Stakeholder Perspectives on mHealth Technologies to Prevent Sitting-Acquired Pressure Injuries in Long-Term Care Facilities: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Adults with Alzheimer disease (AD) or Alzheimer disease and related dementias (ADRD) who require a wheelchair to accommodate disease-associated decline in mobility are at elevated risk for pressure injuries. More than half of residents in long-term care (LTC) facilities in the United States experience AD or ADRD. In LTC facilities, bed-based technologies exist to facilitate pressure injury prevention efforts, but similar technologies have not yet been widely evaluated to address sitting-related pressure injuries. OBJECTIVE: This study aimed to determine preliminary design inputs from care providers for technology to address sitting-related pressure injury prevention in LTC settings. Specifically, we sought to (1) understand the types and use of sitting-related equipment used in LTC for residents with AD or ADRD, (2) identify challenges faced by nurses and other caregivers when repositioning seated residents, and (3) understand care provider preferences for features of future sitting-related feedback technologies designed to facilitate effective and timely repositioning. METHODS: Surveys (n=30) and semistructured interviews (n=9) of administrative and direct care providers in LTC facilities were administered. Survey results were summarized, and we used thematic qualitative analysis of interview responses to develop themes around challenges experienced by care providers and their perceptions about how technologies could facilitate the prevention of sitting-related pressure injuries. RESULTS: Survey respondents endorsed using many sitting surfaces for LTC residents with memory loss, such as padded reclining chairs, bedside or dining chairs, and wheelchairs with cushions. All indicated that shared equipment is provided by the facility, and 43% of respondents reported having access to a seating specialist at their facility. Sitting time was typically up to 12 hours per day. Themes related to pressure injury prevention in the LTC context, specific to those with memory loss, included (1) barriers to repositioning seated residents vary with the degree of memory loss, (2) care providers are aware of guidelines and policies around the 2-hour repositioning schedule, and (3) care providers are interested in technologies that have relative value over added burden. Care providers expressed interest in mobile health (mHealth) technologies that provide automatic repositioning in later stages of memory loss, delivery of cues for residents with mild memory loss to encourage independent repositioning, and tools to monitor resident sitting and pressure-related outcomes. CONCLUSIONS: These findings highlight the complexity of addressing the repositioning needs of seated LTC residents with AD or ADRD using mHealth technologies due to changes as the disease progresses. mHealth technologies should encourage more independence by residents experiencing milder memory loss, with increasing automaticity in repositioning residents in later stages. Both approaches could potentially minimize care provider burden in repositioning seated residents throughout the day. Design, development, and implementation of technologies should carefully weigh benefit versus burden to care providers and residents and continue to engage with them for feedback as development progresses.
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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".