Internet of things-based approach for glycemic control in people with type 2 diabetes: A randomized controlled trial.

AIMS: The utilization of long-term effect of internet of things (IoT) on glycemic control is controversial. This trial aimed to examine the effect of an IoT-based approach for type 2 diabetes. MATERIALS AND METHODS: This randomized controlled trial enrolled 1,159 adults aged 20-74 years with type 2 diabetes with a HbA1c of 6.0-8.9% (42-74 mmol/mol), who were using a smartphone on a daily basis were randomly assigned to either the IoT-based approach group (ITG) or the control group (CTG). The ITG were supervised to utilize an IoT automated system that demonstrates a summary of lifelogging data (weight, blood pressure, and physical activities) and provides feedback messages that promote behavioral changes in both diet and exercise. The primary end point was a HbA1c change over 52 weeks. RESULTS: Among the patients, 581 were assigned to the ITG and 578 were in the CTG. The changes in HbA1c from baseline to the final measurement at 52 weeks [mean (standard deviation)] were -0.000 (0.6225)% in ITG and - 0.006 (0.6449)% in CTG, respectively (P = 0.8766). In the per protocol set, including ITG using the IoT system almost daily and CTG, excluding those using the application almost daily, the difference in HbA1c from baseline to 52 weeks were -0.098 (0.579)% and 0.027 (0.571)%, respectively (P = 0.0201). We observed no significant difference in the adverse event profile between the groups. CONCLUSIONS: The IoT-based approach did not reduce HbA1c in patients with type 2 diabetes. IoT-based intervention using data on the daily glycemic control and HbA1c level may be required to improve glycemic control.

Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements.

BACKGROUND: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. OBJECTIVE: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). METHODS: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. RESULTS: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration-extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. CONCLUSIONS: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day.

Non-exercise Activity Thermogenesis Correlated With Clinical Parameters in Patients With or At-Risk for Chronic Obstructive Pulmonary Disease (COPD): A Pilot Study.

BACKGROUND: Attention to physical activity has grown in patients with chronic obstructive pulmonary disease (COPD), as it serves as a robust indicator for mortality associated with COPD. Non-exercise activity thermogenesis (NEAT) is the energy expenditure due to physical activities besides active sports-like exercises and resistance training in daily life, and decreased NEAT may be related to physical inactivity in patients with COPD. We examined whether NEAT assessed using a questionnaire reflects clinical parameters in patients with or at risk for COPD. METHODS: The study participants consisted of 36 male patients (COPD=28; stage1=6, stage2=14, stage3/4=8, and at-risk for COPD=8) older than 50 years of age. The participants underwent anthropometric measurements, lung function testing, a six-minute walk test, muscle strength testing, and questionnaires, e.g., the COPD assessment test (CAT), modified Medical Research Council (mMRC) dyspnea scale, and Hospital Anxiety and Depression Scale. Image analysis with chest computed tomography (CT) included the number of trunk muscles, bronchial wall thickening, and emphysema (percentage of the lung field occupied by low attenuation area <-950 HU). We evaluated the relationship between these clinical parameters and NEAT questionnaire scores using Pearson correlation analysis and the Tukey-Kramer test. RESULTS: The NEAT score was correlated with the severity of airflow limitation and airway wall thickness measured by chest CT, symptoms evaluated by the mMRC dyspnea scale and CAT, and inspiratory muscle strength and pectoralis muscle area assessed by CT. CONCLUSION: Our study revealed the significance of NEAT as a valuable indicator in assessing the health status of patients with or at risk for COPD. The NEAT score was correlated with various clinical traits, suggesting that incorporating NEAT assessments using a questionnaire can contribute to a comprehensive understanding of the clinical condition in these patients. Further large-scale studies are warranted to validate and generalize these findings across diverse COPD populations.

Effects of energy loads on energy and nutrient absorption rates and gut microbiome in humans: A randomized crossover trial.

