The Effects of Home Automation on Personal and Social Autonomies in Spinal Cord Injury Patients: A Pilot Study
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) is a severe and progressive neurological condition caused by trauma to the nervous system, resulting in lifelong disability and severe comorbidities. This condition imposes serious limitations on everyday life, interfering with patients’ social lives and compromising their quality of life, psychological well-being, and daily living activities. Rehabilitation is essential to helping SCI patients gain more independence in their daily routines. Home automation (HA) systems provide personalized support to users, allowing them to manage various aspects of their living environment, promoting independence and well-being. This study aims to demonstrate the efficacy of an HA system in enhancing personal and social autonomies in SCI patients, resulting in improved cognitive function and reduced anxiety–depressive symptoms compared to traditional training. Methods: We enrolled 50 SCI patients undergoing neurorehabilitation at IRCCS Centro Neurolesi (Messina, Italy). These patients were randomly assigned to one of two groups: a control group (CG) and an experimental group (EG). The CG received traditional training, while the EG underwent HA training. We evaluated the patients before (T0) and after (T1) rehabilitation using various scales, including the Montreal Cognitive Assessment (MoCA), the Beck Depression Inventory (BDI), the Hamilton Rating Scale for Anxiety (HRS-A), the 12-Item Short-Form Survey (SF-12), the Functional Independence Measure (FIM), Activities of Daily Living (ADL), Instrumental Activities of Daily Living Scale (IADL), and the EQ-5D-5L. Results: The effect of the experimental treatment showed an improvement in all patients test scores in the EG, especially regarding cognitive functions, mood disorders, activities of daily living, and quality of life. Conclusion: Our findings suggest that HA may be effective in improving daily autonomy and, in turn, alleviating mood disorders and enhancing psychological well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".