et al., 2021 | 30 | 3,877 males and females; high-income countries | Physical activity questionnaire TEE (50 questionnaires) | — |
| Sharifzadeh et al., 2021 | 15 | 2,058 males and females; high-income countries | Physical activity questionnaire AEE (35 questionnaires) | — |
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Mean differences in TEE or AEE between DLW and the accelerometer were often small on the group level, but the limits of agreement (2 SD) were usually large. Observed correlations between PAL and activity counts vary between 0.06 (Lifecorder) and 0.68 (TracmorD). Interpreting correlations between AEE or TEE and activity counts becomes more difficult as body mass and other characteristics are the main determinants of EE. Output from the 3dNX accelerometer significantly increased the prediction of TEE in addition to FFM. The Tracmor significantly contributed to the prediction of TEE after correcting for sleeping metabolic rate, body mass, or FFM. Likewise, the RT3 significantly contributed to the prediction of TEE and AEE after correction for subject characteristics. When AEE is expressed per kg body mass, correlations with activity counts vary between 0.37 (Actigraph) and 0.79 (Tracmor). |
— | — | Not well done/reported |
| The weighted mean difference was not significant between TEEDLW –TEEPAQ (WMD, –243, 95% CI, –841.4–354.6; I2, 97.9%, p < .0001). | — | — | Not well done/reported |
| A significant difference was found between AEEs examined by various indirect measures and the direct measures derived from DLW (WMD, 414.6; 95% CI, 78.7–750.5; I2, 92%, p < .001) in which AEE assessed by DLW was higher than that measured by PAQ. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Tudor-Locke et al., 2002 | 8 | Males and females; high-income countries | Pedometer versus energy expenditure | — |
| Tudor-Locke et al., 2002 | 8 | Males and females; high-income countries | Pedometer versus energy expenditure | — |
NOTE: AEE = activity energy expenditure; COPD = chronic obstructive pulmonary disease; DLW = doubly labeled water; EE = energy expenditure; ES = effect size; FFM = fat-free mass; IDEEA = Intelligent Device for Energy Expenditure and physical Activity; kcal = kilocalories; kg = kilogram; kJ = kilojoule; LIPA = light-intensity physical activity; MD = mean difference; MET = metabolic equivalent of task; MPA = moderate-intensity physical activity; MPD = mean percentage difference; PA = physical activity; PAL = physical activity level; PAQ = physical activity questionnaire; REE = resting energy expenditure; SD = standard deviation; SWA = SenseWear Armband; TEE = total energy expenditure; VPA = vigorous-intensity physical activity; WMD = weighted mean difference; y = years.
| Quantitative or Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Although a single study comparing pedometer outputs with energy expenditure derived from doubly labeled water reported a significant correlation of r = 0.61 in a patient population, two other studies reported no significant correlations in different populations (no reported r values). | — | — | Not well done/reported |
| Pedometers generally correlate with indirect calorimetry from r = 0.49 to 0.81 | — | — | Not well done/reported |
TABLE J-2 Evidence on the Association of Macronutrient Composition of the Diet on Metabolic Efficiency (Energy Usage or Energy Expenditure): Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome | Quantitative Finding(s) |
|---|---|---|---|---|---|
| Ludwig et al., 2021 | 29 | 617 male and female adults 19–50 y | Low vs. high carbohydrate diet | TEE | Lower carbohydrate diet had lower TEE for studies < 2.5 weeks –50.0 kcal (–77.4, –22.6) |
| Higher TEE among > 2.5 weeks 135.5 kcal (72.0, 198.7). Sensitivity analysis produced similar results | |||||
| Park et al., 2020 | 15 | Adults 19–50 y with obesity or lean/normal weight | — | — | — |
| Quatela et al., 2016 | 19 (related to energy) | Male and female adults 19 y and older | Total energy intake | DIT; RMR | The effect of energy intake on DIT (coefficient, 0.011; standard error, 0.0013; p < .001; 95% CI, 0.0083–0.014) |
| Cisneros et al., 2019 | 15 | 210 male and female adults 19 y and older | type of fatty acid | DIT or EE | No conclusion can be drawn |
| Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Among trials < 2.5 weeks, the lower-carbohydrate diets slightly reduced TEE. | — | I2, 69.8%; p < .001 | Not well done/reported |
| Among trials of > 2.5 weeks, the lower-carbohydrate diet substantially increased TEE—by ~50 kcal/d for every 10% decrease in carbohydrate as % EI—with minimal residual heterogeneity. | I2, 26.4%; p = .255 | ||
| Many studies reported that the main determinant of DIT is the energy content of food, followed by the protein fraction of food. The thermic effect of alcohol is similar to that of protein. Therefore, the main determinants of DIT are the energy content and protein fraction of the diet. | — | Not well done/reported | |
| This model shows that DIT (kJ) increases significantly (p < .001) when the kJ content of meals increases, although this increase is of a small magnitude (coefficient, 0.011). This model predicts that for every 100-kJ increase in energy intake, DIT increases by 1.1 kJ/h. Model 2 produced similar results. (47.4% variance explained in model 1; 70.6% in model 2) | — | Not well done/reported | |
| — | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome | Quantitative Finding(s) |
|---|---|---|---|---|---|
| Wycherley et al., 2012 | 4 | 40 participants | high protein (low fat) vs standard protein (low-fat) | REE (secondary outcome) | ≥ 12 weeks mean difference was 130 kJ/day (range –205.13–465.13); < 12 weeks 838 kJ/day (228.83, 1,447.17). Across all time 595.50 kJ/day (range, 66.95–1,124.05) |
NOTE: CI = confidence interval; DIT = diet-induced thermogenesis; EE = energy expenditure; EI = energy intake; kJ = kilojoule; REE = resting energy expenditure; RMR = resting metabolic rate; TEE = total energy expenditure; y = years.
TABLE J-3 Evidence on the Association of Body Composition on Metabolic Efficiency (Energy Usage or Energy Expenditure): Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome | Quantitative Finding(s) |
|---|---|---|---|---|---|
| Bailly et al., 2021 | 29 for any meta-analysis. 15 assessed TEE (2 using DLW), RMR indirect calorimeter (n = 14) and 9 with portable devices, physical activity measured with accelerometer (n = 5) | Male and female adults 19–50 y; included pregnant women | CT vs. anorexia nervosa or normal BMI | TEE, RMR, RMR/FFM, RQ, AEE, PAL | See Table 7 in Bailly et al., 2021: comparison of CT vs. C |
| Comment: Meta-analysis done only in women, no cohort studies included because risk of bias too high |
NOTE: AEE = activity energy expenditure; BMI = body mass index; C = controls; CT = constitutional thinness; DLW = doubly labeled water; FFM = fat-free mass; PAL = physical activity level; REE = resting energy expenditure; RMR = resting metabolic rate; RQ = respiratory quotient; TEE = total energy expenditure; y = years.
| Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| There was significantly less reduction in REE with a high-protein diet | Provided risk of bias for each included study | I2, 64% | Not well done/reported |
| Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| See Table 9 in Bailly et al., 2021: CT individuals have a lower TEE, REE compared to normal weight; No diff in RQ, AEE, PAL between CT and normal weight; RMR/FFM trend of significant difference such that C < CT (p = .083) | — | — | Partially well done/reported |
TABLE J-4 Evidence on the Effect or Association of Weight Cycling with Metabolic Efficiency (Energy Usage/Expenditure) and Health Outcomes: Systematic Reviews and Observational Studies
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome | Quantitative Finding(s) |
|---|---|---|---|---|---|
| Zou et al., 2021 | 14 | 253,766 males and females 19 y and older | Weight cycling | Type 2 diabetes mellitus | RR, 1.23; 95% CI, 1.07 to 1.41; p = .003 |
| Zou et al., 2019 | 20 | 341,395 males and females 19 y and older | Weight cycling | All-cause mortality | RR, 1.41; 95% CI, 1.27 to 1.57; p = .001 |
| El Ghoch et al., 2018 | — | 38 males and females 19–50 y with obesity | Weight cycling | REE | No change in REE: 1,840.2 ± 397.9 vs. 1,831.9 ± 408.9, p = .78 |
| Nymo et al., 2019 | — | 38 males and females 19–50 y | Weight cycling | REE | REE only 70 kcal lower than baseline |
| Bosy-Westphal et al., 2013 | — | 47 males and females 19–50 y with obesity | Very-low-calorie diet | REE | REE adjusted for changes in organ and tissue masses, remains reduced on weight cyclers, p < .01. |
| Dombrowski et al., 2014 | 45 | 7,788 males and females 19–50 y with overweight and obesity | Diet | Weight cycling | N/A |
| Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Weight cycling increases risk for new-onset diabetes by 23% in persons with BMI < 30 | — | I2, 73.9% | Partially well done/reported |
| Weight cycling increases risk for all-cause mortality by 41%, CVD mortality by 36%, and risk for hypertension by 35% in adults | — | I2, 78.1% | Well done/reported |
| Weight cycling does not appear to adversely affect REE in adults with morbid obesity (BMI ≥ 40) | — | — | — |
| Although weight loss associated with reduced REE, there was no association between REE and weight cycling in adults with class I/II obesity | — | — | — |
| In overweight and obese adults age 22–45, weight cycling shows a reduced REE when adjusted for organ and tissue mass. | — | — | — |
| Behavioral interventions for weight loss maintenance in obese adults reduces risk for weight regain/cycling. | — | I2, 75% | Well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome | Quantitative Finding(s) |
|---|---|---|---|---|---|
| Turicchi et al., 2019 | 43 | 2,379 males and females 19 y and older with overweight and obesity | Diet | Weight cycling | Amount of weight loss: R2, 0.29; p < .001; Rate of weight loss (R2, 0.06; p = .049) |
| Fothergill et al., 2016 | — | 14 males and females 19–50 y with class III obesity | Diet and exercise | TEE and REE | REE reduced 704 ± 427 kcal/d below baseline at 6 years after weight loss (p < .0001) |
| Zhang et al., 2019 | 4 | 92,063 females 19 y and older | Weight cycling | Endometrial cancer | Odds ratio, 1.23 to 2.33 |
NOTE: BMI = body mass index; CI = confidence interval; CVD = cardiovascular disease; N/A = not applicable; REE = resting energy expenditure; RR = relative risk; TEE = total energy expenditure; y = years.
| Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| When controlling for the rate of weight loss, the amount of weight loss significantly predicts weight regain. | 1 study high risk of bias, 4 studies low risk of bias, 38 medium risk of bias | Tau2 | Not done/reported |
| Metabolic adaptation in morbid obesity is associated with the degree of weight loss; REE and TEE remain reduced for 6 years after weight loss even with weight regain or increased physical activity. | — | — | — |
| Weight cycling is associated with 1.2- to 2.3-fold increased risk for endometrial cancer in females age ≥ 18y. | — | — | Partially well done/reported |
TABLE J-5 Evidence on the Effect of Race or Ethnicity on Energy Expenditure
| Author, Year | Populations | Sex | Life Stage |
|---|---|---|---|
| Albu et al., 1997 | B/W | F | Adults |
| Foster et al., 1999 | B/W | F | Adults |
| Jakicic and Wing, 1998 | B/W | F | Adults |
| Mika Horie et al., 2009 | B/W | F | Adults |
| Reneau et al., 2019 | B/W | F/M | Adults |
| Shook et al., 2014 | B/W | F | Adults |
| Olivier et al., 2016 | B/W | F | Adults |
| Sharp et al., 2002 | B/W | F/M | Adults |
| Spaeth et al., 2015 | B/W | F/M | Adults |
| Vander Weg et al., 2004 | B/W | F | Adults |
| Wang et al., 2010 | B/W | F | Adults |
| Adzika Nsatimba et al., 2016 | B/W | F/M | Adults |
| Forman et al., 1998 | B/W | F | Adults |
| Santa-Clara et al., 2006 | B/W | F | Adults |
| Vander Weg et al., 2000 | B/W | F/M | Adults |
| Martin et al., 2004 | B/W | F/M | Adults |
| Most et al., 2018 | B/W | F | Adults |
| Manini et al., 2011 | B/W | F/M | Adults |
| Désilets et al., 2006 | B/W | F/M | Adults |
| Rush et al., 1997 | Maori/W | F | Adults |
| Wouters-Adriaens and Westerterp, 2008 | Asian/W | F/M | Adults |
| Byrne et al., 2003 | B/W | F | Adults |
| Hunter et al., 2000 | B/W | F | Adults |
| Deemer et al., 2010 | Hispanic/W | F | Adults |
| Conclusion Category | Mean Difference (kcal/d) | General Conclusions |
|---|---|---|
| REE difference, adjusted | 180 | Lower REE in B vs. W |
| REE difference, adjusted | 135 | Lower EE in B vs. W |
| REE difference, adjusted | 172 | Lower EE in B vs. W |
| REE difference, adjusted | 200 | Lower EE in B vs. W |
| REE difference, adjusted | 144 | Lower REE in B vs. W, attenuated with inclusion of trunk lean body mass |
| REE difference, adjusted | 101 | Lower EE in B vs. W, also lower fitness levels |
| REE difference, adjusted | 140 | Lower EE in B vs. W |
| REE difference, adjusted | 80 | Lower EE in B vs. W, CARDIA study |
| REE difference, adjusted | 100 | Lower EE in B vs. W |
| REE difference, adjusted | 65 | Prediction equation, lower EE in B |
| REE difference, adjusted | 121 | Lower EE in B vs. W |
| REE difference, adjusted | 250 | Lower EE in B vs. W |
| REE difference, adjusted | 200 | Lower EE in B vs. W |
| REE difference, adjusted | 80 | Lower EE in B vs. W |
| REE difference, adjusted | 78 | Lower EE in B vs. W, no body composition; smokers |
| REE difference, adjusted | 135 | Lower EE in B vs. W; diabetes status |
| REE difference, adjusted | 81 | Early pregnancy; lower REE in B vs. W |
| REE difference, adjusted | 50 | European admixture associated with higher REE; elderly |
| REE difference, adjusted | 110 | Lower EE in B vs. W |
| REE difference, adjusted | 119 | Lower REE in Maori vs. W |
| REE no difference, adjusted | 0 | Equal REE after adjusting for body composition |
| REE no difference, adjusted | 0 | Equal REE after adjusting for detailed composition |
| REE no difference, adjusted | 0 | Equal EE after adjusting for trunk lean body mass |
| REE no difference, adjusted | 0 | Equal REE but unadjusted |
| Author, Year | Populations | Sex | Life Stage |
|---|---|---|---|
| Soares et al., 1998 | Indian/W | F/M | Adults |
| Weyer et al., 1999 | Pima/W | F/M | Adults |
| Javed et al., 2010 | B/W | F/M | Adults |
| Jones et al., 2004 | B/W | F | Adults |
| Gallagher et al., 2006 | B/W | F/M | Adults |
| Gallagher et al., 1997 | B/W | F/M | Adults |
| Song et al., 2016 | Chinese/Indian/Malay | M | Adults |
| Tranah et al., 2011 | B/W | F/M | Adults |
| Glass et al., 2002 | B/W | F | Adults |
| DeLany et al., 2014 | B/W | F | Adults |
| Dugas et al., 2009 | B/W | F | Adults |
| Lam et al., 2014 | B/W | F/M | Adults |
| Weinsier et al., 2000 | B/W | F | Adults |
| Most et al., 2018 | B/W | F | Adults |
| Blanc et al., 2004 | B/W | F/M | Adults |
| Walsh et al., 2004 | B/W | F | Adults |
| Weyer et al., 1999 | Pima/W | F/M | Adults |
| Katzmaryk et al., 2018 | B/W | F/M | Adults |
| Hunter et al., 2000 | B/W | F | Adults |
| Kushner et al., 1995 | B/W | F | Adults |
| Lovejoy et al., 2001 | B/W | F | Adults |
| Saad et al., 1991 | Pima/W | M | Adults |
| Christin et al., 1993 | Pima/W | M | Adults |
| Fontvieille et al., 1994 | Pima/W | F/M | Adults |
| Conclusion Category | Mean Difference (kcal/d) | General Conclusions |
|---|---|---|
| REE no difference - adjusted | 0 | Equal EE after adjusting for body composition |
| REE no difference - adjusted | 0 | Higher TEE in Pima vs. W, equal SMR |
| REE no difference - HMRO | 0 | Equal after adjusting for organ metabolic rate |
| REE no difference - HMRO | 0 | Equal after adjusting for skeletal muscle mass |
| REE no difference - HMRO | 0 | Organ sizes/metabolic rates |
| REE no difference - HMRO | 0 | Body composition differences |
| REE no difference - HMRO | 0 | Lower EE in Asians, equal when adjusting for trunk lean body mass |
| REE no difference - mtDNA | 0 | Equal EE after adjusting for mtDNA haplotypes; elderly |
| REE no difference -unadjusted | 0 | Equal EE |
| TEE difference - adjusted | 233 | Lower EE B vs. W |
| TEE difference - adjusted | 105 | Lower EE in B vs. W |
| TEE difference - adjusted | 52 | Lower EE in B vs. W, develop predictive equation |
| TEE difference - adjusted | 138 | Lower EE in B vs. W |
| TEE difference - adjusted | 230 | Lower SMR and TEE in B vs. W; early pregnancy |
| TEE difference - adjusted | 200 | Lower TEE and REE in B vs. W; elderly |
| TEE difference - unadjusted | 116 | Lower TEE in B vs. W, unadjusted |
| TEE difference (Pima higher) | -44 | |
| TEE no difference - adjusted | 0 | Lower EE in B vs. W |
| TEE no difference - adjusted | 0 | |
| TEE no difference - adjusted | 0 | Equal TEE after adjusting body composition |
| TEE no difference - adjusted | 0 | Lower SMR in B vs. W, equal TEE |
| TEE no difference - adjusted | 0 | Equal 24-hr EE, difference in sympathetic nervous system activity |
| TEE no difference - adjusted | 0 | Equal EE, norepinephrine turnover as predictor |
| TEE no difference - adjusted | 0 | Lower SMR in Pimas |
| Author, Year | Populations | Sex | Life Stage |
|---|---|---|---|
| Tershakovec et al., 2002 | B/W | F/M | Children |
| Wong et al., 1996 | B/W | F | Children |
| Bandini et al., 2002 | B/W | F | Children |
| Morrison et al., 1996 | B/W | F | Children |
| Yanovski et al., 1997 | B/W | F | Children |
| Wong et al., 1999 | B/W | F | Children |
| Sun et al., 2001 | B/W | F/M | Children |
| McDuffie et al., 2004 | B/W | F/M | Children |
| Pretorius et al., 2021 | B/W | F/M | Children |
| Sun et al., 1998 | B/W | F/M | Children |
| Broadney et al., 2018 | B/W | F/M | Children |
| Hanks et al., 2015 | B/W | M | Children |
| Rush et al., 2003 | Maori/Pacific Islander/W | F/M | Children |
| Spurr et al., 1992 | Mestizo/B/Amerindian | F/M | Children |
| Goran et al., 1995 | Mohawk/W | F/M | Children |
| Fontvieille et al., 1992 | Pima/W | F/M | Children |
| Bandini et al., 2002 | B/W | F | Children |
| DeLany et al., 2002 | B/W | F/M | Children |
| Dugas et al., 2008 | Hispanic/W | F | Children |
| Sun et al., 1998 | B/W | F/M | Children |
| Goran et al., 1998 | B/W/Mohawk/Guatemalan | F/M | Children |
| Goran et al., 1995 | Mohawk/W | F/M | Children |
NOTE: AEE = activity energy expenditure; B = Black; BMD = bone mineral density; EE = energy expenditure; F = female; HMRO = high-metabolic-rate organs; kcal/d = kilocalorie per day; M = male; mtDNA = mitochondrial DNA; REE = resting energy expenditure; SMR = sleeping metabolic rate; TEE = total energy expenditure; W = White.
| Conclusion Category | Mean Difference (kcal/d) | General Conclusions |
|---|---|---|
| REE difference - adjusted | 77 | Lower EE in B vs. W, attenuated with inclusion of trunk lean body mass |
| REE difference - adjusted | 52 | Testing REE predictive equations; greater overestimation in B |
| REE difference - adjusted | 62 | Lower REE,TEE, AEE in B vs. W |
| REE difference - adjusted | 120 | Lower REE in B vs. W |
| REE difference - adjusted | 92 | Lower REE in B vs. W |
| REE difference - adjusted | 79 | Lower REE in B vs. W |
| REE difference - adjusted | 45 | Lower REE in B vs. W |
| REE difference - adjusted | 36 | Lower EE in B vs. W; developed predictive equation |
| REE difference - adjusted | 91 | Lower EE in B vs. W |
| REE no difference - adjusted | 0 | Equal EE |
| REE no difference - adjusted | 0 | Equal REE after adjusting for truncal composition |
| REE no difference - adjusted | 0 | Looking at BMD as predictor |
| REE no difference - adjusted | 0 | Equal REE across groups |
| REE no difference - adjusted | 0 | Equal EE across groups |
| REE no difference - adjusted | 0 | Lower EE in W vs. Mohawk |
| REE no difference - adjusted | 0 | Equal REE |
| TEE difference - adjusted | 110 | Lower TEE in B vs. W; prepubertal and pubertal |
| TEE difference - adjusted | 62 | Lower EE B vs. W |
| TEE difference - adjusted | 60 | Equal REE, lower AEE in Hispanic |
| TEE no difference - adjusted | 0 | |
| TEE no difference - adjusted | 0 | Equal REE across groups, lower AEE in Guatemalans |
| TEE no difference - adjusted | 0 |
TABLE J-6 Evidence on How Physical Activity and Energy Expenditure Change Across the Life Span: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Craigie et al., 2011 | 22 | 11,889 males and females, children and adults from high-income countries | Association between physical activity levels at baseline and follow-up | — |
| Craigie et al., 2011 | 13 | 4,999 males and females, children and adults from high-income countries | Maintenance of relative position—physical activity | — |
| Craigie et al., 2011 | 10 | 17,654 males and females, children and adults from high-income countries | The probability of being physically active at followup according to activity at baseline | — |
| Quantitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| In general, the correlation coefficients tended to be stronger in the European studies (ranging from –0.01 to 0.47), compared with Canadian (–0.1 to 0.24), United States (0.01 to 0.17) or Australian studies (0.04 to 0.07). In males, coefficients varied between –0.1 (nonsignificant, at 22-year follow-up) and 0.47 (p < 0.001 for frequency of activity over 8 years). In females, these ranged between –0.04 (nonsignificant over 7 years) and 0.37 (p < .001 over 6 years). |
— | — | Not well done/reported |
| Over 5–8 years follow-up from adolescence between 44% and 59% maintained their tertile position for activity, with higher proportions for males than for females. In the Cardiovascular Risk in Young Finns study participants, the probability of 9-to-18-year-olds remaining active 6 years later (44% of all participants) was significantly weaker than the probability of remaining sedentary (57% of all participants) (p = .002). | — | — | Not well done/reported |
| Four studies reported the probability of being physically active in adulthood using odds ratios. However, a comparison of their findings is complicated by the variation in categories used in their analyses. The Amsterdam Growth and Health Longitudinal Study reported general daily physical activity: those in the lowest quartile for daily physical activity at 13 years old were 3.6 times more likely (95% CI, 2.4–5.4) to be in the lowest quartile 14 years later than those in the 3 higher quartiles at baseline. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Foulds et al., 2013 | 8 | 915 males and females; Native American population in Canada and United States | Average PALs—adults | PAL via DLW and metabolic chamber |
| Foulds et al., 2013 | 2 | 408 males and females; Native American population in Canada and United States | Average PALs—adults | PAL via DLW and metabolic chamber |
| Foulds et al., 2013 | 5 published from 1980 to 1989, 14 from 1990 to 1999, and 20 from 2000 to 2011 | > 100,000 males and females; Native American population in Canada and United States | Physical activity change over time | PAL via self-report |
| Quantitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| Overall average total energy expenditure among Native American adults was found to be 10.53 MJ, with 2.28 MJ of activity energy expenditure. Overall, Native American adults were found to have PAL ratios averaging 1.48. | Citations included in the physical activity behavior assessment consisted of a range of grades from 1A to 3B and an average quality score of 11 out of 15 (range, 6–14) | — | Partially well done/reported |
| Among children at age 5 years, overall average total energy expenditure was found to be 5.93 MJ, with 1.17 MJ of activity energy expenditure, resulting in a PAL ratio of 1.42. Results among other ages of children/youth are not available in the literature. | Citations included in the physical activity behavior assessment consisted of a range of grades from 1A to 3B and an average quality score of 11 out of 15 (range, 6–14) | — | Partially well done/reported |
| More recent reports of physical activity behavior among Native American adults identify individuals as being less active than in the 1990s. Overall, greater proportions of Native American adults from 2000 to 2011 reported inactive levels of activity compared to earlier assessments, with lower proportions reporting insufficient PALs. | Citations included in the physical activity behavior assessment consisted of a range of grades from 1A to 3B and an average quality score of 11 out of 15 (range, 6–14). | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Tanaka et al., 2014 | 10 | 7,238 males and females; children and adolescents; from high-income countries | Longitudinal changes in overall sedentary behavior | Average sedentary behavior change per year via wearable devices |
NOTE: CI = confidence interval; DLW = doubly labeled water; MJ = megajoule; PAL = physical activity level.