OBJECTIVE: This study aimed to determine the effects of different energy loads on the gut microbiota composition and the rates of energy and nutrient excretion via feces and urine. METHODS: A randomized crossover dietary intervention study was conducted with three dietary conditions: overfeeding (OF), control (CON), and underfeeding (UF). Ten healthy men were subjected to each condition for 8 days (4 days and 3 nights in nonlaboratory and laboratory settings each). The effects of dietary conditions on energy excretion rates via feces and urine were assessed using a bomb calorimeter. RESULTS: Short-term energy loads dynamically altered the gut microbiota at the α-diversity (Shannon index), phylum, and genus levels (p < 0.05). Energy excretion rates via urine and urine plus feces decreased under OF more than under CON (urine -0.7%; p < 0.001, urine plus feces -1.9%; p = 0.049) and UF (urine -1.0%; p < 0.001, urine plus feces -2.1%; p = 0.031). However, energy excretion rates via feces did not differ between conditions. CONCLUSIONS: Although short-term overfeeding dynamically altered the gut microbiota composition, the energy excretion rate via feces was unaffected. Energy excretion rates via urine and urine plus feces were lower under OF than under CON and UF conditions.

Prediction of the Physical Activity Level of Community-Dwelling Older Japanese Adults with a Triaxial Accelerometer Containing a Classification Algorithm for Ambulatory and Non-Ambulatory Activities.

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed correction formulae for such populations in Japan. Data for 69 Japanese community-dwelling adults aged 65 to 85 years were used. The total energy expenditure in free-living conditions was measured with the doubly labeled water method and the measured basal metabolic rate. The PAL was also estimated from metabolic equivalent (MET) values obtained with the activity monitor. Adjusted MET values were also calculated with the regression equation of Nagayoshi et al. (2019). The observed PAL was underestimated, but significantly correlated, with the PAL from the ASP. When adjusted using the Nagayoshi et al. regression equation, the PAL was overestimated. Therefore, we developed regression equations to estimate the actual PAL (Y) from the PAL obtained with the ASP for young adults (X) as follows: women: Y = 0.949 × X + 0.205, mean ± standard deviation of the prediction error = 0.00 ± 0.20; men: Y = 0.899 × X + 0.371, mean ± standard deviation of the prediction error = 0.00 ± 0.17.

Delayed Eating Schedule Raises Mean Glucose Levels in Young Adult Males: a Randomized Controlled Cross-Over Trial.

BACKGROUND: 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. OBJECTIVES: We examined the effects of different eating schedules on the 24-h glucose response using a continuous glucose monitor (CGM). METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Relationship between intra-individual variability in nutrition-related lifestyle behaviors and blood glucose outcomes under free-living conditions in adults without type 2 diabetes.

AIMS: This study determined the relationship between intra-individual variability in day-to-day nutrition-related lifestyle behaviors (meal timing, eating window, food intake, movement behaviors, sleep conditions, and body weight) and glycemic outcomes under free-living conditions in adults without type 2 diabetes. METHODS: We analyzed 104 adults without type 2 diabetes. During the 7-day measurement period, dietary intake, movement behaviors, sleep conditions, and glucose outcomes were assessed. Daily food intake was assessed using a mobile-based health application. Movement behaviors and sleep conditions were assessed using a tri-axial accelerometer. Meal timing was assessed from the participant's daily life record. Blood glucose levels were measured continuously using a glucose monitor. Statistical analyses were conducted using a linear mixed-effects model, with mealtime, food intake, body weight, movement behaviors, and sleep conditions as fixed effects and participants as a random effect. RESULTS: Dinner time and eating window were positively significantly correlated with mean (dinner time, p = 0.003; eating window, p = 0.001), standard deviation (SD; both at p < 0.001), and maximum (both at p < 0.001) blood glucose levels. Breakfast time was negatively associated with glucose outcomes (p < 0.01). Sedentary time was positively significantly associated with blood glucose SD (p = 0.040). Total sleep time was negatively significantly correlated with SD (p = 0.035) and maximum (p = 0.032) blood glucose levels. Total daily energy intake (p = 0.001), carbohydrate intake (p < 0.001), and body weight (p < 0.05) were positively associated with mean blood glucose levels. CONCLUSION: Intra-individual variations in nutrition-related lifestyle behaviors, especially morning and evening body weight, and food intake, were associated with mean blood glucose levels, and a long sedentary time and total sleep time were associated with glucose variability. Earlier dinner times and shorter eating windows per day resulted in better glucose control.

