Measurement of children's physical activity using a pedometer with a built-in memory

ObjectivesWe evaluated the accuracy of the Accusplit AH120 pedometer (built-in memory) for recording step counts of children during treadmill walking against (1) observer counted steps and (2) concurrently measured steps using the previously validated Yamax Digiwalker SW-700 pedometer.DesignThis was a cross-sectional validation study performed under controlled settings.MethodsForty five 9–12-year-olds walked on treadmills at speeds of 42, 66 and 90 m/min to simulate slow, moderate and fast walking wearing Accusplit and Yamax pedometers concurrently on their right hip. Observer counted steps were captured by video camera and manually counted. Absolute value of percent error was calculated for each comparison. Bland–Altman plots were constructed to show the distribution of the individual (criterion-comparison) scores around zero.ResultsBoth pedometers under-recorded observer counted steps at all three walk speeds. Absolute value of percent error was highest at the slowest walk speed (Accusplit = 46.9%; Yamax = 44.1%) and lowest at the fastest walk speed (Accusplit = 8.6%; Yamax = 8.9%). Bland–Altman plots showed high agreement between the pedometers for all three walk speeds.ConclusionsUsing pedometers with built-in memory capabilities eliminates the need for children to manually log step counts daily, potentially improving data accuracy and completeness. Step counts from the Accusplit (built-in memory) and Yamax (widely used) pedometers were comparable across all speeds, but their level of accuracy was dependent on walking pace. Pedometers should be used with caution in children as they significantly undercount steps, and this error is greatest at slower walk speeds.     

Effects of toe-out and toe-in gait with varying walking speeds on knee joint mechanics and lower limb energetics

Toe-out/-in gait has been prescribed in reducing knee joint load to medial knee osteoarthritis patients. This study focused on the effects of toe-out/-in at different walking speeds on first peak knee adduction moment (fKAM), second peak KAM (sKAM), knee adduction angular impulse (KAAI), net mechanical work by lower limb as well as joint-level contribution to the total limb work during level walking.Gait analysis of 20 healthy young adults was done walking at pre-defined normal (1.18 m/s), slow (0.85 m/s) and fast (1.43 m/s) walking speeds with straight-toe (natural), toe-out (15° > natural) and toe-in (15° < natural). Repeated measure ANOVA (p < 0.05) with post-hoc Tukey’s test was applied for statistical analysis.Toe-out gait increased fKAM at all walking speeds (highest at normal speed) while toe-in gait reduced fKAM at all speeds (highest at fast walking speed). Toeing-in reduced KAAI at all speeds while toeing-out affected KAAI only at normal speed. Increasing walking speed generally increased fKAM for all foot positions, but it did not affect sKAM considerably. Slowing down the speed, increased KAAI significantly at all foot positions except for toe-in. At slow walking speed, hip and knee joints were found to be major energy contributors for toe-in and toe-out respectively. At higher walking speeds, these contributions were switched. The ankle joint remained unaffected by changing walking speeds and foot progression angles.Toe-out/-in gait modifications affected knee joint kinetics and lower limb energetics at all walking speeds. However, their effects were inconsistent at different speeds. Therefore, walking speed should be taken into account when prescribing toe-out/-in gait.     

Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site

IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3 min on a treadmill for five speeds (5.0 km/h, 6.5 km/h, 8.0 km/h, 10 km/h, 12 km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3 ± 5.2yrs) took part. Step count error ranged from 41.3 ± 13.8% for the wrist-worn Actigraph to only 0.04 ± 4.3% and −0.3 ± 4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p < 0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p = 0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.     

Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body,bag, belt,hand, and pocket

The assessment of spatiotemporal gait parameters is a useful clinical indicator of health status. Unfortunately, most assessment tools require controlled laboratory environments which can be expensive and time consuming. As smartphones with embedded sensors are becoming ubiquitous, this technology can provide a cost-effective, easily deployable method for assessing gait. Therefore, the purpose of this study was to assess the reliability and validity of a smartphone-based accelerometer in quantifying spatiotemporal gait parameters when attached to the body or in a bag, belt, hand, and pocket. Thirty-four healthy adults were asked to walk at self-selected comfortable, slow, and fast speeds over a 10-m walkway while carrying a smartphone. Step length, step time, gait velocity, and cadence were computed from smartphone-based accelerometers and validated with GAITRite. Across all walking speeds, smartphone data had excellent reliability (ICC_2,1 ≥ 0.90) for the body and belt locations, with bag, hand, and pocket locations having good to excellent reliability (ICC_2,1 ≥ 0.69). Correlations between the smartphone-based and GAITRite-based systems were very high for the body (r = 0.89, 0.98, 0.96, and 0.87 for step length, step time, gait velocity, and cadence, respectively). Similarly, Bland-Altman analysis demonstrated that the bias approached zero, particularly in the body, bag, and belt conditions under comfortable and fast speeds. Thus, smartphone-based assessments of gait are most valid when placed on the body, in a bag, or on a belt. The use of a smartphone to assess gait can provide relevant data to clinicians without encumbering the user and allow for data collection in the free-living environment.     

Automatic identification of gait events during walking on uneven surfaces

The accurate detection of gait events is essential for clinical gait analysis. Aside from speed, surface characteristics like planarity and compliance can affect gait kinematics. Therefore detection of kinematic gait events on uneven surfaces may be inaccurate. To date, no study has investigated the possible influence of surface characteristics on gait event detection. Thus, the purpose of this study was to assess and compare the performance of four kinematic-based gait event detection algorithms (horizontal heel-heel displacement, foot velocity, heel/toe-PSIS displacement, peak hip extension) during walking on three surfaces with different degrees of planarity. Kinematic and force plate data were collected on thirteen athletes during two self-selected walking speeds at a normal (1.30 ± 0.03 m/s) and fast pace (1.70 ± 0.10 m/s). Footstrike and toe-off events were calculated by the algorithms and compared to vertical ground reaction force as a reference. The main findings of the study were: (1) surface configuration had an effect on algorithm accuracy (p < 0.010, 0.84 < d < 2.79); (2) the vertical foot-velocity profile provided the lowest RMSE for footstrike (8.8–14.6 ms) during normal walking and toe-off (15.4–24.9 ms) during normal and fast walking on all surfaces; (3) horizontal heel-ankle displacement provided the lowest RMSE for footstrike during fast walking on all surfaces (RMSE: 8.9–13.8 ms). Overall, the vertical foot-velocity algorithm provided low RMSE for all conditions, is easy to apply and thus recommended for gait event detection.     

Reliability of 3D gait data across multiple laboratories

The aim of this study was to analyze the repeatability of gait analysis studies performed across multiple trials, sessions, and laboratories. Ten healthy participants (6 male/4 female, mean age of 30, mean BMI of 24 kg/m²) were assessed in 3 sessions conducted at each of the three Centers of Excellence for Amputee Care within the Department of Defense. For each test session, kinematic and kinetic parameters were collected during five walking trials for each limb. One independent examiner at each site placed markers on the subjects. Biomechanical data were collected at two walking speeds: self-selected and Froude speed. Variability of the gait data was attributed to inter-trial, inter-session, and inter-lab errors for each subject. These error sources were averaged across all ten subjects to obtain a pooled error estimate. The kinematic errors were fairly consistent at the two walking speeds tested. Median inter-lab kinematic errors were <5.0° (median 2.3°) for all joint angle measurements. However, the kinetic error differed significantly between walking speeds. The median inter-lab kinetic error for the self-selected speed was 0.112 N m/kg (ICR 0.091–0.184) with a maximum of 0.226 N m/kg. The errors were greatly reduced when the subjects walked at their Froude speed. The median inter-lab error was 0.048 N m/kg (ICR 0.025–0.078, maximum 0.086). These data demonstrate that it is possible to get reliable data across multiple gait laboratories, particularly when gait speed is standardized across testing sessions. A key similarity between sites was the use of identical anatomical segment definitions for the respective gait models.     