TABLE J-7 Evidence on the Effect of BMI (and Other Measures of Adiposity) on Energy Balance or Energy Expenditure: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Ashtary-Larky et al., 2020 | 7 | 361 males and females 19 y and older with overweight and obesity | Gradual weight loss | Weight change |
| Cheng et al., 2016 | 12 | 1,499 males and females 9–18 y | Pubertal | REE |
| Nunes et al., 2022 | 33 | 2,528 males and females 19 y and older | Weight loss | REE or TEE |
| Quantitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|
| The follow-up duration ranged from 1.0 to over 10.0 years. The age of the participants at baseline ranged from 3.8 to 13.2 years. The overall percentage daily sedentary behavior change per year ranged from –3.8% to 12.5% for boys and from –2.5% to 12.7% for girls, with a weighted mean increase of daily sedentary behavior of +5.7% in boys and 5.8% in girls, equivalent to additional approximately 30 min of daily accelerometer-measured sedentary behavior per year. | Study methodological quality was rated as high with all 10 papers rated as ≥ 70% | — | Partially well done/reported |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Gradual weight loss preserved REE by ~100 kcals compared to rapid weight loss | Gradual weight loss produces less reduction in REE than rapid weight loss and a greater loss of fat mass and percent body fat. | 3/7 low | — | Partially well done/reported |
| REE increases 12% and TEE increases 16% during puberty | Both REE and TEE are significantly higher during puberty. | Medium | — | Partially well done/reported |
| REE and TEE show up to 20% greater decrease than predicted. | In adults, there is adaptive thermogenesis with weight loss leading to a greater than predicted decrease in energy expenditure. | Low to medium | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Schwartz and Doucet, 2010 | 90 | 2,996 males and females 19 y and older with overweight and obesity | Diet or diet plus exercise or diet plus pharmacological intervention | REE |
| Dhurandar et al., 2015 | 32 | 1,680 males and females 19–50 y with normal weight, overweight, and obesity | Diet | Compensation |
| Kee et al., 2012 | 20 | Males and females 19–50 y with morbid obesity (BMI ≥ 40) | BMI | REE |
| Nunes et al., 2021 | 94 males and females 19 y and older with overweight and obesity | Diet; calorie restriction averaged 270 kcal/d | REE | |
| Schwartz et al., 2012 | 90 | 815 males and females 19 y and older with overweight and obesity | Diet or diet plus exercise or diet plus weight loss intervention | REE |
NOTE: BMI = body mass index; kcal = kilocalorie; kg = kilogram; REE = resting energy expenditure; TEE = total energy expenditure; y = years.
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| REE decreases 15 kcal/kg during weight loss. | The 15-kcal/kg decrease in REE during weight loss does not differ by sex. Short interventions (2–6 weeks) have greater decrease in REE than long intervention (> 6 weeks). | — | — | Not well done/reported |
| Diet restriction results in 12–44% less weight loss than predicted. | Energy compensation (intake and/or expenditure) leads to less weight loss than predicted with diet restriction. | Medium | — | Not well done/reported |
| REE ranges 1,800–2,600 kcal in adults with BMI ≥ 40 | REE increases with increasing BMI in morbid obesity (BMI ≥ 40). | — | — | Not well done/reported |
| Reduction in REE ranges –70 to –220 kcal/d more than predicted. | Adaptive thermogenesis occurs with moderate weight loss of 5%. | — | — | Partially well done/reported |
| Reduction in REE 29.1% greater than predicted by Harris-Benedict equation. | Reduction in REE greater than predicted from Harris-Benedict equation, but Harris-Benedict equation after weight loss may overestimate energy intake needs for weight maintenance. | — | — | Not well done/reported |
TABLE J-8 Evidence on How the Increase in Tissue Deposition Associated with Growth During Infancy, Childhood, and Adolescence Influences, Effects, or Contributes to Energy Requirements
| Author, Year | N | Sex | Age (SD) | Ethnicity |
|---|---|---|---|---|
| DeLany et al., 2006 | 28 | F | 10.7 (0.7) | Black |
| 25 | F | 10.6 (0.4) | White | |
| 31 | M | 10.9 (0.8) | Black | |
| 29 | M | 10.9 (0.6) | White | |
| Plachta-Danielzik et al., 2008 | 680 | M | 6–10 y | |
| 684 | F | 6–10 y | ||
| 254 | M | 10–14 y | ||
| 260 | F | 10–14 y | ||
| Wells and Davies, 1998 | 49 | 41% M | 1.5 mo | White |
| 92 | 59% F | |||
| 37 | ||||
| 36 | ||||
| 18 |
NOTE: F = female; g = gram; kcal = kilocalorie; kg = kilogram; M = male; mo = months; SD = standard deviation; wk = weeks; y = years.
TABLE J-9a Evidence on How the Increase in Tissue Deposition Associated with Pregnancy Influences, Effects, or Contributes to Energy Requirements: Nonsystematic Reviews
| Author, Year | N | Age (SD) | BMI Status | Ethnicity |
|---|---|---|---|---|
| Catalano et al., 1998 | 6 normal, 10 GDM/IGT | 31.8 y (5.5) | 20.8 | — |
| Kopp-Hoolihan et al., 1999 | 10 | 29.1 y (5) | 23.1 | — |
| Berggren et al., 2015 | 11 | 29 y (median) | 23.8 | 10 White 1 non-White |
| Okereke et al., 2004 | 8 NGT, 7 GDM | NGT 31.6 y (3.4) | Obese > 25% body fat, 8 NGT 26.2 | — |
| Abeysekera et al., 2016 | 26 | — | — | — |
NOTE: BMI = body mass index; g = gram; FFM = fat-free mass; FM = fat mass; GDM = gestational diabetes mellitus; IGT = impaired glucose tolerance; kcal = kilocalorie; kg = kilogram; NGT = normal glucose tolerance; SD = standard deviation; y = years.
| Weight Gain g/day (SD) | Protein Gain g/day (SD) | FFM Gain g/day (SD) | FM Gain g/day (SD) | Energy Deposition kcal/day (SD) |
|---|---|---|---|---|
| 10.7 (4.3) | 8.1 (1.6) | 2.6 (3.6) | 32.72 | |
| 10.80 (4.7) | 7 (2.3) | 3.8 (3.3) | 42.64 | |
| 12.8 (5.2) | 9.2 (4.3) | 3.5 (5) | 42.22 | |
| 9.7 (6.1) | 7.5 (4.3) | 2.2 (5) | 28.38 | |
| 12.2 kg/4 y | 10.6 kg/4 y | 1.8 kg/4 y | 19.3 (50) | |
| 12.7 kg/4 y | 10.0 kg/4 y | 2.7 kg/4 y | 24.5 (50) | |
| 21.5 kg/4 y | 18.5 kg/4 y | 2.9 kg/4 y | 31.8 (50) | |
| 18.4 kg/4 y | 12.5 kg/4 y | 5.7 kg/4 y | 45.6 (50) | |
| 0.24 kg/wk (0.08) | 3.3 (1.4) | 14.4 (3.2) | 152.0 (4.8) | |
| 0.2 (0.1) | 2.8 (1.7) | 12.8 (3.7) | 134.3 (4.3) | |
| 0.12 (0.1) | 2.5 (1.7) | 3.7 (4) | 46.6 (7.6) | |
| 0.11 0.11) | 2.4 (1.9) | 3.1 (5.2) | 42.8 (9.1) | |
| 0.09 (0.09) | 2.1 (1.6) | 1.7 (3.3) | 28.0 (9.1) |
| Gestational Weight Gain g/day (SD) | Protein Gain g/day (SD) | FFM Gain g/day (SD) | FM Gain g/day (SD) |
|---|---|---|---|
| 13.5 | — | 7.3 kg from preconception to 36 weeks | 2 kg from preconception to 36 weeks |
| 11.6 kg at 36 weeks (4.3) | — | — | 4.5 kg from preconception to 34/36 weeks |
| 17.5 median from preconception to 34/36 weeks | — | 12.2 (median) | 3.5 kg (median) |
| 12.7 kg NGT at 36 weeks | — | 5.8 NGT | 6.9 |
| 10.8 (3.9 kg) from 12–14 to 34–36 weeks | — | 3.9 (2.4) kg | 7.0 (3.6) kg |
TABLE J-9b Evidence on How the Increase in Tissue Deposition Associated with Pregnancy Influences, Effects, or Contributes to Energy Requirements: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Savard et al., 2021 | 32 | Pregnant women, mostly White | Pregnancy | REE/TEE |
NOTE: kcal = kilocalorie; REE = resting energy expenditure; TEE = total energy expenditure.
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Increases in REE ranged from 0.5% to 18.3% (8 to 239 kcal) between early and midpregnancy, from 3.0% to 24.1% (45 to 327 kcal) between mid- and late pregnancy, and from 6.4% to 29.6% (93 to 416 kcal) between early and late pregnancy. The median increases in REE were 5.3% (72 kcal), 9.9% (153 kcal), and 18.0% (252 kcal) between early and mid-, mid- and late, and early and late pregnancy, respectively. Increases in TEE ranged from 4.0% to 17.7% (84 to 363 kcal) between early and midpregnancy, from 0.2% to 30.2% (5 to 694 kcal) between mid- and late pregnancy, and from 7.9% to 33.2% (179 to 682 kcal) between early and late pregnancy, respectively. The median increases in TEE were 6.2% (144 kcal), 7.1% (170 kcal), and 12.0% (290 kcal) between early and mid-, mid- and late, and early and late pregnancy, respectively. |
REE and TEE increase during pregnancy, mainly from early to late and from mid- to late pregnancy. Great variability in the extent to which REE and TEE increase throughout pregnancy. | Huge variability. Inclusion of women with excessive gestational weight gain and sample with small number of overweight or obese women may have led to overestimation of energy requirements. | — | Partially well done/reported |
TABLE J-10a Evidence on How the Increase in Tissue Deposition Associated with Lactation Influences, Effects, or Contributes to Energy Requirements: Nonsystematic Reviews
| Author, Year | N | Age (SD) | BMI Status | Ethnicity |
|---|---|---|---|---|
| Pereira et al., 2019 | 52 and 49 | 32 y (4) | 27.3 (5.6) | White |
| Thakkar et al., 2013 | 50 | 28–33 y | Asian | |
| Nielsen et al., 2011 | 47 and in the end n = 30 with 26 EBF | 33.7 y (4.3) | 25.0 (3.9) | White |
| Nielsen et al., 2013 | — | — | — |
NOTE: BF = breast feeding; BMI = body mass index; DLW = doubly labeled water; EBF = exclusively breast feeding; FFM = fat-free mass; FM = fat mass; g = gram; kcal/d = kilocalories/day; kg = kilogram; kJ = kilojoule; ml = milliliter; pp = postpartum; REE = resting energy expenditure; SD = standard deviation; TEE = total energy expenditure; y = years.
| Weight Gain g/day (SD) | Findings |
|---|---|
| Negative 0.8 BMI units from 3 to 9 months pp | FFM gain of 0.4 g from 3 to 9 months pp FM loss of 2 g REE and TEE measured by whole-body calorimetry. REE increased significantly by 48 (108 kcal day) 3.2% at 3 months; breast milk volume 771 (261) g/d for breast milk energy output of 678 (230) kcal/day. At 9 months breast milk vol 530 (225) g/d for breast milk energy output of 465 (198) kcal/d. 41/52 and 28/49 were BF at 3 and 9 mo. TEE at 9 months 2,028 (286) kcal/d. No difference in TEE between lactating and nonlactating. |
| Energy content of HM at 1 months was 65.92 (9.43) kcal/100 ml, at 3 months 70.24 (22.0). Energy content for milk produced for male infants was greater. Figure 1 shows significant difference at 3 months of 14.8 kcal/100 ml or 24% difference. | |
| Mean weight at 15 days was male 6.72 (0.78) and female 6.30 (0.64); male 7.84 (0.91) and female 7.37 (0.75) at 25 weeks | Mean milk intake (DLW) 923 (SD = 122) g/day at 15 weeks and 997 (SD = 142) g/day at 25 weeks for all infants. For EBF 999 (SD = 146) g/day at 25 weeks. Milk energy content 2.72 (SD = 0.38) at 15 weeks, and 2.62 (SD = 0.40) kg/g at 25 weeks. No difference by sex. Energy intakes male 2,582 (SD = 362) and females 2,403 (SD = 215) kJ/day at 15 weeks and males 2,748 (SD = 480) and females 2,449 (SD = 312) kJ/day at 25 weeks. Significant difference by sex at 25 weeks (Table 2 in paper). However, milk and energy intake decreased from 15 weeks to 25 weeks (Table 3). |
| See Table 2 in Nielsen et al., 2013 | Same as above (Nielsen et al., 2011) but now used DLW to measure TEE |
TABLE J-10b Evidence on How the Increase in Tissue Deposition Associated with Lactation Influences, Effects, or Contributes to Energy Requirements: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Reilly et al., 2005 | 3–4 months, 33; 5–6 months, 6; 6 months, 5 | 3–4 months, 1,041; 5–6 months, 99; at 6 months, 72 mom–infant dyads; exclusively breast feeding | Not applicable | Milk transfer |
NOTE: CI = confidence interval; d = day; g = gram; kcal = kilocalorie; kJ = kilojoule; SD = standard deviation; WHO = World Health Organization.
