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. 2022 Aug 30;13(6):2357–2405. doi: 10.1093/advances/nmac093

TABLE 3.

Differences between Chronotype and Dietary Intake1

Reference Method of assessment Differences between types
MT IT ET P value (ET vs. IT/MT) and other analysis
Total daily energy intake
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool 2114.5 ± 634 kcal/d2 — 2147.4 ± 588 kcal/d3 P-trend = 0.33
Sato-Mito et al., 2011 (56) Dietary history questionnaire 1836 ± 20 kcal/d4 1776 ± 16 kcal/d51803 ± 17 kcal/d61814 ± 17 kcal/d7 1768 ± 18 kcal/d8 P-trend = 0.10
Vera et al., 2018 (71) Single 24-h recalls 1972.8 ± 23.8 kcal/d — 1918.6 ± 24.68 kcal/d P = 0.12P-trend = 0.94
Yoshizaki et al., 2018 (59) A semiquantitative FFQ 1854 ± 29 kcal/d9 1853 ± 27 kcal/d10 1825 ± 26 kcal/d11 P-trend = 0.47
Lucassen et al., 2013 (62) 3-d food recall diary Working day: 2129 ± 631 kcalNonworking day: 2383 ± 928 kcal — Working day: 2276 ± 815 kcalNonworking day: 2378 ± 883 kcal P = 0.37P = 0.92
Mota et al., 2016 (63) 3-d self-administered food diary — — Chronotype scores (toward ET) were negatively associated with daily energy intake; kcal/kg/dETs were associated with higher intake (β coefficient = −0.28) P = 0.02
Maukonen et al., 2019 (78) 48-h dietary recalls over 2 previous consecutive days 7709 (SEM 97) kJ — 7679 (SEM 215) kJ P = 1.00
Maukonen et al., 2017 (79) 48-h dietary recalls 7808 (SEM 170) kJ on weekdays 7960 (SEM 171) kJ on weekdays 7881 (SEM 210) kJ on weekdays P = 1.00
7841 (SEM 283) kJ on weekends 7871 (SEM 283) kJ on weekends 7992 (SEM 367) kJ on weekends P = 1.00
Maukonen et al., 2016 (65) FFQ; Baltic Sea diet score Men: 11,597 (SEM 130) kJ/dWomen: 9489 (SEM 103) kJ/d Men: 11,676 (SEM 90) kJ/dWomen: 9433 (SEM 64) kJ/d Men: 11,776 (SE 159) kJ/dWomen: 9389 (SE 105) kJ/d P-trend = 0.43P-trend = 0.54
Teixeira et al., 2018 (66) 24-h recall 1552.8 [1233.4–2090.6] kcal/d 1734.2 [1356.3–2218.3] kcal/d 1692.9 [1333.8–2197.9] kcal/d P = 0.07
— — Breakfast skippers were negatively associated with energy intake (kcal/d)ET breakfast skippers had higher intake β = −0.25 P < 0.001
Baron et al., 2011 (75) 7-d food logs — 1905 ± 526 kcal/d12 2153 ± 524 kcal/d13248 kcal/d5 P = 0.10
Baron et al., 2013 (68) 7-d food logs — 1905 ± 526 kcal/d12 2153 ± 524 kcal/d13 p > 0.05
Beaulieu et al., 2020 (69) 24-h dietary record tool (myfood24) 1843 ± 681 kcal/d — 1737 ± 659 kcal/d p > 0.05
Zerón-Rugerio et al., 2020 (58) 6-d food logs 1517 ± 404 kcal/d14 1596 ± 425 kcal/d151555 ± 412 kcal/d16 1676 ± 420 kcal/d17 P = 0.45
Total daily carbohydrate intake
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool Carbohydrate: 240.5 ± 79.0 g/d2 — Carbohydrate: 244.9 ± 72.3 g/d3 P-trend = 0.27
Sugar: 103 ± 46.6 g/d2 Sugar: 109 ± 43.9 g/d3 P-trend = 0.02
Fiber:19.9 ± 8.1 g/d2 Fiber: 19.8 ± 7.7 g/d3 P-trend = 0.96
Sato-Mito et al., 2011 (56) Dietary history questionnaire 56.3 ± 0.3 E%4 55.9 ± 0.2 E%555.5 ± 0.3 E%655.4 ± 0.2 E%7 55.1 ± 0.3 E%8 P-trend < 0.01
Vera et al., 2018 (71) Single 24-h recalls 205 ± 3.07 g/d — 194 ± 3.18 g/d P = 0.02P-trend = 0.67
Yoshizaki et al., 2018 (59) A semiquantitative FFQ 235 ± 4.30 g/d9 237 ± 4.0 g/d10 230 ± 3.9 g/d11 P-trend = 0.50
Lucassen et al., 2013 (62) 3-d food recall diary No significant differences in total intakes before and after 20:00 h P = 0.84
Mota et al., 2016 (63) 3-d self-administered food diary — — Chronotype scores were negatively associated with carbohydrate (g/kg/d)ETs had a higher intake (β = –0.26) P = 0.03
Maukonen et al., 2017 (79) 48-h dietary recalls Weekdays: 48.6 (0.6) E% Weekdays: 48.1 (0.6) E% Weekdays: 48.8 (0.7) E% P = 1.00
Weekends: 49.6 (0.8) E% Weekends: 48.8 (0.8) E% Weekends: 47.8 (1.0) E% P = 0.09
ME score was positively associated with carbohydrate intakes on weekendsMTs were associated with higher intake on weekends — — P-trend = 0.04
