Background: Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective: We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods: A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results: On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. Conclusions: Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

Teachers often utilize automatic program repair methods to provide feedback on submitted student code using model answer code. A state-of-the-art tool is Refactory, which achieves a high repair success rate and small patch size (less code repair) by refactoring code to expand the variety of correct code samples that can be referenced. However, Refactory has two major limitations. First, it cannot fix code with syntax errors. Second, it has difficulty fixing code when there are few correct submissions. Herein we propose a new method that combines Refactory and OpenAI's GPT models to address these issues and conduct a performance measurement experiment. The experiment uses a dataset consisting of 5 programming assignment problems and almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. The proposed method improves the repair success rate by 1-21% when the set of correct code samples is sufficient and the patch size is smaller than Refactory alone in 16-45% of the cases. When there was no set of correct code samples at all (only the model answer code was used as a reference for repair), method improves the repair success rate by 1-43% and the patch size is smaller than Refactory alone in 42-68% of the cases.

The purpose of this study was to reveal any association between cardiorespiratory fitness level and excess post-exercise oxygen consumption (EPOC) using three cycling protocols with varying degrees of exercise intensity, i.e., sprint interval training (SIT), high-intensity interval aerobic training (HIAT), and continuous aerobic training (CAT).

We developed a novel method for computing diet‐induced thermogenesis (DIT) in a respiratory chamber and evaluated the validity and reproducibility of the method. We hypothesized that DIT may be calculated as the difference between postprandial energy expenditure (EE) and estimated EE (sum of basal metabolic rate and physical activity (PA)‐related EE). The estimated EE was derived from the regression equation between EE from respiration and PA intensity in the fasting state. It may be possible to evaluate the time course of DIT using this novel technique. In a validity study, we examined whether DIT became zero (theoretical value) for 6 h of fasting in 11 subjects. The mean value of DIT calculated by the novel and traditional methods was 22.4 ± 13.4 and 3.4 ± 31.8 kcal/6 h, respectively. In the reproducibility study, 15 adult subjects lived in the respiratory chamber for over 24 h on two occasions. The DIT over 15 h of postprandial wake time was calculated. There were no significant differences in the mean values of DIT between the two test days. The within‐subject day‐to‐day coefficient of variation for calculated DIT with the novel and traditional methods was approximately 35% and 25%, respectively. The novel method did not have superior reproducibility compared with that of the traditional method. However when comparing the smaller variation in the fasting state than the theoretical value (zero), the novel method may be better for evaluating interindividual differences in DIT than the traditional method and also has the ability to evaluate the time‐course.

Promoting subjective well-being is a crucial challenge in aging societies. In 2022, we launched a community-based intervention trial (the Chofu-Digital-Choju Movement). This initiative centered on fostering in-person and online social connections to enhance the subjective well-being of older adults. This paper describes the study design and baseline survey. This quasi-experimental study involved community-dwelling older adults aged 65–84 years in Chofu City, Tokyo, Japan. A self-administered questionnaire was distributed to 3742 residents (1681 men and 2061 women), and a baseline survey was conducted in January 2022. We assessed subjective well-being (primary outcome); psychosocial, physical, and dietary factors; and the use of information and communication technology variables (secondary outcomes) among the participants. After the intervention involving online classes, community hubs, and community events, a 2-year follow-up survey will be conducted to evaluate the effects of the intervention, comparing the intervention group (participants) with the control group (non-participants). We received 2503 questionnaires (66.9% response rate); of these, the analysis included 2343 questionnaires (62.6% valid response rate; mean age, 74.4 (standard deviation, 5.4) years; 43.7% male). The mean subjective well-being score was 7.2 (standard deviation, 1.9). This study will contribute to the development of a prototype subjective well-being strategy for older adults.

In Japan, there is a lack of physical activity data on health and disease among people with visual impairments, making it difficult to develop specific strategies for health promotion. This study aimed to identify moderate-to-vigorous physical activity (MVPA) among people with visual impairments in Japan, to examine the percentage of them who meet the MVPA recommended activity, and to identify factors related to MVPA. In this cross-sectional study, we analyzed a survey of 169 adults with visual impairments. The relationship between MVPA and related factors was analyzed using binomial logistic regression analysis. The median MVPA was 46 min/day for men and 36 min/day for women, respectively, aged < 65 years, and 26 min/day for men and 34 min/day for women, respectively, aged ≥ 65 years. Seventy-eight percent of the subjects achieved the recommendations by the WHO, and 28% of the subjects < 65 years had achieved the MVPA of 60 min/day recommended by the Japanese Active Guide. Self-efficacy (SE) for PA, recommended PA implementation behavior, subjective walking speed, and exercise habits were significantly associated with MVPA. The current status of MVPA time among people with visual impairments in Japan, as revealed by this study, indicates that it is important to set realistic goals and plan a step-by-step process to achieve the recommendation. In the future, it will be important to develop a program that encourages the improvement of SE and promotes PA.