Background. Comparability of accelerometers in epidemiological studies is important for public health researchers. This study aimed to compare physical activity (light, LPA; moderate, MPA; and moderate-to-vigorous, MVPA) and sedentary behavior (SB) data collected using two Omron triaxial accelerometer generations (Active style Pro, ASP) among a sample of Japanese workers in a free-living environment. Methods. Thirty active and sedentary workers (24–62 years) wore two types of ASP accelerometers, the HJA-350IT (350IT) and the HJA-750C (750C), simultaneously for seven consecutive days to represent a typical week. The accelerometers estimated daily average step counts and time spent per day in LPA, MPA, and MVPA. If a participant had data for ≥4 days (>10 h/day) it was considered valid. The difference and agreement between the two ASPs were analyzed using a paired t-test, intra-class correlation coefficients (ICC), and a Bland–Altman analysis in total and for each type of worker. Results. Among all workers, the 750C measured significantly (p < 0.05) less SB, MPA, MVPA, and more LPA compared with the 350IT. The agreements in ICC were high (ICC ≥ 0.94). Conclusions. Compared with the 350IT, the newer generation 750C ASP accelerometer may not provide equivalent estimates of activity time, regardless of the type of physical activity.

Various accelerometers have been used in research measuring physical activity (PA) and sedentary behavior (SB). This study compared two triaxial accelerometers—Active style Pro (ASP) and ActiGraph (AG)—in measuring PA and SB during work and nonwork days in free-living conditions. A total of 50 working participants simultaneously wore these two accelerometers on one work day and one nonwork day. The difference and agreement between the ASP and AG were analyzed using paired t-tests, Bland–Altman plots, and intraclass coefficients, respectively. Correction factors were provided by linear regression analysis. The agreement in intraclass coefficients was high among all PA intensities between ASP and AG. SB in the AG vertical axis was approximately 103 min greater than ASP. Regarding moderate-to-vigorous-intensity PA (MVPA), ASP had the greatest amount, followed by AG. There were significant differences in all variables among these devices across all day classifications, except for SB between ASP and AG vector magnitude. The correction factors decreased the differences of SB and MVPA. PA time differed significantly between ASP and AG. However, SB and MVPA differences between these two devices can be decreased using correction factors, which are useful methods for public health researchers.

Different models of accelerometer have the potential to provide a different estimate of the same physical activity or sedentary behavior. Our study compared the outputs of the Active Style Pro (ASP) and ActiGraph (AG) devices in assessing predicted metabolic equivalents (METs) for specific activities under laboratory conditions. Thirty healthy young adults wore two hip accelerometers (ASP and AG), simultaneously while performing twenty-two activities (eight sedentary, eight household, and six ambulatory activities) in a controlled laboratory setting. For the AG, predicted METs for each activity was calculated using four equations based on vertical-axis and vector magnitude data. Separate paired t-tests and Bland–Altman analysis examined the difference and agreement in METs between AG using four commonly used equations and ASP measurements for each activity. AG devices using different equations calculated significantly different outcomes for most activities compared with ASP devices. The smallest differences in predicted METs estimates between ASP and AG were observed for ambulatory activities. Ambulatory activities demonstrated the best agreement between ASP and AG regardless of which AG equation was used. Our findings can be used to assist researchers in their selection of accelerometer and output estimation equations for measuring physical activity and sedentary behavior in adults.

Our study examined the associations between neighborhood walkability, frailty, and the incidence of long-term care insurance (LTCI) service needs using a prospective cohort survey in a suburban town in Japan. The final sample for analyses comprised 2867 community-dwelling older adults (mean age: 73.0 years). Neighborhood walkability was measured using the Walk Score^®. A total of 387 participants (13.5%) exhibited frailty. The odds of frailty, adjusted for the covariates (sex, age, educational status, marital status, residential status, employment status, subjective economic status) among participants who lived in somewhat walkable/very walkable areas, was 0.750 (95% Confidence Interval, CI: 0.597–0.943) versus those who lived in car-dependent areas. During the 23-month follow-up, 102 participants needed LTCI services (19.0 per 1000 person-years), 41 of whom (21.0 per 1000 person-years) lived in car-dependent areas, and 61 of whom (17.9 per 1000 person-years) lived in somewhat walkable/very walkable areas. As compared with participants who lived in car-dependent areas, the incidence of LTCI service needs was not significantly lower than that of those who lived in somewhat walkable/very walkable areas. Walk Score^® can provide the critical information for the strategies to improve walkability and prevent older adults’ frailty in less walkable areas, contributing to achieving the United Nation’s Sustainable Development Goals (SDGs).

Background: A composite outcome of hospital-associated complications of older people (HAC-OP; comprising functional decline, delirium, incontinence, falls, and pressure injuries) has been proposed as an outcome measure reflecting quality of acute hospital care. Estimating HAC-OP from routinely collected administrative data could facilitate the rapid and standardized evaluation of interventions in the clinical setting, thereby supporting the development, improvement, and wider implementation of effective interventions. Objective: This study aimed to create a Diagnosis Procedure Combination (DPC) data version of the HAC-OP measure (HAC-OP-DPC) and demonstrate its predictive validity by assessing its associations with hospital length of stay (LOS) and discharge destination. Methods: This retrospective cohort study acquired DPC data (routinely collected administrative data) from a general acute care hospital in Tokyo, Japan. We included data from index hospitalizations for patients aged ≥65 years hospitalized for ≥3 days and discharged between July 2016 and March 2021. HAC-OP-DPC were identified using diagnostic codes for functional decline, incontinence, delirium, pressure injury, and falls occurring during the index hospitalization. Generalized linear regression models were used to examine the associations between HAC-OP-DPC and LOS, and logistic regression models were used to examine the associations between HAC-OP-DPC and discharge to other hospitals and long-term care facilities (LTCFs). Results: Among 15,278 patients, 3610 (23.6%) patients had coding evidence of one or more HAC-OP-DPC (1: 18.8% and ≥2: 4.8%). Using “no HAC-OP-DPC” as the reference category, the analysis showed a significant and graded association with longer LOS (adjusted risk ratio for patients with one HAC-OP-DPC 1.29, 95% CI 1.25-1.33; adjusted risk ratio for ≥2 HAC-OP-DPC 1.97, 95% CI 1.87-2.08), discharge to another hospital (adjusted odds ratio [AOR] for one HAC-OP-DPC 2.36, 95% CI 2.10-2.65; AOR for ≥2 HAC-OP-DPC 6.96, 95% CI 5.81-8.35), and discharge to LTCFs (AOR for one HAC-OP-DPC 1.35, 95% CI 1.09-1.67; AOR for ≥2 HAC-OP-DPC 1.68, 95% CI 1.18-2.39). Each individual HAC-OP was also significantly associated with longer LOS and discharge to another hospital, but only delirium was associated with discharge to LTCF. Conclusions: This study demonstrated the predictive validity of the HAC-OP-DPC measure for longer LOS and discharge to other hospitals and LTCFs. To attain a more robust understanding of these relationships, additional studies are needed to verify our findings in other hospitals and regions. The clinical implementation of HAC-OP-DPC, which is identified using routinely collected administrative data, could support the evaluation of integrated interventions aimed at optimizing inpatient care for older adults.