Determination of preferred walking speed on treadmill may lead to high oxygen cost on treadmill walking

The energy consumption of walking relates to the intensity of physical effort and can be affected by the alterations in walking speed. Therefore, walking speed can be accepted as a crucial, determinant of energy consumption measurement for a walking test. We aimed to investigate the differences in preferred walking speed (PWS) determined both on overground and on a treadmill and, to measure walking energy expenditure and spatio-temporal parameters of gait on a treadmill at both, speeds. Participants (n = 26) walked on a treadmill at two pre-determined speeds for 7 min while, indirect calorimetry measurements were being performed. Spatio-temporal parameters were collected, by video-taping during each walking session on a treadmill. The average overground preferred walking speed (O-PWS) was 85.96 ± 12.82 m/min and the average treadmill preferred walking speed (T-PWS), was 71.15 ± 13.85 m/min. Although T-PWS was lower, oxygen cost was statistically higher when, treadmill walking at T-PWS (0.158 ± 0.02 ml/kg/m) than when the treadmill walking at O-PWS, (0.1480 ± 0.02 ml/kg/m). Cadence (127 ± 9.13 steps/min), stride (134.02 ± 14.09 cm) and step length (67.02 ± 6.90 cm) on the treadmill walking at O-PWS were significantly higher than cadence (119 ± 10 steps/min), stride (117.96 ± 14.38 cm) and step length (59.13 ± 7.02 cm) on the treadmill walking at TPWS. In conclusion, walking on treadmill using O-PWS is more efficient than walking on treadmill using TPWS, in walking tests. Since using T-PWS for treadmill walking tests overestimates the oxygen cost of walking, O-PWS should be used for oxygen consumption measurement during treadmill walking tests.     

Manipulating walking path configuration influences gait variability and six-minute walk test outcomes in older and younger adults

This study determined whether manipulations to walking path configuration influenced six-minute walk test (6MWT) outcomes and assessed how gait variability changes over the duration of the 6MWT in different walking path configurations. Healthy older (ODR) and younger (YNG) (n = 24) adults completed familiarisation trials and five randomly ordered experimental trials of the 6MWT with walking configurations of; 5, 10 and 15 m straight lines, a 6 m by 3 m rectangle (RECT), and a figure of eight (FIG8). Six-minute walk distance (6MWD) and walking speed (m.s⁻¹) were recorded for all trials and the stride count recorded for experimental trials. Reflective markers were attached to the sacrum and feet with kinematic data recorded at 100 Hz by a nine-camera motion capture system for 5 m, 15 m and FIG8 trials, in order to calculate variability in stride and step length, stride width, stride and step time and double limb support time. Walking speeds and 6MWD were greatest in the 15 m and FIG8 experimental trials in both groups (p < 0.01). Step length and stride width variability were consistent over the 6MWT duration but greater in the 5 m trial vs. the 15 m and FIG8 trials (p < 0.05). Stride and step time and double limb support time variability all reduced between 10 and 30 strides (p < 0.01). Stride and step time variability were greater in the 5 m vs. 15 m and FIG8 trials (p < 0.01). Increasing uninterrupted gait and walking path length results in improved 6MWT outcomes and decreased gait variability in older and younger adults.     

