Gait speed and step-count monitor accuracy in community-dwelling older adults

Comparison of step-count outcomes across seven different activity trackers: a free-living experiment with young and older adults

BACKGROUND

There are now many different types of activity trackers, including pedometers and accelerometers, to estimate step counts per day. Previous research has extensively examined step-count measurements using activity trackers across various settings while simultaneously wearing different devices.; however, older adults frequently display distinct walking patterns and gait speeds compared to younger adults. This study aimed to compare the step-count between older and younger adults by having them simultaneously wear seven different activity trackers in free-living experiments.

METHODS

This study included 35 younger adults (21-43 yrs) and 57 physically independent older adults (65-91 yrs). All participants simultaneously wore one pedometer and six activity trackers: ActiGraph GT3X + Wrist and Hip, Omron Active Style Pro HJA-350IT, Panasonic Actimarker, TANITA EZ-064, Yamasa TH-300, and Yamasa AS-200 for seven days. A regression equation was also used to assess inter-device compatibility.

RESULTS

When comparing wrist-worn ActiGraph to the six hip-worn activity trackers, the wrist-worn ActiGraph consistently recorded step counts over 4,000 steps higher than hip-worn activity trackers in both groups (range, 3000-5000 steps). Moreover, when comparing the ActiGraph worn on the wrist to that worn on the hip, the proportion was higher among older adults compared to younger ones (younger: 131%, older: 180%). The Actimarker recorded the highest average step counts among six hip-worn devices, with 8,569 ± 4,881 overall, 9,624 ± 5,177 for younger adults, and 7,890 ± 4,562 for older adults. The difference between the hip-worn ActiGraph and Active Style Pro was just about 70 steps/day overall. The correlation among all devices demonstrated a very high consistency, except for the wrist-worn ActiGraph (r = 0.874-0.978).

CONCLUSIONS

Step counts recorded from seven selected consumer-based and research-grade activity trackers and one pedometer, except for the wrist-worn ActiGraph. showed a variation of approximately 1700 steps (range, 1265-2275 steps) steps for both groups, yet maintained a high correlation with each other. These findings will be valuable for researchers and clinicians as they compare step counts across different studies or representative surveys conducted globally.

Reliability, validity and minimal detectable change of the Chinese Version of the Assessment of Physical Activity in Frail Older People (APAFOP-C)

BACKGROUND

Physical activity (PA) is essential in mitigating frailty syndrome, and it is necessary to measure PA in older adults with frailty. Assessment of Physical Activity in Frail Older People (APAFOP) is a suitable patient-reported outcome measure (PROM) for assessing PA among older adults with frailty. This study aimed to determine the reliability, validity and minimal detectable change of the Chinese version of the APAFOP (APAFOP-C).

METHODS

This cross-sectional validation study was designed to measure the reliability and criterion validity of the APAFOP-C with 124 frail community-residing older adults. APAFOP-C was completed twice within an interval of 7-17 days to determine test-retest reliability. The investigator triangulation method was used to investigate inter-rater reliability, and a pedometer was used as the reference measurement to assess the criterion validity. Reliability and criterion validity were assessed using the intraclass correlation coefficient (ICC2,1), Pearson correlation coefficient for normally distributed variables, Spearman correlation coefficient, Wilcoxon signed-rank test for skewed variables, and the minimal detectable change at 95% level of confidence (MDC95). Agreement assessment was conducted using Bland-Altman plots for inter-rater reliability and criterion validity. Kendall's W test assessed absolute agreement among three raters in inter-rater reliability. The Mann-Whitney U test was used to evaluate whether any particular day was more representative of certain daily activities.

RESULTS

Total PA on any arbitrarily chosen day illustrates daily activity (Z= -0.84, p = 0.40). The APAFOP-C exhibited strong-to-very strong test-retest reliability (ICC2,1=0.73-0.97; Spearman ρ = 0.67-0.89), and the total PA score demonstrated MDC95 < 10%. Inter-rater reliability was also strong-to-very strong (ICC2,1=0.96-0.98; Spearman ρ = 0.88-1.00), and moderate criterion validity when compared with total PA score on pedometer readings (Spearman ρ = 0.61). Limits of agreement among different raters regarding the APAFOP-C and the pedometer were narrow.

CONCLUSION

The APAFOP-C was found to have limited but acceptable psychometric properties for measuring PA among community-dwelling older adults with frailty in China. It was a feasible comparative PROM for assessing PA worldwide. Practitioners can develop individualized exercise programs for frail older adults and efficiently track changes in PA utilizing the APAFOP-C.

A Low or High Physical Activity Level Does Not Modulate Prostate Tumor Tissue Protein Synthesis Rates

INTRODUCTION

Physical activity level has been identified as an important factor in the development and progression of various types of cancer. In this study, we determined the impact of a low versus high physical activity level on skeletal muscle, healthy prostate, and prostate tumor protein synthesis rates in vivo in prostate cancer patients.

METHODS

Thirty prostate cancer patients (age, 66 ± 5 yr; body mass index, 27.4 ± 2.9 kg·m -2 ) were randomized to a low (<4000 steps per day, n = 15) or high (>14,000 steps per day, n = 15) physical activity level for 7 d before their scheduled radical prostatectomy. Daily deuterium oxide administration was combined with the collection of plasma, skeletal muscle, nontumorous prostate, and prostate tumor tissue during the surgical procedure to determine tissue protein synthesis rates throughout the intervention period.

RESULTS

Daily step counts averaged 3610 ± 878 and 17,589 ± 4680 steps in patients subjected to the low and high physical activity levels, respectively ( P < 0.001). No differences were observed between tissue protein synthesis rates of skeletal muscle, healthy prostate, or prostate tumor between the low (1.47% ± 0.21%, 2.74% ± 0.70%, and 4.76% ± 1.23% per day, respectively) and high (1.42% ± 0.16%, 2.64% ± 0.58%, and 4.72% ± 0.80% per day, respectively) physical activity group (all P > 0.4). Tissue protein synthesis rates were nearly twofold higher in prostate tumor compared with nontumorous prostate tissue.

CONCLUSIONS

A short-term high or low physical activity level does not modulate prostate or prostate tumor protein synthesis rates in vivo in prostate cancer patients. More studies on the impact of physical activity level on tumor protein synthesis rates and tumor progression are warranted to understand the potential impact of lifestyle interventions in the prevention and treatment of cancer.

Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

Effects of ActiGraph's filter, epoch length and non-wearing time algorithm on step counts in people with COPD

The influence of the ActiGraph® processing criteria on estimating step counts in chronic obstructive pulmonary disease (COPD) remains uncertain. This study aimed to assess the influence of filters, epoch lengths and non-wearing time (NWT) algorithms on steps/day in people with COPD. ActiGraph GT3X+ was worn on the waist for seven days. Steps were detected using different filters (normal and low-frequency extension [LFE]), epoch lengths (15s and 60s), and NWT algorithms (Choi and Troiano). Linear mixed-effects model was applied to assess the effects of filter, epoch length, NWT algorithm on steps/day. Lin's concordance correlation and Bland-Altman were used to measure agreement. A total of 136 people with COPD (107 male; 69 ± 8 years; FEV1 51 ± 17% predicted) were included. Significant differences were found between filters (p < 0.001), but not between epoch lengths or NWT algorithms. The LFE increased, on average, approximately 7500 steps/day compared to the normal filter (p < 0.001). Agreement was poor (<0.3) and proportional bias was significant when comparing steps/day computed with different filters, regardless of the epoch length and NWT algorithm. Filter choice but not epoch lengths or NWT algorithms seem to impact measurement of steps/day. Future studies are needed to recommend the most accurate technique for measuring steps/day in people with COPD.

