The purpose of this investigation is to test the hypothesis that in humans, eating a relatively big amount of chocolate at the wrong time (bedtime) may disrupt our circadian system (change the circadian phase), while taking this same amount of chocolate in the morning (wake up condition) may synchronize it. Other related factors may be also affected such as total body weight and body fat, dietary habits (total energy intake and macronutrient distribution), the timing of food intake and of sleep, daily rhythms of TAP, microflora composition and postprandial glycemia.
Recent studies suggest that not only "what" the people eat, but also "when" the people eat may have a significant role in obesity treatment and in the regulation of the circadian system. Thus, the hypothesis of this study is eating a relatively big amount of chocolate at the wrong time, bedtime may affect: 1. Metabolism: resting energy expenditure, corrected resting energy respiratory quotient (RQ) 2. Glucose metabolism 3. Total weight loss 4. Food intake, total energy intake, and type of food 5. Microflora (feces) 6. Mood 7. Disrupt our circadian system 7a) Changes in Temperature, Actimetry, and Position 7b) Electrocardiogram (ECG) 7c) Melatonin (two points) cortisol rhythm (three points) While having this same amount of chocolate in the morning (wake up condition) may synchronize it. Other related factors may be also affected such as total body weight and body fat, dietary habits (total energy intake and macronutrient distribution), the timing of food intake and of sleep, daily rhythms of TAP, microflora composition and postprandial glycemia. 19 women (postmenopausal) following the habitual dietary habits of participants (ad libitum) will have 30% of the habitual total daily calories in chocolate of participants (Nestle, "chocolate with milk") during two consecutive weeks each under three conditions: eating chocolate within 1 hour of habitual wake-time, eating chocolate within 1 hours of habitual bedtime, or eating no chocolate. No other chocolate (i.e., none at all in control and in the washout weeks). The protocol will be a randomized, cross-over design, with a 1-week washout between each condition. During the 14 days in each condition, the participants will record sleep and activity schedules by dairy, food intake and food timing by phone application, daily rhythms of wrist temperature, activity and position (TAP).
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Evening/Night Chocolate: Eating 100gr of milk chocolate within 1 hours of habitual bedtime Control Chocolate: Eating no milk chocolate or other chocolate Chocolate Morning: Eating chocolate within one hour of habitual waketime.
Resting Energy Expenditure (Indirect Calorimetry)
The investigators will measure by indirect calorimetry the resting energy expenditure after each condition (no chocolate, Morning Chocolate, Night Chocolate) Changes in Resting Energy Expenditure (Indirect Calorimetry) were determined by changes in resting energy expenditure occurred in the participants between baseline and after 2 weeks in each of the three different experimental conditions. Oxygen (O2) consumption (mL/min) and carbon dioxide (CO2) production (mL/min) were measured. The Respiratory Quotient (RQ) was calculated out of O2 consumption (mL/min) and CO2 production (mL/min). Energy expenditure values (kcal/day) were calculated according to the Weir equation: Metabolic rate (kcal per day) = 1440 (3.9 VO2 + 1.1 VCO2)\*
Time frame: Baseline and after 2 weeks in each condition
Total Body Weight
The investigators will measure total body weight after each condition (no chocolate, Morning Chocolate, Night Chocolate)
Time frame: Baseline and after 2 weeks in each condition
Fragmentation of Wrist Temperature (WT) Daily Rhythm
The investigators will measure the fragmentation parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). Intradaily variability (IV) characterizes the rhythm fragmentation. Changes in daily rhythm of wrist temperature fragmentation were measured by changes in the fragmentation parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm). A low fragmentation involves a low intraday rhythmicity of the circadian rhythm. Its values oscillated between 0, when the wave was perfectly sinusoidal, and 2, when the wave was as Gaussian noise.
Time frame: Baseline and after 2 weeks in each condition
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
Purpose
PREVENTION
Masking
NONE
Enrollment
19
Regularity of Wrist Temperature (WT) Daily Rhythm
The investigators will measure the regularity parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). RegularIty is measured as interdaily stability (IS), that means rhythm stability over different days; it varied between 0 for Gaussian noise to 1 for perfect stability, where the rhythm repeated itself exactly day after day. Changes in daily rhythm of wrist temperature regularity were measured by changes in the regularity parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. A regular rhythm means recurring at uniform intervals. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm).
Time frame: Baseline and after 2 weeks in each condition
Amplitude of Wrist Temperature (WT) Daily Rhythm
The investigators will measure the amplitude parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). In the cosinor analysis to characterize the WT rhythm, we calculate amplitude (difference between the maximum \[or minimum\] value of the cosine function and mesor) Changes in daily rhythm of wrist temperature amplitude were measured by changes in the amplitude parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm). A healthy circadian rhythm is considered as the one with high amplitude.
Time frame: Baseline and after 2 weeks in each condition
Changes in Microbiota Diversity (Inverse Simpson Index)
Microbiota diversity changes were measured using Inverse Simpson Index, accounting for both species richness (number of species) and evenness (distribution of species). A higher Inverse Simpson Index indicates greater diversity because it reduces the impact of dominant species and gives more weight to rarer species. Unlike the regular Simpson Index (which decreases as diversity increases), the inverse form is more intuitive: higher values mean a more diverse microbiota. This index is widely used in microbiome studies to compare microbial diversity across different samples, such as in health versus disease states.
Time frame: Baseline and after 2 weeks in each condition
Concentration of Total Short-chain Fatty Acids (SCFAs)
Short-chain fatty acids (SCFAs) in fecal samples were analyzed using gas-liquid chromatography (GLC) with a GC-FID system. Samples were mixed with NaOH, lyophilized, and homogenized with formic acid, methanol, and an internal standard (2-ethyl butyric acid). After ultrasonic treatment and centrifugation, the supernatant was filtered and injected into an Agilent 7890A GC system equipped with a Nukol capillary column. Helium was used as the carrier gas, and the FID temperature was set at 220°C. SCFAs were identified by comparing retention times with authentic standards, and quantification was performed using calibration curves with a high correlation (R² = 0.99). Total average values of SCFA (acetate, propionate, other minorities) in each condition were calculated.
Time frame: After 2 weeks in each condition
Energy Intake
The investigators will measure changes in total energy intake measured as kilocalories between baseline and after 2 weeks in each condition (no chocolate, Morning Chocolate, Night Chocolate) At baseline and during the 2 weeks of each condition, time-stamped photographs were captured with a cell phone app. Total energy intake during the 14 days of each condition was analyzed with the nutritional evaluation software (Grunumur 2.0 8).
Time frame: Baseline and after 2 weeks in each condition
Macronutrient Composition
The investigators will measure macronutrient composition measured as and grams per day between baseline and after 2 weeks in each condition (no chocolate, Morning Chocolate, Night Chocolate). At baseline and during the 2 weeks of each condition, time-stamped photographs were captured with a cell phone app. Macronutrient composition during the 14 days of each condition was analyzed with the nutritional evaluation software (Grunumur 2.0 8).
Time frame: Baseline and after 2 weeks in each condition