Long-term gait measurements in daily life: Results from the Berlin Aging Study II (BASE-II)

Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults

Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45-79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test-retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test-retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications.

Self-reported and accelerometer-based assessment of physical activity in older adults: results from the Berlin Aging Study II

Physical activity (PA) has a substantial impact on health and mortality. Besides questionnaires that rely on subjective assessment of activity levels, accelerometers can help to objectify an individual's PA. In this study, variables estimating PA and sleep time obtained through the wGT3X-BT activity monitor (ActiGraph LLC, USA) in 797 participants of the Berlin Aging Study II (BASE-II) were analyzed. Self-reports of PA and sleep time were recorded with Rapid Assessment of Physical Activity (RAPA) and the Pittsburgh Sleep Quality Index sleep questionnaire (PSQI). Total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), fasting glucose, and hemoglobin A1c (HbA1c) were determined in an accredited standard laboratory. Of all participants, 760 fulfilled the PA wear-time criteria. In this sample mean age was 75.6 years (SD: 3.8 years, range 66.0-94.1 years) and 53% of the included participants were women. Average wear time was 23.2 h/day (SD 1.3 h/day). Statistically significant differences between RAPA groups were found for all accelerometric variables except energy expenditure. Post-hoc analysis, however, suggested low agreement between subjective and device-based assessment of physical activity. TC, HDL-C, LDL-C, TG, fasting glucose and HbA1c were weakly correlated with accelerometric variables (Pearson's r ≤ 0.25). Device-based average sleep time per night (mean sleep time = 6.91 h, SD = 1.3, n = 720) and self-reported average sleep time per night (mean sleep time = 7.1 h, SD = 1.15 h, n = 410) were in a comparable range and moderately correlated (Pearson's r = 0.31, p < 0.001, n = 410). Results from this study suggest that self-reported PA obtained through the RAPA and device-based measures assessed by accelerometers are partially inconsistent in terms of the physical activity level of the participants. Self-reported and device-based measures of average sleep time per night, however, were comparable.

Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults

This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.

Associations of Gait Speed, Cadence, Gait Stability Ratio, and Body Balance with Falls in Older Adults

To investigate the association between gait speed (GS), cadence (CAD), gait stability ratio (GSR), and body balance (BB) with falls in a large sample of older adults. The analysis included 619 individuals-305 men and 314 women (69.50 ± 5.62 years)-residing in the Autonomous Region of Madeira, Portugal. Mobility in GS, CAD, and GSR was assessed using the 50-foot walk test and BB by the Fullerton Advanced Balance scale. The frequency of falls was obtained by self-report. Linear regression analysis showed that higher performance in GS and BB was able to reduce the risk of falling by up to 0.34 and 0.44 times, respectively. An increase in the GSR value enhanced the risk of falling by up to 0.10 times. Multinomial analysis indicated that, in relation to the highest tertile (reference), older adults classified with GS and BB performance in the lowest tertile (lowest) had an increased chance (OR) of falling by up to 149.3% and 48.8%, respectively. Moreover, in relation to the highest tertile, the performance of the GSR classified in the lowest and medium tercile showed an increase in the chance of falling by up to 57.4% and 56.4%, respectively.

The Role of Physical Function in the Association between Physical Activity and Gait Speed in Older Adults: A Mediation Analysis

Adequate levels of physical function (PF) are essential for vulnerable older adults to perform their daily tasks safely and remain autonomous. Our objective was to explore the mediating role of PF in the relationship between physical activity (PA) and gait speed (GS) in a large sample of older adults from the north of Brazil. This is a cross-sectional study that analyzed 697 older adults (mean age 70.35 ± 6.86 years) who participated in the project "Health, Lifestyle, and Physical Fitness in Older Adults in Amazonas" (SEVAAI). PA was assessed using the Baecke Questionnaire, PF using the Senior Fitness Test, and GS using the 50-foot Walk Test. Mediation pathways were analyzed to test the possible mediating role of PF between specific PA domains (PA-total score, PA-housework, PA-sport, PA-leisure) and GS. Regarding PA-total, the analysis showed that high-performance GS was partially mediated in approximately 19% by better PF performance. Moreover, the PF could partially mediate the association between PA-sport and PA-leisure with GS, at levels of approximately 9% and 46%, respectively. An inverse relationship was observed between PA-housework (sedentary lifestyle) and GS. This association was partially mediated to an extent of approximately 9% by better PF performance. We conclude that PF plays a crucial role in mediating the association between PA and GS among vulnerable older adults.

Step detection and energy expenditure at different speeds by three accelerometers in a controlled environment

Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide. In the USA, less than 10% of the adult population were able to meet the PA recommendations when accelerometers were used to assess PA habituation. Accelerometers significantly differ from each other in step recognition and do not reveal raw data. The aim of our study was to compare a novel accelerometer, Sartorio Xelometer, which enables to gather raw data, with existing accelerometers ActiGraph GT3X+ and activPAL in terms of step detection and energy expenditure estimation accuracy. 53 healthy subjects were divided into 2 cohorts (cohort 1 optimization; cohort 2 validation) and wore 3 accelerometers and performed an exercise routine consisting of the following speeds: 1.5, 3, 4.5, 9 and 10.5 km/h (6 km/h for 2nd cohort included). Data from optimization cohort was used to optimize Sartorio step detection algorithm. Actual taken steps were recorded with a video camera and energy expenditure (EE) was measured. To observe the similarity between video and accelerometer step counts, paired samples t test and intraclass correlation were used separately for step counts in different speeds and for total counts as well as EE estimations. In speeds of 1.5, 3, 4.5, 6, 9 and 10.5 km/h mean absolute percentage error (MAPE) % were 8.1, 3.5, 4.3, 4.2, 3.1 and 7.8 for the Xelometer, respectively (after optimization). For ActiGraph GT3X+ the MAPE-% were 96.93 (87.4), 34.69 (23.1), 2.13 (2.3), 1.96 (2.6) and 2.99 (3.8), respectively and for activPAL 6.55 (5.6), 1.59 (0.6), 0.81 (1.1), 10.60 (10.3) and 15.76 (13.8), respectively. Significant intraclass correlations were observed with Xelometer estimates and actual steps in all speeds. Xelometer estimated the EE with a MAPE-% of 30.3, activPAL and ActiGraph GT3X+ with MAPE percentages of 20.5 and 24.3, respectively. The Xelometer is a valid device for assessing step counts at different gait speeds. MAPE is different at different speeds, which is of importance when assessing the PA in obese subjects and elderly. EE estimates of all three devices were found to be inaccurate when compared with indirect calorimetry.