Fitbit's accuracy to measure short bouts of stepping and sedentary behaviour: validation, sensitivity and specificity study

Objective

This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies.

Methods

Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL.

Results

Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively.

Conclusions

This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

Wearable Device Validity in Measuring Steps, Energy Expenditure, and Heart Rate Across Age, Gender, and Body Mass Index: Data Analysis From a Systematic Review

BACKGROUND

This paper examined whether the criterion validity of step count (SC), energy expenditure (EE), and heart rate (HR) varied across studies depending on the average age, body mass index (BMI), and predominant gender of participants.

METHODS

Data from 1536 studies examining the validity of various wearable devices were used. Separate multilevel regression models examined the associations among age, gender, and BMI with device criterion validity assessed using mean absolute percent error (MAPE) at the study level.

RESULTS

MAPE values were reported in 970 studies for SC, 328 for EE, and 238 for HR, respectively. There were several significant differences in MAPE between age, gender, and BMI categories for SC, EE, and HR. SC MAPE was significantly different for older adults compared with adults. Compared with studies among normal-weight populations, MAPE was greater among studies with overweight samples for SC, HR, and EE. Comparing studies with more women than men, MAPE was significantly greater for EE and HR.

CONCLUSIONS

There are important differences in the criterion validity of commercial wearable devices across studies of varying ages, BMIs, and genders. Few studies have examined differences in error between different age groups, particularly for EE and HR.

Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review

BACKGROUND

Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitoring devices for step counting and distance estimation in adults and/or older adults.

METHODS

Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engineering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid's grids) and data extraction.

RESULTS

This review included 21 studies involving 637 participants (age 30.3 ± 7.5 years (for adults) and 82.7 ± 3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX +, GT3X +/wGT3X + and wGT3X - BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance estimation. The ActiGraph GT3X +/wGT3X + and wGT3X - BT had better criterion validity than the ActiGraph 7164, wGTX + and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X + was good for distance estimation.

CONCLUSION

The ActiGraph wGT3X - BT and GT3X +/wGT3X + have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing.

Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments

BACKGROUND

Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another.

RESEARCH QUESTION

What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials?

METHODS

In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days).

RESULTS

When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P < 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility.

SIGNIFICANCE

Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

Comparison of Self-Reported vs Objective Measures of Long-Term Community Ambulation in Lower Limb Prosthesis Users

Objective

To determine normal variation in walking metrics in a population of lower limb amputees who use lower limb prostheses over a 6-month period and to provide a means to interpret clinically meaningful change in those community walking metrics.

Design

Prospective cohort study monitoring walking behavior and subjective and objective measures of activity.

Setting

Veterans Administration and university amputee clinics.

Participants

86 individuals with lower limb amputation who use protheses.

Interventions

StepWatch activity monitor tracked subjects’ walking for 24 weeks; Global Mobility Change Rating collected weekly.

Main Outcome Measures

Association between change in Global Mobility Change Rating and change in any of the walking metrics.

Results

Walking metrics including step count, cadence, cadence variability, and walking distance in a population of lower limb prosthesis users were obtained. There was a high correlation in the walking metrics indicating higher function with higher functional classification level (K-levels) but also substantial overlap in all metrics and a very weak correlation between subject-reported activity level and objective measures of walking performance.

Conclusion

The overlap in walking metrics with all K-levels demonstrates that no single metric measured by StepWatch can determine K-level with 100% accuracy. As previously demonstrated in other populations, subjects’ interpretations of their general activity level was inaccurate, regardless of their age or activity level. Objective measures of walking appear to provide a more accurate representation of patients’ activity levels in the community than self-report. Therefore, objective measures of walking are useful in supporting K-level determinations. However, clinicians cannot rely on a single metric to determine K-level.

Improving patient selection for use of consumer grade physical activity monitors in the hospital

BACKGROUND

Bedrest is toxic for inpatients and consumer grade physical activity monitors offer an economical solution to monitor patient ambulation. But these devices may not be accurate in debilitated hospitalized patients who frequently ambulate very slowly.

OBJECTIVE

To determine whether measures of physical capacity can help identify inpatients for whom wearable physical activity monitors may accurately measure step count.

METHODS

Prospective observational study of 54 adult inpatients with acute neurological diagnoses. Patients were assessed using 2 physical capacity assessments (Activity Measure for Post-Acute Care Inpatient Mobility Short Form [AM-PAC IMSF] and Katz Activities of Daily Living [ADL] scale). They also completed a 2-minute walk test (2MWT) wearing a consumer grade physical activity monitor.

RESULTS

The wearable activity monitor recorded steps (initiated) in 33 (61%) of the inpatients, and for 94% of inpatients with gait speeds >0.43 m/s. Physical capacity assessments correlated well with gait speed, AM-PAC IMSF r = 0.7, and Katz ADL r = 0.6, p < 0.05. When the physical activity monitor initiated, the mean absolute percent error (SD) comparing device calculated steps to observed steps, was 10% (13). AM-PAC IMSF (T-score >45) and Katz ADL (>5) cutoff scores identified inpatients for whom physical activity monitors initiated with a sensitivity of 94 and 91%, respectively.

CONCLUSIONS

Physical capacity assessments, such as AM-PAC, and Katz ADL, may be a useful and feasible screening strategy to help identify inpatients where wearable physical activity monitors can measure their mobility.

Step Monitor Accuracy During PostStroke Physical Therapy and Simulated Activities

Introduction/Purpose

The amount of stepping activity during rehabilitation post-stroke can predict walking outcomes, although the most accurate methods to evaluate stepping activity are uncertain with conflicting findings on available stepping monitors during walking assessments. Rehabilitation sessions also include non-stepping activities and the ability of activity monitors to differentiate these activities from stepping is unclear. The objective of this study was to examine the accuracy of different activity monitors worn by individuals post-stroke with variable walking speeds during clinical physical therapy (PT) and research interventions focused on walking.

Methods

In Part I, 28 participants post-stroke wore a StepWatch, ActiGraph with and without a Low Frequency Extension (LFE) filter, and Fitbit on paretic and non-paretic distal shanks at or above the ankle during clinical PT or research interventions with steps simultaneously hand counted. Mean absolute percent errors were compared between limbs and tasks performed. In Part II, 12 healthy adults completed 8 walking and 9 non-walking tasks observed during clinical PT or research. Data were descriptively analyzed and used to assist interpretation of Part I results.

Results

Part I results indicate most devices did not demonstrate an optimal limb configuration during research sessions focused on walking, with larger errors during clinical PT on the non-paretic limb. Using the limb that minimized errors for each device, the StepWatch had smaller errors than the ActiGraph and Fitbit (p<0.01), particularly in those who walked < 0.8 m/s. Conversely, errors from the ActiGraph-LFE demonstrated inconsistent differences in step counts between Fitbit and ActiGraph. Part II results indicate that errors observed during different stepping and non-stepping activities were often device-specific, with non-stepping tasks frequently detected as stepping.

Conclusions

The StepWatch and ActiGraph-LFE had smaller errors than the Fitbit or ActiGraph, with greater errors in those walking at slower speeds. Inclusion of non-stepping activities affected step counts and should be considered when measuring stepping activity in individuals post-stroke to predict locomotor outcomes following rehabilitation.