Fiber: 2.5 (0.1) E% on weekdays Fiber: 2.4 (0.1) E% on weekdays Fiber: 2.5 (0.1) E% on weekdays P = 1.00
Fiber: 2.5 (0.1) E% on weekends Fiber: 2.4 (0.1) E% on weekends Fiber: 2.4 (0.1) E% on weekends P = 1.00
Fiber: ME score was positively associated with fiber intakes on weekendsMTs were associated with higher intake — — P-trend = 0.04
Sucrose: 9.5 (0.4) E% on weekdays Sucrose: 9.4 (0.4) E% on weekdays Sucrose: 10.1 (0.5) E% on weekdays P = 0.46
— — Sucrose: Intakes increased with lower ME scores (ET) on weekdays P-trend = 0.02
Sucrose: 10.3 (0.5) E% on weekends Sucrose: 10.0 (0.5) E% on weekends Sucrose: 9.7 (0.7) E% on weekends P = 0.91
Teixeira et al., (66) 24-h food recall Carbohydrate: 198.6 [155.6–275.1] g/d Carbohydrate: 226.4 [169.2–295.5] Carbohydrate: 225.3 [169.9–293.2] P = 0.10
— — Breakfast skippers were negatively associated with carbohydrate intake (g/d)ET breakfast skippers had higher intake (β-coefficient = – 0.19) P < 0.05
Fiber: 16.0 [10.2–21.8] g/d Fiber: 15.8 [10.9–22.1] g/d Fiber: 15.6 [10.6–21.1] g/d P = 0.93
Baron et al., 2011 (75) 7-d food logs — 49 ± 7.9 E%12 49 ± 7.8 E%13 P > 0.05
Baron et al., 2013 (68) 7-d food logs — 237 ± 81 g/d1249 ± 7.9 E%12 260 ± 72 g/d1349 ± 7.8 E%13 P > 0.05
Total daily protein intake
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool 87.4 ± 27.1 g/d2 — 87.7 ± 27.6 g/d3 P-trend = 0.97
Sato-Mito et al., 2011 (56) Dietary history questionnaire 13.5 ± 0.1 E%4 13.6 ± 0.1 E%513.5 ± 0.1 E%613.3 ± 0.1 E%7 13.2 ± 0.1 E%8 P-trend < 0.01
Vera et al., 2018 (71) Single 24-h recalls 83.01 ± 1.11 g/d — 82.34 ± 1.15 g/d P = 0.68P-trend = 0.94
Yoshizaki et al., 2018 (59) A semiquantitative FFQ 66.0 ± 1.2 g/d9 64.1 ± 1.1 g/d10 63.4 ± 1.0 g/d11 P-trend = 0.08
Lucassen et al., 2013 (62) 3-d food recall diary No significant difference in total intakes before and after 20:00 h P = 0.89
Mota et al., 2016 (63) 3-d self-administered food diary — — Chronotype score was negatively associated with protein intake (g/kg/d)ETs had a higher intake (β-coefficient = −0.23) P = 0.04
Maukonen et al., 2017 (79) 48-h dietary recalls 17.3 (0.3) E% on weekdays 17.4 (0.3) E% on weekdays 16.4 E% (0.3) on weekdays P = 0.02
Teixeira et., 2018 (66) 24-h food recall 71.9 [55.0–97.2] g/d 79.3 [60.0–100.2] g/d 75.6 [57.3–105.8] g/d P = 0.16
Baron et al., 2011 (75) 7-d food logs — 14 ± 2.7 E%12 15 ± 2.0 E%13 P > 0.05
Baron et al., 2013 (68) 7-d food logs — 69 ± 21 g/d (14%)12 84 ± 26 g/d (15%)13 P > 0.05
Total daily fat intake
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool Fat: 84.4 ± 31.6 g/d2 — Fat: 85.6 ± 30.0 g/d3 P-trend = 0.43
Saturated fat: 28.2 ± 11.3 g/d2 — Saturated fat: 28.8 ± 11.3 g/d3 P-trend = 0.50
Polyunsaturated fat: 18.5 ± 7.7 g/d2 — Polyunsaturated fat: 18.5 ± 7.3 g/d3 P-trend = 0.85
Monounsaturated fat: 30.4 ± 12.3 g/d2 — Monounsaturated fat: 31.0 ± 11.3 g/d3 P-trend = 0.24
Cholesterol: 304.3 ± 139.6 g/d2 — Cholesterol: 308.0 ± 147.9 g/d3 P-trend = 0.73
Sato-Mito et al., 2011 (56) Dietary history questionnaire Fat: 28.9 ± 0.2 E%4 Fat: 29.3 ± 0.2 E%529.7 ± 0.2 E%629.9 ± 0.21E%7 Fat: 30.1 ± 0.2 E%8 P-trend < 0.01
Cholesterol: 168 ± 3 mg/1000 kcal4 Cholesterol: 169 ± 2 mg/1000 kcal5165 ± 2 mg/1000 kcal6161 ± 2 mg/1000 kcal7 Cholesterol: 162 ± 3 mg/1000 kcal8 P-trend < 0.05
Vera et al., 2018 (71) Single 24-h recalls 93.79 ± 1.500 g/d — 93.03 ± 1.54 g/d P-trend = 0.73P-trend = 0.49
Yoshizaki et al., 2018 (59) A semiquantitative FFQ 65.8 ± 1.3 g/d9 66.0 ± 1.2 g/d10 66.3 ± 1.10 g/d11 P-trend = 0.88
Lucassen et al., 2013 (62) 3-d food recall diary No significant differences in total intakes before and after 20:00 h P = 0.14
Mota et al., 2016 (63) 3-d self-administered food diary — — Chronotype score was negatively associated with cholesterol intake (mg/d)ETs had a higher intake (β-coefficient = −0.24) P = 0.04
