Use of smartphones and tablets after acquired brain injury to support cognition
Bibliographic record
Abstract
Objectives To describe the use of mobile devices after acquired brain injury (ABI), from the perspectives of injured individuals and significant others, and to examine factors associated with mobile device use for cognition.Methods Cross-sectional study with 50 adults with moderate/severe traumatic brain injury or stroke (42% women; mean of 50.7 years old, 4.6 years post-ABI), and 24 significant others. Participants completed questionnaires on mobile technology, cognitive functioning and the impact of technology.Results Of 45/50 adults with ABI who owned a smartphone/tablet, 31% reported difficulties in using their device post-injury, 44% had received support, and 46% were interested in further training. Significant others reported motor/visual impairments and the fear of becoming dependent on technology as barriers for mobile device use, and 65% mentioned that their injured relative needed additional support. Mobile device use for cognition was common (64%), predicted in a regression model by lower subjective memory and more positive perception of the psychosocial impacts of technology, and also associated in univariate analyses with younger age, lower executive functioning, and greater use of memory strategies.Conclusion Using mobile devices for cognition is common post-ABI but remains challenging for a significant proportion. Developing training approaches may help supporting technology use.IMPLICATIONS FOR REHABILITATIONUsing mobile electronic devices (smartphones and tablets) is common after acquired brain injury (ABI) but is challenging for a significant proportion of individuals.After the ABI, close to 50% of individuals receive support in using their mobile device, mostly from family members and friends, but rarely from rehabilitation clinicians or technology specialists.In a sample of 50 adults with ABI, more frequent use of mobile devices to support cognition was associated with poorer subjective memory and executive functioning, greater use of memory strategies, more positive perception of the psychosocial impacts of technology, and younger age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".