Experiences With an In-Bed Real-Time Motion Monitoring System on a Geriatric Ward: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Older adults now make up about two-thirds of hospital admissions, with up to 50% experiencing cognitive impairments such as dementia. These patients often struggle with adherence to care plans and maintaining regular day or night cycles, presenting challenges for nurses. Hospitals are typically unprepared to manage this patient population, resulting in increased nurse workload and challenges like managing motor agitation, which can lead to falls or accidental removal of medical devices. OBJECTIVE: This study aimed to (1) assess how an in-bed real-time motion monitoring system (IRMS) impacts nurses' perceptions of physical and mental stress, (2) evaluate the IRMS's effect on the care process, (3) explore ethical implications like patient autonomy and privacy, and (4) understand how nurses acquire knowledge about the technology and how this affects their assessment of the IRMS. METHODS: The IRMS, which provides real-time motion monitoring and bed edge or exit information, was implemented in the geriatric ward of a university medical center. The study followed a monocentric, explorative evaluation design using a mixed methods approach. It lasted 24 weeks and had two phases. In Phase 0 (6 weeks), patients received standard care. In Phase 1 (18 weeks), the IRMS was introduced. Initial data were gathered through focus groups and participant observations during manufacturer training sessions. At the end of the intervention, a survey, a second focus group, and an interview were conducted to capture nurses' experiences. The study follows the Good Reporting of a Mixed Method Study (GRAMMS) checklist for reporting. RESULTS: Initial training sessions with 12 participants (10 nurses and 2 physiotherapists) showed varying levels of engagement, with the second session demonstrating more optimism and interprofessional collaboration. A total of 10 questionnaires were completed (10/21, 48%). Survey results showed that 80% (8/10) of nurses found the IRMS valuable for assessing the quality of work, and 90% (9/10) were willing to continue using it. The system was regarded as reliable for monitoring bed edge and exit events. Usability was positively rated, with minimal concerns about documentation burden. Focus group discussions (n=3 per session) indicated that nurses viewed the system as reliable and appreciated its role in reducing anxiety related to fall prevention. However, concerns about patient privacy and monitoring were raised. Nurses expressed a willingness to continue using the IRMS but reaffirmed their ability to care for patients without it. CONCLUSIONS: Nurses had a generally positive attitude toward the IRMS, recognizing its benefits, particularly for nighttime monitoring. Although its effectiveness in preventing falls remains inconclusive, the system helps reduce nurses' fear of falls and enhances their responsiveness. The study highlights the broader impact of the IRMS beyond fall prevention and stresses the importance of thoughtful integration into health care practice.
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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 | 0,011 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».