Maukonen et al., 2017 (79) 48-h dietary recalls Fat: 31.7 (0.6) E% on weekdays Fat: 32.1 (0.6) E% on weekdays Fat: 32.3 (0.7) E% on weekdays P = 0.81
Fat: 31.1 (0.7) E% on weekends Fat: 32.0 (0.7) E% on weekends Fat: 33.1 (0.9) E% on weekends P = 0.05
Fat: Inversely associatedHigher intake on weekends P-trend < 0.05
SFAs: 11.6 (0.3) E% on weekdays SFAs: 11.9 (0.3) E% on weekdays SFAs: 11.8 (0.3) E% on weekdays P = 1.00
SFAs: 11.2 (0.4) E% on weekends SFAs: 11.7 (0.4) on weekends SFAs: 12.2 (0.5) E% on weekends P = 0.06
ME score was inversely associated on weekendsETs were associated with higher intake of SFAs P-trend < 0.05
Maukonen et al., 2016 (65) FFQ; Baltic Sea diet score Fat: 32 E% in men Fat: 32 E% in men Fat: 32 E% in men P-trend = 0.67
Fat: 30 E% in women Fat: 31 E% in women Fat: higher intake of 31 E% in women P-trend = 0.02
Teixeira et al., 2018 (66) 24-h food recall Fat: 45.2 [33.9–69.2] g/d Fat: 53.7 [38.6–69.8] g/d Fat: 53.6 [34.7–74.5] g/d P = 0.10
Breakfast skippers were negatively associated with fat intake (g/d)ET breakfast skippers had higher intake (β-coefficient = –0.18) P < 0.05
Cholesterol: 180.5 [102.7–278.9] mg/d Cholesterol: 208.0 [142.1–296.6] mg/d Cholesterol: 193.5 [127.6–297] mg/d P = 0.18
Baron et al., 2011 (75) 7-d food logs — 38 ± 7.2 E%12 35 ± 7.7 E%13 P > 0.05
Baron et al., 2013 (68) 7-dfood logs — 78 ± 23 g/d (38%)12 82 ± 24 g/d (35%)13 P > 0.05
Total daily micronutrient intake
Sato-Mito et al., 2011 (56) Dietary history questionnaire Potassium: 1094 ± 12 mg/1000 kcal4 Potassium: 1101 ± 10 g/1000 kcal51084 ± 11 mg/1000 kcal61083 ± 10 mg/1000 kcal7 Potassium: 1046 ± 11 mg/1000 kcal8 P-trend < 0.05
Magnesium: 120 ± 1 mg/1000 kcal4 Magnesium: 121 ± 1 mg/1000 kcal5120 ± 1 mg/1000 kcal6119 ± 1 mg/1000 kcal7 Magnesium: 115 ± 1 mg/1000 kcal8 P-trend < 0.05
Iron: 3.73 ± 0.04 mg/1000 kcal4 Iron: 3.72 ± 0.03 mg/1000 kcal53.70 ± 0.03 mg/1000 kcal63.70 ± 0.03 mg/1000 kcal7 Iron: 3.59 ± 0.04 mg/1000 kcal8 P-trend < 0.05
Zinc: 4.12 ± 0.02 mg/1000 kcal4 Zinc: 4.14 ± 0.02 mg/1000 kcal54.11 ± 0.02 mg/1000 kcal64.07 ± 0.02mg/1000 kcal7 Zinc: 4.04 ± 0.02 mg/1000 kcal8 P-trend < 0.05
Vitamin A: 308 ± 10 Inline graphicg/1000 kcal4 Vitamin A: 294 ± 9 Inline graphicg/1000 kcal5287 ± 9 Inline graphicg/1000 kcal6297 ± 9 Inline graphicg/1000 kcal7 Vitamin A: 271 ± 10 Inline graphicg/1000 kcal8 P-trend < 0.05
Vitamin D: 3.7 ± 0.1 Inline graphicg/1000 kcal4 Vitamin D: 3.7 ± 0.1 Inline graphicg/1000 kcal53.6 ± 0.1 Inline graphicg/1000 kcal63.5 ± 0.1 Inline graphicg/1000 kcal7 Vitamin D: 3.4 ± 0.1 Inline graphicg/1000 kcal8 P-trend < 0.01
Pyridoxine: 0.53 ± 0.01 mg/1000 kcal4 Pyridoxine: 0.54 ± 0.01 mg/1000 kcal50.53 ± 0.01 mg/1000 kcal60.52 ± 0.01 mg/1000 kcal7 Pyridoxine: 0.51 ± 0.01 mg/1000 kcal8 P-trend < 0.01
Riboflavin: 0.70 ± 0.01 mg/1000 kcal4 Riboflavin: 0.69 ± 0.01 mg/1000 kcal50.69 ± 0.01 mg/1000 kcal60.69 ± 0.01 mg/1000 kcal7 Riboflavin: 0.67 ± 0.01 mg/1000 kcal8 P-trend < 0.01
Thiamine: 0.41 ± 0.003 mg/1000 kcal4 Thiamine: 0.42 ± 0.003 mg/1000 kcal50.41 ± 0.003 mg/1000 kcal60.41 ± 0.003 mg/1000 kcal7 Thiamine: 0.40 ± 0.004 mg/1000 kcal8 P-trend < 0.01
Folate: 156 ± 2 Inline graphicg/1000 kcal4 Folate: 155 ± 2 Inline graphicg/1000 kcal5153 ± 2 Inline graphicg/1000 kcal6155 ± 2 Inline graphicg/1000 kcal7 Folate: 145 ± 2 Inline graphicg/1000 kcal8 P-trend < 0.01
Calcium: 275 ± 4 mg/1000 kcal4 Calcium: 273 ± 4 mg/1000 kcal5269 ± 4 mg/1000 kcal6266 ± 4 mg/1000 kcal7 Calcium: 251 ± 4 mg/1000 kcal8 P-trend < 0.001
Total daily intake of food groups
Sato-Mito et al., 2011 (56) Dietary history questionnaire Alcohol: 0.19 ± 0.05 E%4 Alcohol: 0.13 ± 0.04 E%50.24 ± 0.05 E%60.29 ± 0.04 E%7 Alcohol: 0.44 ± 0.05 E%8 P-trend < 0.01
Rice: 171.4 ± 2.9 g/1000 kcal4 Rice: 167.7 ± 2.5 g/1000 kcal5158.0 ± 2.6 g/1000 kcal6153.6 ± 2.5 g/1000 kcal7 Rice: 150.0 ± 2.8 g/1000 kcal8 P-trend < 0.001
