Monique A. M. Berger

Purpose: To assess the acquisition of assistive products (APs) and unmet needs regarding APs in patients after stroke. Methods: In this prospective cohort study patients reported their acquisition of APs in the past 6 months at 6, 12, 24, and 30 months after starting stroke rehabilitation. At 12 months unmet needs related to APs were assessed with four items of the Longer-term Unmet Needs after Stroke. Results: 836 patients (median age 63 years, 38.5% female) were included. In the first year after starting stroke rehabilitation, 50.0% of patients had acquired no APs, 22.4% had acquired 1–2 APs, and 27.6% ≥ 3 APs. Most frequently a shower chair (32.8%) and grab bars (26.3%) were acquired. After this year, fewer APs were acquired, most frequently a walker (8.3%) and orthopaedic shoes (7.9%). Unmet needs related to APs were reported by 20.2% of patients. Patients with ≥3 APs reported more unmet needs than patients without APs or with 1–2 APs. Conclusion: One year after starting stroke rehabilitation, 50.0% of patients acquired APs, while one in five patients still had an unmet need related to APs. Health professionals should be alert to these unmet needs, especially in patients with stroke who already have several APs.

Background: Stroke patients are less physically active than the general population. Various determinants can affect post-stroke physical activity; understanding these determinants can help tailor targeted interventions. The Determinants of Physical Activity Questionnaire (DPAQ), based on the Theoretical Domains Framework, assesses determinants of physical activity. This study aimed to translate the DPAQ into Dutch and evaluate its content validity (comprehensibility, relevance and comprehensiveness) in a stroke population and their peers without stroke. Methods: The DPAQ was translated according to international guidelines. Content validity was assessed at an outpatient neurorehabilitation clinic. Seven stroke patients completed the DPAQ using think aloud and cognitive debriefing. Audio-recorded data were transcribed, coded and analysed. The DPAQ was adapted and re-tested in seven other stroke patients and seven peers. Results: The translation and content validity test resulted in the following adaptations: the DPAQ introduction was clarified; the response option ‘Do not want to answer’ was changed to ‘Cannot answer’; and some negatively phrased items were converted to positive. Of the adapted version, most items were found comprehensible and relevant by both stroke patients and peers, except for items on coping planning (complex sentence structure and abstract item-wording) and goal conflict (irrelevant for some patients and peers). The DPAQ was found comprehensive for both patients and peers. Conclusions: A Dutch version of the DPAQ was created, which was considered comprehensible, relevant and comprehensive for both stroke patients and peers without stroke.

This chapter provides an analysis of the transformative role of wearables in enhancing athletic performance and supporting individualised training in Paralympic and adaptive sports. It begins with an overview of how wearables influence performance metrics, particularly within the unique demands of adaptive sports, and discusses current practical applications and potential future developments. Insights are offered into various wearable technologies, ranging from heart rate monitors and electromyography (EMG) sensors to more advanced systems such as IMUs and GPS trackers. The use of wearables in measuring both internal and external load is described, with their role in performance and training management. A dedicated section focuses on the customisation of sports equipment through wearable data, enabling optimised performance and injury prevention. Subsequent sections cover the application of wearables in monitoring training outcomes, performance tracking, and their role in sports classification. Future advancements are also considered, including the integration of AI, smart textiles, and personalised solutions tailored to athletes’ specific needs, while ethical concerns such as data privacy are addressed. The chapter concludes by emphasising the ongoing potential of wearable technology to bridge the gap between scientific research and real-world sports practice, paving the way for smarter, data-driven approaches in adaptive sports.

Wheelchair optimisation in clinical settings often relies on expert opinion from static wheelchair seating posture. A wheelchair ergometer provides biomechanical and physiological insights during propulsion. This study assessed the reliability of propulsion technique and physiological parameters in submaximal and sprint tests and used these values to compare two hand rim types. Nineteen non-wheelchair users completed two exercise blocks per hand rim type (Gekko vs. conventional) on a wheelchair ergometer. Each block included a 4-min submaximal test and a 30 s sprint. The intraclass correlation coefficient (ICC), standard error of measurement (SEM) and smallest detectable change (SDC) were calculated to assess reliability for one to four exercise blocks. Hand rim differences were analysed using a mixed-effects model. Submaximal propulsion technique showed good to excellent reliability (ICC = 0.75-1.00), while physiological variables and sprint propulsion technique had moderate to excellent reliability (ICC = 0.5-0.95). SDCs ranged from 12 to 29% (submaximal) and 13 to 25% (sprint), except for negative power variables (25-70%). Averaging the two, three or four tests reduced SDCs by 29%, 42% and 50%, respectively, compared to one test. The Gekko rim outperformed the conventional rim in negative power (submaximal) and in distance, velocity and power (sprints). The submaximal test demonstrated better reliability and lower SDCs than the sprint test. The Gekko rim performed better at group level but not for every individual. Using SDCs instead of group results enhances clinical relevance. Future research should validate these findings in wheelchair users to support evidence-based wheelchair setup recommendations.Implications for rehabilitationThe study showed that submaximal propulsion technique has good-to-excellent reliability, while submaximal physiological parameters and sprint propulsion technique showed moderate-to-excellent reliability. Smallest detectable change (SDC) values varied, with higher values seen in negative power and physiological parameters, and generally higher SDCs in the sprint test.Although group-level statistics showed significant differences in propulsion technique (favouring the Gekko hand rim), individual analysis using SDC cut-offs revealed variations among participants. This highlights that wheelchair adaptations should be evaluated at an individual level, as group results may not capture important differences that matter for specific users.To detect the most meaningful individual differences, it is recommended to average the results of four tests. However, since the most significant reduction in SDCs occurs between one and two tests, at least two tests should be conducted when time or physical capacity is limited.The findings are not only relevant for comparing hand rims. SDC thresholds can be used to assess a wide range of individual factors, such as propulsion technique, fitness or wheelchair features (e.g., seat height). These thresholds can help tailor interventions based on the unique needs of each wheelchair user.

