Digital and Mobile Technologies to Promote Physical Health Behavior Change and Provide Psychological Support for Patients Undergoing Elective Surgery: Meta-Ethnography and Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Digital technology has influenced many aspects of modern living, including health care. In the context of elective surgeries, there is a strong association between preoperative physical and psychological preparedness, and improved postoperative outcomes. Health behavior changes made in the pre- and postoperative periods can be fundamental in determining the outcomes and success of elective surgeries. Understanding the potential unmet needs of patients undergoing elective surgery is central to motivating health behavior change. Integrating digital and mobile health technologies within the elective surgical pathway could be a strategy to remotely deliver this support to patients. OBJECTIVE: This meta-ethnographic systematic review explores digital interventions supporting patients undergoing elective surgery with health behavior changes, specifically physical activity, weight loss, dietary intake, and psychological support. METHODS: A literature search was conducted in October 2019 across 6 electronic databases (International Prospective Register of Systematic Reviews [PROSPERO]: CRD42020157813). Qualitative studies were included if they evaluated the use of digital technologies supporting behavior change in adult patients undergoing elective surgery during the pre- or postoperative period. Study quality was assessed using the Critical Appraisal Skills Programme tool. A meta-ethnographic approach was used to synthesize existing qualitative data, using the 7 phases of meta-ethnography by Noblit and Hare. Using this approach, along with reciprocal translation, enabled the development of 4 themes from the data. RESULTS: A total of 18 studies were included covering bariatric (n=2, 11%), cancer (n=13, 72%), and orthopedic (n=3, 17%) surgeries. The 4 overarching themes appear to be key in understanding and determining the effectiveness of digital and mobile interventions to support surgical patients. To successfully motivate health behavior change, technologies should provide motivation and support, enable patient engagement, facilitate peer networking, and meet individualized patient needs. Self-regulatory features such as goal setting heightened patient motivation. The personalization of difficulty levels in virtual reality-based rehabilitation was positively received. Internet-based cognitive behavioral therapy reduced depression and distress in patients undergoing cancer surgery. Peer networking provided emotional support beyond that of patient-provider relationships, improving quality of life and care satisfaction. Patients expressed the desire for digital interventions to be individually tailored according to their physical and psychological needs, before and after surgery. CONCLUSIONS: These findings have the potential to influence the future design of patient-centered digital and mobile health technologies and demonstrate a multipurpose role for digital technologies in the elective surgical pathway by motivating health behavior change and offering psychological support. Through the synthesis of patient suggestions, we highlight areas for digital technology optimization and emphasize the importance of content tailored to suit individual patients and surgical procedures. There is a significant rationale for involving patients in the cocreation of digital health technologies to enhance engagement, better support behavior change, and improve surgical outcomes.
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 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 | 0,024 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,016 |
| Bibliométrie | 0,014 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».