StepWatch accuracy during walking,running, and intermittent activities

IntroductionThe purpose of this study was two-fold: 1) to investigate effects of cadence and sensitivity settings for the StepWatch (SW3) on step count accuracy over a wide range of ambulatory speeds, and 2) to compare the preprogrammed “quick start” settings to modified settings during intermittent lifestyle activities.MethodsPart 1: Fifteen participants (18–57 years of age) performed two trials of treadmill walking and running at ten speeds ranging from 26.8 to 268 m min⁻¹ while wearing four SW3 devices. During the first trial, the cadence setting was maintained while sensitivity was varied; in the second trial sensitivity was maintained while the cadence setting was varied. Part 2: Fifteen participants performed four intermittent activities and drove an automobile while wearing two SW3 devices, one with preprogrammed settings and the other with the modified settings determined in Part 1.ResultsPart 1: The modified settings (cadence setting of 70% of default and sensitivity of 16) provided the greatest step counting accuracy across a wide range of speeds reporting 96.0–104% of actual steps between 53.6 and 268 m min⁻¹. Part 2: The preprogrammed settings tended to have higher accuracy for light household tasks (recording 88% to 94% of actual steps) than the modified settings (recording 82% to 86% of actual steps) which showed a trend towards higher accuracy for tennis (recording 93% vs. 89% of actual steps) (p < 0.05).ConclusionThe preprogrammed “quick start” StepWatch settings should be used with individuals who do not engage in running and vigorous sports. However, for individuals who engage in running and tennis, use of modified settings may result in improved step counting accuracy.     

Meaningful change in measures of gait variability in older adults

ObjectivesGait variability is an important indicator of impaired mobility in older adults; however, little is known about the meaning of change in gait variability over time. This study estimated clinically meaningful change in measures of gait variability using both distribution- and anchor-based approaches.DesignCommunity-based observational cohort study.SettingBronx County and the research center at Albert Einstein College of Medicine.ParticipantsOf 1148 participants in the Einstein Aging Study, 241 had quantitative gait assessments in two consecutive years between 2001 and 2005.MeasurementsGait variables were collected using a 12-foot instrumented walkway as participants walked at their normal walking speed. Gait variability was defined as the within-person standard deviation (SD) across steps in two 12-foot walks. Distribution-based meaningful change estimates used Cohen's effect size (0.2 for small and 0.5 for moderate effects). Anchor-based estimates were obtained using dichotomous and ordinal self-reported walking ability ratings as anchors.ResultsDistribution-based estimates for small and substantial changes of variability measures were: stance time 0.005 and 0.014 s; swing time 0.003 and 0.009 s; step length 0.24 and 0.61 cm; and step width 0.03 and 0.08 cm. Among those reporting no change in walking ability, measures of gait variability were stable over 1 year. Among those reporting a decline in walking, stance time and swing time variability increased. Among those reporting an improvement in walking, only step length variability improved.ConclusionPreliminary criteria for meaningful change are 0.01 s for stance time and swing time variability and 0.25 cm for step length variability. These estimates may identify important changes over time in both clinical settings and research studies.     

Old adult fallers display reduced flexibility of arm and trunk movements when challenged with different walking speeds

Specific patterns of pelvic and thorax motions are required to maintain stability during walking. This cross-sectional study explored older-adults’ gait kinematics and their kinematic adaptations to different walking speeds, with the purpose of identifying mechanisms that might be related to increased risk for falls. Fifty-eight older adults from self-care residential facilities walked on a treadmill, whose velocity was systematically increased with increments of 0.1 meters/second (m/s) from 0.5 to 0.9 m/s, and then similarly decreased. Thorax, pelvis, trunk, arms, and legs angular total range of motion (tROM), stride time, stride length, and step width were measured. Twenty-one of the subjects reported falling, and 37 didn’t fall. No significant effect of a fall history was found for any of the dependent variables. A marginally significant interaction effect of fall history and walking speed was found for arms’ tROM (p = 0.098). Speed had an effect on many of the measures for both groups. As the treadmill’s velocity increased, the non-fallers increased their arm (15.9 ± 8.6° to 26.6 ± 12.7°) and trunk rotations (4.7 ± 1.9° to 7.2 ± 2.8°) tROM, whereas for the fallers the change of arm (14.7 ± 14.8° to 20.8 ± 13°) and trunk (5.5 ± 2.9° to 7.3 ± 2.3°) rotations tROM were moderate between the different walking speeds. We conclude that walking speed manipulation exposed different flexibility trends. Only non-fallers demonstrated the ability to adapt trunk and arm ROM to treadmill speed i.e., had a more flexible pattern of behavior for arm and trunk motions, supporting the upper-body’s importance for stability while walking.     

Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.This paper presents a new gait database called MAREA (n = 20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios.     

The high cost of swing leg circumduction during human walking

Humans tend to walk economically, with preferred step width and length corresponding to an energetic optimum. In the case of step width, it is costlier to walk with either wider or narrower steps than normally preferred. Wider steps require more mechanical work to redirect the body’s motion laterally with each step, but the cost for narrower steps remains unexplained. Here we show that narrow steps are costly because they require the swing leg to be circumducted around the stance leg. Healthy adults (N = 8) were tested walking with varying levels of circumduction, induced through lightweight, physical obstructions (“Fins”) attached medially to the lower legs, during treadmill walking at fixed speed (1.25 m s⁻¹) and step width. The net rate of metabolic energy expenditure increased approximately with the square of circumduction amplitude, by about 50% for an amplitude (measured at mid-swing) of about 18 cm. Subjects also generated greater stance leg torque and more arm motion to counter the circumduction, among other compensatory motions that may contribute to energy expenditure. The costs of producing and countering lateral leg motion partially explains the poorer economy of some gait pathologies where circumduction may occur, for example stiff-knee gait. And for healthy individuals, it explains how the energetically optimal average step width, along with the additional variability inherent with multiple steps, should be narrow enough to avoid excessive redirection of the body, yet wide enough to avoid costly circumduction. Humans appear to prefer a step width that compromises between the competing energetic costs for either wider or narrower steps.     

Influence of non-level walking on pedometer accuracy

The YAMAX Digiwalker pedometer has been previously confirmed as a valid and reliable monitor during level walking, however, little is known about its accuracy during non-level walking activities or between genders. Subsequently, this study examined the influence of non-level walking and gender on pedometer accuracy. Forty-six healthy adults completed 3-min bouts of treadmill walking at their normal walking pace during 11 inclines (0–10%) while another 123 healthy adults completed walking up and down 47 stairs. During walking, participants wore a YAMAX Digiwalker SW-700 pedometer with the number of steps taken and registered by the pedometer recorded. Pedometer difference (steps registered ? steps taken), net error (% of steps taken), absolute error (absolute % of steps taken) and gender were examined by repeated measures two-way ANOVA and Tukey's post hoc tests. During incline walking, pedometer accuracy indices were similar between inclines and gender except for a significantly greater step difference (?7 ± 5 steps vs. 1 ± 4 steps) and net error (?2.4 ± 1.8% for 9% vs. 0.4 ± 1.2% for 2%). Step difference and net error were significantly greater during stair descent compared to stair ascent while absolute error was significantly greater during stair ascent compared to stair descent. The current study demonstrated that the YAMAX Digiwalker SW-700 pedometer exhibited good accuracy during incline walking up to 10% while it overestimated steps taken during stair ascent/descent with greater overestimation during stair descent. Stair walking activity should be documented in field studies as the YAMAX Digiwalker SW-700 pedometer overestimates this activity type.     

Variability of gait,bilateral coordination,and asymmetry in women with fibromyalgia

