Naoki Deguchi

Purpose: To investigate the association between preoperative extracellular water–to–total body water ratio (ECW/TBW) and time to walking independence among female patients undergoing total hip arthroplasty (THA). Methods: This retrospective cohort study included female patients who underwent primary THA between January and December 2022. Preoperative ECW/TBW was measured using bioelectrical impedance analysis and dichotomized at 0.400. The primary outcome was time to walking independence within 14 postoperative days. Cox proportional hazards models assessed the association between ECW/TBW and walking independence, adjusting for age, comorbidities, nutritional status, skeletal muscle mass index, non-operated knee extensor strength, walking pain, and maximum walking speed. Model performance was evaluated using likelihood ratio tests, Harrell’s C-index, and time-dependent area under the curve (AUC). Results: Among 142 patients, 118 (83.1%) achieved walking independence within 14 days. Patients with ECW/TBW ≥ 0.400 achieved walking independence later than those with ECW/TBW < 0.400 (log-rank p < 0.001). In multivariable analysis, ECW/TBW ≥ 0.400 was associated with delayed walking independence (hazard ratio 0.29, 95% CI 0.16–0.52). Including ECW/TBW improved model fit and increased the time-dependent AUC at postoperative day 14. Conclusion: Higher preoperative ECW/TBW is associated with delayed walking independence after THA and may complement preoperative assessment when predicting early postoperative functional recovery.

Background While accurate sleep measurement is vital for older adults, the validity of actigraphy (AG) in free-living environments remains controversial, particularly given the flexible sleep-wake schedules common in this demographic. To address this uncertainty, we assessed the accuracy of wrist AG against in-home portable electroencephalography (EEG) among community-dwelling older adults. Methods Community-dwelling older adults underwent concurrent sleep monitoring using a portable EEG device and a wrist-worn AG for five consecutive nights whenever possible, with monitoring extended to up to seven nights when feasible. Key sleep parameters, including total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency, were derived from both devices. Measurement agreement was assessed using Bland-Altman plots and multilevel modeling, while reliability and accuracy were quantified via intraclass correlation coefficients (ICCs) and mean absolute percentage error (MAPE). Results Forty-nine adults contributed 217 nights of recordings. On average, AG slightly overestimated TST and sleep efficiency and underestimated SOL and WASO compared with EEG. Single-measure ICCs were 0.73 for TST and 0.38 for WASO (0.84 and 0.55 for averages across nights), and the MAPE was 11% for TST but exceeded 50% for SOL and WASO, indicating poor accuracy for these indices. Conclusion In community-dwelling older adults, wrist AG yielded acceptably accurate estimates of average TST, supporting its use in epidemiological monitoring of sleep duration. However, large errors for SOL and WASO indicate that portable EEG- or polysomnography-based assessment remains indispensable when precise evaluation of sleep initiation and nocturnal wakefulness is required.

Sleep health is essential for older adults. However, validity of wrist- and waist-worn devices for assessing sleep under free-living conditions remains unclear. This study evaluated the accuracy of a wrist-worn smartwatch (Silmee W22) and a waist-worn activity monitor (MTN-221) in measuring key sleep parameters, using portable electroencephalography (EEG; Insomnograf K2) as the reference. Healthy older adults wore all devices simultaneously for at least three nights. Total sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency were analyzed using Bland– Altman plots, multilevel models, and intraclass correlation coefficients (ICCs). Fifty-five participants completed the study, yielding valid EEG-paired data for 49 participants with Silmee W22 (238 nights) and 53 with MTN-221 (265 nights). Silmee W22 overestimated total sleep time by 35 min and sleep efficiency by 8.1%, whereas MTN-221 overestimated it by 3 min and sleep efficiency by 1.0%. Both devices underestimated sleep onset latency and wake after sleep onset, with greater discrepancies observed as the estimated values increased. ICCs for total sleep time were 0.60–0.75 for Silmee W22 and 0.66–0.79 for MTN-221, while agreement for sleep onset latency and wake after sleep onset remained lower. While Silmee W22 did not provide sufficiently accurate estimates of total sleep time, MTN-221 yielded estimates that may offer practical benefits for large-scale sleep monitoring in older adults. In both devices, estimates of sleep onset latency, wake after sleep onset, and sleep efficiency should be interpreted with caution due to misclassification of quiet wakefulness. Further algorithm refinement is warranted.

