Perceptions of senior citizens on the use and desired features of a wristband for maintaining, strengthening, and regaining hand and finger function
Bibliographic record
Abstract
The objective of this study was to understand whether seniors would wear a wristband technology to help them improve, retain, regain, or strengthen hand and finger function and to gather information about the desired features of the technology to enhance compliance in use. The strength and functioning of the hand and fingers decrease as people age and can have a detrimental impact on the individual’s quality of life. Studies have shown that regular exercise of the hands can help the individual maintain hand strength and improve function. Two self-reported, online questionnaires were designed and administered to seniors. Of the 105 surveyed, 62% indicated they would wear a wristband. The top desired wristband features identified were ease of putting the device on, unobtrusiveness and comfort of the device with a desired price point of $99 or less. The majority of seniors surveyed were interested in wearing the wristband; however, results revealed that the wristband would need to be tailored for this population for use and uptake of the wristband. The results of this study provide insight into the features and functionalities of a wristband that would enhance user compliance in seniors who wished to improve hand and finger function.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".