Long-term Effects of the Use of a Step Count-Specific Smartphone App on Physical Activity and Weight Loss: Randomized Controlled Clinical Trial.

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.

Does earlier acquisition of motor competence promote pubertal physical activity in Japanese elementary school children: A 4-year follow-up study.

This 4-year follow-up study investigated which profile of motor competence (MC) in early childhood more strongly correlated with moderate-to-vigorous physical activity (MVPA) in early pubescence. A total of 247 first-grade children (152 boys) aged 6 to 7 years old participated in the baseline measurement. MVPA was measured using an accelerometer annually until the fifth grade. Body fat percentage was assessed using dual-energy X-ray absorptiometry at baseline. MC included fundamental movement skills (FMS) and motor performance, which were assessed in terms of locomotor and object control. FMS was assessed by the Test of Gross Motor Development-2, while motor performance (locomotor: run, hop, jump, and side-step, object control: throw, kick, and hand dribble) was assessed by Japanese Physical Fitness and Motor Abilities Test at baseline. Multiple regression analysis showed that locomotor and object control performance were significant predictors of MVPA in later grades in boys but not in girls. These associations between locomotor performance and MVPA mainly remained significant after controlling for body fat and MVPA at baseline. FMS in both genders was not a significant predictor of MVPA in early pubescence. This study suggests that appropriate strategies for increasing MVPA from prepubescence to early pubescence may differ between boys and girls.

Neighborhood-level socioeconomic factors moderate the association between physical activity and relative age effect: a cross-sectional survey study with Japanese adolescents.

BACKGROUND: Relative age effect is defined as a phenomenon where children born early generally perform better than children born later in the same cohort. Physical activity is an important factor that might be influenced by the relative age effect. Socioeconomic factors (e.g., parent's income, education level) are also associated with the adolescent's physical activity. However, no existing study has examined whether socioeconomic factors moderate the relative age effect on the adolescent's physical activity. This study aims to clarify whether and how birth month and socioeconomic factors relate to organized sports and physical activity among adolescents in Japan. METHODS: We conducted a questionnaire survey targeting 21,491 adolescents who live in a widespread neighborhood. We included 8102 adolescents (4087 males and 4015 females: mean age 13.1 ± 1.4) in the analysis. Based on the participants' birth months, we divided them into four groups (April to June, July to September, October to December, January to March). We asked participants to report their organized sports participation. Using the International Physical Activity Questionnaire for Japanese Early Adolescents, we identified their moderate to vigorous physical activity (MVPA). Neighborhood-level socioeconomic factors (areal deprivation, average annual income, education level) were analyzed based on national surveys, such as the population census. We performed multilevel logistic and linear regression analysis for organized sports participation and MVPA, respectively. Moreover, a simple slope analysis was implemented if the interaction between birth month and socioeconomic factor was significant in the multilevel linear regression analysis. RESULTS: Among males, relatively younger adolescents (adolescents who were born later in the same grade) were less likely to participate in organized sports activites (OR=0.90, 95% CI 0.82-0.97, p<0.05), while both males and females engaged in less MVPA (b=-0.54, b=-0.25, p< 0.01, respectively). We observed an interaction between birth month and socioeconomic factors. Among males in low-income neighborhoods, and females in more deprived neighborhoods, relatively younger adolescents engaged in less MVPA. CONCLUSIONS: Socioeconomic factors moderate the relative age effect on adolescents' physical activity. The relative age effect on adolescents' physical activity might be more likely to appear among adolescents from socioeconomically disadvantaged neighborhoods.

