Time-restricted meal timing -
But Scheer said more research is needed before he's comfortable making any recommendations. In the second study, firefighters in San Diego, California, followed a Mediterranean diet rich in fruit, vegetables, fish and olive oil for 12 weeks.
Seventy firefighters ate their meals within a hour window, while the rest generally ate over 13 hours. The firefighters logged their meals in an app and wore wearable devices to help researchers track their blood sugar levels.
Most participants in the hour group ate between the hours of 8 or 9 a. though they occasionally strayed outside the window, extending to an or hour period. Among healthy firefighters, time-restricted eating showed "favorable effects that should translate into less built-up plaque in the arteries and less cardiovascular disease," Peterson said.
The firefighters in that group also reported an improved quality of life. Among firefighters with pre-existing risk factors for heart disease, time-restricted eating decreased blood pressure and blood sugar levels. Panda said past research in animals has shown that during periods of fasting, "organs get some rest from digesting food so they can divert their energy towards repairing cells.
A fasting period also seems to allow for the break down of built-up toxins, Panda said. And Peterson added that during fasts, the body can get rid of sodium, which in turn lowers blood pressure.
She said she wouldn't be surprised if we eventually see national recommendations about eating windows or meal times in the next five to 10 years in the U. Aria Bendix is the breaking health reporter for NBC News Digital. IE 11 is not supported.
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Featured NBC News Now Nightly Films Stay Tuned Special Features Newsletters Podcasts Listen Now. We examined changes in timing of eating in relation to sleep following a randomized controlled trial of a week unrestricted eating non-TRE vs. restricted eating TRE: self-selected eating window of 8 h intervention.
We characterized the change in timing of eating and eating patterns from pre- to end-intervention, evaluated whether the TRE intervention reduced late-night eating, and examined associations between timing of eating and health outcomes.
We hypothesized that the TRE intervention would reduce late-night eating and that earlier timing of the last eating occasion would be associated with improved metabolic health outcomes. This is a secondary analysis of the See Food Study, a randomized controlled trial examining the impact of TRE on body composition and glycemic outcomes NCT Details of the study have been previously reported in the primary outcomes manuscript 2.
The study was approved by the University of Minnesota's Institutional Review Board IRB and all participants provided written informed consent before study participation. They had a stable sleep and work schedule no shift work, bed and wake times no more than 2 h variance on 6 out of 7 days, and no more than 4 h variance on the 7 th day.
Participants were excluded if they were pregnant or anticipated becoming pregnant, nursing, or had any significant medical problems such as diabetes or cardiovascular disease based on a screening visit and laboratory evaluation.
After informed consent, participants were randomized to either TRE or non-TRE intervention, stratified by sex and age ±45 years. The TRE group self-selected a daily 8 h eating window, such as 10 am-6 pm or 11 am-7pm, which they were instructed to consistently maintain for the 12 week intervention.
During the 8 h eating window, the TRE group could eat ad libitum. Outside the eating window, the TRE group could only drink water and take their medications.
No other dietary recommendations were given to the TRE group. The non-TRE group was asked to eat ad libitum per their typical eating habits. All participants were asked to document all eating occasion except water or medications using the mobile application, the myCircadian Clock mCC: www.
org throughout the entire study. Height, weight, body composition, oral glucose tolerance test, and hemoglobin A1c HbA1c were obtained at pre-intervention and week end-intervention, and participants wore an ActiGraph Link ActiGraph, Pensacola, FL to monitor sleep and activity for 2 weeks just prior to randomization pre-intervention and for 2 weeks just prior to study conclusion week 10—12; end-intervention.
Each participant was instructed to document all oral intake including food, beverages along with an identifying text entry, using the mCC mobile application from the pre-intervention period through Week 12 of the study. The data were time-stamped and adherence to logging TRE and non-TRE group and compliance to eating windows TRE group were derived from the mCC data.
Criteria for adherence have been previously described 2. Summary metrics of meal timing were determined from the mCC data. As previously described, eating occasions were classified as any food or beverage intake event within 15 min of each other 2. Medication and water were excluded from the analysis.