Vegetables: 126.7 ± 3.1 g/1000 kcal4 Vegetables: 127.5 ± 2.6 g/1000 kcal5121.9 ± 2.8 g/1000 kcal6121.3 ± 2.6 g/1000 kcal7 Vegetables: 109.8 ± 2.9 g/1000 kcal8 P-trend < 0.001
Pulses: 26.6± 0.8 g/1000 kcal4 Pulses: 25.8 ± 0.7 g/1000 kcal525.7 ± 0.7 g/1000 kcal624.7 ± 0.7 g/1000 kcal7 Pulses: 22.5 ± 0.8 g/1000 kcal8 P-trend < 0.001
Eggs: 19.3 ± 0.6 g/1000 kcal4 Eggs: 19.4 ± 0.5 g/1000 kcal518.1 ± 0.5 g/1000 kcal617.1 ± 0.5 g/1000 kcal7 Eggs: 17.4 ± 0.6 g/1000 kcal8 P-trend < 0.001
Noodles: 28.8 ± 1.4 g/1000 kcal4 Noodles: 33.6 ± 1.2 g/1000 kcal536.5 ± 1.2 g/1000 kcal638.5 ± 1.2 g/1000 kcal7 Noodles: 46.4 ± 1.3 g/1000 kcal8 P-trend < 0.001
Dairy: 77.4 ± 3.1 g/1000 kcal4 Dairy: 76.5 ± 2.6 g/1000 kcal574.3 ± 2.8 g/1000 kcal671.4 ± 2.6 g/1000 kcal7 Dairy: 65.6 ± 2.9 g/1000 kcal8 P-trend < 0.05
Confections: 42.5 ± 1.0 g/1000 kcal4 Confections: 41.8 ± 0.8 g/1000 kcal544.8 ± 0.9 g/1000 kcal646.8 ± 0.9 g/1000 kcal7 Confections: 46.7 ± 1.0 g/1000 kcal8 P-trend < 0.05
Meat: 33.1 ± 0.7 g/1000 kcal4 Meat: 34.0 ± 0.6 g/1000 kcal534.8 ± 0.6 g/1000 kcal633.4 ± 0.6 g/1000 kcal7 Meat: 35.7 ± 0.7 g/1000 kcal8 P-trend < 0.05
Vera et al., 2018 (71) Single 24-h recalls — — Lower intake of cereals P < 0.05Other analysis:ETs have 1.3 times higher odds for alcohol (OR: 1.52; 95% CI: 1.25, 1.86; P < 0.001)
Najem et al., 2020 (76) Yale Food Addiction Scale (YFAS) — — — Other analysis:ME score negatively correlated with number of units of caffeine-containing beverages/dETs were associated with higher intake of units of caffeine beverages/d (r = – 0.14, P = 0.00)
Lázár et al., 2012 (73) Medical Questionnaire Alcohol: reported lower intake — — P < 0.001
Daily caffeine intake was associated with diurnal preference; MTs reported lower intake — — P < 0.05
Yoshizaki et al., 2018 (59) A semiquantitative FFQ Potatoes and starches intake: higher intake of 36.4 ± 1.7 g/d9 Potatoes and starches: 32.7 ± 1.6 g/d10 Potatoes and starches: 30.9 ± 1.5 g/d11 P-trend = 0.04
Green/yellow vegetables: higher intake of 76.2 ± 2.2 g/d9 Green/yellow vegetables: 67.1 ± 2.1 g/d10 Green/yellow vegetables: 65.4 ± 2.0 g/d11 P-trend < 0.001Other analysis:MTs9 were associated with a higher intake (β = 0.15, P < 0.001)
White vegetables: higher intake of 123 ± 3.7 g/d9 White vegetables: 112 ± 3.4 g/d10 White vegetables: 112 ± 3.3 g/d11 P-trend = 0.01Other analysis:White vegetables: associated with high chronotype scoreMTs9 were associated with a higher intake (β = 0.11, P < 0.001)
Fruit: higher intake of 81.9 ± 3.8 g/d9 Fruit: 72.7 ± 3.5 g/d10 Fruit: 59.9 ± 3.4 g/d11 P-trend < 0.001Other analysis:Fruit: associated with high chronotype scoreMTs9 were associated with a higher intake (β = 0.11, P < 0.001)
Algae: higher intake of 4.6 ± 0.2 g/d9 Algae: 4.3 ± 0.2 g/d10 Algae: 4.1 ± 0.2 g/d11 P-trend = 0.02Other analysis:Algae: associated with high chronotype scoreMTs9 were associated with a higher intake (β = 0.10, P < 0.001)
Confectioneries/savory snacks: 80.7 ± 2.9 g/d9 Confectioneries/savory snacks: 89.2 ± 2.7 g/d10 Confectioneries/savory snacks: 94.9 ± 2.6 g/d11 P-trend = 0.001Other analysis:Confectioneries/savory snacks: negatively associated with high chronotype scoreETs11 were associated with a higher intake (β = –0.10, P < 0.001)
Sugar-sweetened beverages: 42.7 ± 5.4 g/d9 Sugar-sweetened beverages: 43.8 ± 5.0 g/d10 Sugar-sweetened beverages: 60.8 ± 4.9 g/d11 P-trend = 0.01Other analysis:Sugar-sweetened beverages: negatively associated with high chronotype scoreETs11 were associated with a higher intake (β = –0.13, P < 0.001)
Silva et al., 2016 (60) FFQ — — Meat: ET associated with a higher intake (β = 0.21) P = 0.003
Mota et al., 2016 (63) 3-d self-administered food diary — — Chronotype score was negatively associated with:Intake of sweets (servings/d)ETs had a higher intake (β-coefficient = −0.27)Vegetable intake (servings/d)ETs had a higher intake (β-coefficient = −0.26)

