Aim: To examine whether mild early time-restricted eating (eating dinner at 18:00 vs. at 21:00) improves 24-h blood glucose levels and postprandial lipid metabolism in healthy adults. Methods: Twelve participants (2 males and 10 females) were included in the study. In this 3-day (until the morning of day 3) randomized crossover study, two different conditions were tested: eating a late dinner (at 21:00) or an early dinner (at 18:00). During the experimental period, blood glucose levels were evaluated by each participant wearing a continuous blood glucose measuring device. Metabolic measurements were performed using the indirect calorimetry method on the morning of day 3. The study was conducted over three days; day 1 was excluded from the analysis to adjust for the effects of the previous day’s meal, and only data from the mornings of days 2 and 3 were used for the analysis. Results: Significant differences were observed in mean 24-h blood glucose levels on day 2 between the two groups (p = 0.034). There was a significant decrease in the postprandial respiratory quotient 30 min and 60 min after breakfast on day 3 in the early dinner group compared with the late dinner group (p < 0.05). Conclusion: Despite a difference of only 3 h, eating dinner early (at 18:00) has a positive effect on blood glucose level fluctuation and substrate oxidation compared with eating dinner late (at 21:00).

The current study examined how body weight and lifestyle fluctuate between spring, autumn, and winter in Japanese female college students and whether weight gain is associated with changes in physical activity, food intake, and sleep. We measured body weight and lifestyle factors in 31 participants from May 2017 to January 2018. Weight was measured daily in participants’ homes. Physical activity and sleep were measured for three weeks in three seasons using two accelerometers. Food intake was assessed using a validated food frequency questionnaire. Body weight significantly decreased in autumn compared with spring (p < 0.001). Body weight in winter tended to increase compared with autumn (p = 0.052). Step counts and energy intake were significantly different between seasons (p < 0.05). Total time in bed was not significantly different between seasons. In comparisons of changes in lifestyle patterns from autumn to winter between the weight gain (≥0.5 kg) and weight maintenance groups, seasonal changes in lifestyle factors were not significantly different between groups (p > 0.05). The results indicated that body weight and lifestyle were affected by seasonal variability in female college students, but no significant relationships existed between seasonal weight gain and changes in lifestyle patterns.

Body composition changes with age, with fat mass (FM) increasing and fat-free mass (FFM) decreasing. Higher physical activity and high or adequate protein intake are thought to be beneficial in preventing the loss of skeletal muscle mass in the elderly. We aimed to investigate the relationships between physical activity, protein intake, and FFM in older people with mild disability. Total energy expenditure (TEE) under free-living conditions was assessed using the doubly-labelled water (DLW) method, and physical activity was measured using a triaxial accelerometer. Dietary intake was assessed using a self-recorded food intake diary during the DLW period. Percent FFM was significantly positively correlated with protein intake and physical activity level (PAL) after adjustment for age and sex (protein intake r = 0.652, p < 0.001, PAL r = 0.345, p = 0.011). In multiple linear regression analysis, when PAL, moderate-to-vigorous physical activity (MVPA), or protein intake were included, 31%, 32%, and 55%, respectively, of the variation in %FFM was explained. Moreover, the addition of both PAL/MVPA and protein intake explained 61%/60%, respectively, of the variation in %FFM. Either protein intake above the currently recommended level or higher levels of physical activity would be beneficial for the maintenance of high %FFM.

Purpose: To examine how physical activity (PA) and sitting time (ST) are associated with mortality in older Japanese adults.Methodology: We used the data of 10 233 older Japanese adults aged ≥65 years who provided valid responses to the International Physical Activity Questionnaire‐Short Form (IPAQ‐SF) by a mail survey. Both PA and ST were assessed using the IPAQ‐SF. The results were classified into high or low categories using ≥3.0 metabolic equivalent PA (150 min/week) and ST (300 min/day) into the following four groups: High PA (HPA)/Low ST (LST), HPA/High ST (HST), Low PA (LPA)/LST, and LPA/HST. Mortality data were collected from July 30, 2011, to November 30, 2016. We assessed the interaction of PA and ST status with the risk of all‐cause mortality using the multivariable Cox proportional‐hazards model.Results: A total of 1014 people were recorded to have died during a median follow‐up period of 5.3 years (51 553 person‐years). After adjustment for confounders, the risk of mortality was higher in the LPA/HST group than in all other groups (HPA/LST: reference; HPA/HST group: hazard ratio [HR] 0.86 (95% confidence interval [CI]: 0.66 to 1.12); LPA/LST group: HR 1.09 (95% CI: 0.88 to 1.35); LPA/HST group: HR 1.36 (95% CI: 1.10 to 1.67); and multiplicative interaction: HR 1.44 (95% CI: 1.07 to 1.94)).Conclusions: The risk of mortality associated with LPA/HST depends on the level of PA, duration of ST, and their interaction with each other. Our results may be useful in ameliorating the adverse effects leading to mortality in individuals with lower PA, by reducing ST.