Physical Activity in De Novo Parkinson Disease: Daily Step Recommendation and Effects of Treadmill Exercise on Physical Activity

OBJECTIVE

People with Parkinson disease (PD) have low physical activity (PA) levels and are at risk for cardiovascular events. The 3 purposes of this study were to determine a step threshold that corresponds to meeting aerobic PA guidelines, determine effects of treadmill exercise on PA, and quantify the relationship between changes in daily steps and fitness.

METHODS

This was a secondary analysis of the Study in Parkinson's Disease of Exercise trial, which randomized participants to high-intensity treadmill exercise, moderate-intensity treadmill exercise, or usual care for 6 months. Daily steps and moderate- to vigorous-intensity PA (MVPA) were assessed at baseline and once each month using an activity monitor. Fitness was assessed via graded exercise test at baseline and at 6 months. A step threshold that corresponds to meeting PA guidelines was determined by receiver operating characteristic curves. The effect of treadmill exercise on PA was examined in those below the step threshold (ie, the least active participants). Pearson r correlations determined the relationship between daily steps and fitness.

RESULTS

Individuals with de novo PD (n = 110) were included. Those with ≥4200 steps were 23 times more likely (95% CI = 7.72 to 68) to meet PA guidelines than those with <4200 steps. For those with <4200 steps at baseline (n = 33), only those in the high-intensity exercise group increased daily steps (median of differences = 1250 steps, z = -2.35) and MVPA (median of differences = 12.5 minutes, z = -2.67) at 6 months. For those with <4200 steps, changes in daily steps were not associated with changes in fitness (r = .183).

CONCLUSION

In people with PD and <4200 daily steps at baseline, high-intensity treadmill exercise increased daily steps and MVPA, but these changes were not associated with changes in fitness.

IMPACT

People with PD should be encouraged to take ≥4200 daily steps to meet PA guidelines through walking.

Validity of Hip and Ankle Worn Actigraph Accelerometers for Measuring Steps as a Function of Gait Speed during Steady State Walking and Continuous Turning

This study aimed to investigate the accuracy and reliability of hip and ankle worn Actigraph GT3X+ (AG) accelerometers to measure steps as a function of gait speed. Additionally, the effect of the low frequency extension filter (LFEF) on the step accuracy was determined. Thirty healthy individuals walked straight and walked with continuous turns in different gait speeds. Number of steps were recorded with a hip and ankle worn AG, and with a Stepwatch (SW) activity monitor positioned around the right ankle, which was used as a reference for step count. The percentage agreement, interclass correlation coefficients and Bland–Altmann plots were determined between the AG and the reference SW across gait speeds for the two walking conditions. The ankle worn AG with the default filter was the most sensitive for step detection at >0.6 m/s, whilst accurate step detection for gait speeds < 0.6 m/s were only observed when applying the LFEF. The hip worn AG with the default filter showed poor accuracy (12–78%) at gait speeds < 1.0 m/s whereas the accuracy increased to >87% for gait speeds < 1.0 m/s when applying the LFEF. Ankle worn AG was the most sensitive to measure steps at a vast range of gait speeds. Our results suggest that sensor placement and filter settings need to be taken into account to provide accurate estimates of step counts.

Objectively assessed physical activity patterns and physical function in community-dwelling older adults: a cross-sectional study in Taiwan

OBJECTIVES

To objectively assess light physical activity (PA), moderate-to-vigorous PA (MVPA), step counts and number of 10 min MVPA bouts and their association with physical function among older adults.

DESIGN

Cross-sectional design.

SETTING

Urban community setting in Taiwan.

PARTICIPANTS

127 Taiwanese older adults aged over 65 years (mean age=70.8±5.3 years; 72% women).

PRIMARY AND SECONDARY OUTCOME MEASURES

Triaxial accelerometers were used to measure PA variables for 10 hours/day for seven consecutive days. Then, five physical function components (handgrip strength, single-leg stance, 5-metre walk speed, timed up and go and sit-to-stand test) were measured. Multiple linear regressions were used to perform separate analyses for older men and women.

RESULTS

For older women, daily MVPA time (β: 0.39, 95% CI: 0.12, 0.64; p=0.004), daily step counts (β: 0.46, 95% CI: 0.12, 0.78; p=0.009) and number of 10 min MVPA bouts (β: 0.27, 95% CI: 0.001, 0.53; p=0.049) were positively associated with handgrip strength after adjusting for accelerometer wear time, sedentary time and other confounders. Furthermore, daily MVPA time was positively associated with a single-leg stance (β: 0.25, 95% CI: 0.02, 0.49; p=0.036) and higher daily step counts were associated with shorter walking speed performance (β: -0.31, 95% CI: -0.57, -0.001; p=0.049). None of the variables of the objectively assessed PA patterns was associated with physical function outcomes among older men due to their small sample size.

CONCLUSIONS

Daily MVPA, MVPA bouts of at least 10 min and accumulated daily steps are important for improving physical function among older women. Future prospective research should establish causal associations between PA patterns and functional ability among older adults.

Validation of different stepping counters during treadmill and over ground walking

BACKGROUND

Commercially available physical activity trackers are very popular in the general population and are increasingly common in clinical and research settings. The marketfor activity trackers are rapidly expanding, requiring them to be validated on an ongoing basis. Different approaches have been used for validating these devices. Studies using treadmills shows good step-counting accuracy although test performed in field tests settings are limited.

RESEARCH QUESTION

Does step-counting validity differ between a field test and a treadmill protocol for different types of activity trackers?

METHODS

Thirty healthy subjects participated in this study, mean age was 28.2 (± 4.33) years, body mass 78.9 (± 12.9) kg, and height 178.5 (± 9.7) cm. A treadmill protocol with three different walking speeds (2, 3 and 4 km/h) and a 982 m field test was used. During the tests, participants' feet were filmed using a waist-mounted camera. The number of steps were extracted from the video data and used for comparison with four different step counters: a) Polar M200; b) Polar A300; c) Dunlop pedometer; d) Samsung Galaxy S9 smartphone. Validity and agreement determined was determined with the use of Bland-Altman plot and Spearman's correlation.

RESULTS

Validity was higher for the field test compared to the 4 km/h treadmill test for all tested devices. The smartphone was the most accurate in terms of error, validity and agreement for both the treadmill and field test. All devices performed poorly for the 2 km/h treadmill test and only the smartphone performed well at 3 km/h.

SIGNIFICANCE

The results of this study show that step counting validity and error obtained during treadmill walking is not similar to a field test. Future validation studies of activity trackers should consider this when designing a protocol. The smartphone had the lowest mean bias during the field test.

"Study protocol for the ≥65 years NOrthern jutland Cohort of Fall risk Assessment with Objective measurements (the NOCfao study)"

Background

Accidental falls are common among community-dwellers, probably due to the level of physical activity and impaired postural stability. Today, fall risk prediction tools’ discriminative validity are only moderate. In order to increase the accuracy, multiple variables such as highly validated objective field measurements of physical activity and impaired postural stability should be adressed in order to predict falls. The main aim of this paper is to describe the ≥65 years NOrthern jutland Cohort of Fall risk Assessment with Objective measurements (NOCfao) investigating the association between physical activity and impaired postural stability and the risk of fall episodes among community-dwelling older adults.

Methods

The study consists of a baseline session where the participants are asked to respond to three questionnaires, perform physical tests (i.e., measuring strength in the upper and lower extremities, balance, and walking speed), participate in an assessment of pain sensitivity, and to wear an ankle mounted pedometer for measuring physical activity for 5 days. Subsequently, the fall incidences and the circumstances surrounding the falls during the previous 1 to 2 months will be recorded throughout a one-year follow-up period.

