The Apple Watch Series 10 might be last year's model, but its specs prove it's basically as capable as the Series 11. The main difference? It's cheaper.

An anonymous reader quotes a report from The Guardian: Whether it is doing sums or working out what to text your new date, some tasks produce a furrowed brow. Now scientists say they have come up with...

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Background: Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (eg, Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect. Objective: We aimed to suggest that wear time of a wearable device may provide additional information about perinatal mental health to facilitate screening and early detection of PPD. We proposed that wear time of a wearable device may also be valuable for managing other mental health disorders. Methods: Using the All of Us Research Program dataset, we identified females who experienced childbirth with and without PPD using computational phenotyping. We compared the percentage of days and number of hours per day females with and without PPD wore Fitbit devices during prepregnancy, pregnancy, postpartum, and PPD periods, determined by electronic health records. Comparisons between females with and without PPD were conducted using linear regression models. We also assessed the correlation between Fitbit wear time consistency (measured as the maximum number of consecutive days the Fitbit was worn) during prepregnancy and PPD periods in females with and without PPD using the Pearson correlation. All analyses were run with Bonferroni correction. Results: Our findings showed a strong trend, although nonsignificant after multiple testing correction, that females in the PPD cohort wore their Fitbits more than those in non-PPD cohort during the postpartum (PPD cohort: mean 69.9%, 95% CI 42.7%-97%; non-PPD cohort: mean 50%, 95% CI 25.5%-74.4%; P=.02) and PPD periods (PPD cohort: mean 66.6%, 95% CI 37.9%-95.3%; non-PPD cohort: mean 46.4%, 95% CI 20.5%-72.2%; P=.02). We found no difference in the number of hours per day females in the PPD and non-PPD cohorts wore their Fitbit during any period of pregnancy. Finally, there was no relationship between the consistency of Fitbit wear time during prepregnancy and PPD periods (r=–0.05, 95% CI –0.46 to 0.38; P=.84); however, there was a trend, though nonsignificant, in Fitbit wear time consistency among females without PPD (r=0.25, 95% CI –0.02 to 0.49; P=.07). Conclusions: We hypothesize that increased Fitbit wear time among females with PPD may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that wear time patterns of a wearable device combined with digital biomarkers such as sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns and facilitating timely screenings, which may have implications for other mental health disorders.

Meta is racing to replace smartphones with Meta smart glasses and AR wearables. With cutting-edge features and bold investments, the future of tech is changing fast.

Background: The risk of developing heat-related illness (HRI) increases when personal protective equipment (PPE) is worn, especially in hot and humid environments. While cooling strategies are effective, they must be applied preemptively or delivered promptly, which can be difficult if individuals are working in dangerous environments or wearing contaminated PPE. Wearable sensors can be leveraged to continuously monitor health including heart rate, respiration rate, blood oxygen levels, and physical activity. Metrics can be acquired, and alerts can be generated to inform individuals, colleagues, and/or supervisors of deteriorating health status. Objective: Evaluate the use of wearable sensors for monitoring the real-time health of individuals wearing PPE to mitigate risk of developing an HRI and enable timely intervention. Methods: Individuals participating in planned field trainings/exercises were asked to wear Garmin Fenix 6 smartwatches before, during, and after the event to monitor health and recovery. These individuals wore PPE during the training/exercise. Results: Two pilot studies were conducted using the Garmin Fenix 6 smartwatch to assess wearability, data quality, and data accuracy. Conclusions: The Garmin Fenix 6 smartwatch is an appropriate choice for continuously monitoring the health of individuals wearing PPE. The watch is well tolerated for extended wear periods and does not interfere with PPE. Data quality and accuracy is sufficient for monitoring heart rate and predicting core body temperature. Clinical Trial: The work described in this paper is not part of an ongoing clinical trial. The University of North Carolina Institutional Review Board approved this protocol [22-0817].