Impact of non-exercise activity thermogenesis on physical activity in patients with COPD.

Physical inactivity is associated with comorbidities and mortality in chronic obstructive pulmonary disease (COPD) patients. Although non-exercise activity thermogenesis (NEAT) is important for evaluating the physical activity level (PAL) of patients with chronic diseases, it has not yet been assessed in COPD patients. This study included male patients with COPD (n = 28) and high risk for COPD (n = 8). Total energy expenditure (TEE) and basal metabolic rate (BMR) were measured using the doubly labeled water (DLW) method and indirect calorimetry, respectively. PAL was calculated as TEE/BMR, while the NEAT was obtained from a questionnaire. Physical activity was also assessed using an accelerometer. The total NEAT score was correlated with PAL (r = 0.534, P < 0.001), while PAL was correlated more strongly with the non-locomotive NEAT score (r = 0.548, P < 0.001) than the locomotive NEAT score (r = 0.278, P = 0.10). Regarding accelerometer-obtained data, this questionnaire mainly reflected steps/day and the duration of light locomotive and non-locomotive daily activities. The NEAT score is a possible option for evaluating PAL in daily clinical practice. The present results indicated that non-locomotive activity may have a greater impact on PAL than locomotive activity in COPD patients.

Results from the Japan 2022 report card on physical activity for children and youth.

Background/Objective: The 2020 Tokyo Olympics and Paralympic was held in 2021, although postponed due to the spread of COVID-19. This event might have an impact on physical activity (PA) of children and adolescents, but the national data on PA during the pandemic were not available. Therefore, the goal of the 2022 Japan Report Card on Physical Activity for Children and Youth (The 2022 Japan Report Card) is to assess and track levels of health behaviors related to PA, and health outcomes in Japanese children and adolescents, and environments and government strategy for PA just before the pandemic. Methods: The 2022 Japan Report Card consists of health behaviors and outcomes (8 indicators), and influences on health behaviors (4 indicators). Nationally representative data were used to score the indicators. Results: The key five health behaviors and outcomes (Overall PA: B-; Organized Sport: B-; Active Transportation: A-; Physical fitness: B, Weight status: A) were favorable. Sedentary Behavior and Sleep received C- and D- grades, respectively. Active Play could not be graded (INC). In the influences domain, Family and Peers was graded as C-, while School (B+), Community and Environment (B), and Government (B) were favorable. Conclusions: The 2022 Japan Report Card shows that Japanese children and adolescents had favorable levels of overall PA, active transportation to and from school, and weight status, and there was a generally favorable environment for PA and health, though sedentary behavior and sleep were unfavorable. Future nationally representative surveys on active play are needed.

Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments.

BACKGROUND: Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another. RESEARCH QUESTION: What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials? METHODS: In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days). RESULTS: When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P < 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility. SIGNIFICANCE: Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

Possible association of high-density lipoprotein cholesterol levels with trunk muscle deficits and decrease in energy expenditure in patients with or at risk for COPD: A pilot study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is associated with low muscle mass and function caused by malnutrition and physical inactivity. We aimed to investigate possible associations between serum biomarkers and clinical traits including computed tomography-derived muscle measurements and energy expenditure indices in COPD. METHODS: Total energy expenditure (TEE) was measured by the doubly labeled water method, while physical activity level (PAL) was calculated as TEE/basal metabolic rate. Cross-sections and densities of pectoralis, rectus abdominis, and erector spinae muscles were measured. Serum biomarkers included adiponectin, insulin-like growth factor-1, and high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol (C). RESULTS: HDL-C levels were significantly correlated with all muscle areas, densities, and TEE. Only LDL-C levels were correlated with PAL. CONCLUSIONS: HDL-C level was a potential biomarker for trunk muscle volumes and functions, as well as total energy expenditure in COPD.

An Earlier First Meal Timing Associates with Weight Loss Effectiveness in A 12-Week Weight Loss Support Program.