Discussion

This study will add to the present-day understanding of the association between physical activity and impaired postural stability and the risk of fall episodes among community-dwelling older adults. These data will provide valid and reliable information on the relationship between these variables and their significance for community-dwelling older adults.

Trial registration

ClinicalTrials.gov identifier: NCT2995317. Registered December 13th, 2016.

Accuracy of Apple Watch fitness tracker for wheelchair use varies according to movement frequency and task

OBJECTIVES

Individuals with disabilities have high prevalence of sedentary lifestyle, obesity, and cardiometabolic disease. Physical activity monitors (i.e., step counters) are ill-suited for tracking wheelchair pushes. The study purpose was to investigate the validity of a consumer-level fitness tracker (Apple Watch) designed for wheelchair users.

METHODS

Validation study. A total of 15 wheelchair users with disabilities and 15 able-bodied individuals completed 3-min bouts of wheelchair propulsion on a treadmill and arm ergometry at pre-determined cadences as well as overground obstacle and Figure 8 courses. Tracker stroke counts were compared against direct observation.

RESULTS

We found no interaction of tracker counts and ability status across all tasks (P≥0.550), so results are presented for the combined sample. For treadmill tasks, Bland-Altman analysis (bias±limits of agreement) showed good agreement for only higher-rate fixed-frequency tasks (-15±48, -1±14, 0±5, and 0±27 for low, moderate, high, and variable cadence, respectively). Mean absolute percentage error (MAPE) was 22%, 3%, 1%, and 6%, respectively. Intraclass correlation coefficients (ICCs) (95% confidence intervals) were -0.18 (-0.51-0.20), 0.47 (0.13-0.71), 0.98 (0.96-0.99), and 0.22 (-0.16-0.54). We found significant overestimation by the tracker at low frequency (P<0.01). Arm ergometry showed good agreement across all cadences (0±5, -1±3, 0±8, 6±6). MAPE was 1%, 1%, 1%, and 4%. ICCs were 0.88 (0.77-0.94), 0.95 (0.89-0.97), 0.88 (0.76-0.94), and 0.97 (0.87-0.97). We found minimal (2rpm) but significant differences at variable cadence (P<0.01). Overground tasks showed poor agreement for casual-pace and fast-pace obstacle course and Figure 8 task (-5±18, 0±23, and -18±32, respectively). MAPE was 15%, 18%, 21% and ICCs were 0.90 (0.79-0.95), 0.79 (0.59-0.90), and 0.82 (0.64-0.91). Significant differences were found for propulsion at casual pace (P<0.01) and the Figure 8 task (P<0.01).

CONCLUSIONS

Apple Watch is suitable for tracking high-frequency standardized (i.e., treadmill) pushing and arm ergometry but not low-frequency pushing or overground tasks.

Feasibility and Effectiveness of an Individualized 12-Week "Uptime" Participation (U-PART) Intervention in Girls and Women With Rett Syndrome

BACKGROUND

Girls and women with Rett Syndrome (RTT) have low levels of daily physical activity and high levels of sedentary time. Reducing sedentary time and enhancing "uptime" activities, such as standing and walking, could be an important focus for interventions to address long-term health and quality of life in RTT.

OBJECTIVE

The aim of the study was to evaluate the feasibility and health-related effects of an individualized 12-week uptime participation (U-PART) intervention in girls and women with RTT.

DESIGN

The study used a single-group pretest-posttest design with 4 assessments (2 baseline, postintervention, and follow-up).

METHODS

A participation-based intervention employing a whole-day approach was used. During a 12-week intervention period, individualized programs focused on participation in enjoyable uptime activities in home, school/day center, and community settings. Feasibility was assessed with a study-specific questionnaire. Primary outcome measures were sedentary time and daily step count. Secondary outcomes were gross motor skills, walking capacity, quality of life, and goal attainment scaling.

RESULTS

Fourteen girls and women who were 5 to 48 years old and had RTT participated. The U-PART intervention was perceived as feasible by caregivers. Similar scores were observed at baseline assessments in all outcomes. Positive effects with small to medium effect sizes (0.27-0.54) were seen in sedentary time (- 4%), daily step count (+ 689 steps/d), walking capacity (+ 18.8 m), quality of life (+ 2.75 points), and goal attainment scaling after the intervention. Positive effects were maintained in sedentary time (- 3.2%) and walking capacity (+ 12.1 m) at short-term follow-up.

LIMITATIONS

This study was limited by the lack of a control group. However, participants acted as their own control, and the stable baseline period partially mitigated this issue.

CONCLUSIONS

The U-PART intervention was found to be feasible and effective in the short term in girls and women with RTT.

Mobile Health Technologies for Continuous Monitoring of Cancer Patients in Palliative Care Aiming to Predict Health Status Deterioration: A Feasibility Study

Background: Unplanned readmissions or emergency visits (EVs) after discharge from hospital are frequent in patients in palliative care. Strategies to anticipate and prevent rapid deterioration of health are needed. Objective: Assessing feasibility and predictive ability of remote monitoring using wearables. Design: Prospective observational feasibility study in a single center. Setting/Subjects: Thirty cancer patients with an estimated life expectancy of >8 weeks to <12 months, aged >18 years and being discharged from inpatient to outpatient care were included. Measurements: Patients were provided with a smartphone, including the preinstalled "Activity Monitoring" application and a sensor-equipped bracelet. Follow-up was 12 weeks. Both devices recorded several features (e.g., vital signs). Visual analog scale (VAS) for pain and distress was reported once daily and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) once weekly. Statistical methods were applied to explore relationship between sensor data, self-reports, and EVs or readmissions or death. Results: Between February 2017 and May 2018, 30 patients were included. Twenty-five of 30 participants (83%) completed 12 weeks of follow-up. On average, bracelet was worn on 53% and smartphone on 85% of study days. Completion rate of daily digital questionnaires for subjective ratings was 73%. Eight unplanned hospital readmissions occurred. Ratings of pain, distress, and QLQ-C30 scores were not associated with readmission, whereas resting heart rate, resting heart rate variability, as well as speed of steps differed significantly in patients with and without readmission. Conclusions: Monitoring of palliative cancer patients using wearables is feasible. First results indicate that mobile health features might be promising biomarkers to predict unplanned readmissions.

Are cost-effective technologies feasible to measure gait in older adults? A systematic review of evidence-based literature

BACKGROUND

Unrestricted by time and place, innovative technologies seem to provide cost-effective solutions for gait assessment in older adults.

OBJECTIVE

The objective of this study is to provide an overview of gait assessment for older adults by investigating critical gait characteristics of older adults, discussing advantages and disadvantages of the current gait assessment technologies, as well as device applicability.

METHODS

The Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed during the review. Inclusion criteria were: (1) Sample consisting of adults older than 60 years; (2) qualitative, quantitative, or mixed-method researches using one or more specific gait assessment technologies; and (3) publication in English between 2000 and 2018.

RESULTS

In total, twenty-one studies were included. Gait speed, stride length, frequency, acceleration root mean square, step-to-step consistency, autocorrelation, harmonic ratio were reported in the existing literatures to be associated with falls. The enrolled studies address the use of pedometer, wearable accelerometer-based devices, Kinect, Nintendo Wii Balance Board as cost-effective gait assessment technologies.