P = 0.03

P = 0.04
Chronotype score was positively associated with oil and fat intake (servings/d)MTs had a higher intake (β-coefficient = 0.27) — — P = 0.03
Maukonen et al., 2017 (79) 48-h dietary recalls Alcohol: 4.6 (1.5) g on weekdays Alcohol: 4.3 (1.5) g on weekdays Alcohol: 9.7 (1.9) g on weekdaysAlcohol: Intakes increased with lower ME scores (ET) on weekdays P = 0.57P-trend = 0.04
Maukonen et al., 2016 (65) FFQ; Baltic Sea diet score Cereals: women 85 g/d and men 89 g/d Cereals: women: 79 g/d and men: 84 g/d Cereals: women 74 g/d and men 78 g/d P-trend < 0.001
Fish: men 55 g/d Fish: men 53 g/d Fish: Men consumed less, 49 g/d P-trend < 0.05
Alcohol: women 3.6 g/d and men 10.6 g/d Alcohol: women: 4.4 g/d and men: 11.8 g/d Alcohol: Consumed more. Women 5.1 g/d and men 13.3 g/d Women: P-trend < 0.001Men: P-trend = 0.003
Li, Wu et al., 2018 (74) Sugary beverage consumption: number of bottles or tins consumed per day last week — — — Other analysis: Negative direct effects were found between chronotype and sugary beverage consumptionMTs had lower intake (β = − 0.15, SE = 0.03, P < 0.01)
Culnan et al., 2013 (72) Gray–Donald Eating Patterns Questionnaire Alcohol: Baseline: No difference in alcohol intake P > 0.05
Caffeine: No difference between chronotypes at baseline P > 0.05
— — At follow-up: More ETs reported drinking alcohol [χ2(1, n = 54) = 5.94] P < 0.05
At follow-up: ETs not more likely to change alcohol drinking status throughout study [χ2(1, n = 54) = 3.19] P > 0.05
Baron et al., 2011 (75) 7-d food logs — Fruit and vegetables: 3.4 ± 1.8 servings/d12 Fruit and vegetables: lower intake of 1.9 ± 1.1 servings/d13 P < 0.01Other analysis:Fruit and vegetable intakes were negatively correlated with sleep timing (r = –0.49, P < 0.01)ITs12 were associated with higher intakes
Fast-food meals: 3.0 ± 1.8 servings/wk12 Fast-food meals/wk: higher intake of 5.2 ± 3.8 servings/wk13 P < 0.05
Full-calorie sodas: 1.3 ± 2.5 servings/wk12 Full-calorie sodas: higher intake of 4.5 ± 6.5 servings/wk13 P < 0.05
— Caffeinated drinks: 7.3 ± 6.5 servings/wk12 Caffeinated drinks: trend for higher intake: 13.0 ± 12.6 servings/wk13 P < 0.10
Muscogiuri et al., 2020 (70) PREDIMED questionnaire — — — Other analysis:Food intake negatively associated with chronotype scoreETs associated with a higher OR for:Red/processed meat <1/d (OR: 1.05; 95% CI: 1.02, 1.08; P < 0.001); butter, cream, margarine <1/d (OR: 1.05; 95% CI: 1.02, 1.08;; P = 0.001)Commercial sweets/confectionary ≤2/wk (OR: 1.04; 95% CI: 1.01, 1.06; P = 0.007)Soda drinks <1/d (OR: 1.04; 95% CI: 1.01, 1.07; P = 0.001)Food intake positively associated with chronotype score. MTs were associated with a higher OR for:EVOO >4 tbs (OR: 1.03; 95% CI: 1.00, 1.06; P = 0.01)Vegetables ≥2 servings/d (OR: 1.05; 95% CI: 1.02, 1.07; P < 0.001)Fruit ≥3 servings/d (OR: 1.07; 95% CI: 1.04, 1.10; P < 0.001)Fish/seafood ≥3/wk (OR: 1.037; 95% CI: 1.00, 1.06; P = 0.02)Poultry more than red meats (OR: 1.05; 95% CI: 1.03, 1.08; P < 0.001)Tree nuts ≥3/wk (OR: 1.03; 95% CI: 1.00, 1.06; P = 0.01)Wine (glasses) ≥7/wk (OR: 1.05; 95% CI: 1.01, 1.09; P = 0.004) Most predictive factor of chronotype score among single contributing PREDIMED food items and score:Both MTs (R2 = 0.18, P < 0.001)
and ETs (R2 = 0.23, P = 0.02) most influenced by PREDIMED score and IT most influenced by butter, cream, and margarine <1/d (R2 = 0.09, P = 0.04)
Maukonen et al., 2017 (79) 48-h dietary recalls Alcohol: 1.8 (0.7) g after 20:00 on weekdays Alcohol: 1.9 (0.7) g after 20:00 on weekdays Alcohol: 4.0 (0.9) g after 20:00 on weekdays P = 0.09
— — Alcohol: Intake increased with lower ME score values (ET) after 20:00 on weekdays P-trend < 0.05
Daily energy distribution
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool — — — Other analysis:Higher energy intake in morning window (within 2 h after getting out of bed) associated with lower OR for overweight/obese in MT2 (OR: 0.32; 95% CI: 0.16, 0.66; P-trend = 0.0006)
— — — Other analysis:Higher energy intake at nighttime window (within 2 h before bedtime), associated with higher OR for overweight/obese in ETs3 (OR: 4.94; 95% CI: 1.61, 15.1; P-trend = 0.01)
Muñoz et al., 2020 (57) Hypocaloric dietary treatment according to the Spanish Federation of Nutrition, Food and Dietetics guidelines Breakfast 30%, midmorning 10%, lunch 35%, midafternoon 5%, and dinner 20% — Breakfast 20%, midmorning 5%, lunch 35%, midafternoon 10%, and dinner 30% —
Maukonen et al., 2017 (79) 48-h dietary recalls 99% of MTs had energy intake >0 kJ on weekday mornings by 10:00 — 80% of ETs had energy intake >0 kJ on weekday mornings by 10:00 —
— — Weekday mornings: 350 kJ (4% TEI) lower energy intake as compared with MTs P < 0.001
— — Weekend mornings by 10:00: 380 kJ lower energy than MTs P = 0.004
81% of MTs had energy intake >0 kJ on weekday evenings by 20:00 — 94% of ETs had energy intake >0 kJ on weekday evenings by 20:00 —
— — Weekday evenings by 20:00: 430 kJ (6% TEI) more energy than MTs P < 0.001
— — Weekend evenings by 20:00: 590 kJ (7% TEI) more energy than MTs P < 0.001
— — Cumulative energy intake of ET: Weekdays: lower from the beginning of the day until 22:00 Weekends: lower from the beginning of the day until 01:00 —
Weekends: 3 peaks of energy intake of the same height at 08:00, 12:00, and 17:00 —

