Biopsychosocial Profiles of Patients With Cardiac Disease in Remote Rehabilitation Processes: Mixed Methods Grounded Theory Approach
Notice bibliographique
Résumé
BACKGROUND: Digital development has caused rehabilitation services and rehabilitees to become increasingly interested in using technology as a part of rehabilitation. This study was based on a previously published study that categorized 4 groups of patients with cardiac disease based on different experiences and attitudes toward technology (e-usage groups): feeling outsider, being uninterested, reflecting benefit, and enthusiastic using. OBJECTIVE: This study identifies differences in the biopsychosocial profiles of patients with cardiac disease in e-usage groups and deepen the understanding of these profiles in cardiac rehabilitation. METHODS: Focus group interviews and measurements were conducted with 39 patients with coronary heart disease, and the mean age was 54.8 (SD 9.4, range 34-77) years. Quantitative data were gathered during a 12-month rehabilitation period. First, we used analysis of variance and Tukey honestly significant difference test, a t test, or nonparametric tests-Mann-Whitney and Kruskal-Wallis tests-to compare the 4 e-usage groups-feeling outsider, being uninterested, reflecting benefit, and enthusiastic using-in biopsychosocial variables. Second, we compared the results of the 4 e-groups in terms of recommended and reference values. This analysis contained 13 variables related to biomedical, psychological, and social functioning. Finally, we formed biopsychosocial profiles based on the integration of the findings by constant comparative analysis phases through classic grounded theory. RESULTS: The biomedical variables were larger for waistline (mean difference [MD] 14.2; 95% CI 1.0-27.5; P=.03) and lower for physical fitness (MD -0.72; 95% CI -1.4 to -0.06; P=.03) in the being uninterested group than in the enthusiastic using group. The feeling outsider group had lower physical fitness (MD -55.8; 95% CI -110.7 to -0.92; P=.047) than the enthusiastic using group. For psychosocial variables, such as the degree of self-determination in exercise (MD -7.3; 95% CI -13.5 to -1.1; P=.02), the being uninterested group had lower values than the enthusiastic using group. Social variables such as performing guided tasks in the program (P=.03) and communicating via messages (P=.03) were lower in the feeling outsider group than in the enthusiastic using group. The feeling outsider and being uninterested groups had high-risk lifestyle behaviors, and adherence to the web-based program was low. In contrast, members of the being uninterested group were interested in tracking their physical activity. The reflecting benefit and enthusiastic using groups had low-risk lifestyle behavior and good adherence to web-based interventions; however, the enthusiastic using group had low self-efficacy in exercise. These profiles showed how individuals reflected their lifestyle risk factors differently. We renamed the 4 groups as building self-awareness, increasing engagement, maintaining a healthy lifestyle balance, and strengthening self-confidence. CONCLUSIONS: The results facilitate more effective and meaningful personalization guidance and inform the remote rehabilitation. Professionals can tailor individual web-based lifestyle risk interventions using these biopsychosocial profiles.
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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 | 0,015 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 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 ».