CONCLUSIONS

Gait parameters and assessment approaches for older adults are diverse. Cost-effective technologies such as a wearable accelerometer-based device, Kinect, and the Nintendo Wii Balance Board provide potential alternatives for gait assessment with acceptable validity and reliability compared with sophisticated devices. The popularity and development of cost-effective devices have made large-scale data collection for gait assessment possible in the daily environment. Further study could involve older adults and their family members/caregivers in use of these technologies to design elderly-friendly products.

Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients

BACKGROUND

Mobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited.

OBJECTIVE

The aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population.

METHODS

Twenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms.

RESULTS

Particularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s).

CONCLUSION

With the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds.

Sensitivity to Physical Activity Predicts Daily Activity Among Pain-Free Older Adults

Objective

Prior research indicates that older adults with knee osteoarthritis have increased sensitivity to physical activity (SPA) and respond to physical activities of stable intensity with increases in pain. Whether SPA is present in healthy older adults without chronic pain and predicts functional outcomes remains relatively unexplored. The purpose of this study was to determine the degree of SPA in healthy older adults in response to a standardized walking task, and whether SPA was associated with temporal summation of pain, pain-related fear of movement, and functional outcomes.

Methods

Fifty-two older adults without chronic pain completed self-reported measures of activity-related pain and physical function, completed the Six-Minute Walk Test (6MWT), underwent quantitative sensory testing to measure temporal summation of heat pain, and wore an accelerometer for one week to measure physical activity behavior. Subjects rated overall bodily discomfort (0-100 scale) prior to and during each minute of the 6MWT. An SPA index was created by subtracting the initial bodily discomfort ratings from the peak ratings.

Results

Repeated-measures analysis of variance indicated that bodily discomfort significantly increased across the walking task, with approximately 60% of the sample experiencing SPA. Hierarchical regressions indicated that greater SPA was associated with fewer average steps per day and greater activity-related pain. Additionally, analyses revealed that temporal summation of pain and pain-related fear of movement significantly predicted the degree of SPA on the walking task.

Conclusions

These findings shed light on potential mechanisms underlying SPA in older adults and suggest that SPA might be a risk factor for reduced physical activity.

Accelerometry data in health research: challenges and opportunities

Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

Are Older Adults without a Healthy Diet Less Physically Active and More Sedentary?

Few studies on older populations consider several energy balance-related behaviors together. This cross-sectional study compared subjectively and objectively measured physical activity (PA) and sedentary behavior (SB) patterns between older adults with and without a healthy diet. We recruited 127 community-dwelling older Taiwanese adults (69.9 ± 5.0 years); data were collected during April and September 2018. Objectively measured total PA, moderate-to-vigorous PA, light PA, step count, total sedentary time, duration of sedentary bouts, number of sedentary bouts, and number of sedentary breaks were assessed using activity monitors. Subjectively measured PA and SB were measured using the International Physical Activity Questionnaire and Sedentary Behavior Questionnaire for Older Adults. Chi-square tests and independent sample t-tests were performed. For subjective measures, older adults without a healthy diet spent significantly less total leisure time on PA and more leisure sitting time than those with a healthy diet. For objective measures, older adults without a healthy diet spent less time on light PA and had a higher total sedentary time, duration of sedentary bouts, times of sedentary bouts, and times of sedentary breaks than those with a healthy diet. Regardless of the use of objective or subjective measurements, older adults without a healthy diet engaged in a more inactive and sedentary lifestyle. These findings have implications for health promotion practitioners in designing tailored interventions.

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

Design and validation of a simple automated optical step counting method for treadmill walking

BACKGROUND

Reliable step counting is a critical part of locomotion research. Current counting methods can be inaccurate, time consuming, expensive or encumbering to the subject. Here, we present a camera-based optical method for automatically counting steps.

METHODS

Fifteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) and once at varying speed (1.21-2.68 m/s) for 90 s. Subjects had visual marker affixed to their left foot while walking. Video was recorded synchronously at low- and high-resolution during trials. The step count found manually from the video was compared to an automated video analysis system using the two configurations of the optical system.

RESULTS

Bland-Altman plots, Intra-class correlation coefficients (ICC) and relative error comparison were used for quantitative assessment of device reliability. Reliability of optical method was high (ICC ≥0.98).

CONCLUSIONS

The method produces accurate step count results for the range of speeds tested. They use customisable open-source software and off-the-shelf hardware. The method has a low cost of implementation compared to many consumer products and grants researchers access to the raw sensor data.

Validation of the Fitbit Flex in an Acute Post-Cardiac Surgery Patient Population

Purpose: This study examined the validity of the Fitbit Flex activity monitor for step count and distance walked among post-cardiac surgery patients. Method: Participants (n=20) from a major urban cardiac surgery centre were recruited 1-2 days before hospital discharge. The Fitbit Flex step count and distance walked outputs and video recording of each participant performing the 6-minute walk test were collected. Fitbit Flex output was compared with criterion measures of manual step count obtained from the video recording and manual measurement of distance walked. Statistical analysis compared the output and criterion measures using paired sample t-tests, Pearson correlation coefficients, Lin's concordance correlations, and Bland-Altman plots. Sub-analysis compared slower walking (<0.8 m/s; n=11) and faster walking (≥0.8 m/s; n=8) group speeds (1 participant was excluded from analysis). Results: Steps counted and distance walked were significantly different between the Fitbit Flex outputs and criterion measures (p<0.05). The Fitbit Flex steps counted and distance walked showed moderate association with manual measure steps counted (r=0.67) and distance walked (r=0.45). Lin's concordance coefficients revealed a lack of agreement between the Fitbit Flex and the criterion measurement of both steps counted (concordance correlation coefficient [CCC]=0.43) and distance walked (CCC=0.36). The percentage of relative error was -18.6 (SD 22.7) for steps counted and 25.4 (SD 45.8) for distance walked. Conclusions: The Fitbit Flex activity monitor was not a valid measure of step count and distance walked in this sample of post-cardiac surgery patients. The lack of agreement between outputs and criterion measures suggests the Fitbit Flex alone would not be an acceptable clinical outcome measure for monitoring walking progression in the early postoperative period.

Accuracy and Acceptability of Commercial-Grade Physical Activity Monitors in Older Adults

The aim of this study was to evaluate accuracy of seven commercial activity monitors in measuring steps in older adults with varying walking abilities and to assess monitor acceptability and usability. Forty-three participants (age = 87 ± 5.7 years) completed a gait speed assessment, two walking trials while wearing the activity monitors, and questionnaires about usability features and activity monitor preferences. The Accusplit AX2710 Accelerometer Pedometer had the highest accuracy (93.68% ± 13.95%), whereas the Fitbit Charge had the lowest (39.12% ± 40.3%). Device accuracy varied based on assistive device use, and none of the monitors were accurate at gait speeds <0.08 m/s. Barriers to monitor usability included inability to apply monitor and access the step display. Monitor accuracy was rated as the most important feature, and ability to interface with a smart device was the least important feature. This study identified the limitations of the current commercial activity monitors in both step counting accuracy and usability features for older adults.