[The first steps after a proximal femoral fracture : Sensor-based mobility exploration in geriatric trauma patients]

BACKGROUND

Sensor-based monitoring allows continuous observations of patient mobilization after proximal femoral fractures. A wrist-worn motion tracker allows long-term observation that is low in interruption and constraints for subjects.

OBJECTIVE

Description of steps development after hip fracture surgery on a specialized geriatric trauma ward and beyond.

MATERIAL AND METHODS

In the explorative long-term field research study, an applicable motion tracker observed steps per day of 20 patients (80% female, mean age 85.2 years ± 7.86 years) for 10 weeks. Weekly mean values (days 1-7, 8-14 etc.) of steps per day formed the database for descriptive analysis (mean, SD, min, max, median).

RESULTS

During observation weeks (ow) a positive development of steps took place. A mean increase factor of 1.285 (±0.351) occurred from ow 1 (M = 353.57 ± 310.15) to ow 10 (M = 2482.07 ± 1374.12). The highest increase by a factor of 1.8 could be reported from ow 2 (M = 556.27 ± 478.11) to ow 3 (M = 1024.86 ± 921.24) as well as from ow 6 (M = 1268.21 ± 880.47) to 7 (M = 2367.14 ± 1680.08). A slight decrease of steps occurred from ow 4 (M = 1208.27 ± 1210.45) to ow 5 (0.99-fold) and from ow 9 (M = 2689.98 ± 2339.71) to 10 (0.92-fold). High ranges and standard deviations in relation to the mean occurred constantly. The presence of several step development groups could be presumed.

CONCLUSION

Motion tracker and the variable steps per day can represent the ability to walk within an everyday environment, with a possible underestimation of < 10%. Differences regarding observation lengths and disruptions occurred. Cluster analysis should detect group attributes of different courses of development in subsequent studies.

Real-world gait speed estimation, frailty and handgrip strength: a cohort-based study

Gait speed is a reliable outcome measure across multiple diagnoses, recognized as the 6th vital sign. The focus of the present study was on assessment of gait speed in long-term real-life settings with the aim to: (1) demonstrate feasibility in large cohort studies, using data recorded with a wrist-worn accelerometer device; (2) investigate whether the walking speed assessed in the real-world is consistent with expected trends, and associated with clinical scores such as frailty/handgrip strength. This cross-sectional study included n = 2809 participants (1508 women, 1301 men, [45-75] years old), monitored with a wrist-worn device for 13 consecutive days. Validated algorithms were used to detect the gait bouts and estimate speed. A set of metrics were derived from the statistical distribution of speed of gait bouts categorized by duration (short, medium, long). The estimated usual gait speed (1-1.6 m/s) appears consistent with normative values and expected trends with age, gender, BMI and physical activity levels. Speed metrics significantly improved detection of frailty: AUC increase from 0.763 (no speed metrics) to 0.798, 0.800 and 0.793 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Similarly, speed metrics also improved the prediction of handgrip strength: AUC increase from 0.669 (no speed metrics) to 0.696, 0.696 and 0.691 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Forward stepwise regression showed that the 95th percentile speed of gait bouts with medium duration (30-120 s) to be the best predictor for both conditions. The study provides evidence that real-world gait speed can be estimated using a wrist-worn wearable system, and can be used as reliable indicator of age-related functional decline.

Intra-day variation in daily outdoor walking speed among community-dwelling older adults

Background

Walking speed is an important measure associated with health outcomes in older individuals, such as dependency and death. This study aimed to examine whether the walking speed of community-dwelling older adults varies between time periods within a day, as measured outdoors in daily life. We aimed to determine the types of walking speed variations and examine the factors associated with them.

Methods

Daily life outdoor walking speed was measured in 92 participants (average age 71.9 years±5.64) using a GPS smartphone app for 1 month. Average walking speeds for five time periods were analyzed with a linear mixed model. Intra-day walking speed variation patterns were classified by latent class analysis. Factors associated with the class were identified by logistic regression analysis.

Results

A statistically significant difference in average walking speed was found between early morning (1.33 m/s), and afternoon (1.27 m/s) and evening (1.26 m/s) (p < 0.01). The intra-day variation in walking speed was attributed to variation in cadence. Two classes were identified: (1) fast walking speed with large variation and (2) slow walking speed with little variation; hypertension and frailty level were associated with the class.

Conclusion

The results suggest that there is intra-day variation in walking speed in daily life, wherein the speed is the fastest early in the morning and slower in the afternoon and evening. A larger variation in the walking speed was related to the health status without hypertension or frailty. These results suggest that if a person shows less intra-day variation in walking speed, this could be a sign that they are susceptible to hypertension and an increased frailty level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02349-w.

Correction: Long-term gait measurements in daily life: Results from the Berlin Aging Study II (BASE-II)

[This corrects the article DOI: 10.1371/journal.pone.0225026.].