Showing posts with label upper limb. Show all posts
Showing posts with label upper limb. Show all posts
Thursday, September 24, 2026
Measuring arm function early after stroke: is the DASH good enough?
Despite a growing call to use patient-reported outcomes in clinical research, few are available for measuring upper limb function post-stroke. We examined the Disabilities of the Arm, Shoulder and Hand (DASH) to evaluate its measurement performance in acute stroke. In doing so, we compared results from traditional and modern psychometric methods. 172 people with acute stroke completed the DASH. Those with upper limb impairments completed the DASH again at 6 weeks (n=99). Data (n=271) were analysed using two psychometric paradigms: traditional psychometric (Classical Test Theory, CTT) analyses examined data completeness, scaling assumptions, targeting, reliability and responsiveness; Rasch Measurement Theory (RMT) analyses examined scale-to-sample targeting, scale performance and person measurement. CTT analyses implied the DASH was psychometrically robust in this sample. Data completeness was high, criteria for scaling assumptions were satisfied (item-total correlations 0.55-0.95), ...
Monday, September 21, 2026
Health-convergence-based muscle synergy variability for quantifying neuromuscular control responses evoked by upper limb rehabilitation robot training in stroke patients
With NO protocol created and distributed to all 10 million yearly survivors; COMPLETELY FUCKING USELESS!
Health-convergence-based muscle synergy variability for quantifying neuromuscular control responses evoked by upper limb rehabilitation robot training in stroke patients
Abstract
Background
Upper limb rehabilitation robot-assisted training (UE-RAT) facilitates post-stroke motor recovery. Its efficacy is evaluated using clinical scales. However, these scales mainly reflect overall motor impairment and functional performance, lacking indices to assess patients’ neuromuscular control during training, thereby limiting precise evaluation of training effects. Therefore, this study quantified neuromuscular control in stroke patients through muscle synergy analysis with fine-grained movement decomposition and proposed a muscle synergy variability index based on healthy synergy templates to measure convergence toward healthy neuromuscular control.
Methods
Eighteen age-matched healthy subjects and 62 stroke patients were recruited. Patients underwent 4-week UE-RAT in assistive, active, or resistive modes according to their functional impairment. Electromyographic and kinematic data were synchronously recorded from the affected upper limb weekly. Continuous tasks were segmented into four specific movement types based on motion trajectories. Muscle synergy patterns were extracted from healthy subjects for each movement segment and clustered to construct reference synergies. Using this benchmark, the similarity and coverage of patients’ single-movement synergies relative to reference synergies were calculated to derive the variability index (Vvariability). Finally, standardized by healthy controls, the neuromuscular control deviation score (DS) was quantified for each training mode.
Results
The proposed Vvariability significantly distinguished patients from healthy controls across all training modes (P < 0.05). Longitudinal observation demonstrated average reductions in Vvariability of 5.7%-11.2%, 13.2%-25.5%, and 14.0%-25.1% after training in the assistive, active, and resistive groups, respectively (all P < 0.05). After adjustment for clinical covariates, DS remained significantly different across training modes (P < 0.05), with the highest deviations in the severely impaired assistive group (0.79–1.19) and the lowest in the least impaired resistive group (0.26–0.42). DS was significantly negatively correlated with FMA-UE scores (r= -0.317 to -0.526) and exhibited stronger correlations than conventional synergy-based metrics, including synergy structural similarity (r = 0.020–0.448) and synergy merging (r= -0.171 to -0.408), supporting its potential as a complementary quantitative measure of neuromuscular control deviation during robotic rehabilitation.
Conclusion
The proposed Vvariability can quantify deviations of stroke patients’ neuromuscular responses from healthy patterns during UE-RAT, providing an objective tool to assess task suitability and supporting neuromuscular control-guided precision rehabilitation.
Sunday, September 20, 2026
Implementing Evidence‐Based Somatosensory Rehabilitation After Stroke: Reported Practice, Documented Practice, and Barriers
Finally got around to thinking about implementing protocols from Margaret Yekutiel writing a whole book about this in 2001, 'Sensory Re-Education of the Hand After Stroke'.
THAT IS MASSIVE INCOMPETENCE!
Implementing Evidence‐Based Somatosensory Rehabilitation After Stroke: Reported Practice, Documented Practice, and Barriers
Abstract
Introduction
Occupational therapists play a key role in assessing and treating somatosensory impairments post stroke including proprioception (limb position sense), touch, and temperature discrimination. Although research evidence and clinical guidelines offer strategies for assessing and treating this condition, there is limited information on how well these strategies are being implemented in routine practice. We aimed to describe the current practice of occupational therapists in somatosensory assessment and treatment of the upper limb of stroke survivors and to understand barriers and enablers to evidence‐based care.
Methods
This observational study encompassed (1) a cross‐sectional survey measuring self‐reported use of somatosensory assessment and treatment strategies and perceived barriers to implementation and (2) a medical file audit to report on the use of somatosensory assessment and treatment with stroke patients. Data were analyzed descriptively, with enablers and barriers to evidence use classified using the three domains of the Capability, Opportunity, Motivation–Behavior (COM‐B) change model.
Results
Of 50 files audited, 26 patients had documented upper limb impairment. Of these, 54% had sensation assessed. Treatment strategies were documented for 7 out of 12 patients with identified impairment. These findings aligned with therapist self‐report, indicating that most clinicians used sensory assessment and treatment strategies less than once per month. Therapists reported capability (lack of skills and skill confidence) and opportunity (lack of time and opportunity to practice skills) as barriers to evidence‐based practice. Occupational therapists expressed motivation to change and improve their skills in evidence‐based sensory assessment and treatment.