Physical Activity, Nutrition, Cognition, Neurophysiology, and Short-Time Synaptic Plasticity in Healthy Older Adults: A Cross-Sectional Study

The aging brain undergoes remodeling processes because of biological and environmental factors. To counteract brain aging, neuronal plasticity should be preserved. The aim of this study was to test if the capacity of generating short-time synaptic plasticity in older adults may be related to either physical activity, nutritional status, cognition, or neurophysiological activity. Thirty-six participants (mean age 73.3 ± 5.9 years) received transcranial magnetic stimulation in combination with peripheral nerve stimulation to experimentally induce short-time synaptic plasticity by paired associative stimulation (PAS). Adaptations in neuronal excitability were assessed by motor-evoked potential (MEP) in the right m. tibialis anterior before and after PAS. The Physical Activity Questionnaire 50+ and the StepWatchTM captured physical activity levels. Nutritional status was assessed by the Mini Nutritional Assessment. Cognition was assessed by reaction time for a divided attention test and with the Montreal Cognitive Assessment. Neurophysiological activity was assessed by electroencephalography during the divided attention test. MEPs of the highest stimulation intensity resulted significantly different comparing before, 5 min, or 30 min after PAS (p < 0.05). Data-driven automatic hierarchical classification of the individual recruitment curve slopes over the three-time points indicated four different response types, however, response groups did not significantly differ based on physical activity, nutritional status, cognition, or neurophysiological activity. In a second-level analysis, participants having an increased slope showed a significant higher energy expenditure (z = -2.165, p = 0.030, r = 0.36) and revealed a significant higher power activity in the alpha frequency band (z = -2.008, p = 0.046, r = 0.37) at the prefrontal-located EEG electrodes, compared to the participants having a decreased slope. This study hints toward older adults differing in their neuronal excitability which is strongly associated to their short-time synaptic plasticity levels. Furthermore, a physically active lifestyle and higher EEG power in the alpha frequency band seem to be connected to the capacity of generating long-term potentiation-like synaptic plasticity in older adults. Future studies should consider more sensitive assessments and bigger sample sizes to get a broad scope of the older adults' population.

Accuracy of Objective Physical Activity Monitors in Measuring Steps in Older Adults

Objective: The aim of this study is to evaluate accuracy of research activity monitors in measuring steps in older adults with a range of walking abilities. Method: Participants completed an initial assessment of gait speed. The accuracy of each monitor to record 100 steps was assessed across two walking trials. Results: In all, 43 older adults (age 87 ± 5.7 years, 81.4% female) participated. Overall, the StepWatch had the highest accuracy (99.0% ± 1.5%), followed by the ActivPAL (93.7% ± 11.1%) and the Actigraph (51.4% ± 35.7%). The accuracy of the Actigraph and ActivPAL varied according to assistive device use, and the accuracy of all three monitors differed by gait speed category (all p < .05). StepWatch was highly accurate (⩾97.7) across all conditions. Discussion: The StepWatch and ActivPAL monitor were reasonably accurate in measuring steps in older adults who walk slowly and use an assistive device. The Actigraph significantly undercounted steps in those who walk slow or use an assistive device. Researchers should consider gait speed and the use of assistive devices when selecting an activity monitor.

Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica

BACKGROUND

Activity monitors provide objective measurements of physical activity, however, the accuracy of these devices in people with polymyalgia rheumatica (PMR) is unknown. Therefore, this study aimed to obtain preliminary evidence of the accuracy of two activity monitors and explore if clinical and gait-related factors altered device accuracy in people with PMR.

METHODS

The ActiGraph with low frequency extension (+LFE) and standard (-LFE) algorithms, Fitbit-Zip (waist) and Fitbit-Zip (shirt) were concurrently tested using a two-minute walk test (2MWT) and stairs test in 27 people with PMR currently treated with prednisolone. To determine accuracy, activity monitor step-count was compared to a gold-standard step-count (GSSC; calculated from video recording) using Bland-Altman plots.

RESULTS

The Fitbit-Zip (waist) achieved closest agreement to the GSSC for the 2MWT (mean bias (95%CI): 10 (-3, 23); 95%LOA: -55, 74). The ActiGraph (+LFE) achieved closest agreement to the GSSC for the stairs test (mean bias (95%CI): 0 (-1, 1); 95%LOA: -5, 5). The ActiGraph (-LFE) performed poorly in both tests. All devices demonstrated reduced accuracy in participants with lower gait velocity, reduced stride length, longer double-limb support phase and greater self-reported functional impairment.

CONCLUSION

Our preliminary results suggest that in controlled conditions, the Fitbit-Zip fairly accurately measures step-count during walking in people with PMR receiving treatment. However, device error was greater than data published in healthy people. The ActiGraph may not be recommended without activation of the LFE. We identified clinical and gait-related factors associated with higher levels of functional impairment that reduced device accuracy. Further work is required to evaluate the validity of the activity monitors in field conditions.

Accuracy and precision of consumer-level activity monitors for stroke detection during wheelchair propulsion and arm ergometry

The purpose of this study was to evaluate whether consumer-level activity trackers can estimate wheelchair strokes and arm ergometer revolutions. Thirty able-bodied participants wore three consumer-level activity trackers (Garmin VivoFit, FitBit Flex, and Jawbone UP24) on the wrist. Participants propelled a wheelchair at fixed frequencies (30, 45 and 60 strokes per minute (spm)) three minutes each and at pre-determined varied frequencies, (30-80 spm) for two minutes. Participants also freely wheeled through an obstacle course. 10 other participants performed arm-ergometry at 40, 60 and 80 revolutions per minute (rpm), for three minutes each. Mean percentage error (MPE(SD)) for 30 spm were ≥46(26)% for all monitors, and declined to 3-6(2-7)% at 60 spm. For the obstacle course, MPE ranged from 12-17(7-13)% for all trackers. For arm-ergometry, MPE was at 1-96(0-37)% with the best measurement for the Fitbit at 60 and 80 rpm, and the Garmin at 80rpm, with MPE = 1(0-1)%. The consumer-level wrist-worn activity trackers we tested have higher accuracy/precision at higher movement frequencies but perform poorly at lower frequencies.

Validation of the ADAMO Care Watch for step counting in older adults

Background

Accurate measurement devices are required to objectively quantify physical activity. Wearable activity monitors, such as pedometers, may serve as affordable and feasible instruments for measuring physical activity levels in older adults during their normal activities of daily living. Currently few available accelerometer-based steps counting devices have been shown to be accurate at slow walking speeds, therefore there is still lacking appropriate devices tailored for slow speed ambulation, typical of older adults.

This study aimed to assess the validity of step counting using the pedometer function of the ADAMO Care Watch, containing an embedded algorithm for measuring physical activity in older adults.

Methods

Twenty older adults aged ≥ 65 years (mean ± SD, 75±7 years; range, 68–91) and 20 young adults (25±5 years, range 20–40), wore a care watch on each wrist and performed a number of randomly ordered tasks: walking at slow, normal and fast self-paced speeds; a Timed Up and Go test (TUG); a step test and ascending/descending stairs. The criterion measure was the actual number of steps observed, counted with a manual tally counter. Absolute percentage error scores, Intraclass Correlation Coefficients (ICC), and Bland–Altman plots were used to assess validity.

Results

ADAMO Care Watch demonstrated high validity during slow and normal speeds (range 0.5–1.5 m/s) showing an absolute error from 1.3% to 1.9% in the older adult group and from 0.7% to 2.7% in the young adult group. The percentage error for the 30-metre walking tasks increased with faster pace in both young adult (17%) and older adult groups (6%). In the TUG test, there was less error in the steps recorded for older adults (1.3% to 2.2%) than the young adults (6.6% to 7.2%). For the total sample, the ICCs for the ADAMO Care Watch for the 30-metre walking tasks at each speed and for the TUG test were ranged between 0.931 to 0.985.

