eHealth: Towards improving self-management of acute pain in older adults following a fracture
Bibliographic record
Abstract
Pain is often poorly managed both in-hospital and post-hospital discharge and is associated with harmful outcomes such as increased falls, loss of autonomy and low quality of life and can, in certain instances, evolve into chronic pain. Mobile applications (apps) downloadable on devices such as smartphones and tablets can offer targeted interactive interventions to support patients in acute pain self-management following hospital discharge. However, little is known about the availability and quality of mobile apps for acute pain management in older adults, and the current levels of technology adoption and electronic health (eHealth) literacy in a population of older adults seen in orthopedic clinics. Furthermore, it is important to seek the voice of clinicians when developing a mobile app so that the content is evidence-based, credible and of high value. In this scholarly work, we sought to 1) identify mobile apps currently available for self-management of acute pain and characterize their features (content and functionalities), 2) identify the current level of technology adoption and eHealth literacy among older adults who recently suffered a fracture, to determine if use of mobile digital devices to optimize care interventions would be feasible, acceptable and 3) identify what clinicians believe are the most important content or functionalities to include in a mobile application for self-management of acute pain in older adults following a recent skeletal fracture. Following an environmental scan, where we were unable to identify high quality mobile apps for the self-management of acute pain, we conducted two surveys. In the first, we invited adults ≥50 years with a recent fracture to complete a self-administered survey composed of 21 closed-ended questions, including the eHealth literacy scale (eHEALS). A total of 401 participants completed the survey (women: 64%; ≥65 years old: 59%). Most respondents (81%) owned at least one mobile device: smartphone (49%), tablet (45%). The majority (65%) of older adults participating in this survey used the Internet in the 6 months prior to the survey (50-64 years: 84%; 65-74 years: 76%; ≥75 years: 61%) and approximately 69% of those who used the Internet had high eHealth literacy (eHEALS ≥26). Among adults ≥75 years who reported owning a smartphone and/or tablet, 60% had recently used the Internet and 64% indicated being interested in using technology to improve their health. Although the eHEALS scores and technology adoption in the ≥75 years group were significantly lower compared to younger age groups, our results do support the development of mobile applications for the management of acute pain in this patient population with recent fractures. In the second study, we surveyed clinicians across Canada with expertise in osteoporosis, fractures, rehabilitation and pain management using a snowball sampling method. The survey constituted of one question sent through email asking for recommendations for the most important content or functionalities to include in a mobile app for the self-management of acute pain following a recent fracture. Forty-two clinicians responded to our survey (response rate 1st wave 60%; 2nd wave 60%) and 230 references were extracted. Appropriate medical information, pain management modalities, pain self-management strategies were the most cited content recommendations while the primary app functions highlighted were the ability to receive direct feedback from the app (interactiveness), pain self-monitoring and access to healthcare providers. This work will support the development of a mobile app that will meet evidence-based standards and will support self-management of acute pain following a skeletal fracture in older individuals. This will improve quality of life by reducing pain levels while engaging in activities of daily living and promote healthy lifestyle behaviors to reduce the risk of subsequent injurious falls
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".