A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits - PubMed (original) (raw)

A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits

Shubhroz Gill et al. Cell Metab. 2015.

Abstract

A diurnal rhythm of eating-fasting promotes health, but the eating pattern of humans is rarely assessed. Using a mobile app, we monitored ingestion events in healthy adults with no shift-work for several days. Most subjects ate frequently and erratically throughout wakeful hours, and overnight fasting duration paralleled time in bed. There was a bias toward eating late, with an estimated <25% of calories being consumed before noon and >35% after 6 p.m. "Metabolic jetlag" resulting from weekday/weekend variation in eating pattern akin to travel across time zones was prevalent. The daily intake duration (95% interval) exceeded 14.75 hr for half of the cohort. When overweight individuals with >14 hr eating duration ate for only 10-11 hr daily for 16 weeks assisted by a data visualization (raster plot of dietary intake pattern, "feedogram") that we developed, they reduced body weight, reported being energetic, and improved sleep. Benefits persisted for a year.

Copyright © 2015 Elsevier Inc. All rights reserved.

PubMed Disclaimer

Figures

Figure 1

Figure 1

A scalable method to monitor daily patterns of dietary intake in humans. (a) Schematic of the smartphone-based approach to monitor human eating pattern used to monitor all ingestion events for (b) three week period in healthy adults. Polar plot of (c) all or (d) calorie containing (≥5 kcal) ingestion events of each individual plotted against time of the day (radial axis) in each concentric circle. Data from 156 individuals are shown.

Figure 2

Figure 2

Human eating lacks 3-meals-a-day structure. (a) Percentage of all ingestion events in 1 h bin shows the nadir at 4 am. (b) The fraction of events with <5kcal also reaches its peak at 4am. Therefore we considered 4 am (instead of the midnight) as the beginning of the metabolic day. (c) Representative scatter plot of ingestion events of 11 subjects during the observation period shows the lack of clustering of events into three principal bins for most individuals and a large variation in the total number of events. (d) Number of ingestion events/day in all subjects binned into 10 deciles show a wide distribution of number of total and calorie containing events every day. (e) Frequency distribution and cumulative frequency of inter-meal-interval for the entire cohort. (f) Percentage of calories remaining to be consumed in each hourly bin shows >75% of all calories are consumed after mid-day. (g) Time (median + 25%–50% range and 10–90% interval) at which percentage of maintenance calories consumed in 10% increments are shown.

Figure 3

Figure 3

Activity and eating duration in adult humans. (a) Representative actogram and light exposure pattern (from a wrist-worn device) of a subject for 3 weeks overlaid with ingestion events (from smartphone app) shows that the latter occur erratically throughout the active period. (b) Wakeful activity duration in a subset of the subjects is shown. Each horizontal bar shows the interval between average wake up and bedtime (+ s.e.m., up to 21 days of monitoring). (c) Time interval between waking up and the first caloric ingestion or the last caloric ingestion and going to bed. Bars (orange and blue, y-axis) indicate the percent of the individuals for whom actigraphy was performed with the indicated number of hours (x-axis, 1 h bins) from waking up to the first caloric intake or from the last caloric intake to sleep. Cumulative percentages (secondary y-axis) are shown in color-matched lines. Median time of (d) first and (e) last caloric event of all individuals on different days of the week. Median (25–75% interval in box, 10–90% interval in lines) local time is shown.

Figure 4

Figure 4

Daily duration of caloric intake. (a) Eating duration of all individuals is shown in the order of late (top) to early (bottom) nighttime fasting onset time. (b) The time of last caloric intake weakly positively correlates with the time of first caloric intake. (c) The daily duration of eating does not correlate with the time of first caloric intake (d) but weakly positively correlates with the time of the last caloric intake. The subjects’ daily eating duration does not correlate with their (e) body mass index (BMI, kg/m2) (f) Frequency distribution (red bars) and cumulative percentage (black line and blue filled area) of eating duration (hours).

Figure 5

Figure 5

Improved eating pattern reduces body weight in healthy overweight individuals. (a) Schematic of the study design to test the effect of eating pattern on body weight. (b) Representative “feedogram” of a participant during baseline and during intervention. The times of ingestion events are denoted as prominent black rectangles along the 24 h day represented in each horizontal line (x-axis). Yellow represents the time between 6 am and 6 pm. Eating duration during baseline and intervention are shown as broken lines. (c) The daily eating duration of each individual during baseline (red) and intervention (blue) plotted against the local time (y-axis). (d) Scatter plot and average (± s.e.m.) change in body weight in 8 participants during 3 weeks of baseline monitoring, after 16 weeks of intervention and after 1 year. (e) Average (+ s.e.m.) body weight at the end of 3 weeks baseline, after 16 weeks of monitored intervention and at 1 y. (f) Average (+ s.e.m.) of subjective measures of energy level, hunger and sleep in subjects. These subjective measures were assessed on a scale of 1 to 10, with 10 being the preferred (healthier) end of the range. Higher numbers thus indicated healthier values for the quantity, i.e., more energetic, less hunger at bedtime and more sleep satisfaction. *: p<0.05, T-test.

Similar articles

Cited by

References

    1. Adamovich Y, Rousso-Noori L, Zwighaft Z, Neufeld-Cohen A, Golik M, Kraut-Cohen J, Wang M, Han X, Asher G. Circadian clocks and feeding time regulate the oscillations and levels of hepatic triglycerides. Cell metabolism. 2014;19:319–330. - PMC - PubMed
    1. Asher G, Schibler U. Crosstalk between components of circadian and metabolic cycles in mammals. Cell metabolism. 2011;13:125–137. - PubMed
    1. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384:45–52. - PubMed
    1. Blood ML, Sack RL, Percy DC, Pen JC. A comparison of sleep detection by wrist actigraphy, behavioral response, and polysomnography. Sleep. 1997;20:388–395. - PubMed
    1. Cappuccio FP, Cooper D, D’Elia L, Strazzullo P, Miller MA. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. European heart journal. 2011;32:1484–1492. - PubMed

Publication types

MeSH terms

Grants and funding

LinkOut - more resources