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| At 3–4 months: The weighted mean milk transfer was 779 g/d (SD = 40), and the unweighted mean was 796 g/d (SD = 48) (95% CI, 778; 812 g/day). At 5–6 months: Weighted mean milk transfer was 826 g/d (SD = 39). The unweighted mean was 816 g/d (SD = 42) (95% CI, 772; 860 g/d. At 6 months: Weighted mean milk transfer was 894 g/d (SD = 87) and unweighted mean transfer 883 g/d (SD = 89) (95% CI, 790; 975 g/d). Changes in breast milk transfers between 2 and 5 months from nine studies reported no marked increase in milk transfer over the periods of time measured, and most described the pattern of change in intake over time as a “plateau” in milk transfer after 3 months. The weighted mean metabolizable energy content of milk from 25 papers of 777 mom–infant pairs was 2.6 (SD = 0.2) kJ/g (equivalent to 0.62 kcal/g) (see Table 4 in Reilly et al., 2005). | Cross-sectional studies of milk transfer suggest that it typically varies between approximately 779 g/d at age 3–4 months (for which there was a great deal of evidence: 33 studies of 1,041 mother–infant pairs and approximately 894 g/d at age 6 months (for which evidence was limited: five studies with 72 possibly highly selected mother–infant pairs; longitudinal studies, in contrast, did not suggest any marked increase in milk transfer over time during the period of 3–6 months. The metabolizable energy content of breast milk is approximately 2.6 kJ/g. They speculate that using lower values for breast-milk energy content than the 0.67 to 0.68 kcal/g used in WHO reviews might alter the apparent adequacy of exclusive breastfeeding to 6 months of age. | Risk of bias was provided for included studies. | — | Partially well done/reported |
TABLE J-11 Evidence on the Calorie Intake Needed to Achieve Weight Loss (if Overweight), Weight Maintenance (for All Individuals), or Weight Gain (if Underweight): Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Heymsfield et al., 2007 | 10 | 150 obese subjects on low-calorie diet and patients with reduced obesity | Relationship between measured and predicted TEE among reduced obesity after long-term (≥ 26 weeks) weight loss treatment | TEE-DLW or indirect calorimeter |
NOTES: DLW = doubly labeled water; kcal = kilocalorie; kg = kilogram; LCD = low-calorie diet; TEE = total energy expenditure.
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Mean difference between measured and predicted TEE for all reduced obesity subjects 20.1 kcal/day (–58, –155) % difference 1.3% (–1.7, –8.5). From the DLW studies—difference in –518 kcal/day. Reduction in energy intake of ~500 kcal/day had a weight loss of 30 kg. | Limited literature, but findings support that low patient adherence is the main basis for modest weight loss associated with LCD. Obese subjects have weight loss < 50% of expected for the degree of prescribed LCD energy deficit. TEE in the reduced obesity state is close to predicted in never obese subjects (1%). | — | — | Not well done/reported |
TABLE J-12 Evidence on the Association Between Weight Change and Chronic Disease Outcomes: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Alharbi et al., 2021 | 2 | 715 community-dwelling males and females 65 y and older; not all from high-income countries | Intentional weight loss | All-cause mortality risk |
| Alharbi et al., 2021 | 23 | 1,210,116 community-dwelling males and females 65 y and older; not all from high-income countries | Weight gain | All-cause mortality risk |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 0.92 (0.54–1.54) | In this small sample of older adults, intentional weight loss was not associated with all-cause mortality. More research is needed on the effect of intentional weight loss on all-cause mortality or the reasons for intentional weight loss in older community-dwelling adults. Older, community-dwelling adults with very small sample size and no information on how weight loss was measured |
good | Moderate heterogeneity p = .99; I2 = 56% |
Well done/reported |
| RR (95% CI) = 1.10 (1.02–1.17) | No information on whether weight gains or losses were intentional Weight gain had a small, but significant association with all-cause mortality. In community-dwelling older adults, weight gains are associated with an increased risk of all-cause mortality relative to stable weight. Weight gain data were a mixture of measured and self-reported. Need research on reason for weight gain. |
Most were good | Low heterogeneity p = .01; I2 = 41% |
Well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Alharbi et al., 2021 | 4 | 6,901 community-dwelling males and females 65 y and older; not all from high-income countries | Weight fluctuation | All-cause mortality risk |
| Capristo et al., 2021 | 17 | 39,875 males and females ≥ 18 y with overweight or obesity; not all from high-income countries | Weight loss associated with anti-obesity medications | All-cause mortality |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 1.66 (1.28–2.15) | No information on whether weight gains or losses were intentional A 63% increased risk of all-cause mortality with weight fluctuation compared to stable weight reference In community-dwelling older adults, weight fluctuations are associated with an increased risk of all-cause mortality relative to stable weight. Weight fluctuation data were a mixture of measured and self-reported. Need research on effect of intentional vs. unintentional weight fluctuations. |
Most were good | No significant heterogeneity p = .31; I2 = 14.6% |
Well done/reported |
| OR (95% CI): 1.03 (0.87–1.21) | No significant reduction in risk of all-cause mortality with weight-lowering drugs compared with placebo or no treatment. There was a weak, but statistically significant, linear association between all-cause mortality and magnitude of weight loss (ß = 0.0007, p = .045). A weight loss of 20 kg would lower mortality by 1.4% and a 30-kg weight loss by 2.1%. |
Suboptimal quality | No significant heterogeneity I2 = 0%; p = 1.0 |
Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Capristo et al., 2021 | 8 | 28,657 males and females ≥ 18 y with overweight or obesity; not all from high-income countries | Weight loss associated with antiobesity medications | Cardiovascular mortality |
| Capristo et al., 2021 | 7 | 30,404 males and females ≥ 18 y with overweight or obesity; not all from high-income countries | Weight loss associated with anti-obesity medications | Myocardial infarction |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Although unable to demonstrate a superiority of antiobesity medications over placebo, meta-regression showed that even a small weight reduction tends to reduce all-cause mortality in obesity. The health status of participants is not described. |
||||
| OR (95% CI): 0.92 (0.72–1.18) | No significant decrease in the risk of CVD death with antiobesity drugs Linear association between CVD mortality and magnitude of weight loss was not significant. Unable to demonstrate an effect of weight-loss medications on CVD mortality in trials with an average of 52 weeks of follow-up. Unclear as to the health status of participants |
Suboptimal quality | No significant heterogeneity I2 = 0%; p = .79 |
Not well done/reported |
| OR (95% CI): 1.01 (0.86–1.19 | No significant decrease in the risk of myocardial infarction with antiobesity drugs. Unable to demonstrate an effect of weight-loss medications on myocardial infarction in trials with an average of 52 weeks follow-up. Unclear as to the health status of participants or if these were incidence cases |
Suboptimal quality | No heterogeneity I2 = 0%, t2 = 0, p = .87 |
Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Capristo et al., 2021 | 4 | 21,584 males and females ≥ 18 y with overweight or obesity; not all from high-income countries | Weight loss associated with anti-obesity medications | Stroke |
| Chan et al., 2019 | 8 | 1,373 females ≥ 18 y; underweight women (BMI < 18.5) excluded; not all from high-income countries | Adult weight loss of unknown intention | Premenopausal breast cancer |
| Chan et al., 2019 | 14 | 8,283 females ≥ 18 y; underweight women (BMI < 18.5) excluded; not all from high-income countries | Adult weight loss of unknown intention | Postmenopausal breast cancer |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| OR (95% CI): 0.93 (0.72–1.20) | Unable to demonstrate effect of weight loss medications on stroke Unclear as to the health status of participants or if these were incidence cases |
Suboptimal quality | No heterogeneity I2 = 0%, t2 = 0, p = .49 |
Not well done/reported |
| RR (95% CI): 0.85 (0.74–0.99) | Inverse associations for premenopausal breast cancers when comparing any weight loss of unknown intention from age 18 y to study baseline with stable weight The results were not robust and require further confirmation. |
Most studies considered average to good quality. Higher or lower RoB studies on average did not find statistically different associations in the subgroup meta-analyses. | I2 = 0%, p = .93 | Not well done/reported |
| RR (95% CI): 0.90 (0.81–0.99) | Inverse associations for postmenopausal breast cancers when comparing any weight loss of unknown intention from age 18 y to study baseline with stable weight. The results were not robust and require further confirmation. |
Most studies considered average to good quality. Higher or lower RoB studies on average did not find statistically different associations in the subgroup meta-analyses. | I2 =24%, p heterogeneity = 0.20 | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Chan et al., 2019 | 9 | Females ≥ 18 y; underweight women (BMI < 18.5) excluded; not all from high-income countries | Adult weight gain per 5 kg (of unknown intention) | Premenopausal breast cancer |
| Chan et al., 2019 | 16 | Females ≥ 18 y; underweight women (BMI < 18.5) excluded; not all from high-income countries | Adult weight gain per 5 kg (of unknown intention) | Postmenopausal breast cancer |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 1.00 (0.97–1.03) | No association of weight gain and breast cancer in premenopausal women | Most studies considered average to good quality. Higher or lower RoB studies on average did not find statistically different associations in the subgroup meta-analyses. | I2 = 20.7%, p = .265 | Not well done/reported |
| RR (95% CI) = 1.07 (1.11–1.23) | Positive association of weight gain and breast cancer in postmenopausal women | Most studies considered average to good quality. Higher or lower RoB studies on average did not find statistically different associations in the subgroup meta-analyses. | I2 = 64%; p heterogeneity ≤ .001 | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Hao et al., 2021 | 19 | 862,177 females ≥ 19 y; American, European, Australia, Asian (Japanese, Chinese) | Highest adult weight gain since early adulthood for both whole adulthood and hormone-changed menopause stages | Onset of breast cancer or total cancers |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Highest vs. lowest weight gain and premenopausal risk: RR = 1.00 (95% CI, 0.83, 1.21); postmenopausal risk: RR = 1.55 (95% CI, 1.40, 1.71). Dose–response: RR per 5-mg weight gain: 1.08 (95% CI, 1.07, 1.09). Weight gain since menopause: RR = 1.59 (95% CI, 1.23, 2.05). | Weight gain in Asian women had a much stronger effect (34%) than in other countries. No significant findings among premenopausal women: RR, 1.00; 95% CI, 0.83–1.21 Dose–response analysis confirmed a significant increased risk of 8% of developing postmenopausal breast cancer with each 5-kg increment in adult weight gain for Western women, but about a 34% stronger risk in Asian women. No significant finding among premenopausal women. Higher weight gain since menopause associated with increased postmenopausal breast cancer risk based on comparison of highest vs. lowest adult weight gain. For postmenopausal women, there was a significant effect of weight gain since menopause on breast cancer risk. The effect is strongest in Asian women. No effect of weight gain on breast cancer risk in premenopausal women. The majority of participants came from Europe, United States, United Kingdom, Canada, Australia. Only a small minority were from China or Japan. |
No data | Highest vs. lowest weight gain in premenopausal women: I2 = 24.9%. Postmenopausal women I2 = 47.2%. Dose–response: postmenopausal I2 = 69.4%. | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Jayedi et al., 2018 | 5 | 134,247 males and females; general population > 18 y with > 1 y followup; high-income countries | Weight gain equal to a 1-unit increment in BMI (both self-reported and measured weights) | Hypertension incidence |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| There was a linear association between weight gain and risk of hypertension (p non-linearity = 0.58) | There was a linear association between weight gain and risk of hypertension (p non-linearity = 0.58) Adjustment for baseline blood pressure attenuated the associations, but results remained significant, indicating that adiposity increases the risk of hypertension independently of baseline blood pressure. Greater risk in self-reported subgroup compared with measured. Preventing weight gain in adults is a useful approach for reducing the risk of hypertension. The study provided evidence of the role of weight gain in hypertension risk. One limitation was the failure of included studies to control for salt intake or renal function. Some evidence of publication bias. |
No data | I2 = 77.8%. p heterogeneity = 0.001 | Well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Jayedi et al., 2020 | Of 11 studies with data on CVD mortality, 5 had data on participants without preexisting CVD | < 505,802 males and females ≥ 18 y reporting unintended weight gain during adulthood or before assessment; Europe (13), United States (8), Asia (2), Australia (1), Middle East (1) | Weight gain during adulthood | CVD mortality in persons without preexisting CVD |
| Jayedi et al., 2020 | 2 | 118,140 males and females ≥ 18 y reporting unintended weight gain during adulthood or before assessment; Europe (13), United States (8), Asia (2), Australia (1), Middle East (1) | Weight gain during adulthood | CVD incidence |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 1.14 (1.02 to 1.26) for a 5-kg increment in body weight | A nonlinear dose–response analysis indicated that the risk of CVD mortality did not change materially with weight gain of 0 to 5 kg and then increased sharply at weight gain of > 6 kg. Measuring weight gain during adulthood may be better than static, cross-sectional assessment of weight because it considers trend over time, and thus, can be used as a supplementary approach to predict CVD. Adult weight gain could increase the risk of CVD incidence and mortality. Slightly more than half of the studies relied on self-reported weight gain, which could have attenuated relationships. |
Out of a possible score of 9, 1/3 of the studies were rated as 7 and 2/3 as 8. | I2 = 84%, p heterogeneity = < 0.001; p heterogeneity between subgroups = 0.15 | Partially well done/reported |