To be included for analysis, all participants were required to have at least 1 day of meal timing data where at least 2 eating occasions i.
Timing of eating occasions were expressed as absolute clock time and relative to sleep and wake as defined by actigraphy scoring.
These classifications were newly developed for the purpose of the current analyses. The categorial classifications of the beginning and end eating windows were combined to create a composite meal timing classification. All potential meal timing classifications are described in the Supplementary Table.
These classifications were also used to approximate different TRE patterns: early-TRE, late-TRE, or intermediate-TRE pattern. Intermediate-TRE was defined as delaying the first eating occasion and advancing the last meal of the day first eating occasion occurring between 3.
The Actigraph Link ActiGraph, Pensacola FL , a triaxial accelerometer, was worn on the non-dominant wrist for 2 weeks at pre-intervention and again at end-intervention. Data were collected in 60 second epochs and scored using the Cole-Kripke algorithm. Participants reported their typical weekday and weekend bed and wake times, which were used to facilitate actigraphy scoring.
Sleep variables calculated included bedtime, waketime, and total sleep time. At pre-intervention and end-intervention, height and weight were measured by study staff. DXA GE Healthcare Lunar DXA; General Electric Medical Systems, Madison, WI assessed body composition using standard positioning and imaging protocols.
Markers of insulin sensitivity were measured at pre-intervention and end-intervention using an oral glucose tolerance test OGTT following an 8 h fast.
After baseline sampling, 75 g glucose Trutol 75 Glucose Tolerance Beverage, Thermo Scientific, Waltham, MA was consumed orally and plasma glucose and serum insulin were sampled every 30 min over 2 h.
Meal timing data with adequate documentation on the mCC app were aligned with sleep data such that intake events during the day were associated with the following night of sleep. If sleep aligned data were not available, the preceding meal timing data were excluded.
To be included for analyses, at least 2 days of aligned meal timing data and sleep data were required. One individual was excluded from the non-TRE at end-intervention because they only provided 1 day of sleep data. In the TRE group, one individual did not have any meals logged during the sleep measurement period.
Thus, the end-intervention analysis included 8 individuals in the non-TRE group and 10 individuals in the TRE group. Demographic characteristics were summarized with descriptive statistics. Independent samples t -tests were used to compare pre-intervention demographic, sleep, and meal timing variables between the TRE and non-TRE groups.
Linear regression analyses examined associations between intervention group and change in meal timing and sleep, controlling for pre-intervention values. Pearson product moment correlations were also used to examine associations between timing of the last meal and health outcomes at pre-intervention BMI, HbA1c, fasting glucose, HOMA-IR, and Matsuda Index.
SPSS Statistics 26 and R statistical software version 4. Eleven participants completed the TRE intervention No participants were taking any medication for dysglycemia or weight loss treatment during the study. On average, participants had 8 ± 3.
There were no significant between group differences. We evaluated the relationship between late night eating and health outcomes at pre-intervention Supplementary Figure. There were no other significant associations between health outcomes and the timing of last eating occasions. There were also no significant associations at end-intervention after accounting for pre-intervention values.
Between the TRE and non-TRE groups, there were no differences in the timing of eating occasions during the pre-intervention period. At end-intervention, the TRE group significant delayed the time of the first eating occasion of the day, both when expressed by absolute clock time and as hours since wake Table 1 ; Figure 1.
In contrast, the timing of the first and last eating occasions in the non-TRE group remained similar between pre- to end-intervention. Figure 1. Timing of eating for each day at Pre- and End-Intervention.
Individual participants are represented by different colored points. The non-TRE participants are depicted on the top, while the TRE participants are depicted on the bottom; each participant is depicted once at both Baseline Left and End Intervention Right.
A shift toward the line of identity blue solid line , would be indicative of a shortened eating window consistent with TRE. Upper left box represents an early-TRE eating pattern, whereas the lower right box represents a late TRE eating pattern.
The middle box represents an intermediate-TRE eating pattern which the timing of the first and last meals were shifted by similar magnitudes. Eating pattern was categorized using the timing of the first and last eating occasion of the day Table 2. Finally, no participants in the TRE group elected to follow a Late-TRE pattern.