Background: Social frailty poses a potential risk even for relatively healthy older adults, necessitating development of early detection and prevention strategies. Recently, consumer-grade wearable devices have attracted attention due to their ability to continuously collect physiological and activity-related data. These data can potentially be used to calculate digital biomarkers for screening social frailty in older adults. Objective: The objective of this study was to explore digital biomarkers associated with social frailty using sensor data recorded via Fitbit devices and evaluate their relationship with health outcomes in older adults. Methods: This cross-sectional study was conducted in 102 community-dwelling older adults. Participants attending frailty prevention programs wore devices from the Fitbit Inspire series on their nondominant wrist for at least 7 consecutive days, during which step count and heart rate data were collected. Standardized questionnaires were used to assess physical functions, cognitive functions, and social frailty, and based on the scores, the participants were categorized into 3 groups: robust, social prefrailty, and social frailty. The sensor data were analyzed to calculate nonparametric and extended cosinor rhythm metrics, along with heart rate–related metrics. Results: The final sample included 86 participants who were categorized as robust (n=28, 33%), social prefrailty (n=39, 45%), and social frailty (n=19, 22%). The mean age of the participants was 77.14 (SD 5.70) years, and 91% (78/86) were women. Multinomial logistic regression analysis revealed that a step-based rhythm metric (intradaily coefficient of variation) was significantly associated with social frailty (odds ratio 1.05, 95% CI 1.01-1.11; P=.01). The heart rate metrics, including the delta resting heart rate and time of transition from rest to activity, showed significant associations with both social prefrailty (odds ratio 0.82, 95% CI 0.68-0.99; P=.04) and social frailty (odds ratio 0.69, 95% CI 0.50-0.95; P=.01). Specifically, delta resting heart rate, defined as the difference between the overall average heart rate and resting heart rate, exhibited significant negative associations with social prefrailty (odds ratio 0.82, 95% CI 0.68-0.97; P=.02) and social frailty (odds ratio 0.74, 95% CI 0.58-0.94; P=.02). Furthermore, analysis using a linear regression model revealed a significant association between the intradaily coefficient of variation and the word list memory score, a measure of cognitive decline (β=−0.04; P=.02). Conclusions: This study identified associations between novel rhythm and heart rate metrics calculated from the step count and heart rate recorded by Fitbit devices and social frailty. These findings suggest that consumer-grade wearable devices, which are low cost and accessible, hold promise as tools for evaluating social frailty and its risk factors through enabling the calculation of digital biomarkers. Future research should include larger sample sizes and focus on the clinical applications of these findings.

The device moves beyond simple vibrations to produce a refined and diverse range of haptic sensations. Most haptic technologies today are limited to delivering simple vibrations. However, human skin is...

Fitbit is overhauling its appHealth Metrics have been revamped to look cleaner and more modernFitbit has also pushed the Google account switch deadline to 2026 After a fairly bleak run, there are two...

The Redmi Watch 5 is perfect for those looking to track workouts with ease.

Wearable technology is becoming essential in daily life. Its applications span health monitoring, virtual reality, brain-computer interfaces, and smart clothing. These devices are continuously evolvin...

This might be the Pixel Watch 3's most important update yet -- and it brings something even the Apple Watch can't do.