| RR (95% CI) = 1.12 (1.10, 1.13) for a 5-kg increment in body weight | In five studies in which participants with preexisting CVD were excluded, the RR (95% CI) = 1.14 (1.02 to 1.26). I2 = 84% (p < .001) and between group heterogeneity = 0.15. Measuring weight gain during adulthood may be better than a static, cross-sectional measurement of weight (e.g., BMI) for predicting CVD risk. Adult weight gain may be associated with a higher risk of CVD. |
Out of a possible score of 9, 1/3 of the studies were rated as 7 and 2/3 as 8. | I2 = 6%, p heterogeneity = 0.30 | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Karahalios et al., 2017 | 18 | Healthy adults measured between middle and older age No data on number of participants |
Weight at baseline and follow-up based on measured weight (subgroup analysis) | All-cause mortality |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| No data | Used results from a subgroup of participants whose weights were based on measured values rather on the full sample that combined measured and self-reported weights. Weight gain in middle-aged to older adults is associated with muscle-mass decreases and fat-mass increases, with the largest increase in visceral and abdominal fat. Weight gain from middle to older adulthood was associated with a slightly increased risk of all-cause mortality. Studies using self-reported measures of weight at baseline and follow-up had higher HRs than studies with measured weight. None of the participants were underweight at baseline. |
No data | I2 = 64.4%, tau2 = 0.16. Ratio of HRs = 1.00 | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Karahalios et al., 2017 | 11 | Healthy adults measured between middle and older age No data on number of participants |
Measured weights at baseline and followup. Largest weight gain from baseline to follow-up. Included both intentional and unintentional weight gain. Excluded studies that investigated weight gain from early adulthood to middle age; included studies of weight gain from middle age to older age. | CVD mortality |
| Karahalios et al., 2017 | 2 | Healthy adults measured between middle and older age No data on number of participants |
Intentional weight loss (measured and self-reported) | All-cause mortality |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 1.14 (0.97, 1.35) | Studies that used self-reported measures of weight gain had higher HRs (HR = 1.41, 95% CI = 0.97, 2.05. Studies with normal weight or overweight/obese participants gave similar HRs to studies that combined all participants. The effect of baseline weight on association is unknown. Weight gain in midlife is associated with increased risk of CVD mortality. |
No data | I2 = 58.2%, tau2 = 0.029. Ratio of HRs = 1.00 The time between weight measurements (i.e., > 10 y or < 10 y) explained much of the heterogeneity. Studies with > 10 y between weight measurements had higher HRs than studies with < 10 y |
Partially well done/reported |
| HR = 1.44 (95% CI = 1.03, 2.00) | Results from weight-loss studies with measured weights and including both intentional and unintentional weight loss were similar: HR = 1.40 (95% CI = 1.14, 1.71); Unintentional weight loss might reflect an underlying disease, resulting in excess mortality. Only two studies had data on intentional weight loss. |
No data | No data | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| LeBlanc et al., 2018 | 9 | Males and females ≥ 19 y; high-income countries Included studies with ≥ 12 months follow-up and participants ≥ 18 y with above normal weight. Excluded studies with participants with chronic diseases or secondary causes of obesity. |
Behavior-based weight loss | Diabetes incidence in prediabetic participants |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI): 0.67 (0.51 to 0.89) | Weight loss interventions associated with a decreased risk of type 2 diabetes in prediabetic participants up to 36 months of follow-up. Behavior-based weight-loss interventions were associated with more weight loss than controls. Weight loss maintenance interventions were associated with less weight regain than control conditions over 12 to 18 months Behavior-based weight loss interventions were associated with more weight loss and a lower risk of developing diabetes than control conditions. Weight-loss medications were associated with higher rates of harms than behavior-based interventions. Infrequent reporting of CVD, cancer, and all-cause mortality precluded summarizing data for these outcomes. |
— | I2 = 49.2%, p = .46. The consistency across interventions and subgroups suggests that benefits are likely dependent on individual, social, and environmental factors more than intervention characteristics. |
Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Ma et al., 2017 | 24 | 15,176 males and females age ≥ 19 y with obesity | Dietary weight loss ± physical activity. All but 1 of the diets were low fat. Followup for ≥ 1 y. | New CVD events |
| Ma et al., 2017 | 19 | 6,330 males and females age ≥ 19 y with obesity | Dietary weight loss ± physical activity. All but 1 of the diets were low fat. Followup for ≥ 1 y. | New cancers |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 0.93 (0.83, 1.04 | Similar results when using ACC/AHA definitions. “New CVD events” was a secondary outcome. Predominantly in middle-aged adults, the authors were unable to show effects of weight loss on new CVD events. There were fewer trials and much uncertainty for this outcome. Because all but one study used a low-fat, weight-reducing diet, the results are relevant only to this cause of weight loss. |
— | I2 = 0%, p = .829 | Partially well done/reported |
| RR (95% CI) = 0.92 (0.63, 1.36) | “New cancers” was a secondary outcome. Predominantly in middle-aged adults, the authors were unable to show effects of weight loss on new cancer events. There were fewer trials and much uncertainty for this outcome. Because all but one study used a low-fat, weight-reducing diet, the results are relevant only to this cause of weight loss. |
— | I2 = 0%; p = .992. | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Ma et al., 2017 | 34 | Males and females age ≥ 19 y with obesity | Dietary weight loss ± physical activity. All but 1 of the diets were low-fat. Followup for ≥ 1 y. | All-cause mortality |
| Sun et al., 2021 | 6 studies in meta-analysis | 128,164 males and females, from childhood to adulthood; mixed race/ethnicity; not all from high-income countries Age at baseline weight assessment < 20 y |
Those with (1) normal weight in childhood and overweight/obese in adulthood; (2) overweight/obese in childhood and adulthood; (3) overweight/obese in childhood and normal weight in adulthood | T2D |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR (95% CI) = 0.82 (0.71, 0.95) | Predominantly in middle-aged adults, weight-loss diets, usually low in fat and saturated fat, with or without exercise advice or programs, may reduce premature all-cause mortality in adults with obesity. Because all but one study used a low-fat, weight-reducing diet, the results are relevant only to this cause of weight loss. |
— | I2 = 0%. p = .945 | Partially well done/reported |
| Compared to normal weight in childhood and adulthood, ORs (95% CI) of adult T2D were: (1) 3.40 (2.71 to 4.25) for normal child weight but overweight/obese adult weight; (2) 3.94 (3.05 to 5.08) for overweight/obese in childhood and adulthood; (3) 1.37 (1.10 to 1.70) for overweight/obese in childhood but normal weight in adulthood | Those who developed excess weight in adulthood or were persistently overweight/obese in childhood and adulthood had increased risk of T2D. Those with excess child weight but normal adult weight had a much reduced increase in risk. NOTE: They also assessed a number of other CVD risk factors, including dyslipidemia, nonalcoholic fatty liver disease, metabolic syndrome, inflammation, left ventricular hypertrophy, and subclinical CVD markers. All showed increased OR in the incident and persistent obesity groups, and most were NS for resolved obesity. |
Study quality ranged from 6 to 8 out of 9 (moderate to high quality) | Heterogeneity assessed. After subgroup analyses by child age (< 11 and > 11 years) and adult age (< 30 and > 30 years); definition of childhood overweight and obesity (U.S. CDC and international BMI percentile); measured vs. self-reported weight and height, the heterogeneity disappeared. | Partially or not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Sun et al., 2021 | 4 studies in meta-analysis (vs. 10 in review) | 30,309 males and females, from childhood to adulthood; mixed race/ethnicity; not all from high-income countries Age at baseline weight assessment < 20 y |
Those with (1) normal weight in childhood and overweight/obese in adulthood; (2) overweight/obese in childhood and adulthood; (3) overweight/obese in childhood and normal weight in adulthood | Hypertension |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Compared to normal weight in childhood and adulthood, ORs (95% CI) of adult hypertension were: (1) 2.69 (2.07 to 3.49) for normal child weight but overweight/obese adult weight; (2) 3.49 (2.21 to 5.05) for overweight/obese in childhood and adulthood; (3) 1.25 (0.73 to 2.13) for overweight/obese in childhood but normal weight in adulthood | Incident and persistent overweight/obesity are associated with increased risk of adult hypertension. Resolved obesity is not. | Study quality ranged from 6 to 8 out of 9 (moderate to high quality) | Heterogeneity assessed. After subgroup analyses by child age (< 11 and > 11 years) and adult age (< 30 and > 30 years); definition of childhood overweight and obesity; measured vs. self-reported weight and height, the heterogeneity disappeared. | Partially or not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Sun et al., 2021 | 4 studies in meta-analysis | 87,556 males and females, from childhood to adulthood; mixed race/ethnicity; not all from high-income countries Age at baseline weight assessment < 20 y |
Those with (1) normal weight in childhood and overweight/obese in adulthood; (2) overweight/obese in childhood and adulthood; (3) overweight/obese in childhood and normal weight in adulthood | Adult cardiovascular disease (CHD, CVD, stroke, heart failure) |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Compared to normal weight in childhood and adulthood, ORs (95% CI) of adult CVD were: (1) 2.76 (1.79 to 4.27) for normal child weight but overweight/obese adult weight (2) 3.04 (1.69–5.46) for overweight/obese in childhood and adulthood; (3) 1.22 (0.92–1.62) for overweight/obese in childhood but normal weight in adulthood | Incident and persistent overweight/obesity are associated with increased risk of adult CVD. Resolved obesity is not. | — | Heterogeneity assessed. After subgroup analyses by child age (< 11 and > 11 years) and adult age (< 30 and > 30 years); definition of childhood overweight and obesity; measured vs. self-reported weight and height, the heterogeneity disappeared. | Partially or not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Wang et al., 2021 | 20 | 38,141 males and females ≥ 19 y; from United States, Europe, Nigeria, Australia, South Korea | Weight loss | Diagnosis of dementia |
| Zhang et al., 2019 | 15 | 623,973 males and females ≥ 19 y; from United States, South Korea, Australia, Germany, UK | Weight fluctuation episodes | All-cause mortality |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR = 1.26, 95% CI 1.15 to 1.38 | Subgroup analysis by baseline BMI identified that weight loss in normal weight participants had similar dementia risk (1.21, 95% CI 1.06–1.38) to weight loss in overweight/obese individuals (1.22, 1.11–1.34). Weight loss may be associated with increased risk of dementia. Maintaining stable weight may help prevent dementia. Information was not available on whether weight loss was intentional or not. |
12 studies were high quality (score of 7–9) and 8 were medium quality (4–6) | Subgroup analyses conducted (degree of weight loss, dementia subtype, diagnostic criteria for dementia, country, sex, age, baseline BMI, baseline health status, duration of follow-up, and adjusted factors). In most cases, results were consistent among subgroups. | Well done/reported |
| Overall HR for group with greatest weight fluctuation (vs. group with most stable weight) was 1.45 (95% CI 1.29 to 1.63) | Weight fluctuation might be associated with an increased risk of all-cause mortality. | Newcastle scores ranged from 5 to 9 (moderate to high quality) | Heterogeneity assessed by meta-regression, sensitivity analyses, and stratified analyses according to prespecified study characteristics. Overall conclusion was not changed. | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Zou et al., 2019 | 20 | 341,395 males and females ≥ 19 y | Weight fluctuation (studies varied in how this was measured) | All-cause mortality |
| Zou et al., 2019 | 11 | 245,109 males and females ≥ 19 y | Weight fluctuation (studies varied in how this was measured) | CVD mortality |
| Zou et al., 2019 | 6 | 172,709 males and females ≥ 19 y | Weight fluctuation (studies varied in how this was measured) | Cancer mortality |
| Zou et al., 2019 | 5 | 122,920 males and females ≥ 19 y | Weight fluctuation (studies varied in how this was measured) | CVD morbidity |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR = 1.41 (95% CI 1.27, 1.57) | Relationship between weight fluctuation and all-cause mortality did not differ by BMI or age or by how weight fluctuation was measured (continuous or categorical) Body-weight fluctuation is associated with higher all-cause mortality. Future study needed to determine causal links. Studies included weight fluctuation measured either as categorical (episodes of a given magnitude) or continuous (e.g., intrapersonal variation of weight). Most studies did not indicate if weight fluctuation was intentional or not. |
Most studies were high quality | Analysis of heterogeneity was significant. Contributing factors included study location, duration, quality, weight ascertainment measured or self-reported, adjustment for physical activity and energy intake. | Partially well done/reported |
| RR = 1.36 (95% CI 1.22, 1.52) | Relationship between weight fluctuation and CVD mortality was observed in those with normal weight and overweight but not with obesity or by how weight fluctuation was measured (continuous or categorical) | 11 of 11 studies were high quality | Heterogeneity NS | Partially well done/reported |
| RR = 1.01 (95% CI, 0.90, 1.13) | Body weight fluctuation is NOT associated with cancer mortality. | 6 of 6 studies were high quality | Heterogeneity NS | Partially well done/reported |
| RR = 1.49 (95% CI, 1.26, 1.76) | Body weight fluctuation is associated with CVD | 3 of 5 studies were high quality | Significant. Appeared to be affected by method of weight ascertainment | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Predictor or Intervention or Comparator | Primary Outcome |
|---|---|---|---|---|
| Zou et al., 2019 | 4 | 144,256 males and females ≥ 19 y | Weight fluctuation (studies varied in how this was measured) | Hypertension |
NOTE: ACC = American College of Cardiology; AHA = American Heart Association; BMI = body mass index; CHD = coronary heart disease; CI = confidence interval; CVD = cardiovascular disease; HR = hazard ratio; kg = kilogram; m = meter; NS = non-significant; OR = odds ratio; RoB = risk of bias; RR = relative risk; T2D = type 2 diabetes; y = year.