Background: Wearable Internet of Things (IoT) devices are powerful tools for remotely collecting intensive longitudinal data. The TDK Silmee W22, a wristband-type wearable IoT device with a built-in 3-axis acceleration sensor, provides minute-by-minute physical activity data such as estimated metabolic equivalents (METs) and step counts. These measurements can be aggregated to daily estimates; however, their accuracies have not been fully explored in adults under free-living settings. Objective: This study aims to assess the agreement between the TDK Silmee W22 and the research-grade activity monitor, ActiGraph wGT3X-BT, in estimating daily steps and time spent in moderate to vigorous physical activity (MVPA ≥3 METs) in adults under free-living settings. Methods: A convenience sample of young to older adults was recruited from communities across several prefectures in Japan. Participants concurrently wore a TDK Silmee W22 on their nondominant wrist and an ActiGraph wGT3X-BT on the left side of the waist during waking hours for 7 consecutive days. Data were aggregated to daily steps and time spent in MVPA (≥1952 vertical axis counts/minute for ActiGraph) for each participant. A valid day was defined as having ≥ 10 hours of accumulated ActiGraph wear time with ≥100 and <50,000 accumulated steps from both devices. Each valid day was classified as either an active day (≥10,000 steps/day or ≥21.4 minutes MVPA per day) or an inactive day. Bland-Altman plots combined with multilevel analysis and κ statistics were used to assess the agreement between physical activity estimates from the devices. Results: A total of 129 participants (n=66, 51.2% women) aged 23‐89 years provided the final dataset of 884 observations (5 to 7 daily observations/participant). The TDK Silmee W22 estimated an overall mean of 6369 (SE 242) steps/day and 40.3 (SE 1.9) minutes/day spent in MVPA. Although Bland-Altman plots suggested no obvious proportional bias, fixed biases were observed; the TDK Silmee W22 estimated −1203 steps/day (95% limits of agreement [LoA] −4202 to 1796) and +5 minutes/day (LoA−23 to 34) spent in MVPA compared with those estimated by the ActiGraph wGT3X-BT. TDK Silmee W22 and ActiGraph wGT3X-BT, respectively, classified 14% and 23.1% as active by daily step counts (κ=0.65, 95% CI 0.59‐0.72), and 70.4% and 60.9% as active by daily time spent in MVPA (κ=0.64, 95% CI 0.59‐0.69), both indicating moderate agreement. Conclusions: TDK Silmee W22 underestimated step counts and overestimated time spent in MVPA compared with the research-grade ActiGraph wGT3X-BT, which may lead to misclassification of active and inactive days. Caution is warranted when using TDK Silmee W22 data over relatively short periods, as discrepancies—particularly when compared with research-grade monitors—may affect feedback or goal setting.

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Aim: The Questionnaire for Medical Checkup of Old‐Old (QMCOO) is a 15‐item dichotomous questionnaire developed for the early detection and intervention of frailty in a nationwide health checkup program targeting the old‐old (i.e. aged ≥75 years). The Kihon Checklist (KCL) is a 25‐item questionnaire widely used for screening and self‐monitoring frailty status in administrative settings. With fewer items than the KCL, the QMCOO might expedite the frailty screening process. This study tested whether the QMCOO shows noninferiority in detecting frailty compared with the KCL.Methods: Overall, 645 participants aged ≥75 years in the Itabashi Longitudinal Study on Aging were assessed for their frailty status according to the revised Japanese version of the Cardiovascular Health Study criteria. They also completed the QMCOO and the KCL simultaneously. We compared the discriminative performance of the two questionnaires using non‐inferiority testing with an operationally defined non‐inferiority margin of 10% of the area under the receiver operating characteristic curve computed from the KCL.Results: The prevalence of frailty was 8.8%. The area under the receiver operating characteristic curve for the QMCOO in determining frailty was 0.76 (95% CI 0.70, 0.82), and the corresponding area under the receiver operating characteristic curve for the KCL was 0.77 (95% CI 0.69, 0.84). The QMCOO was not inferior to the KCL for frailty discrimination (P for non‐inferiority = 0.006).Conclusions: The accuracy of the QMCOO for determining frailty was not inferior to that of the KCL. The QMCOO might be more acceptable and useful, as it can be applied in a shorter time with fewer questions than the KCL. Geriatr Gerontol Int 2024; 24: 176–181.