Mental Health Needs of Families of Patients in Intensive Care Units and the Role of Mobile Health: Survey Study
Notice bibliographique
Résumé
BACKGROUND: The experiences of patients with COVID-19 and their families manifested the most devastating effects of family separation since the 1918 Spanish influenza pandemic and, with it, a call for solutions to patient isolation and its effect on family mental health. OBJECTIVE: This study examined the recent experiences of families of critical care (intensive care unit; ICU) patients related to anxiety and depression (AD), satisfaction with clinician-family communication, and counseling from mental health and social services. This study explored correlations between these factors and family interest in mobile health (mHealth) designed to improve information flow and communication from patient bedside to remote families. METHODS: Using a 36-question quantitative survey, we collected 97 responses over 6 months. We selected participants by using a convenience sampling strategy. To analyze data, we applied descriptive and inferential statistics. Participants represented a spectrum of ages, relationships to patients, and races (n=78, 80% White; n=17, 18% Black; n=2, 2% other races). Approximately 17% (n=16) of the patients were admitted for cancer, 13% (n=13) were admitted for COVID-19, and 21% (n=20) were admitted for other conditions. RESULTS: The mean score for remote families' satisfaction with patient health updates from the bedside and mental health services was 2.94 (SD 1.31), whereas that for phone communication was lower on average. The mean scores of family AD levels were elevated, and levels were higher among family members during the ICU stay than after discharge. These findings confirmed evidence of a negative correlation between transportation difficulties and satisfaction with the frequency of information provided (r=-0.284; P=.005), suggesting that, with the increase in transportation challenges, families become less satisfied with the frequency of patient health information. Family members expressed strong interest in using mHealth information and communication services (mean 8.34, SD 1.98) and having easy access to social workers to manage AD (mean 8.29, SD 2.03). Families experiencing higher levels of anxiety during patients' ICU stays had significantly greater interest in the use of an mHealth app that would provide direct access to social workers (r=0.326; P<.001), in using an mHealth videoconferencing app (r=0.319; P=.002), and in overall mHealth app use (r=0.322; P<.001). CONCLUSIONS: Family members experienced high levels of AD during patient ICU admission, as well as after discharge even though their mental health challenges were reduced. Families were highly dissatisfied with the frequency of health updates, with lower satisfaction reported among those who faced difficulties arranging transportation or lived further from the hospital. Modest but statistically significant correlations were observed between family members' reported mental health status during ICU stays and an interest in an mHealth app that could provide access to real-time bedside information, facilitate communication with bedside nurses, and support connections with social workers.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».