Weekdays: energy intake peaks on weekdays are an hour later than MTs

Weekends: 6 peaks of energy intakeHighest peak at 19:00

 —
Sucrose: 12.5 (1.2) E% after 20:00 on weekdays Sucrose: 13.4 (1.2) E% after 20:00 on weekdays Sucrose: 1.1 E% units more after 20:00 on weekdays 13.6 (1.5) E% P < 0.05
Sucrose: 10.2 (1.9) E% after 20:00 on weekends Sucrose: 13.8 (1.9) after 20:00 on weekends Sucrose: 3.1 E% units more by 20:00 on weekends 13.3 (2.5) E% P < 0.05
Maukonen et al., 2019 (78) (78) 48-h dietary recalls covering 2 previous consecutive days 1596 (41%) kJ in the morning — 340 kJ less energy in the morning—1252 (90%) kJ P < 0.01
953 (43%) kJ in the evening — 450 kJ more in the evening—1402 (97%) kJ P < 0.001
— — — Other analysis:% TEI in the morning and obesity risk had a significant interaction between % TEI in the morning and chronotype on increase in weight (≥5%) (P = 0.025) and increase in BMI (≥5%) (P = 0.012)
Baron et al., 2011 (75) 7-d food logs — Caloric intake after 20:00: 376 ± 237 kcal/d12 Caloric intake after 20:00: 754 ± 373 kcal/d13 P < 0.001Other analysis:ETs were associated with more calories consumed after 20:00 (β = 0.45, r2Δ = 0.18, P = 0.001)12
— Caloric intake at breakfast: 355 ± 133 kcal/d12 Caloric intake at breakfast: 285 ± 143 kcal/d13 P > 0.05
— Caloric intake at lunch: 528 ± 188 kcal/d12 Caloric intake at lunch: 503 ± 378 kcal/d13 P > 0.05
— Caloric intake for snacks: 405 ± 284 kcal/d12 Caloric intake for snacks: 536 ± 323 kcal/d13 P > 0.05
— Caloric intake at dinner: 630 ± 198 kcal/d12 Caloric intake at dinner: 825 ± 352 kcal/d13 P < 0.05
— Caloric intake after dinner: 150 ± 151 kcal/d12 Caloric intake after dinner: 208 ± 166 kcal/d13 P > 0.05
— — Cumulative energy intake across the day; 1-h incrementsFewer calories at 9:0013 P < 0.001
— — Fewer calories at 10:00, 11:00, and 12:0013 P = 0.001
— — Afternoon: intake increased steeply, and caloric intake matched and began to exceed normal sleepers around average dinner time13 —
— ITs reached a plateau as early as 21:0012 Caloric intake of late sleepers continued to rise after 23:0013 —
Lucassen et al., 2013 (62) 3-d food recall Working days: 299 ± 354 kcal after 20:00 — Consumed more calories after 20:00 on working days 677 ± 460 kcal P < 0.001
Nonworking days: 327 ± 354 kcal after 20:00 — Consumed more calories after 20:00 on nonworking days 537 ± 480 kcal/d P = 0.03
Zerón-Rugerio et al., 2020 (58) 6-d food logs and Quality Index Food Consumption Pattern Breakfast: 24.8 (10.4) % of kcal14 Breakfast: 26.9 (10.4) % of kcal1526.5 (6.9) % of kcal16 Breakfast: 22.8 (8.3) % of kcal17 P = 0.26
Lunch: 31.3 (7.5) % of kcal14 Lunch: 29.5 (10.2) % of kcal1533.7 (10.5) % of kcal16 Lunch: 30.9 (9.6) % of kcal17 P = 0.36
Dinner: 18.0 (10.4) % of kcal14 Dinner:18.6 (9.8) % of kcal1520.7 (9.1) % of kcal16 Dinner: 23.5 (11.3) % of kcal17 P-trend = 0.02
Daily carbohydrate distribution
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool — — — Other analysis:In MTs,2 the highest quintile of % carbohydrate intake in the morning (within 2 h after getting out of bed) is associated with 80% decrease in risk for being overweight/obese (OR: 0.2; 95% CI: 0.10, 0.42; P-trend < 0.0001)In ETs,3 the highest quintile of % carbohydrate intake during the evening (within 2 h before bedtime) is associated with an increase in OR for being overweight/obese (OR: 4.48; 95% CI: 1.64, 12.2; P-trend = 0.01)
In ETs,3 the highest quintile of % sugar intake at night (within 2 h before bedtime) is associated with a 3-fold increase in OR for being overweight/obese (OR: 3.11; 95% CI: 1.17, 8.22; P-trend = 0.02) In MTs,2 the highest quintile of % sugar intake during the morning (within 2 h after getting out of bed) (OR: 0.23; 95% CI: 0.11, 0.49; (P-trend = 0.0003), % fiber (OR: 0.31; 95% CI: 0.15, 0.65; P-trend = 0.0008) was associated with a decrease in OR for being overweight/obese
Maukonen et al., 2017 (79) 48-h dietary recalls Intake by 10:00 on weekdays: 52.8 (1.3) E% Intake by 10:00 on weekdays: 50.5 (1.3) E% Intake by 10:00 on weekdays: 47.1 (1.6) E% P < 0.001 P-trend < 0.001
Intake after 20:00: 48.8 (2.0) E% on weekdays Intake after 20:00: 51.3 (2.0) E% on weekdays Intake after 20:00: 51.2 (2.4) E% on weekdays P-trend = 0.01
— — CHO intakes increased with lower ME score values (ET) after 20:00 on weekdays P-trend < 0.05
Intake by 10:00 on weekends: 52.6 (2.6) E% Intake by 10:00 on weekends: 48.3 (2.4) E% Intake by 10:00 on weekends: 48.5 (3.1) E% P-trend = 0.003
Intake after 20:00: 46.3 (3.4) on weekends Intake after 20:00: 50.3 (3.4) on weekends Intake after 20:00: 49.8 (4.4) on weekends P = 1.00
Baron et al., 2013 (68) 7-d food logs — After 20:00: 47 ± 31 g (19%)12 After 20:00: higher intake 87 ± 39 g (33%)13 P < 0.01Other analysis:After 20:00: Moderate positive correlation with midpoint of sleepETs13 were associated with higher intake (r = 0.52, P < 0.001)
Daily protein distribution
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool — — — In MTs,2 the highest % protein intake during the morning (within 2 h after getting out of bed) was associated with a 61% decrease in OR for being overweight/obese (OR: 0.39; 95% CI: 0.19, 0.81; P-trend = 0.03) In ETs,3 the highest % protein intake consumed at night (2 h before bedtime) is associated with 3.7-fold increase in OR for being overweight/obese (OR: 3.74; 95% CI: 1.33, 10.5; P-trend = 0.02)
