Acceptance of Enhanced Robotic Assistance Systems in People With Amyotrophic Lateral Sclerosis–Associated Motor Impairment: Observational Online Study
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive paresis of the extremities and the loss of manual functioning. Due to the severe functional impairment that the disease entails, ALS requires the provision of comprehensive nursing care and a complex set of assistive technology devices. To relieve caregivers and promote autonomy of people with ALS, robotic assistance systems are being developed. This trial aims to evaluate the acceptance of technology, in general, and of robotic arm assistance among people with ALS in order to lay the groundwork for the development of a semiautomatic robotic arm that can be controlled by humans via a multimodal user interface and that will allow users to handle objects and attend to their own bodies. OBJECTIVE: The aim of this study was to perform a systematic analysis of technology commitment and acceptance of robotic assistance systems from the perspective of physically limited people living with ALS. METHODS: The investigation was conducted as a study of a prospective cohort. Participants were only included if they had received a medical diagnosis of ALS. Data collection took place via an online questionnaire on the Ambulanzpartner Soziotechnologie internet platform. Technological commitment was measured using the Neyer short scale. Furthermore, a multidimensional questionnaire was specially developed to analyze participant acceptance of robotic arm assistance: the Acceptance Measure of Robotic Arm Assistance (AMRAA). This questionnaire was accompanied by a video introducing the robot arm. ALS severity was ascertained using the ALS Functional Rating Scale-Extended (ALSFRS-EX). RESULTS: A total of 268 people with ALS participated in the survey. Two-thirds of the participants were male. The overall mean ALS severity score was 42.9 (SD 11.7) points out of 60 on the ALSFRS-EX, with the most relevant restrictions on arms and legs (<60% of normal functioning). Technological commitment ranked high, with the top third scoring 47.2 points out of 60. Younger participants and males showed significantly higher values. The AMRAA score was, again, significantly higher among younger participants. However, the gender difference within the overall cohort was not significant. The more limited the arm functioning of participants according to the ALSFRS-EX subscale, the higher the acceptance rate of robotic assistance. This relationship proved significant. CONCLUSIONS: People with ALS display high technological commitment and feel positive about using technological assistance systems. In our study, younger participants were more open to technology use, in general, and robotic assistance, in particular. Self-appraisal of technology acceptance, competence, and control conviction were generally higher among men. However, any presumed gender difference vanished when users were asked to rate the anticipated usefulness of the technology, in particular the robotic arm. The acceptance was also reflected in users' increased willingness to use a robotic arm as the functionality of their own arms decreased. From the perspective of people with ALS, robotic assistance systems are critical to promoting individual autonomy. Another key consideration in the development of future assistive technologies should be the reduction of caregiver burden. TRIAL REGISTRATION: German Clinical Trials Register DRKS00012803; https://tinyurl.com/w9yzduhd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".