TABLE J-13 Evidence on the Association Between BMI and Chronic Disease, Including All-Cause Mortality: Systematic Reviews and Observational Studies
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Azizpour et al., 2018 | 16 | 8,397 including 3,577 cases | 1–18 y | Females and males | ≥ 25.0 and ≥ 30.0 |
| Sharma et al., 2019 | 52 | 1,553,683 | 5–13 y | Females and males | ≥ 85th percentile |
| Hidayat et al., 2019 | 6 | 13,510 cases | Pregnancy | Females | ≥ 25.0 |
| Xiao et al., 2021 | 103 | 1,826,454 including 120,696 cases | Prepregnancy | Females | ≥ 25.0 |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| RR = 1.35, 95% CI, 1.14, 1.61 | Body weight fluctuation is associated with hypertension | Not reported | Heterogeneity NS | Partially well done/reported |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Asthma | Overweight 1.64 (95% CI 1.13–2.38); obese 1.92 (1.39–2.65) | Risk for asthma in children and adolescents who are overweight or obese is 64–92% higher compared to underweight/normal weight. | p = 0.312; P = 0.09 | — |
| Child/adolescent prediabetes, HTN, NAFLD | Prediabetes: 1.4 (1.2–1.6); HTN: 4.0 (2.8–5.7); NAFLD: 26.1 (9.4–72.2) | Children and adolescents (age 5–13) with overweight or obesity (≥ 85th percentile) are 1.4 times more likely to have prediabetes; those with obesity are 4.4 times more likely to have high blood pressure and 26.1 times more likely to have NAFLD. | — | Partially well done/reported |
| Child-onset T1DM | Overweight 1.09 (1.03–1.15); obese 1.25 (1.16–1.34) | Each 5-unit increase in maternal BMI associated with 10% increased risk for child-onset T1DM. Association was nonlinear, with steeper increase in risk at BMI ≥ 26.0 | p = 0.23 | — |
| Gestational diabetes | 2.64 (1.56–4.45) | Prepregnancy overweight or obesity increases risk 2.64-fold for having gestational diabetes. | — | Partially well done/reported |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Ibe and Smith, 2014 | BRFSS (Behavioral Risk Factor Surveillance System) | 1,168,418 | 18–64 | Females | ≥ 25.0 |
| Jayedi et al., 2022 | 182 | 5,585,850, including 228,695 cases | > 18 | Females and males | > 20 |
| Khadra et al., 2019 | 11 | 60,118 | 19–50 | Females and males | ≥ 25.0 |
| Larsson et al., 2021 | 47 | 218,792 | > 18 | Females and males | ≥ 25.0 |
| Yu et al., 2022 | 82 | 2,690,000 | > 18 | Females and males | ≥ 25.0 |
| Jayedi et al., 2018 | 50 | 2,255,067, including 190,320 cases | > 18 | Females and males | > 20 |
| Zhou et al., 2018 | 57 | 830,685, including 125,071 cases | > 18 | Females and males | |
| Rexrode et al., 2001 | Physicians Health Study | 16,164, including 552 cases | 40–84 | Males | ≥ 27.6 |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| T2DM | 3.57 (3.52–3.63) | Adjusting for age, race, physical activity, and year of survey response, results indicate a 3.5-fold increase in diabetes in females with BMI > 25. | — | — |
| T2DM | 1.72 (1.65–1.81) | Each 5-unit increase in BMI above 20.0 associated with 72% increased risk for T2DM, with steep upward curve at BMI > 25 in younger adults. | — | — |
| T2DM | 1.38 (1.27–1.50) | Sarcopenic obesity is associated with a 38% increased risk for T2DM compared to nonsarcopenic obesity. | — | — |
| T2DM | 2.03 (1.88–2.19) | Mendelian randomization (genetically predicted) studies show high adult BMI is a causal risk factor for T2DM, with a 2-fold increased risk for T2DM when BMI ≥ 25. | — | — |
| Prediabetes, T2DM | Prediabetes overweight and obesity: 1.24 (1.19–1.28); T2DM overweight: 2.24 (1.95–2.56); obese: 4.56 (3.69–5.64) | Overweight and obesity are associated with a 24% increased risk for prediabetes. Overweight is associated with a 2-fold increased risk and obesity a 4.5-fold increased risk for T2DM. | — | Partially well done/reported |
| HTN | 1.49 (1.41–1.58) | Each 5-unit increase in BMI above 20.0 is associated with 49% increased risk for HTN. | 0.0001 | — |
| HTN | BMI 18.5: 1.27 (1.20–1.35), BMI 25.0: 2.07 (1.34–2.46), BMI 30: 3.13 (2.49–3.93) | Risk for HTN increases at least 50% for every 5-unit increase in BMI. | — | Partially well done/reported |
| CHD | 1.73 (1.29–2.32) | Males with BMI ≥ 27.6 have a 73% increased risk for a CHD event. | — | — |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Kim et al., 2000 | Framingham Heart Study | 1,882 | 30–62 | Males | ≥ 23.8 |
| Kim et al., 2000 | Framingham Heart Study | 2,373 | 30–62 | Females | ≥ 27.6 |
| Liu et al., 2018a | 43 | 4,432,475, including 102,466 cases | > 18 | Females and males | > 23.5 |
| Dugani et al., 2021 | 16 | 12,700,000 | > 18 | Females (18–65) and males (18–55) | ≥ 25.0 and ≥ 30.0 |
| Meigs et al., 2006 | Community Longitudinal Study | 2,902 | > 18 | Females and males | ≥ 25.0 |
| Darbandi et al., 2020 | 38 | 137,256 | > 18 | Females and males | ≥ 30.0 |
| Kim et al., 2021 | 77 | 30,000,000 | > 18 | Females and males | > 20 |
| Church et al., 2005 | Aerobics Center Longitudinal Study | 2,316 | > 20 | Males with T2DM | ≥ 25.0 |
| Jarvis et al., 2020 | 14 | 1,930,000, including 49,451 cases | > 18 | Females and males | > 30.0 |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| CHD | 1.28 (1.00–1.65) | In males, the relative risk for CHD is 28% at BMI ≥ 23.8, 45% at BMI ≥ 25.9 and 53% at BMI ≥ 28.2 | — | — |
| CHD | 1.56 (1.16–2.08) | In females with BMI ≥ 27.6, there is a 56% increased risk for developing CHD. | — | — |
| Stroke | 1.10 (1.06–1.13) | Risk of stroke increases by 10% for every 5-unit increase in BMI > 23.5, and is greater for males than for females. | p = 0.06 | Well done/reported |
| Premature MI | Males 1.94 (1.47–2.56); females 1.28 (0.95–1.73) | Males in overweight or obese BMI categories have almost a 2-fold increased risk for premature MI. | — | — |
| CVD | Overweight: 3.01 (1.68–5.41) | Adults with overweight/obesity have a 3-fold increased risk for CVD. | — | — |
| CVD | BMI: AUC 0.66 (0.63–0.69); WC: AUC 0.69 (0.64–0.74); WHR: AUC 0.69 (0.66–0.73) males, 0.71 (0.68 = 0.73) females | BMI, WC, and WHR have moderate power to identify risk for CVD. In adults, WC and WHR predict CVD better than BMI. | p < 0.001 | — |
| CVD | 1.10 (1.01–1.210 for hemorrhagic stroke; 1.49 (1.40–1.60) for HTN | Mendelian randomization (genetically predicted) studies show high BMI is a causal risk factor for CVD outcomes; each 5-unit increase in BMI increases risk for CVD events. | — | — |
| CVD mortality | 2.70 (1.40–5.10) | Overweight and obese males with diabetes have similar 2.7-fold increased risk for CVD-mortality. | — | — |
| NAFLD | 1.20 (1.12–1.28) | BMI > 30 is associated with 20% increased risk for severe liver disease. | — | — |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Campbell et al., 2016 | 14 | 1,570,000, including 2,162 cases | > 18 | Females and males | ≥ 25.0 |
| Sohn et al., 2021 | 28 | 8,135,906 | > 18 | Females and males | ≥ 25.0 |
| Byun et al., 2022 | 37 | 1,849,875, including 39,733 cases | ≤ 30 | Females | 13.2–32.5 |
| Byun et al., 2022 | 10 | 662,779, including 4,539 cases | ≤ 30 | Females | 15.3–32.5 |
| Byun et al., 2022 | 6 | 496,391, including 2,692 cases | ≤ 30 | Females | 14.6–32.5 |
| Fang et al., 2018 | 325 | 1,525,052 | > 18 | Females and males | > 20.0 |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Hepatocellular carcinoma | 1.21 (1.09–1.35) | Compared with normal weight BMI, persons with overweight, class I obesity, class II obesity, and class III obesity were associated with 21%, 87%, 142%, and 116%, respectively, increased risk of liver cancer. | — | — |
| Hepatocellular carcinoma | 1.69 (1.50–1.90) | Risk for liver cancer increases in a BMI-dependent manner with a 36% increased risk for BMI > 25, 77% increased risk for BMI > 30, a 3-fold increased risk for BMI > 35 (and a 70% increased risk overall for BMI ≥ 25.0). | — | Well done/reported |
| Breast cancer (premenopausal) | 0.84 (0.81–0.87) | Each 5-unit increase in early-life BMI is associated with 16% reduced premenopausal breast cancer risk. | p < 0.001 | — |
| Endometrial cancer | 1.40 (1.25–1.57) | Each 5-unit increase in early-life (age ≤ 25 y) BMI associated with 1.4-fold increased endometrial cancer risk. | p < 0.001 | — |
| Ovarian cancer | 1.15 (1.07–1.23) | Each 5-unit increase in early-life (age ≤ 25 y) BMI is associated with 15% increased risk for ovarian cancer | p < 0.001 | — |
| Cancer (23 tissue types) | Endometrial: 1.48 | Every 5-unit increase in BMI is associated with increased risk for 18 types of tissue cancers. The strongest positive association is between BMI and endometrial cancer (RR = 1.48). BMI was negatively associated with the risk of oral cavity, lung, and premenopausal breast cancers. | — | — |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Gao et al., 2019 | 27 | 28,784,269, including 127,161 cases | > 18 | Females and males | ≥ 25.0 |
| Gu et al., 2022 | 52 | 279,499, including 51,704 cases | > 18 | Males | ≥ 25.0 |
| Hidayat et al., 2018a | 56 | 56,744 | ≤ 30 | Females and males | ≥ 20.0 |
| Hidayat et al., 2018b | 22 | 7,000,000, including 20,000 cases | > 18 | Females and males | ≥ 20.0 |
| Li et al., 2016 | 12 | 5,902 cases | > 18 | Females and males | ≥ 25.0 |
| O’Sullivan et al., 2022 | 20 | 47,692 cases | ≤ 50 | Females and males | ≥ 30.0 |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Lung cancer | BMI: 0.77 (0.72–0.82); WC: 1.24 (1.13–1.35) | BMI is inversely associated with lung cancer risk. When controlling for BMI, high waist circumference associates with lung cancer risk. | p = 0.005 | — |
| Prostate cancer | 0.99 (0.99–1.00) | Higher BMI associated with 1% decreased risk for localized prostate cancer. | — | — |
| Cancer (8 types) | Each 5-unit increase in early-life (≤ 30 y) BMI is associated with 1.88-fold increased risk for esophageal cancer, 1.31-fold increased risk for liver cancer, 1.17-fold increased risk for pancreatic cancer, 1.59-fold increased risk for gastric cancer, 1.22-fold for kidney cancer, and 1.45-fold increased risk for endometrial cancer. | — | — | |
| Non-Hodgkin’s lymphoma | 1.13 (1.06–1.20) | Each 5-unit increase in BMI is associated with 6% increased risk for NHL, with no difference by sex. Further, each 5-unit increase in BMI in early adulthood (18–21 y) is associated with 11% increased risk for NHL. | — | — |
| Gallbladder cancer | Overweight: 1.10 (0.98–1.23); Obese 1.58 (1.43–1.75) | The pooled risk for gallbladder cancer at BMI ≥ 25 for overweight is 10% and obesity 58%, and risk increases by 4% for each 1-unit increase in BMI. | — | — |
| Colorectal cancer—early onset | Obese: 1.54 (1.01 – 2.35) | Obesity (BMI ≥ 30) is associated with a 54% increased risk of early onset (≤ 50 y) colorectal cancer, with males at higher risk than females. | — | Well done/reported |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Li et al., 2021 | 6 | 8,150,473, including 11,299 cases | ≤ 55 | Females and males | ≥ 25.0 |
| Liu et al., 2018b | 24 | 8,953,478, including 15,535 cases | > 18 | Females and males | > 20 |
| Youssef et al., 2021 | 31 | 24,489,477, including 86,097 cases | > 18 | Females and males | < 18.5, ≥ 25.0 |
| Jiang et al., 2019 | 9 | 96,213 | ≥ 65 | Females and males | > 28 |
| Mortensen et al., 2021 | 35 | 1,508,366 | > 50 | Females and males | < 18.5 |
| Jiang et al., 2019 | 37 | 320,594 | ≥ 65 | Females and males | < 23 and > 33.0 |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Colorectal cancer—early onset | Overweight 1.32 (1.19–1.47); obese 1.88 (1.40–2.54) | Overweight and obesity (BMI ≥ 25) are associated with a 42% increased risk of early-onset (age ≤ 55) colorectal cancer. | p = 0.60 | — |
| Kidney cancer | Overweight: RR 1.35 (1.27–1.43); obese RR 1.76 (1.61–1.91) | Risk of kidney cancer increases 6% for every 1-unit increase in BMI > 20. | — | Well done/reported |
| Thyroid cancer | Underweight: 0.68 (0.65–0.72); overweight: 1.26 (1.24–1.28); obese: 1.50 (1.45–1.55) | Overweight and obesity are associated with a 26% and 50% increased risk of thyroid cancer, with risk greater in females than males. Having an underweight BMI decreases risk by 32%. | — | Not well done/reported |
| Disability | 1.19 (1.01–1.40) | BMI 24.0–28.0 decreases risk by 4% for disability in adults age ≥ 65 years, but BMI > 28 increases disability risk by 19%. | — | — |
| Fragility hip fracture | 2.83 (1.82–4.39) | BMI < 18.5 is associated with almost a 3-fold increased risk for fragility hip fracture, whereas BMI > 30 may be protective. | Partially well done/reported | |
| All-cause mortality | BMI < 18.5:1.69 (1.57–1.83); BMI 18.5–22.9: 1.17 (1.12–1.22); BMI 23.0–27.9:0.91 (0.88–0.94); BMI 28.0–32.9: 0.98 (0.94–1.03); BMI > 33.0:1.32 (1.15–1.51) | BMI < 23.0 and > 33.0 increase risk for all-cause mortality in adults ≥ 65 years | — | — |