Recent studies have reported that meal timing may play an important role in weight regulation, however it is unknown whether the timing of meals is related to the amount of weight loss. This study aimed to examine the relationship between indices of meal timing and weight loss during weight loss intervention in adults. A 12-week weight loss support program was conducted for 97 adults (age: 47.6 ± 8.3 years, BMI: 25.4 ± 3.7 kg/m2). After the program, body weight decreased by -3.0 ± 2.7%. Only the start of the eating window was positively correlated with the weight change rate in both sexes (men: r = 0.321, p = 0.022; women: r = 0.360, p = 0.014). The participants were divided into two groups based on the start of the eating window as follows: the early group (6:48 ± 0:21 AM) and the late group (8:11 ± 1:05 AM). The weight loss rate in the early group was significantly higher (-3.8 ± 2.7%) than that in the late group (-2.2 ± 2.5%). The present results showed that the start of the early eating window was associated with weight loss and suggested paying attention to meal timing when doing weight loss.

Association Between Age of Achieving Gross Motor Development Milestones During Infancy and Body Fat Percentage at 6 to 7 Years of Age.

OBJECTIVES: The later achievement of gross motor milestones during infancy is associated with adiposity in early childhood. However, the associations between gross motor development and adiposity after entering primary school are unclear. This study examined the associations between the ages at which six gross motor milestones were achieved and adiposity during early school years. METHODS: This retrospective study was conducted in 2012 and 2013. Data were collected from 225 first-grade primary school children (mean age, 6.9 years; 39% girls). Adiposity was assessed using dual-energy X-ray absorptiometry and expressed as body fat percentage. Data describing the ages of achieving six gross motor milestones (holding head up, sitting, crawling, standing supported, walking supported, and independent walking) were obtained from the Maternal and Child Health Handbooks. RESULTS: Mean body fat percentage was 21.7%. Multiple linear regression analyses revealed that later ages of achieving crawling (p < .001 [95% confidence interval: 0.33-1.16]), standing supported (p < .001 [95% confidence interval: 0.64-1.65]), and walking supported [p = .013 (95% confidence interval: 0.13-1.07)] were associated with increased fat. However, the ages of achieving holding head up (p = .053), sitting (p = .175), and independent walking (p = .736) were not statistically significant. CONCLUSIONS: Achieving crawling, standing supported, and walking supported later predict increased body fat when aged 6-7 years. The practice of observing gross motor milestone achievements may allow early targeted interventions to optimize body composition before beginning school and thereby, potentially prevent childhood obesity.

Age-Related Decline in Physical Activity Level in the Healthy Older Japanese Population.

The Dietary Reference Intakes 2020 divided the older population into those aged 65-74 y and those over 75 y old. However, physical activity level in each age group was not specified. This study examined age-related differences in physical activity level among healthy Japanese older people, and the effect of lifestyles on these differences. In total, 70 people (22 men, 48 women) aged 65-85 y old participated in this study. Total energy expenditure was measured using the doubly labeled water method, and basal metabolic rate using expired gas concentration and volume. The Physical Activity Scale for the Elderly and a triaxial accelerometer were used to assess physical activities. Physical activity level was significantly higher among 65-74 y old (median 1.86) than those over 75 y old (1.76). However, the Physical Activity Scale for the Elderly did not show any significant differences between the age groups. The duration of physical activity with 3.0-5.9 metabolic equivalents was longer for both locomotive and household activities among 65-74 y old than those over 75 y old. Younger participants walked a median of 6,364 steps a day, compared with 4,419 steps for older people. The 65-74 y old participants involved in paid work or who habitually exercised, and those over 75 y old taking more than 40 min a day of moderate to vigorous physical activity, and walking more than the median level for their sex and age group had significantly higher physical activity levels.

Association of Neighborhood Food Environment and Physical Activity Environment With Obesity: A Large-Scale Cross-Sectional Study of Fifth- to Ninth-Grade Children in Japan.