Maukonen et al., 2017 (79) 48-h dietary recalls Protein intake after 20:00 on weekdays: 12.4 (0.8) E% Protein intake after 20:00 on weekdays: 13.1 (0.8) E% Protein intake after 20:00 on weekdays: 13.4 (0.9) E% P-trend = 0.04
— — Protein intakes increased with lower ME score values (ET) after 20:00 on weekdays P-trend < 0.05
Protein intake after 20:00 on weekends: 11.6 (1.3) E% Protein intake after 20:00 on weekends: 12.7 (1.3) E% Protein intake after 20:00 on weekends: 14.2 (1.7) E% P = 0.25
Intake by 10:00 on weekdays: 14.8 (0.9) E% Intake by 10:00 on weekends: 13.6 (0.9) E% Intake by 10:00 on weekdays: 13.6 (0.9) E% P < 0.001P-trend < 0.001
Intake by 10:00 on weekends: 14.8 (0.9) E% Intake by 10:00 on weekends: 13.6 (0.9) E% Intake by 10:00 on weekends: 11.4 (1.2) E% P < 0.003 P-trend < 0.001
Baron et al., 2013 (68) 7-d food logs — After 20:00: 15 ± 12 g (21%)12 More protein at dinner13 P < 0.01
After 20:00: 32 ± 16 g (37%)13 P > 0.01Other analysis:After 20:00: Moderate positive correlation with midpoint of sleepETs5 were associated with higher intake (r = 0.53 P < 0.001)
Daily fat distribution
Xiao et al., 2019 (77) 24-Hour Dietary Assessment Tool No association between timing of fat intake and BMI2,3 Other analysis:No association between total fat intake during the morning (within 2 h after getting out of bed) (P-trend = 0.47), cholesterol (P-trend = 0.35), saturated fat (P-trend = 0.90), and monounsaturated fat (P-trend = 0.42) and OR of being overweight/obese in MTs2No association between total fat intake during night (2 h before bedtime) (P-trend = 0.30), cholesterol (P-trend = 0.06), saturated fat (P-trend = 0.34), monounsaturated fat (P-trend = 0.31), and polyunsaturated fat (P-trend = 0.08) and OR of being overweight/obese in ETs3
Maukonen et al., 2017 (79) 48-h dietary recalls Fat: 23.8 (1.0) E% by 10:00 on weekdays Fat: 23.3 (1.0) E% by 10:00 on weekdays Fat: 19.6 (1.2) E% by 10:00 on weekdays P < 0.001 P-trend = 0.002
Fat: 22.6 (1.6) E% by 10:00 on weekends Fat: 20.3 (1.5) E% by 10:00 on weekends Fat: 18.8 (2.0) E% by 10:00 on weekends P-trend = 0.001
Fat: 21.5 (1.2) E% after 20:00 on weekdays Fat: 23.4 (1.2) E% after 20:00 on weekdays Fat: 26.1 E% after 20:00 on weekdays [26.1 (1.5) E%] P = 0.0025P-trend < 0.001
Fat: 17.3 (2.0) E% after 20:00 on weekends Fat: 20.0 (2.0) E% after 20:00 on weekends Fat: on weekends after 20:00 [26.0 (2.6) E%] P < 0.001
SFAs: 9.0 (0.5) E% by 10:00 on weekdays SFAs: 9.5 (0.5) E% by 10:00 on weekdays SFAs: 7.3 (0.6) E% by 10:00 on weekdays P = 0.002 P-trend = 0.02
SFAs: 8.3 (0.7) E% by 10:00 on weekends SFAs: 7.1 (0.7) E% by 10:00 on weekends SFAs: 6.4 (0.9) E% by 10:00 on weekends P < 0.05
SFAs: 8.8 (0.6) E% after 20:00 on weekdays SFAs: 9.7 (0.6) E% after 20:00 on weekdays SFAs: 10.3 (0.7) E% after 20:00 on weekdays P < 0.03
SFAs: 6.8 (1.0) E% after 20:00 on weekends SFAs: 7.9 (1.0) E% after 20:00 on weekends SFAs: on weekends after 20:00 [10.3 (1.2) E%] P < 0.003
Baron et al., 2013 (68) 7-d food logs — After 20:00: 16 ± 12 g (19%)12 After 20:00: 30 ± 17 g (35%)13 P < 0.05
— 4 h before sleep: 11 ± 9 g (16%)12 Consumed less fat in the 4 h before sleep 10 ± 12 g (12%)13 P < 0.01Other analysis:After 20:00: Moderate positive correlation with midpoint of sleepETs13 were associated with higher intake (r = 0.48, P < 0.01)
Adherence to guidelines
Najem et al., 2020 (76) YFAS — — Chronotype scores were negatively correlated with YFAS scores (r = – 0.10)ETs were associated with a higher YFAS score P = 0.10
Zeron-Rugerio et al., 2019 (64) MD Quality Index for Children and Adolescents — — Lower adherence to the MD (β = 0.019) P = 0.06
Maukonen et al., 2016 (65) FFQ and Baltic Sea diet score — — Lower adherence to the Baltic Sea diet score P-trend < 0.05
De Amicis et al., 2020 (67) 14-item adherence to traditional MD questionnaire Higher adherence (7 ± 2) — Lower adherence (6 ± 2) P < 0.05
Culnan et al., 2013 (72) Gray–Donald Eating Patterns Questionnaire — Junk food consumption did not vary by chronotype at baseline P > 0.05
After 8-wk, chronotype was not associated with change in scores on the Junk Food subscale P > 0.05
Muscogiuri et al., 2020 (70) PREDIMED (Prevención con Dieta Mediterránea) questionnaire PREDIMED score: 8.8 ± 1.9 PREDIMED score: 7.0 ± 1.5 PREDIMED score: 5.1 ± 1.8 (lowest score) P < 0.001Other analysis:Chronotype score was positively associated to PREDIMED scoreMTs were associated with a higher PREDIMED score (r = 0.59, P < 0.001)
Low adherence to MD: 3 (3.0%) subjects Low adherence to MD: 6 (12.0%) subjects Low adherence to MD: 12 (54.5%) subjects P < 0.001
Average adherence to MD: 58 (58.0%) subjects Average adherence to MD: 42 (84.0%) Average adherence to MD: 10 (45.5%) P = 0.001
High adherence to MD: 9 (39.0%) subjects High adherence to MD: 2 (4.0%) subjects High adherence to MD: 0 (0%) P < 0.001
Zerón-Rugerio et al., 2020 (58) 6-d food logs and Quality Index Food Consumption Pattern Diet quality: 57.9 ± 6.814 Diet quality: 60.7 ± 8.11564.0 ± 9.816 Diet quality:67.3 ± 9.417 P < 0.001 or P-trend < 0.001
1