PurposeTo analyze how fibromyalgia affected the variability, asymmetry, and bilateral coordination of gait walking at comfortable and fast speeds.Methods65 fibromyalgia (FM) patients and 50 healthy women were analyzed. Gait analysis was performed using an instrumented walkway (GAITRite system). Average walking speed, coefficient of variation (CV) of stride length, swing time, and step width data were obtained and bilateral coordination and gait asymmetry were analyzed.ResultsFM patients presented significantly lower speeds than the healthy group. FM patients obtained significantly higher values of CV_StrideLength (p = 0.04; p < 0.001), CV_SwingTime (p < 0.001; p < 0.001), CV_StepWidth (p = 0.004; p < 0.001), phase coordination index (p = 0.01; p = 0.03), and p_CV (p < 0.001; p = 0.001) than the control group, walking at comfortable or fast speeds. Gait asymmetry only showed significant differences in the fast condition.ConclusionFM patients walked more slowly and presented a greater variability of gait and worse bilateral coordination than healthy subjects. Gait asymmetry only showed differences in the fast condition. The variability and the bilateral coordination were particularly affected by FM in women. Therefore, variability and bilateral coordination of gait could be analyzed to complement the gait evaluation of FM patients.     

Mobile inertial sensor based gait analysis: Validity and reliability of spatiotemporal gait characteristics in healthy seniors

Gait analysis is commonly used to identify gait changes and fall risk in clinical populations and seniors. Body-worn inertial sensor based gait analyses provide a feasible alternative to optometric and pressure based measurements of spatiotemporal gait characteristics. We assessed validity and relative and absolute reliability of a body-worn inertial sensor system (RehaGait^®) for measuring spatiotemporal gait characteristics compared to a standard stationary treadmill (Zebris^®). Spatiotemporal gait parameters (walking speed, stride length, cadence and stride time) were collected for 24 healthy seniors (age: 75.3 ± 6.7 years) tested on 2 days (1 week apart) simultaneously using the sensor based system and instrumented treadmill. Each participant completed walking tests (200 strides) at different walking speeds and slopes. The difference between the RehaGait^® system and the treadmill was trivial (Cohen’s d <0.2) except for speed and stride length at slow speed (Cohen’s d, 0.35 and 0.49, respectively). Intraclass correlation coefficients (ICC) were excellent for temporal gait characteristics (cadence and stride time; ICC: 0.99–1.00) and moderate for stride length (ICC: 0.73–0.89). Both devices had excellent day-to-day reliability for all gait parameters (ICC: 0.82–0.99) except for stride length at slow speed (ICC: 0.74). The RehaGait^® is a valid and reliable tool for assessing spatiotemporal gait parameters for treadmill walking at different speeds and slopes.     

Validation of a pouch-mounted activPAL3 accelerometer

The activPAL accelerometer is a commonly used device for the assessment of physical activity in cross-sectional and intervention research. These devices are usually attached directly to the skin; however, recent studies report problems such as skin irritation associated with this attachment method and therefore adequate alternate methods are needed. The aim of this study was to validate the use of an elasticised pouch to secure an activPAL3c (PAL Technologies, Glasgow, UK) accelerometer for the assessment of sedentary and physical activity behaviours during laboratory and free-living conditions. Twenty-eight healthy adults wore two activPAL3c accelerometers, one secured in an elasticised pouch, and one directly attached to the skin, on the anterior surface of the right thigh during laboratory-based walking at a self-selected pace, treadmill walking at 0.89 m s⁻¹, 1.56 m s⁻¹ and running at 2.2 m s⁻¹, and during free-living conditions. Paired samples t-tests and intraclass correlation coefficients were used to investigate the difference and agreement between accelerometer outputs. No statistically significant difference in step count between pouch-mounted and skin-mounted activPAL3c accelerometers was evident during walking at any speed under laboratory conditions. No statistically significant difference in step count, upright time, sitting time or postural transitions was found between pouch-mounted and skin-mounted activPAL3c accelerometers during free-living conditions. Intraclass correlation coefficients showed a high to very high level of agreement between pouch-mounted and skin-mounted activPAL3c accelerometers for each outcome variable. The use of an elasticised pouch to secure the activPAL3c accelerometer appears to be a valid method of attachment and may offer advantages over direct skin mounting.     