Misalignment of meals to the biological clock may cause adverse effects on glucose metabolism. However, the effects of repeated different eating schedules (early compared with late) on glucose concentration throughout the day are poorly understood. We examined the effects of different eating schedules on the 24-h glucose response using a continuous glucose monitor (CGM). Eight young adult males (age, 20.9 ± 3.4 y; body mass index: 21.3 ± 1.8 kg/m²) each followed 2 different eating schedules (early [08:30, 13:30, and 19:30] and late [12:00, 17:00, and 23:00]) in random order. These diet interventions were conducted for 8 d, with an experimental period of 3 d and 2 nights (from dinner on day 7) after 7 d of free living. The 3 meals in each intervention were nutritionally equivalent (55% carbohydrate, 15% protein, and 30% fat). The 24-h mean interstitial glucose concentration on day 8 was obtained under controlled conditions using the CGM (primary outcome). These concentrations were compared among the following 3 schedules using Dunnett's test, with the early eating schedule as reference (1 compared with 2 and 1 compared with 3): 1) early eating schedule (control), 2) late eating schedule according to the clock time (08:00 on day 8 to 08:00 on day 9), and 3) late eating schedule according to the time elapsed since the first meal for 24 h. The 24-h mean ± SD interstitial glucose concentrations when participants followed the late eating schedule were higher than those when they followed the early eating schedule in terms of clock time (91.2 ± 2.9 compared with 99.2 ± 4.6 mg/dL, P = 0.003) and time elapsed (91.2 ± 2.9 compared with 98.3 ± 3.8 mg/dL, P < 0.001). A late eating schedule increases the mean 24-h interstitial glucose concentration in young adult males. This insight will have useful implications in determining meal timings, especially for those with conditions such as diabetes.

Background: Some studies on weight loss promotion using smartphone apps have shown a weight loss effect but not an increase in physical activity. However, the long-term effects of smartphone apps on weight loss and increasing physical activity have not been rigorously examined to date. Objective: The aim of this study was to assess whether the use of a smartphone app will increase physical activity and reduce body weight. Methods: In this parallel randomized clinical trial, participants recruited between April 2018 and June 2019 were randomized in equal proportions to a smartphone app group (n=55) or a control group (n=54). The intention-to-treat approach was used to analyze the data from December 2019 through November 2021. Before the intervention, an hour-long lecture on weight loss instruction and increasing physical activity was conducted once for both groups. Participants in both groups were instructed to weigh themselves immediately after waking up at least once daily from the start of the intervention. Monthly emails were sent advising the participants in both groups on how to lose weight and increase physical activity in order to maintain or increase motivation. Participants in the smartphone app group were instructed to open the app at least once a day to check their step count and rank. The primary outcome was daily accelerometer-measured physical activity (step count) and the secondary outcome was body weight. Since there was a significant difference in the wear time of the accelerometer depending on the intervention period (P<.001), the number of steps and moderate-to-vigorous physical activity were also evaluated per wear time. Results: The mean age of the 109 participants in this study was 47 (SD 8) years. At baseline, the mean daily total steps were 7259 (SD 3256) steps per day for the smartphone app group and 8243 (SD 2815) steps per day for the control group. The difference in the step count per wear time between preintervention and postintervention was significantly different between the app group and the control group (average difference [95% CI], 65 [30 to 101] steps per hour vs –9 [–56 to 39] steps per hour; P=.042). The weight loss was –2.2 kg (SD –3.1%) in the smartphone app group and –2.2 kg (SD –3.1%) in the control group, with no significant difference between the groups. In addition, when divided into weekdays (Monday through Friday) and weekends (Saturday and Sunday), there was a significant interaction between step counts (P=.004) and MVPA (P=.003) during the intervention, with the app group showing higher interaction on weekends than the control group. Conclusions: In this trial, the group with the smartphone app intervention showed increased physical activity, especially on weekends. However, this increased physical activity did not lead to increased weight loss. Trial Registration: University Hospital Medical Information Network UMIN000033397; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000037956