Intensity of daily physical activity–a key component for improving physical capacity after minor stroke?

Purpose: Elucidating the complex interactions between physical activity (PA), a multidimensional concept, and physical capacity (PC) may reveal ways to improve rehabilitation interventions. This cross-sectional study aimed to explore which PA dimensions are related to PC in people after minor stroke. Materials and methods: Community dwelling individuals >6 months after minor stroke were evaluated with a 10-Meter-Walking-Test (10MWT), Timed-Up & Go, and the Mini Balance Evaluation System Test. The following PA outcomes were measured with an Activ8 accelerometer: counts per minute during walking (CPMwalking; a measure of intensity), number of active bouts (frequency), mean length of active bouts (distribution), and percentage of waking hours in upright positions (duration). Multivariable linear regression models, adjusted for age, sex and BMI, were used to assess the relationships between PC and PA outcomes. Results: Sixty-nine participants [62.2 ± 9.8 years, 61% male, 20 months post onset (IQR 13.0–53.5)] were included in the analysis. CPMwalking was significantly associated to PC in the 10MWT (std. β = 0.409, p = 0.002), whereas other associations between PA and PC were not significant. Conclusions: The PA dimension intensity of walking is significantly associated with PC, and appears to be an important tool for future interventions in rehabilitation after minor stroke.Implications for rehabilitation It is recommended to express physical activity after minor stroke in multiple dimensions such as intensity, frequency, duration and distribution. In particular, intensity of physical activity measured with accelerometer counts is most closely related to physical capacity. The findings of this study underline the importance of being physically active beyond a certain intensity. In future development of interventions and guidelines that aim to promote daily physical activity, intensity should be taken into account

Smallest detectable change in propulsion wheelchair tests on a wheelchair ergometer

The wheelchair ergometer (Lode Esseda) can be used to monitor propulsion variables of wheelchair users, for example to evaluate wheelchair adaptations. In order to interpret the outcomes of the measurements and to support clinical decision making, it is important to distinguish real changes in propulsion technique and physiological outcomes from measurement errors

Determining the difference between wheelchair adaptations, i.e. hand rim types, by using propulsion wheelchair tests on a wheelchair ergometer

Wheelchair adaptations are mainly based on expert opinion obtained from observation. The Lode Esseda wheelchair ergometer provides objective data and may therefore support clinical decision-making

Effectiveness of healthcare interventions using objective feedback on physical activity: A systematic review and meta-analysis

Study selection: Randomized controlled trials published after 2007 with (former) healthcare patients ≥ 21 years of age were included if physical activity was measured objectively using a wearable monitor for both feedback and outcome assessment. The main goal of included studies was promoting physical activity. Any concurrent strategies were related only to promoting physical activity. Data extraction: Effect sizes were calculated using a fixed-effects model with standardized mean difference. Information on study characteristics and interventions strategies were extracted from study descriptions. Data synthesis: Fourteen studies met the inclusion criteria (total n = 1,902), and 2 studies were excluded from meta-analysis. The overall effect size was in favour of the intervention groups (0.34, 95% CI 0.23–0.44, p < 0.01). Study characteristics and intervention strategies varied widely. Conclusion: Healthcare interventions using feedback on objectively monitored physical activity have a moderately positive effect on levels of physical activity. Further research is needed to determine which strategies are most effective to promote physical activity in healthcare programmes. Lay Abstract Wearable technology is progressively applied in health care and rehabilitation to provide objective insight into physical activity levels. In addition, feedback on physical activity levels delivered by wearable monitors might be beneficial for optimizing their physical activity. A systematic review and meta-analysis was conducted to evaluate the effectiveness of interventions using feedback on objectively measured physical activity in patient populations. Fourteen studies including 1902 patients were analyzed. Overall, the physical activity levels of the intervention groups receiving objective feedback on physical activity improved, compared to the control groups receiving no objective feedback. Mostly, a variety of other strategies were applied in the interventions next to wearable technology. Together with wearable technology, behavioral change strategies, such as goal-setting and action planning seem to be an important ingredient to promote physical activity in health care and rehabilitation. LinkedIn: https://www.linkedin.com/in/hanneke-braakhuis-b9277947/ https://www.linkedin.com/in/moniqueberger

Rolstoelen: technologie, optimalisatie en individuele afstemming

De rolstoel: het icoon voor handicap, dat ding waar je in ‘belandt’, meestal afgebeeld in zwaar roestvrijstalen uitvouwbare uitvoering, bedoeld om in voortgeduwd te worden. Die rolstoel, die interesseert ons niet! Welke dan wel? De rolstoel die de gebruiker zijn individuele vrijheid teruggeeft, en stimuleert tot bewegen. Bovendien, de rolstoel die de gebruiker de fysieke activiteit die nog inzetbaar is duurzaam laat gebruiken. Dat wil zeggen inspanning van het bovenlichaam zonder overbelasting en pijn

The wheelchair ergometer for adjustments in manual wheelchair use in action!

Wheelchair ergometer Obtaining objective propulsion data to analyze the interface of user and wheelchair in action to contribute to the advice for adjustments. Active lifestyle A well-adjusted wheelchair can contribute to an active lifestyle, maximal participation in society and avoiding overload. Current status Protocol consisted of 30s sprint, driving at comfortable speed and maintaining given constant speed

Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations

Possibilities of using the Rollz Motion Smart for gait analysis in rehabilitation of stroke patients

The Rollz Motion Smart rollator detects posture, gait and activity of a user and provides feedback. • Various programs to train the user and optimize walking performance. • Measuring gait parameters like velocity, step time, step length, distance between person and rollator. • Visual, tactile and auditory cues help the user to take the first step or maintain a suitable walking rhythm

Wheelchair Mobility Performance enhancement by changing wheelchair properties: what is the effect of grip, seat height and mass?

Purpose: To provide insight on the effect of wheelchair settings on wheelchair mobility performance (WMP). Methods: Twenty elite wheelchair basketball athletes of low (n = 10) and high classification (n = 10) were tested in a wheelchair-basketball-directed field test. Athletes performed the test in their own wheelchairs, which were modified for 5 additional conditions regarding seat height (high–low), mass (central–distributed), and grip. The previously developed inertial-sensor-based WMP monitor was used to extract wheelchair kinematics in all conditions. Results: Adding mass showed most effect on WMP, with a reduced average acceleration across all activities. Once distributed, additional mass also reduced maximal rotational speed and rotational acceleration. Elevating seat height had an effect on several performance aspects in sprinting and turning, whereas lowering seat height influenced performance minimally. Increased rim grip did not alter performance. No differences in response were evident between low- and high-classified athletes. Conclusions: The WMP monitor showed sensitivity to detect performance differences due to the small changes in wheelchair configuration. Distributed additional mass had the most effect on WMP, whereas additional grip had the least effect of conditions tested. Performance effects appear similar for both low- and high-classified athletes. Athletes, coaches, and wheelchair experts are provided with insight into the performance effect of key wheelchair settings, and they are offered a proven sensitive method to apply in sport practice, in their search for the best wheelchair–athlete combination. https://doi.org/10.1123/ijspp.2017-0641 LinkedIn: https://www.linkedin.com/in/rienkvdslikke/ https://www.linkedin.com/in/annemarie-de-witte-9582b154/ https://www.linkedin.com/in/moniqueberger

Obtaining wheelchair kinematics with one sensor only? The trade-off between number of inertial sensors and accuracy for measuring wheelchair mobility performance in sports

In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1◦/s for wheelchair angular velocity when compared with the reference system. The two IMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0◦/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available