Finally, there were 2 individuals in the non-TRE group who adopted a Late-TRE eating pattern where they delayed both their first and last eating occasions. We investigated change in late-night eating, or the timing of the last eating occasion relative to bedtime expressed as hours before bed from pre- to end-intervention.
At pre-intervention, all participants had at least one instance of eating within 2 h of bedtime. Pre-intervention values and change in actigraphy-estimated sleep variables are reported by group in Table 3.
Compared to the recommended 7—9 h 24 , participants in both groups on average obtained insufficient sleep on weekdays and weekends at pre-intervention and end-intervention. Average sleep duration at pre-intervention for the entire sample was 6. No significant differences in weekday or weekend sleep variables were found between the TRE and Non-TRE groups at either pre-intervention or end-intervention.
Changes in all actigraphy variables from pre- to end-intervention were not significantly associated with group. Analyses revealed a significant association between sleep duration and change in eating window, controlling for pre-intervention eating window.
Table 3. Pre-intervention and change from pre- to end-intervention for actigraphy-estimated sleep by group. In this examination of participants following a randomized-controlled trial of a self-selected TRE intervention, we found that the TRE group significantly delayed the first eating occasion of the day and advanced the last eating occasion of the day, with most participants following an Early or Intermediate TRE eating pattern in the TRE group.
In contrast, most participants in the non-TRE group maintained an Early-Late eating pattern such that their food consumption occurred across the entire day. Notably, only 4 participants in the TRE condition were consistent across days in their eating pattern, while most participants had a combination of both Early and Intermediate eating.
Findings from the current study suggest that TRE intervention does not worsen sleep, as we found no change in objectively measured sleep duration or timing from pre- to end-intervention for either group.
Moreover, greater restriction of the eating window was associated with longer sleep duration. However, study participants on average obtained less than the recommended 7—9 h of sleep per night, suggesting insufficient sleep. As insufficient sleep is independently associated with obesity, insulin resistance, type 2 diabetes, and metabolic syndrome 25 — 27 , a focus on TRE's effects on sleep may be warranted.
Future research incorporating behavioral strategies to enhance sleep during TRE intervention could be considered. Late-night eating was significantly reduced in the TRE group, with participants completely ceasing food consumption within 2h of bedtime at end-intervention.
This was in contrast to the non-TRE group which did not change their late-night eating habits. Prior studies have shown that late-night eating is associated with obesity, dyslipidemia, hyperglycemia, and metabolic syndrome 28 , consistent with our findings that late-night eating was associated with higher fasting glucose and HbA1c.
Sutton et al. investigated the effects of an early TRE intervention 6-h eating window which the end time was set to on weight and metabolic outcomes.
Despite no change in weight, they found improvements in insulin sensitivity, beta cell responsiveness, and blood pressure 4. In contrast, Weiss recently showed that in a TRE intervention which restricted the eating window to —, there were no changes in glycemic outcomes Data from the present study are consistent with previous reports in the literature.
Together, these findings suggest that reduced late-night eating may be one mechanism by which TRE might improve metabolic measures.
Further studies on the timing of TRE are warranted to understand which components of TRE interventions drive the reported weight and metabolic related changes.
The field of TRE suggests restricting eating to a certain time of day affects daily activity-rest rhythms and the intrinsic circadian clock Specifically, TRE may alter the circadian misalignment that commonly occurs in today's society due in part to activity and eating through the day and night facilitated by artificial light and the prevalence of personal electronic devices Because participants in the current study self-selected their eating window, it is possible that they chose their TRE schedule based on their convenience, regardless of their natural circadian rhythm.
A longitudinal study of the British Birth Cohort showed that more irregular eating patterns were associated with future risk for metabolic syndrome and obesity Others have demonstrated that consistency in the timing of other lifestyle behaviors, like exercise, is related to improved weight loss 32 , thus considering the variability of behavior timing may be an important consideration for future studies.