Conclusion

These findings provide evidence that the ADAMO Care Watch demonstrated highly accurate measurements of the steps count in all activities, particularly walking at normal and slow speeds. Therefore, these data support the inclusion of the ADAMO Care Watch in clinical applications for measuring the number of steps taken by older adults at normal, slow walking speeds.

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison

Background

Activity monitoring is necessary to investigate sedentary behavior after a stroke. Consumer wearable devices are an attractive alternative to research-grade technology, but measurement properties have not been established.

Objective

The purpose of this study was to determine the accuracy of 2 wrist-worn fitness trackers: Fitbit Charge HR (FBT) and Garmin Vivosmart (GAR).

Methods

Adults attending in- or outpatient therapy for stroke (n=37) wore FBT and GAR each on 2 separate days, in addition to an X6 accelerometer and Actigraph chest strap monitor. Step counts and heart rate data were extracted, and the agreement between devices was determined using Pearson or Spearman correlation and paired t or Wilcoxon signed rank tests (one- and two-sided). Subgroup analyses were conducted.

Results

Step counts from FBT and GAR positively correlated with the X6 accelerometer (ρ=.78 and ρ=.65, P<.001, respectively) but were significantly lower (P<.01). For individuals using a rollator, there was no significant correlation between step counts from the X6 accelerometer and either FBT (ρ=.42, P=.12) or GAR (ρ=.30, P=.27). Heart rate from Actigraph, FBT, and GAR demonstrated responsiveness to changes in activity. Both FBT and GAR positively correlated with Actigraph for average heart rate (r=.53 and .75, P<.01, respectively) and time in target zone (ρ=.49 and .74, P<.01, respectively); these measures were not significantly different, but nonequivalence was found.

Conclusions

FBT and GAR had moderate to strong correlation with best available reference measures of walking activity in individuals with subacute stroke. Accuracy appears to be lower among rollator users and varies according to heart rhythm. Consumer wearables may be a viable option for large-scale studies of physical activity.

The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds

Accelerometer-based commercial activity trackers are a low-cost and convenient method for monitoring and assessing health measures such as gait. However, the accuracy of these activity trackers in slow walking conditions on a minute-by-minute basis is largely unknown. In this study, the accuracy of a hip-worn commercial activity tracker (FitBit Ultra) was examined through step counts. Accuracy was evaluated through four minute trials of treadmill walking at speeds representative of older adults (0.9, 1.1, and 1.3m/s). Minute-by-minute step count was extracted through the FitBit API and compared it to observer counted steps through video recordings. Results highlighted a significant over-reporting of steps at the highest speed, and a significant under-reporting of steps at the slowest speed, with the FitBit Ultra failing to count steps for one or more minutes at the slowest speed for 11 participants. This study highlights problems with using the FitBit Ultra by slow-walking populations, and recommends that researchers and clinicians should carefully consider the trade-off between accuracy and convenience when using commercial activity trackers with slow-walking populations.

Assessment of Physical Activity of Hospitalised Older Adults: A Systematic Review

BACKGROUND

The assessment of physical activity levels of hospitalised older people requires accurate and reliable measures. Physical activities that older people in hospital commonly engage in include exercises and walking. Measurement of physical activity levels of older inpatients is essential to evaluate the impact of interventions to improve physical activity levels and to determine associations between physical activity in hospital and other health-related outcome measures.

OBJECTIVE

To determine which measures are used to measure physical activity of older people in hospital, and to describe their properties and applications.

METHOD

A systematic review of four databases: Medline, Embase, CINAHL and AMED was conducted for papers published from 1996 to 2016. Inclusion criteria were participants aged ≥ 65 years and studies which included measures of physical activity in the acute medical inpatient setting. Studies which specifically assessed the activity levels of surgical patients or patients with neurological conditions such as stroke or brain injury were excluded. All study designs were included in the review.

RESULTS

18 studies were included from 127 articles selected for full review. 15 studies used objective measures to measure the physical activity of older inpatients: 11 studies used accelerometers and four used direct systematic observations. Seven accelerometers were identified including the StepWatch Activity Monitor, activPAL, GENEActiv, Kenz Lifecorder EX, Actiwatch-L, Tractivity and AugmenTech Inc. Pittsburgh accelerometer. Three studies used a subjective measure (interviews with nurses and patients) to classify patients into low, intermediate and high mobility groups. The StepWatch Activity Monitor was reported to be most accurate at step-counting in patients with slow gait speed or altered gait. The activPAL was reported to be highly accurate at classifying postures.

CONCLUSION

Physical activity levels of older inpatients can be measured using accelerometers. The accuracy of the accelerometers varies between devices and population-specific validation studies are needed to determine their suitability in measuring physical activity levels of hospitalised older people. Subjective measures are less accurate but can be a practical way of measuring physical activity in a larger group of patients.

A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6%, and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.

Patterns of sedentary time and ambulatory physical activity in a Danish population of girls and women with Rett syndrome

BACKGROUND

Rett syndrome (RTT) is a rare neurodevelopmental disorder leading to multiple disabilities and high dependency on caregivers. This study aimed to: (1) describe the patterns of sedentary time and daily steps and (2) identify the association of individual and environmental characteristics with sedentary time.

METHODS

All Danish females with RTT older than 5 years of age and with a MECP2 mutation were invited to participate. The activPAL and StepWatch Activity Monitor (SAM) were worn by participants for at least four days. Sedentary time and step counts were plotted by time to examine daily activity patterns. Associations between sedentary time and individual and environmental covariates were assessed with linear regression models.

RESULTS

The median (interquartile range) age of participants was 22.0 (14.3-36.5) years. On average 83.3% (standard deviation 13.9%) of waking hours were spent in sedentary behaviours (n = 48) and the median (interquartile range) daily step count was 5128 (2829-7704) (n = 28). Females older than 33.5 years, and those unable to walk independently were more sedentary.

CONCLUSIONS

This study demonstrated high levels of sedentary time and low daily step counts in a Danish population of females with RTT. Advancing age and lower walking skills were associated with higher levels of sedentary time. Implications for Rehabilitation Sedentary lifestyles in individuals with disabilities have a negative impact on health and quality of life. High levels of sedentary time and low daily step counts were demonstrated in a Danish population of females with Rett syndrome. Advancing age and inability to walk independently were strongly associated with higher levels of sedentary time in females with Rett syndrome. Understanding patterns of sedentary behaviour and physical activity can aid health care professionals in developing health-promoting physical activity interventions.

Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting

BACKGROUND

Cellular mobile telephone technology shows much promise for delivering and evaluating healthcare interventions in cost-effective manners with minimal barriers to access. There is little data demonstrating that these devices can accurately measure clinically important aspects of individual functional status in naturalistic environments outside of the laboratory.

OBJECTIVE

The objective of this study was to demonstrate that data derived from ubiquitous mobile phone technology, using algorithms developed and previously validated by our lab in a controlled setting, can be employed to continuously and noninvasively measure aspects of participant (subject) health status including step counts, gait speed, and activity level, in a naturalistic community setting. A second objective was to compare our mobile phone-based data against current standard survey-based gait instruments and clinical physical performance measures in order to determine whether they measured similar or independent constructs.

