Stakeholder Perspectives on mHealth Technologies to Prevent Sitting-Acquired Pressure Injuries in Long-Term Care Facilities: Mixed Methods Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».