Notably, 6 participants were classified as having an Inconsistent meal timing pattern at end-intervention. Further study considering the impact of meal timing variability in the context of TRE and impact on health outcomes is suggested.
Additionally, future research assessing chronotype morning vs. evening preference and circadian rhythms directly via melatonin in conjunction with both self-selected and assigned TRE schedules may help to better elucidate how TRE interacts with circadian rhythms to effect weight loss.
Strengths of the current study include objective measurement of sleep variables, real-time assessment of dietary timing, and a randomized, age- and sex-matched non-TRE control group. Having the control group provided critical comparison, as several participants still shifted their eating patterns despite being instructed to maintain their usual eating habits.
Limiting factors include the small sample size, which limited our power and reduced our ability to control for potential covariates in analyses, though the current sample is similar to many of the existing TRE studies 3 , As food intake was captured only by images and text description in the mCC app, we could not quantify energy intake, another acknowledged limitation.
Thus, interpretation of results should be considered in the context of this preliminary pilot study. A majority of participants were female, which may limit generalizability of findings to males.
Part of the inclusion criteria for the current study required participants to have a relatively stable sleep schedule; thus, our findings may not be representative of those with more variable schedules.
These data emphasize the importance of assessing adherence to intervention and control group requirements in behavioral interventions particularly in those interventions which are popular in the mainstream media. The TRE intervention did not negatively impact sleep, and those with greater restriction of their eating window also had longer sleep duration.
We recommend applying the meal timing classification methodology to future trials of TRE in larger sample sizes. Future research examining eating variability, directly intervening on sleep habits, and comparing Early vs.
Late TRE, as well as Intermediate TRE as a novel framework in larger samples may help elucidate mechanisms responsible for the effect of TRE on weight loss in individuals with obesity.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The studies involving human participants were reviewed and approved by University of Minnesota IRB.
LC, SS, and JB: conceptualization. LC, DM, SP, and EM: methodology. SS and JB: formal analysis and writing—original draft preparation. LC: investigation and funding acquisition. SS, JB, EM, SP, DM, and LC: writing—review and editing.
All authors have read and agreed to the published version of the manuscript. This work was supported by the Healthy Foods Healthy Lives program 17SFR-2YR50LC to LC and the National Institutes of Health NIH National Center for Advancing Translational Sciences, UL1TR; NIH NIDDK, 1K23DK to SS.
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Author SP has authored the book The Circadian Code, for which he receives author royalties and in which he specifically recommends time restricted eating.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.
Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Gill S, Panda S. A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits.
Cell Metab. doi: PubMed Abstract CrossRef Full Text Google Scholar. Chow LS, Manoogian ENC, Alvear A, Fleischer JG, Thor H, Dietsche K, et al.
Time restricted eating effects on body composition and metabolic measures in humans who are overweight: a feasibility study. Obesity Silver Spring. Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, et al.
Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metabol.
Background: Time Time-resticted eating TRElimiting Time-restricted meal timing to a specific daily window, is timimg novel dietary intervention, but Time--restricted mechanisms by Time-restricted meal timing Giming results in weight loss remain unclear. The goal of the current study Sugar crash symptoms to examine changes in eating patterns, sleep, and late-night eating, and associations with health outcomes in a secondary analysis of a week self-selected TRE intervention. All participants logged oral intake using the myCircadian Clock mobile application throughout the entire intervention. Anthropometrics, HbA1c, an oral glucose tolerance test, and 2 weeks of actigraphy monitoring were completed at pre-intervention and end-intervention. Independent samples t -tests compared differences between groups. Data are presented as mean ± standard deviation. There are a number of Quick and easy sports meals Probiotics for respiratory health to mfal weight. A relatively new approach Time-restrifted time-restricted timkng also Time-restricted meal timing intermittent fasting timig limiting meals to timiny certain window of time each day — was put to the test in a small randomized trial. Findings were published April 21,in The New England Journal of Medicine. Researchers in China randomly apportioned obese men and women into two groups. One group was told to limit daily calorie intake 1, to 1, calories or men, and 1, to 1, calories for women. The other group was told to follow the same calorie limits but to eat only between 8 a.
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