Background: Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored. Objective: This study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use. Methods: In total, 19 community-dwelling participants were real-world monitored for 2.5 hours using a lower back wearable device and a reference system combining inertial modules, distance sensors, and pressure insoles. A total of 6 DMO estimates from 164 WBs from 58% (11/19) of the participants (aged 71-90 years; assessed 32-390 days after surgery; Short Physical Performance Battery [SPPB] scores of 3-12; gait speed range 0.39-1.34 m/s) were assessed against the reference system at the WB and participant level. We stratified by WB duration (all WBs, WBs of >10 seconds, WBs of 10-30 seconds, and WBs of >30 seconds) and lower versus higher SPPB scores and observed whether days since surgery and walking aid use affected DMO accuracy and reliability. Results: Across WBs, walking speed and distance ranged from 0.25 to 1.29 m/s and from 1.7 to 436.5 m, respectively. Estimation of walking speed, cadence, stride duration, number of steps, and distance stratified by WB duration showed intraclass correlation coefficients (ICCs) ranging from 0.50 to 0.99 and mean relative errors (MREs) from –6.9% to 12.8%. Stride length estimation showed poor reliability, with ICCs ranging from 0.30 to 0.49 and MREs from 6.1% to 13.2%. Walking speed and distance ICCs in the higher–SPPB score group ranged from 0.85 to 0.99, and MREs ranged from –10.1% to –1.7%. In the lower–SPPB score group, walking speed and distance ICCs ranged from 0.17 to 0.99, and MREs ranged from 13.5% to 32.6%. There was no discernible effect of time since surgery or walking aid use. Conclusions: In total, 5 accurate and reliable real-world DMOs were identified in older adults after hip fracture: walking speed, cadence, stride duration, number of steps, and distance. Accuracy and reliability of most DMOs improved when excluding WBs of 30 seconds than for WBs of 10 to 30 seconds and for participants with higher physical function. DMOs capture daily gait as early as 1 month after surgery also in people using walking aids. However, as most WBs in this cohort were short, there was a trade-off between improving accuracy and reliability by excluding short WBs and losing a substantial amount of data. These results have important implications for establishing the clinical validity of DMOs and evaluating the effects of interventions on daily-life gait, thereby facilitating the design of optimal care pathways.

Background: Football is the most widely played sport globally but carries a high risk of lower limb injuries, particularly among youth athletes. Real-time biomechanical monitoring can play a critical role in injury prevention. However, traditional lab-based systems are often complex and impractical for field use. Recent advances in wearable technology, such as inertial sensors and smart socks, provide more accessible solutions for movement analysis. The DAid® smart sock system is a promising tool, but further evidence is needed to support its use in lower extremity functional assessments. Objective: This correlational study aimed to investigate the correlation between lower limb joint angles, muscle activity, and plantar pressure distribution during the "Single Leg Squat" (SLS) and its variations in youth football players, using wireless wearable sensors in a field-based setting. Methods: Thirty-two youth football players (16 males, 16 females; mean age 14.6 ± 0.5 years) performed SLS movements while wearing the NOTCH® inertial motion sensors, DAid® smart socks (plantar pressure), and PLUX muscleBAN EMG system. Spearman's correlation was used to explore relationships between hip, knee, and ankle joint kinematics, muscle activity (gluteus medius [GM], gluteus maximus [GMx], vastus lateralis [VL], biceps femoris [BF]), and changes in center of pressure (COP) on the plantar surface. Results: A strong positive correlation was found between hip adduction and medial foot pressure (COP1X: r = 0.785, p < .001). Knee flexion was strongly correlated with GM (r = 0.809) and GMx (r = 0.841) muscle activity. Hip internal rotation also showed moderate to strong correlations with both COP variables (COPY1: r = 0.585) and GMx activation (r = 0.477). Significant gender-specific differences were identified: males showed stronger correlations between joint angles and muscle activation, while females demonstrated a strong correlation between knee flexion and overall plantar pressure (COP2W: r = 0.818). Several moderate correlations (0.35 < r < 0.47) further confirmed interactions between joint movement, muscle activity, and plantar pressure. Conclusions: The findings support the #feasibility and utility of wireless wearable sensors—including inertial measurement units and smart socks—for in-field biomechanical analysis in youth football players. The study confirmed key relationships between joint mechanics and plantar pressure distribution, suggesting their relevance in injury risk screening. The DAid® smart sock system, in particular, demonstrated reliable performance for assessing medial-lateral loading patterns associated with hip and knee movement. These insights may help guide neuromuscular training and individualized injury prevention strategies in young athletes.

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