Values reported as mean ± SD unless stated otherwise. P-trend refers to the continuous association between the Morning–Eveningness Questionnaire (MEQ) or Munich Chronotype Questionnaire (MCTQ) score and exposures of interest. CHO, carbohydrate; E%, Percentage of energy intake; ET, evening type; EVOO, Extra-virgin olive oil; IT, intermediate type; PREDIMED, Prevención con Dieta Mediterránea; MD, Mediterranean diet; ME, morning-eveningness; MT, morning type; NS, XXX; TEI, Total energy intake; YFAS, Yale Food Addiction Scale.

2

Earlier chronotype was defined as a chronotype earlier than the median (03:04 h).

3

Later chronotype was defined as a chronotype later than the median (03:04 h).

4

Based on earliest midpoint of sleep quintiles.

5

Based on midpoint of sleep quintile 2.

6

Based on midpoint of sleep quintile 3.

7

Based on midpoint of sleep quintile 4.

8

Based on latest midpoint of sleep quintiles.

9

Based on MEQ score tertile 1: 34–53.

10

Based on MEQ score tertile 2: 54–59.

11

Based on MEQ score tertile 3: 60–76.

12

Based on normal sleep timing (midpoint 04:08 h).

13

Based on late sleep timing (midpoint of sleep 07:15 h).

14

Based on wakeup time <07:52 h and early bedtime <23:48 h and defined as early bedtime/early rise (EE).

15

Based on early bedtime (<23:48 h) and late rise (wakeup time ≥07:12 h) and defined as early bedtime/late rise (EL).

16

Based on late bedtime (≥23:48 h) and wakeup time (<07:52 h) and defined as late bedtime/early rise (LE).

17

Based on late bedtime (≥23:48 h) and late rise (wakeup time ≥07:12 h) and defined as late bedtime/late rise (LL).