| Author, Year | Number of Studies | Number of Participants | Age or Life Stage | Sex | BMI Cut Point for Risk |
|---|---|---|---|---|---|
| Kitahara et al., 2014 | 20 | 9,564 | > 18 | Females and males | Class III obesity |
NOTE: AUC = area under the curve; BMI = body mass index; BRFSS = Behavioral Risk Factor Surveillance System; CHD = coronary heart disease; CVD = cardiovascular disease; HTN = hypertension; kg = kilogram; m = meter; MI = myocardial infarction; NAFLD = nonalcoholic fatty liver disease; RR = relative risk; NHL = non-Hodgkin’s lymphoma; T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus; WC = waist circumference; WHR = waist–hip ratio; y = year.
TABLE J-14 Evidence on the Degree of Systematic Bias or Random Error of Energy Intake as Assessed by Self-Report Compared to Doubly Labeled Water Studies: Systematic Reviews
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Burrows et al., 2019 | 36 | 2,834 male and female adults, including pregnant women; not all high-income countries | Food record/TEE from DLW | EI-TEE |
| Burrows et al., 2019 | 24 | 3,295 male and female adults, including pregnant women; not all high-income countries | 24-hour recall/TEE from DLW | EI-TEE |
| Burrows et al., 2019 | 21 | 2,997 male and female adults, including pregnant women; not all high-income countries | FFQ/TEE from DLW | EI-TEE |
| Burrows et al., 2019 | 5 | 71 male and female adults, including pregnant women; not all high-income countries | Diet history/TEE from DLW | EI-TEE |
| Primary Outcome | Quantitative Finding(s) | Clinical Interpretation | Risk of Bias | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| All-cause mortality | BMI 40–59: 1.40 (1.31–1.51) | Adults with BMI 40–49 have a 2.3- to 3.3-fold increased risk for death, those with BMI 50–59 have a 3.5 to 5.9 increased risk for death, and risks are greater for males than for females. | — | — |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Most studies found underreporting by 11–41% | The food record is likely to significantly underreport EI when compared to TEE measured via the DLW method. | 29/36 positive quality; 7/36 neutral quality | — | Partially well done/reported |
| EI underreported by 8–30% in almost all studies | EI tends to be underreported on 24-hour recalls. | 16/24 positive; 8/24 neutral | — | Partially well done/reported |
| Significant underreporting found in all studies using an FFQ | FFQs tend to underestimate energy intake, particularly at the individual level. | 14/21 positive; 7/21 neutral | — | Partially well done/reported |
| Underreporting in 4 of 5 studies, ranging from 1 to 47% | Diet histories tend to underreport EI. | 4/5 positive; 1/5 neutral | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Burrows et al., 2020 | 5 | 106 male and female children and adolescents | FFQ/TEE from DLW | EI-TEE |
| Burrows et al., 2020 | 4 | 66 male and female children and adolescents | WFR/TEE from DLW | EI-TEE |
| Burrows et al., 2020 | 3 | 108 male and female children and adolescents | Remote food photography/TEE from DLW | EI-TEE |
| Burrows et al., 2020 | 2 | 52 male and female children and adolescents | 24-hour recall/TEE from DLW | EI-TEE |
| Burrows et al., 2020 | 1 | 29 male and female children and adolescents | Precoded food record/TEE from DLW | EI-TEE |
| Capling et al., 2017 | 11 | 109 adolescent and adult male and female athletes; includes pregnant women; not all from high-income countries | Food record/DLW | EI-TEE |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Significant underreporting in 3 of 5 studies (–7% to –23% of estimated EI); other 2 studies were small (n = 9 or 12), one had a higher mean EI on FFQ vs. TEE from DLW, the other was lower | FFQ has limitations for assessing EI, especially at the individual level. | 4/5 positive quality; 1 neutral quality | — | Partially well done/reported |
| Significant underreporting in 1 of 4 studies (–10% of estimated EI) | Only 1 study concluded the tool may be useful in individual children; it may not be accurate at the individual level. | 4/4 positive | — | Partially well done/reported |
| Differences ranged from –16% to +7%. One study found no significant difference between reported and measured values; one found remote food photography method was not valid at the individual or group level. | There is limited ability to assess EI at the individual level. | — | — | Partially well done/reported |
| One study found a difference of –23 (± 442 kcal); the second found a difference of –0.9% | The 24-hour recall was valid on the group level, but not at the individual level. | 1/2 positive; 1/2 neutral | — | Partially well done/reported |
| Overreporting by +24% (p < .0001); mean difference of 726 kJ/day | Method overestimated EI. | 1/1 positive | — | Partially well done/reported |
| Mean difference EI-TEE: –19%; –2,793 ± 1,134 kJ/day absolute difference; Effect size –1.01 (95% CI, –1.3, –0.7) | The food record is likely to significantly underreport estimated EI when compared with TEE estimated via DLW in athletes. | fair to moderate for most studies | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Gemming et al., 2015 | 2 | 82 male and female adults; not all from high-income countries | Image-based food record /TEE from DLW | EI-TEE |
| Gemming et al., 2015 | 1 | 14 male and female adults; not all from high-income countries | Image-assisted 24-hour recall /TEE from DLW | EI-TEE |
| Ho et al., 2020 | 6 | 205 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/DLW | Total energy intake |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Remote food photography underestimated by –6% to –26% in overweight and obese adults | Image-based food records are likely to underestimate EI. | — | — | Not well done/reported |
| Image-assisted 24-hour recall overestimated by +7.6% | Image-assisted methods may overestimate EI. | — | — | Not well done/reported |
| Four studies reported a lower mean EI as estimated by the IBDA method; two studies reported agreement and no bias between the IBDA and DLW. The weighted mean difference for IBDA and DLW methods was –448.04 kcal (–755.52, –140.56), but heterogeneity between studies was very high (I2 = 95%), indicating substantial variability between studies. | A large weighted mean difference in energy intake showed significant energy underreporting with the IBDA methods when compared with DLW. | The overall quality of the 6 studies ranged from good to very good. Two studies were rated as very good quality with 9–10 points, and 4 studies were rated as good quality, with 7–8 points. | Heterogeneity between studies was very high (I2 = 95%), indicating substantial variability between studies. | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Ho et al., 2020 | 4 | 142 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/24-hour dietary recall | Total energy intake |
| Ho et al., 2020 | 6 | 266 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/weighted food record | Total energy intake |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| One study showed a significant positive correlation for EI between the IBDA and 24-hour methods, another study showed that the IBDA method underreported EI when compared with the 24-hour method, and the other two studies provided mean estimates but not statistical analyses. Weighted mean difference in EI for IBDAs and 24-hour recalls was –91.53 kcal (–151.45, 46.13); heterogeneity was high (I2 = 76%), indicating some variability between studies. | No statistically significant differences were found in the weighted mean differences of energy intake between the IBDAs and the 24-hour recalls. | The overall quality of the 4 studies ranged from good to very good. One study was rated as very good quality with 9–10 points, and 3 studies were rated as good quality with 7–8 points. | Heterogeneity was high (I2 = 76%), indicating some variability between studies. | Partially well done/reported |
| Three studies reported good agreement in estimated EI, two studies reported an underestimation of EI using the IBDA methods, and one study reported an overestimation of EI using the IBDA method. Weighted mean difference in EI for IBDA and WFR was –52.66 kcal (–151.45, 46.13); Heterogeneity was high (I2 = 66%), indicating some variability between studies. | No statistically significant differences were found in the weighted mean differences of energy intake between the IBDAs and the WFRs. | The overall quality of the 6 studies ranged from good to very good. Two studies were rated as very good quality, with 9–10 points, and 4 studies were rated as good quality, with 7–8 points. | Heterogeneity was high (I2 = 66%), indicating some variability between studies. | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Ho et al., 2020 | 3 | 103 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/24-hour dietary recall | Macro-nutrients |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| One study showed a significant positive correlation for all three macronutrients, one study observed a significant difference in carbohydrate but not protein or fat intake, and the other study provided mean estimates but not statistical analyses. WMD in carbohydrate intake was –15.52 g (95% CI: –41.34, 10.30); heterogeneity was I2 = 66% (p = .05). WMD in protein intake was 2.06 g (–3.16, 7.28); heterogeneity was I2 = 0% (p = .95). WMD in fat intake was –2.90 g (–8.34, 2.55); heterogeneity was I2 = 0% (p = .44). | No statistically significant differences in the weighted mean difference of carbohydrate, protein, or fat intake were observed between the IBDA and 24-hour recall methods. | The overall quality of the 3 studies ranged from good to very good. One study was rated as very good quality, with 9–10 points, and 2 studies were rated as good quality, with 7–8 points. | Heterogeneity was high (I2 = 66%) for carbohydrate intake, indicating some variability between studies, but was not present for protein (I2 = 0%; p = .95) or fat intake (I2 = 0%; p = .44). | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Ho et al., 2020 | 6 | 256 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/WFR | Macro-nutrients |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Three studies reported good agreement in estimated macronutrients between the two methods, two studies reported no difference in macronutrient intake between the IBDA and WFR, and one study reported that the IBDA overestimated carbohydrate, protein, and fat intake. WMD in carbohydrate intake for IBDAs and WFRs was –6.71 g (–20.2, 6.79); heterogeneity was I2 = 63% (p = 0.02). WMD in protein intake for IBDAs and WFRs was –0.85 g (–6.10, 4.40); heterogeneity was high (I2 = 77%). WMD in fat intake for IBDAs and WFRs was –0.30 g (–2.65, 2.05); heterogeneity was low (I2 = 21%; p = .28). | No statistically significant differences in the WMD of carbohydrate, protein, or fat intake were observed between the IBDA and WFR methods. | The overall quality of the 6 studies ranged from good to very good. Two studies were rated as very good quality, with 9–10 points, and 4 studies were rated as good quality, with 7–8 points. | Heterogeneity was moderate to high for carbohydrate (I2 = 63%; p = .02) and protein intake (I2 = 77%; p < .01), but low for fat intake (I2 = 21%; p = .28). | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Ho et al., 2020 | 2 | 53 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/24-hour dietary recall | Micro-nutrients |
| Ho et al., 2020 | 3 | 152 children and adults, males and females; includes pregnant women | Image-based dietary assessment method/WFR | Micro-nutrients |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| One study showed a significant positive correlation with iron and vitamin C, and the other study provided mean estimates but not statistical analyses. WMD in iron intake for IBDAs and 24-hour recall was 0.39 mg (95% CI: –0.81, 1.59); heterogeneity was I2 = 0% (p = .38). WMD in vitamin C intake was 9.14 mg (–13.16, 31.45); heterogeneity was I2 = 0% (p = .56). | No statistically significant differences were found in the WMDs of iron or vitamin C intake. | One study was rated as very good quality, with 9–10 points, and 1 study was rated as good quality, with 7–8 points. | Heterogeneity was not present for iron (I2 = 0%; p = .38) or vitamin C intake (I2 = 0%; p = .56). | Partially well done/reported |
