Everyday technology and assistive technology supporting everyday life activities in adults living with COPD – a narrative literature review
Bibliographic record
Abstract
People living with chronic obstructive pulmonary disease (COPD) encounter challenges in everyday life activities due to symptoms like breathlessness and fatigue. Compensatory strategies, such as using everyday technology (mechanical, electronic and digital equipment and functions encountered daily) and assistive technology (products, instruments, or equipment adapted or designed to improve functioning of people with disabilities), are crucial for supporting everyday life activities; thus, it is essential to explore therapeutic potentials of these technologies. The present review aims to synthesise research literature concerning the use of everyday technology and assistive technology to support everyday activities among persons living with COPD. A narrative review was conducted with a systematic search in five bibliographic databases. Three sets of search terms were used: (i) everyday technology, assistive technology, and related terms, (ii) everyday life activities and related terms, and (iii) chronic obstructive pulmonary disease and related terms. Screening resulted in 26 included articles.Following the American Occupational Therapy Association framework, the identified articles show six categories of everyday life activities supported by everyday technologies and assistive technologies: health management, social participation, activities of daily living, instrumental activities of daily living, leisure, and rest and sleep. Most articles focus on everyday technology for health management; however, everyday technology may hold unexpected potential to support a broader array of everyday life activities. Little is known about assistive technology to support everyday life activities for people with COPD, though it is described as crucial for independence and energy conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".