Objective: This study examined the relationship between neighborhood food and physical activity environment, and obesity among elementary and junior high school students in Japan. Methods: The participants were fifth- to ninth-grade children (n=7277), who were attending municipal schools in Japan. Percent overweight (POW) was calculated using their age, gender, height, and weight, which were collected through a questionnaire. A POW of < 20% was considered non-obese, while ≥ 20% was considered obese. Furthermore, using a geographic information system, we investigated the density of convenience stores, fast-food stores, casual restaurants, supermarkets and department stores, parks, sports facilities, stations, and intersections in the school district. Additionally, from the census, we obtained information regarding the population density of the municipality where the participants' schools were located. Multiple logistic regression analysis was used to examine the relationship between obesity and food environment (the food environment model), between obesity and physical activity environment (the physical activity environment model), and among obesity, food, and physical activity environment (the food and physical activity environment model). Results: In the food environment model and the food and physical activity environment model, the density of convenience stores showed a significant positive association. In the physical activity environment model, the density of stations showed a significant negative association. Conclusion: This study's findings can contribute to the development of appropriate community interventions for improving children's health in Japan and similar areas.

Evaluation of energy intake by brief-type self-administered diet history questionnaire among male patients with stable/at risk for chronic obstructive pulmonary disease.

BACKGROUND AND OBJECTIVE: Weight loss and reduced fat-free mass are independent risk factors for mortality among patients with chronic obstructive pulmonary disease (COPD). These factors are important for determining diet therapy and examining the validity of assessment for energy intake (EI). We assessed the agreement of EI between a brief-type self-administered diet history questionnaire (BDHQ) and the doubly labelled water (DLW) method among male patients with stable/at risk for COPD. METHOD: In this cross-sectional observational study, data for 33 male patients were analysed. At the first visit, EI was estimated using a BDHQ (EIBDHQ). Total energy expenditure (TEE) was measured during 13-15 days by the DLW method, while corrected EI was calculated using the TEE and weight change during the DLW period (EIDLW). The difference between EIBDHQ and EIDLW was evaluated by the Bland-Altman method. Multiple regression analysis was used to determine the proportion of variance in the difference between EIBDHQ and EIDLW, as determined by the patient's characteristics. RESULTS: EIBDHQ was 2100 (95% CI: 1905 to 2295) kcal/day in the total population. A fixed bias was observed between EIBDHQ and EIDLW as -186 (95% CI: -422 to 50) kcal/day, while a proportional bias was not detected by the Bland-Altman analysis. Age, weight, anxiety and interleukin 6 were responsible for 61.7% of the variance in the difference between both EIs in a multiple regression model. CONCLUSIONS: The BDHQ underestimated EI among male patients with stable/at risk for COPD, but this estimation error was within an acceptable range compared with previous studies. EIBDHQ precision might be improved by considering common COPD traits, including inflammatory condition and mental state.

Impact of walking aids on estimating physical activity using a tri-axial accelerometer in frail older adults.

OBJECTIVES: This study aimed to compare the estimation error of physical activity level (PAL) estimated using a tri-axial accelerometer between an independent walking group and an assisted walking group with walking aids. METHODS: Subjects were 6 older adults who could walk independently and 10 older adults requiring walking assistance during gait. Total energy expenditure (TEE) was measured using the doubly labelled water (DLW) method over 2 weeks and PAL was calculated as the measured TEE divided by the basal metabolic rate measured using indirect calorimetry (PALDLW). The participants wore a tri-axial accelerometer (Active style Pro HJA-750C) on the waist simultaneously as the DLW period, and the estimated PAL was derived from it (PALACC). RESULTS: The median PAL estimation error in the assisted walking group was -0.30 kcal/day (range: -0.77 to -0.01 kcal/day) and more underestimated than that in the independent walking group (p=0.02). The estimation error of PALACC was significantly correlated with PALDLW (r=-0.80, p<0.01). CONCLUSIONS: Using the accelerometer, PAL was underestimated for older adults who used walking aids but not for those who walked independently under free-living conditions.