Self-care Behaviors and Technology Used During COVID-19: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Self-care behaviors are essential for people living with chronic conditions; however, the outbreak of the COVID-19 pandemic has imposed additional complications on their daily routines. Few studies have analyzed how self-care behaviors have changed during COVID-19 and the role of digital technology, especially among people with chronic conditions. OBJECTIVE: This study aims to review how self-care behaviors have changed for people with chronic conditions during the COVID-19 pandemic, and what technology they have adopted to manage their conditions during that period. METHODS: A systematic review was conducted using narrative synthesis. Data were extracted from PubMed, MEDLINE, EMBASE, PsycINFO, CINAHL, and Google Scholar, including articles from December 2019 onward. Eligible studies focused on adults diagnosed with chronic conditions undertaking any self-care tasks in line with the middle-range theory of self-care of chronic illness (ie, self-care maintenance, monitoring, and management). The methodological quality of the included articles was assessed with the McMaster Critical Review Forms for Quantitative or Qualitative Studies. RESULTS: In total, 36 primary research articles were included. Changes to self-care behaviors during COVID-19 among people with chronic conditions were organized according to the middle-range theory of self-care of chronic illness focusing on self-care maintenance (ie, medication adherence, physical activity, and diet control), self-care monitoring (ie, monitoring signs and symptoms), and self-care management (ie, consultations with health care providers). Positive self-care behaviors observed include the following: individuals trying to maintain good glycemic control during COVID-19 increased their medication adherence in 27% (10/36) of studies; and diet control improved in 50% (18/36) of studies. Negative self-care behaviors observed include the following: decline in physical activities and increased sedentariness were observed in 65% (23/36) of studies; poor diet control was observed in 57% (21/36) of studies; and self-monitoring of health status dropped in 43% (15/36) of studies. The use of technology to support self-care of chronic conditions during COVID-19 was reported in 72% (26/36) of studies. The actual use of telehealth in place of physical consultations during COVID-19 was observed in 50% (18/36) of studies, and other digital technologies (eg, social media apps, smartphone apps, web-based platforms, and web browsing) were used in 50% (18/36) of studies. Telehealth was discussed and recommended as the default technology in delivering future health care services during COVID-19 and beyond in 77% (28/36) of studies. CONCLUSIONS: This review highlighted the necessity to rethink how models of self-care should continue to address the demands of chronic conditions while being responsive to the imminent threats of infectious diseases. Perhaps the silver lining of COVID-19 is that adoption of digital technology (especially telehealth) among a vast cross-section of people with chronic conditions is possible. Future research should investigate effective ways to incorporate evidence-based digital health tools into these new models of self-care that address the challenges of chronic and infectious conditions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 tête enseignante, 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 ».