Conclusion
Our findings highlight ongoing gaps between evidence and practice. Therapists report being motivated to provide evidence‐based care but require support to overcome barriers, such as training and access to resources. Medical record audits aligned with survey findings; however, there were differences between reported and documented use and scope of sensory assessment and treatment in practice.
Sunday, September 13, 2026
A reduced sensor configuration for upper extremity monitoring after stroke
Survivors don't give a flying fuck about monitoring; WHERE ARE THE EXACT RECOVERY PROTOCOLS?
Not solving stroke is the absolute stupidity out there! You're all fired! Your comeuppance/screaming when you are the 1 in 4 per WHO that has a stroke will be soul satisfying.
A reduced sensor configuration for upper extremity monitoring after stroke
Abstract
Background
Home rehabilitation for stroke survivors is crucial for promoting upper extremity (UE) movements, improving functional ability, and enhancing independence and quality of life.
(100% recovery protocols would accomplish all this and deliver what the survivors want; 100% recovery! And you're too fucking stupid to see this?) Wearable sensors enable monitoring of movement trends and progress during home rehabilitation. The feasibility of using sensors in the homes of stroke survivors depends both on user acceptability and the accuracy of the sensors in capturing the complexity of movement. The aim of this study was to identify a reduced and more practical sensor configuration that preserves the ability to capture movement complexity for monitoring UE movements in stroke survivors during daily activities at home. This was informed by aligning user acceptability data with movement complexity measures derived from a nine-sensor reference configuration in a home-like environment.Methods
Eleven chronic stroke survivors were observed in a natural or simulated home environment while attempting to attach and detach sensors and wearing them while performing self-chosen activities of daily living. Nine inertial measurement unit sensors were placed on the participants’ UEs and sternum. Acceptability was assessed with a custom-made questionnaire consisting of 14 items scored on a 1–5 Likert scale, and five open-ended questions. Furthermore, information entropy was calculated to determine the minimal number of sensors needed to capture movement complexity.
Results
Total acceptability of wearing the 9 sensors was high, with a median score of 57 out of 70 (81%; IQR = 11), whereas usability was moderate, with a median score of 11 out of 20 (55%; IQR = 5.5). The usability challenges were related mostly to attaching and detaching sensors on the hands and non-affected UE. The minimal sensor configuration to capture behavior complexity consisted of three-to-four sensors, placed on the sternum, non-affected and affected forearms, with or without affected upper arm.
Conclusions
The 9-sensor configuration had high acceptability, but lower usability. A reduced configuration with three-to-four sensors was sufficient to maintain the accuracy of the sensors, while potentially increasing stroke survivors’ usability. In turn, this may increase the feasibility of wearing sensors at home for stroke survivors.
Friday, September 11, 2026
Efficacy of a soft robotic exoskeleton for upper-limb rehabilitation in subacute stroke inpatient rehabilitation: a randomised controlled trial
Since it didn't work, what exactly does your doctor have to get you 100% recovered? Nothing? Just useless words of encouragement!
THAT IS PURE INCOMPETENCE!
Efficacy of a soft robotic exoskeleton for upper-limb rehabilitation in subacute stroke inpatient rehabilitation: a randomised controlled trial
Abstract
Background
Soft robotic devices may be a useful adjunct for stroke rehabilitation by providing higher intensity training, while allowing comfort and portability compared to rigid exoskeletons. We evaluated the efficacy of a sensor-assisted soft robotic glove (SSR) during inpatient stroke rehabilitation.
Methods
This assessor-blinded, randomised controlled trial was conducted in 2 inpatient rehabilitation units. Adults aged 21–90 years old, with ischaemic or haemorrhagic stroke and unilateral upper-limb impairment were recruited between July 2023 and May 2025. Participants were randomised to either [1] SSR or [2] supervised Graded Repetitive Arm Supplementary Programme (GRASP) training for up to 15 sessions, 30–45 min/session; 5 days/week for up to 3 weeks. The primary outcome was Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Secondary outcomes included Stroke Upper Limb Capacity Scale, the DuruÖz Hand Index, and Stroke Impact Scale-16 (SIS-16). Outcomes were assessed at baseline (T1), post-intervention (T2) and follow-up at 3 months (T3).
Results
Recruitment ceased before the planned sample size was reached due to funding constraints. Therefore, the trial was underpowered to detect the prespecified between-group difference. Of 323 screened individuals, 61 were randomised. 3 participants did not complete T2 assessment resulting in a modified intention-to-treat population of 58 (SSR n = 28, control n = 30). Both groups showed improvement across primary and secondary outcomes, with no statistically significant differences observed between them. In a post-hoc exploratory subgroup analysis with baseline FMA-UE < 35 and ≥ 10 sessions (n = 38), a statistically significant difference in FMA-UE distal sub-score at T2 was observed, favouring the SSR group (mean difference 3.06, 95% confidence interval [CI] 0.02 to 6.09, p = 0.049) but not at T3. A numerically higher proportion of participants in the SSR group achieved minimal clinically important difference value for SIS-16 (77.8% vs. 55.0%, adjusted odds ratio 6.52 (95% CI 1.00 to 42.58, absolute risk difference 22.8% [95% CI -7.2% to 47.4%], p = 0.050) at T2, but did not reach statistical significance.
Conclusion Words matter, desc
Sensor-assisted soft robotic glove training did not demonstrate superiority(So failure! Words matter, so describe it properly!) over supervised GRASP for upper limb function post-stroke. Exploratory subgroup analyses identified a signal suggesting improved distal hand motor recovery in participants with greater baseline impairment with sufficient therapy exposure.
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