METHODS

A total of 43 ambulatory, independently dwelling older adults were recruited from Nebraska Medicine, including 25 (58%, 25/43) healthy control individuals from our Engage Wellness Center and 18 (42%, 18/43) functionally impaired, cognitively intact individuals (who met at least 3 of 5 criteria for frailty) from our ambulatory Geriatrics Clinic. The following previously-validated surveys were obtained on study day 1: (1) Late Life Function and Disability Instrument (LLFDI); (2) Survey of Activities and Fear of Falling in the Elderly (SAFFE); (3) Patient Reported Outcomes Measurement Information System (PROMIS), short form version 1.0 Physical Function 10a (PROMIS-PF); and (4) PROMIS Global Health, short form version 1.1 (PROMIS-GH). In addition, clinical physical performance measurements of frailty (10 foot Get up and Go, 4 Meter walk, and Figure-of-8 Walk [F8W]) were also obtained. These metrics were compared to our mobile phone-based metrics collected from the participants in the community over a 24-hour period occurring within 1 week of the initial assessment.

RESULTS

We identified statistically significant differences between functionally intact and frail participants in mobile phone-derived measures of percent activity (P=.002, t test), active versus inactive status (P=.02, t test), average step counts (P<.001, repeated measures analysis of variance [ANOVA]) and gait speed (P<.001, t test). In functionally intact individuals, the above mobile phone metrics assessed aspects of functional status independent (Bland-Altman and correlation analysis) of both survey- and/or performance battery-based functional measures. In contrast, in frail individuals, the above mobile phone metrics correlated with submeasures of both SAFFE and PROMIS-GH.

CONCLUSIONS

Continuous mobile phone-based measures of participant community activity and mobility strongly differentiate between persons with intact functional status and persons with a frailty phenotype. These measures assess dimensions of functional status independent of those measured using current validated questionnaires and physical performance assessments to identify functional compromise. Mobile phone-based gait measures may provide a more readily accessible and less-time consuming measure of gait, while further providing clinicians with longitudinal gait measures that are currently difficult to obtain.

Promise of Wearable Physical Activity Monitors in Oncology Practice

Commercially available physical activity monitors provide clinicians an opportunity to obtain oncology patient health measures to an unprecedented degree. These devices can provide objective and quantifiable measures of physical activity, which are not subject to errors or bias of self-reporting or shorter duration of formal testing. Prior work on so-called quantified-self data was based on older-generation, research-grade accelerometers, which laid the foundation for consumer-based physical activity monitoring devices to be validated as a feasible and reliable tool in patients with cancer. Physical activity monitors are being used in chronic conditions including chronic obstructive pulmonary disease, congestive heart failure, diabetes mellitus, and obesity. Differing demographics, compounded with higher symptom and treatment burdens in patients with cancer, imply that additional work is needed to understand the unique strengths and weaknesses of physical activity monitors in this population. Oncology programs can systematically implement these tools into their workflows in an adaptable and iterative manner. Translating large amounts of data collected from an individual physical activity monitoring device into clinically relevant information requires sophisticated data compilation and reduction. In this article, we summarize the characteristics of older- and newer-generation physical activity monitors, review the validation of physical activity monitors with respect to health-related quality-of-life assessments, and describe the current role of these devices for the practicing oncologist. We also highlight the challenges and next steps needed for physical activity monitors to provide relevant information that can change the current state of oncology practice.

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels

Objectives:

Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research.

Methods:

To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving.

Results:

On five of the seven models, the inter-class correlations coefficient (ICC _(3,1)) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC _(3,1): 0.992-0.998. The false detection rates differed between the sitting conditions.

Conclusions:

These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation.

Validity and reliability of accelerometry in identification of lying, sitting, standing or purposeful activity in adult hospital inpatients recovering from acute or critical illness: a systematic review

Objective:

To investigate the validity and reliability of accelerometers to detect lying, sitting and standing postures or purposeful activity in hospitalized adults recovering from acute or critical illness.

Data sources:

CINAHL, MEDLINE, EMBASE, AMED, Cochrane Library, PEDro, PsycINFO and SPORTDiscuss were searched from inception to June 2017. Professional networks and reference lists of relevant articles were also searched. The main selection criteria were hospitalized adults with acute or critical illness and studies investigating the validity or reliability of accelerometers to identify body position or purposeful activity.

Review methods:

Two authors individually assessed study eligibility and independently undertook methodological quality assessment and data extraction from selected articles. A narrative synthesis of the data was undertaken.

Results:

Fifteen studies, collectively enrolling 385 hospitalized participants, were identified. Populations included stroke, the elderly, acute exacerbation of chronic respiratory disease, abdominal surgery and those recovering from critical illness. Correlations of r = 0.36 to 0.98 and levels of agreement of κ = 0.28 to 0.98 were reported for identification of lying, sitting or standing postures. Correlations of r = 0.4 to 0.8 with general activity were found, with r = 0.94 and 0.96 reported for step count. The reliability of accelerometry measurement was investigated in one study evaluating step count quantification (intraclass correlation coefficient (ICC) = 0.99, 95% confidence interval (CI) = 0.99–1.00).

Conclusion:

The validity of accelerometers to determine lying, sitting and standing postures or quantify purposeful activity within hospitalized acute or critically ill populations is variable. The reliability of accelerometry measurement within this setting remains largely unexplored.

Fitness band accuracy in older community dwelling adults

Fitness bands are widely available and assist with tracking the number of steps taken. However, for older people with slow gaits, shorter step widths and/or use of ambulatory devices, the accuracy of fitness bands for step counting has not been well studied. Using four commercially available fitness bands (Garmin Vivofit2™, Fitbit Flex™, Up3™ and Microsoft Band™), we studied 30 older people with varying ambulatory abilities. We videotaped participants walking and compared the videotaped step count with the fitness band counts. Only 5 of the 30 participants had accurate readings within a ±20 percent accuracy for all four bands. There was no relationship between the step speed and accuracy of the fitness bands. Participants using walkers and walking sticks had none of the bands that met the ±20 percent accuracy. Canes were more variable with accuracy. Fitness band manufacturers may need to tune their algorithms for use by older people.

Factors Associated With Ambulatory Activity in De Novo Parkinson Disease

BACKGROUND AND PURPOSE

Objective ambulatory activity during daily living has not been characterized for people with Parkinson disease prior to initiation of dopaminergic medication. Our goal was to characterize ambulatory activity based on average daily step count and examine determinants of step count in nonexercising people with de novo Parkinson disease.

METHODS

We analyzed baseline data from a randomized controlled trial, which excluded people performing regular endurance exercise. Of 128 eligible participants (mean ± SD = 64.3 ± 8.6 years), 113 had complete accelerometer data, which were used to determine daily step count. Multiple linear regression was used to identify factors associated with average daily step count over 10 days. Candidate explanatory variable categories were (1) demographics/anthropometrics, (2) Parkinson disease characteristics, (3) motor symptom severity, (4) nonmotor and behavioral characteristics, (5) comorbidities, and (6) cardiorespiratory fitness.

RESULTS

Average daily step count was 5362 ± 2890 steps per day. Five factors explained 24% of daily step count variability, with higher step count associated with higher cardiorespiratory fitness (10%), no fear/worry of falling (5%), lower motor severity examination score (4%), more recent time since Parkinson disease diagnosis (3%), and the presence of a cardiovascular condition (2%).

DISCUSSION AND CONCLUSIONS

Daily step count in nonexercising people recruited for this intervention trial with de novo Parkinson disease approached sedentary lifestyle levels. Further study is warranted for elucidating factors explaining ambulatory activity, particularly cardiorespiratory fitness, and fear/worry of falling. Clinicians should consider the costs and benefits of exercise and activity behavior interventions immediately after diagnosis of Parkinson disease to attenuate the health consequences of low daily step count.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A170).