Objective: Benefits of physical activity (PA) on sleep in people with axial SpondyloArthritis (axSpA) are largely unknown. Our aim is to explore the relationships between PA and sleep on both a group level and an individual level using Wearable Activity Trackers (WATs) and machine learning. Methods: A sample of 64 axSpA participants received a WAT to monitor their PA and sleep. Participants with more than 30 days data of PA and sleep duration were included in the analyses. Spearman’s correlation and the machine learning technique Subgroup Discovery were used to determine relationships between PA during the three prior days and light and deep sleep duration. Results: Number of daily steps (n = 64) was (median (first quartile (Q1) - third quartile (Q3) )) 4026 (1915 - 6549), total sleep (daily light and deep sleep) duration of the participants was 7 h 29 min (6 h 41 min - 8 h 8 min). Nearly 30% (n = 18) of the participants were eligible for inclusion in analyses (> 30 days of data). No significant relationships between prior PA and sleep were obtained on a group level. On an individual level, for 8 of the 18 included participants, significant relationships (p < 0.05) could be identified between PA during the three prior days and daily sleep duration. These significant relationships differed from participant to participant with a varying qualification of PA (number of steps, intensity level PA) and relevant time window (previous one, two or three days). Conclusion: Significant relationships between PA and daily sleep duration could be obtained on an individual level with details of the significant relationships varying between participants. Registration number: Netherlands Trial Register NL8238, included in the International Clinical Trial Registry Platform (ICTRP) (https://trialsearch.who.int/Trial2.aspx?TrialID=NL8238).

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.

Within rehabilitation, there is great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self or attendant pushed wheelchair propulsion. The validity was determined by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.

An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. To monitor the mechanical power during wheelchair sports practice, inertial measurement units (IMUs) might be used. However, a well-founded and unambiguous theoretical framework that follows the dynamics of manual wheelchair propulsion is required to validly apply IMUs for mechanical power assessment in wheelchair sports. Such a framework does not yet exist. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to create a set of guidelines on how to use IMUs to monitor mechanical power during wheelchair propulsion supported by experimental data. After verifying the theoretical framework, an IMU model was defined. Subsequently, the validity of the IMU model and underlying assumptions was determined. Therefore, power was estimated from IMU data during wheelchair propulsion and was subsequently compared to gold standard optical motion capture data. Data was collected from eleven participants without wheelchair experience propelled an all-court sports wheelchair on a large treadmill. At the same time, kinematics were measured using two IMUs and an optical motion capture system. The results reveal that, with a proper drag or deceleration test, one IMU on the wheelchair frame and one IMU on the wheel axis, decent power estimations can be obtained in daily wheelchair (sports) practice. To conclude, the theoretical framework and the resulting IMU-based power is thus well suitable to estimate mechanical power during straight-line wheelchair propulsion in wheelchair court sports and daily wheelchair practice, and it is an important first step towards feasible power estimations in all wheelchair (sports) situations.

To assess the reporting quality of interventions aiming at promoting physical activity (PA) using a wearable activity tracker (WAT) in patients with inflammatory arthritis (IA) or hip/knee osteoarthritis (OA). A systematic search was performed in eight databases (including PubMed, Embase and Cochrane Library) for studies published between 2000 and 2022. Two reviewers independently selected studies and extracted data on study characteristics and the reporting of the PA intervention using a WAT using the Consensus on Exercise Reporting Template (CERT) (12 items) and Consolidated Standards of Reporting Trials (CONSORT) E-Health checklist (16 items). The reporting quality of each study was expressed as a percentage of reported items of the total CERT and CONSORT E-Health (50% or less = poor; 51–79% = moderate; and 80–100% = good reporting quality). Sixteen studies were included; three involved patients with IA and 13 with OA. Reporting quality was poor in 6/16 studies and moderate in 10/16 studies, according to the CERT and poor in 8/16 and moderate in 8/16 studies following the CONSORT E-Health checklist. Poorly reported checklist items included: the description of decision rule(s) for determining progression and the starting level, the number of adverse events and how adherence or fidelity was assessed. In clinical trials on PA interventions using a WAT in patients with IA or OA, the reporting quality of delivery process is moderate to poor. The poor reporting quality of the progression and tailoring of the PA programs makes replication difficult. Improvements in reporting quality are necessary.