Validation of a pouch-mounted activPAL3 accelerometer

The activPAL accelerometer is a commonly used device for the assessment of physical activity in cross-sectional and intervention research. These devices are usually attached directly to the skin; however, recent studies report problems such as skin irritation associated with this attachment method and therefore adequate alternate methods are needed. The aim of this study was to validate the use of an elasticised pouch to secure an activPAL3c (PAL Technologies, Glasgow, UK) accelerometer for the assessment of sedentary and physical activity behaviours during laboratory and free-living conditions. Twenty-eight healthy adults wore two activPAL3c accelerometers, one secured in an elasticised pouch, and one directly attached to the skin, on the anterior surface of the right thigh during laboratory-based walking at a self-selected pace, treadmill walking at 0.89 m s⁻¹, 1.56 m s⁻¹ and running at 2.2 m s⁻¹, and during free-living conditions. Paired samples t-tests and intraclass correlation coefficients were used to investigate the difference and agreement between accelerometer outputs. No statistically significant difference in step count between pouch-mounted and skin-mounted activPAL3c accelerometers was evident during walking at any speed under laboratory conditions. No statistically significant difference in step count, upright time, sitting time or postural transitions was found between pouch-mounted and skin-mounted activPAL3c accelerometers during free-living conditions. Intraclass correlation coefficients showed a high to very high level of agreement between pouch-mounted and skin-mounted activPAL3c accelerometers for each outcome variable. The use of an elasticised pouch to secure the activPAL3c accelerometer appears to be a valid method of attachment and may offer advantages over direct skin mounting.     

Bracing the trunk and neck in young adults leads to a more aged-like gait

Older individuals typically walk at slower speeds, with shorter step lengths, greater step widths and spend a larger proportion of the gait cycle in double stance. Changes in neck and trunk mobility may underlie some of the changes in walking seen with increasing age. Consequently, this study was designed to assess whether externally increasing trunk/neck stiffness in young adults leads to similar changes in gait pattern observed with aging. Twelve young adults (20–29 years), sixteen old adults (60–69 years) and fifteen older adults (70–79 years) walked across a 20′ pressure sensitive GAITRite© instrumented walkway at their preferred speed. The young adults also walked under three bracing conditions: (1) Neck braced, (2) Trunk braced, and (3) Neck and Trunk braced. The results revealed that the old and older age groups walked significantly slower, with a shorter step length and with a narrower base of support (p’s < 0.05) compared to the young adults. In young adults, combined neck and trunk bracing led to reduced walking speed, shorter step length, wider base of support and a larger proportion of the gait cycle spent in double stance (p’s < 0.05). The walking speed and step length of older adults remained less than fully braced young adults (p’s < 0.05). Overall these results indicate that artificially stiffening the trunk and neck of young individuals leads to systematic gait changes similar to aging. Consequently, age-related changes in mobility of the neck and torso may in part contribute to the decrements in walking seen for older adults.     

Wearable lower limb haptic feedback device for retraining Foot Progression Angle and Step Width

Technological developments in the last decade have enabled the integration of sensors and actuators into wearable devices for gait interventions to slow the progression of knee osteoarthritis. Wearable haptic gait retraining is one area which has seen promising results for informing modifications of gait parameters for reducing knee adduction moments (KAM) during walking. Two gait parameters which can be easily adjusted to influence KAM include foot progression angle (FPA) and step width (SW). The purpose of this study was to: (1) determine whether a custom haptic ankle bracelet using binary vibrotactile and tactile apparent movement feedback could retrain ten healthy subjects to walk with a modified FPA and SW within a short training session with 80% accuracy; and (2) whether there was a difference between the number of steps required to complete the retraining task based on the two feedback schemes being tested. Retraining multiple gait parameters using a single device was a novel aspect of this work and we found that nine out of ten subjects were able to retrain their gait using the ankle bracelet in both feedback schemes to within 2° and 39 mm of target FPA and SW respectively. We also found no difference in the number steps required for completion between the two schemes (p > 0.05). Future research will investigate the device performance with patients with knee osteoarthritis and the effective change in KAM by modifying a combination of FPA and SW.