| One study showed a significant positive correlation with iron and vitamin C for the IBDA and the WFR, another study showed a significant positive correlation with vitamin C, and the other study showed no difference in micronutrient intake (both iron and vitamin C) between the two methods. The WMD in iron intake was –0.19 g (95% CI: –0.78, 0.40); heterogeneity was I2 = 3% (p = .36). The WMD in vitamin C intake was –10.97 g (–39.95, 18.01); heterogeneity was I2 = 89% (p < .01). | No statistically significant differences were found in the WMDs of iron or vitamin C intake. | The overall quality of the 3 studies ranged from good to very good. One study was rated as very good quality, with 9–10 points, and 2 studies were rated as good quality, with 7–8 points. | Heterogeneity was minimal for iron intake (I2 = 3%; p = 0.36) but quite substantial for vitamin C intake (I2 = 89%; p < .01). | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Tugault-Lafleur et al., 2017 | 15 | 2,576 school-aged children | School meal recalls/observational method (i.e., in-person meal observations, digital photography, WMD | Relative accuracy |
| Tugault-Lafleur et al., 2017 | 1 | 24 school-aged children | Estimated food records/observational method (i.e., in-person meal observation | Relative accuracy |
| Tugault-Lafleur et al., 2017 | 1 | 46 school-aged children | FFQs/4-day estimated food record | Relative accuracy |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Poor accuracy for individual foods reported (omission and intrusion rates > 15%, n = 8 of 12 studies). Acceptable accuracy when reporting amounts consumed (n = 4 of 5 studies). Acceptable energy report rates (n = 2 of 3 studies). | The relative accuracy of school meal recalls is poor for individual foods reported but is acceptable for reporting the estimated energy intake of a group. | — | — | Not well done/reported |
| Pearson correlations ranged from r = 0.16 to r = 0.85 for different meal components (mean r = 0.66) under a daily monitoring approach. For the weekly monitoring approach, Pearson correlation coefficients ranged from r = –0.21 to r = 0.69 (mean, r = 0.25) | The estimated food record had acceptable accuracy with daily monitoring but poor accuracy with weekly monitoring. | — | — | Not well done/reported |
| The Pearson correlation coefficients were r = 0.71, 0.70, and 0.69 for beverages, snacks, and total fruits and vegetables, respectively. Mean, r = 0.69 for all food and beverage items; p < .05. | Acceptable accuracy for measuring select beverages and snack foods; the majority of the 19 questions assessing in-school dietary intakes were significantly associated with amounts obtained from the estimated food record. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Tugault-Lafleur et al., 2017 | 2 | 1,149 school-aged children | DP methods/WFRs | Relative accuracy |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| In the first study, correlation coefficients indicated strong positive correlations, ranging from 0.89 to 0.97, and no statistically significant differences were found in mean amounts for differences in lunch meal components estimated by using the DP and the WFRs. Bland-Altman analyses suggested a tendency to slightly underestimate fruit (mean bias, –4.27 g) and vegetables (mean bias, 6.19g). In the second study, all 11 school meal items had a correlation coefficient > 0.70, with correlations ranging from r = 0.76 to r = 0.98, except for leafy greens (r = 0.59) and lasagna (r = 0.62). The group’s mean for meal items was within 1 g of the reference method (i.e., WFRs), and no evidence of bias in Bland-Altman analyses. | The findings from the two studies suggest that the DP method is a valid method for estimating the dietary intakes, in terms of the types and amounts of foods consumed, of both home-packed and school lunches. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Tugault-Lafleur et al., 2017 | 2 | 282 school-aged children | The SFC/observational method (i.e., in-person meal observations, DP, WFRs) | Relative accuracy and reliability |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| In the first study, the mean difference in estimated EI between the WFTR and the SFC was 15 kJ (95% CI: 107 to 138; p > .05), providing acceptable accuracy to measure energy intake for the group. The second study showed that the ICCs for intrarater reliability ranged from 0.57 to 1.0 for different meal components, suggesting good intrarater reliability. The ICCs for interrater reliability tended to be higher (> 0.7). Thus, interrater reliability was deemed acceptable for most meal components (all except noodles and leftovers). | The relative accuracy of the SFC for measuring energy intake is acceptable. The SFC has acceptable interrater reliability and good intrarater reliability. | — | — | Not well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Wehling and Lusher, 2019 | 13 | 4,172 obese adults (BMI ≥ 30) | Diet records/reference method for assessing energy intake | Accuracy of self-report EI via diet records |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Among the obese population, nine studies reported the percent of the population who underreported EI, with estimates ranging from 19% to 82% underreporting depending on the study setting (clinical vs. free living) and the demographic characteristics of the study population; another study reported 79.6% mean reporting accuracy of EI; one study reported overall misreport of energy intake, which was 46%. | The present findings show a consistent and clear link between underreporting of energy intake and an obese BMI in a considerable number of papers included. | The quality of the included papers generally ranged between 50% and 100%. The most common result was 63% (11 studies), which was primarily due to non-random sampling and using specific groups. Eight studies had small samples that were unlikely to result in adequate power for the statistics applied. The majority of studies were at least average (7) or large (19), suggesting a higher generalizability. | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Wehling and Lusher, 2019 | 12 | 6,363 obese adults (BMI ≥ 30) | 24-hour dietary recall/reference method for assessing energy intake | Accuracy of self-report EI via 24-hour recall |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| Among the obese population, one study found that reporting of actual intake ranged from 90% to 98% depending on whether the participant had binge eating disorder. Another study found that participants who underreport EI are more likely to be overweight/obese (61.7%; p = .032), and a different study showed that underreporting is associated with older age, higher BMI (p < .01), and female sex (p < .001). Similarly, Lichtman et al. found that obese participants under diet resistance underreported intake by 20% (p < .05). Whereas, two other studies found that underreporting among the obese population was not significantly different than among those with a normal weight (30.3% vs. 31.1%), and that BMI has no effect on the accuracy of self-reported EI (p = .19). | The present findings show a consistent and clear link between underreporting of energy intake and an obese BMI in a considerable number of papers included. | The quality of the included papers generally ranged between 50% and 100%. The most common result was 63% (11 studies), which was primarily due to non-random sampling and using specific groups. Eight studies had small samples that were unlikely to result in adequate power for the statistics applied. The majority of studies were at least average (7) or large (19), suggesting a higher generalizability. | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Wehling and Lusher, 2019 | 9 | 22,104 obese adults (BMI ≥ 30) | FFQ/reference method for assessing energy intake | Accuracy of self-reported EI via FFQs |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| The average proportion of underreporting in five studies ranged from 16.8% to 77.5%, depending on the study setting (clinical vs. free living) and the demographic characteristics of the study population. One study reporting overall misreport indicated that 46% of obese adults misreport EI. One study reported a small influence of BMI on underreporting of EI among postmenopausal women (8.1%), whereas another study reported considerable underreporting of energy among obese twins, when compared with their normal-weight twin counterparts (3.2 ± 1.1 MJ/day; p = .036). A different study among obese females found that underreporting was significantly higher among obese individuals when compared with those in lower BMI categories (p < .05), but underreporting varied across dietary instruments, and the FFQ had the lowest accuracy. | The present findings show a consistent and clear link between underreporting of energy intake and an obese BMI in a considerable number of papers included. | The quality of the included papers generally ranged between 50% and 100%. The most common result was 63% (11 studies), which was primarily due to non-random sampling and using specific groups. Eight studies had small samples that were unlikely to result in adequate power for the statistics applied. The majority of studies were at least average (7) or large (19), suggesting a higher generalizability. | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Wehling and Lusher, 2019 | 4 | 1,217 obese adults (BMI ≥ 30) | Food diaries/reference method for assessing energy intake | Accuracy of self-reported EI via food diaries |
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| One study found that low energy reporters have significantly higher BMI when compared with non–low energy reporters, regardless of sex (27.5 vs. 25.7 in males, 27.99 vs. 25.4 in females) and that obesity is the highest predictor (p < .01) of underreporting of energy. Another study showed that 52% of overweight and obese unsuccessful dieters underreported their EI, whereas a different study found that underreporting is considerable for obese twins, when compared with their normal-weight twin counterparts (3.2 ± 1.1 MJ/day; p = .036). Lastly, another study found that obese females underreport their energy by 8.8% and that obese females consumed significantly more energy (especially from the energy-dense category) when compared with their non-obese female counterparts. | The present findings show a consistent and clear link between underreporting of EI and an obese BMI in a considerable number of papers included. | The quality of the included papers generally ranged between 50% and 100%. The most common result was 63% (11 studies), which was primarily due to non-random sampling and using specific groups. Eight studies had small samples that were unlikely to result in adequate power for the statistics applied. The majority of studies were at least average (7) or large (19), suggesting a higher generalizability. | — | Partially well done/reported |
| Author, Year | Number of Studies | Sample Characteristics | Intervention/Comparator | Primary Outcome |
|---|---|---|---|---|
| Wehling and Lusher, 2019 | 3 | 23,482 obese adults (BMI ≥ 30) | DHQ/reference method for assessing energy intake | Accuracy of self-reported EI via the DHQ |
NOTE: BMI = body mass index; CI = confidence interval; DHQ = diet history questionnaire; DLW = doubly labeled water; DP = digital photography; EI = energy intake; FFQ = food frequency questionnaire; FR = food record; IBDA = image-based dietary assessment; ICC = intraclass correlation coefficient; MJ = megajoule; SFC = School Food Checklist; TEE = total energy expenditure; WFR = weighted food record; WMD = weighted mean difference.
| Quantitative Finding(s) | Qualitative Finding(s) | Risk of Bias | Heterogeneity of Studies | Overall AMSTAR2 Rating |
|---|---|---|---|---|
| One study found that 17.5% of obese females and 5.5% of obese males underreport EI, and that no significant differences in accuracy of reporting exists when compared with nonobese females and males. Similarly, another study showed that underreporting is more common among those with a BMI > 30, and energy underreporting in this population is approximately 91% on a DHQ. Lastly, another study found that approximately 16% of obese adults were overreporters and 66% were underreporters. The mean level of underreporting was approximately 18.0 ± 29.1% | The present findings show a consistent and clear link between underreporting of EI and an obese BMI in a considerable number of papers included. | The quality of the included papers generally ranged between 50% and 100%. The most common result was 63% (11 studies), which was primarily due to non-random sampling and using specific groups. Eight studies had small samples that were unlikely to result in adequate power for the statistics applied. The majority of studies were at least average (7) or large (19), suggesting a higher generalizability. | — | Partially well done/reported |
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