Psychosomatic Rehabilitation Patients and the General Population During COVID-19: Online Cross-sectional and Longitudinal Study of Digital Trainings and Rehabilitation Effects
Notice bibliographique
Résumé
Background The COVID-19 pandemic has largely affected people’s mental health and psychological well-being. Specifically, individuals with a pre-existing mental health disorder seem more impaired by lockdown measures posing as major stress factors. Medical rehabilitation treatment can help people cope with these stressors. The internet and digital apps provide a platform to contribute to regular treatment and to conduct research on this topic. Objective Making use of internet-based assessments, this study investigated individuals from the general population and patients from medical, psychosomatic rehabilitation clinics. Levels of depression, anxiety, loneliness, and perceived stress during the COVID-19 pandemic, common COVID-19–related worries, and the intention to use digital apps were compared. Furthermore, we investigated whether participating in internet-delivered digital trainings prior to and during patients’ rehabilitation stay, as well as the perceived usefulness of digital trainings, were associated with improved mental health after rehabilitation. Methods A large-scale, online, cross-sectional study was conducted among a study sample taken from the general population (N=1812) in Germany from May 2020 to April 2021. Further, a longitudinal study was conducted making use of the internet among a second study sample of psychosomatic rehabilitation patients at two measurement time points—before (N=1719) and after (n=738) rehabilitation—between July 2020 and April 2021. Validated questionnaires and adapted items were used to assess mental health and COVID-19–related worries. Digital trainings were evaluated. Propensity score matching, multivariate analyses of covariance, an exploratory factor analysis, and hierarchical regression analyses were performed. Results Patients from the psychosomatic rehabilitation clinics reported increased symptoms with regard to depression, anxiety, loneliness, and stress (F4,2028=183.74, P<.001, η2p=0.27) compared to the general population. Patients perceived greater satisfaction in communication with health care professionals (F1,837=31.67, P<.001, η2p=0.04), had lower financial worries (F1,837=38.96, P<.001, η2p=0.04), but had higher household-related worries (F1,837=5.34, P=.02, η2p=0.01) compared to the general population. Symptoms of depression, anxiety, loneliness, and perceived stress were lower postrehabilitation (F1,712=23.21, P<.001, η2p=0.04) than prior to rehabilitation. Psychosomatic patients reported a higher intention to use common apps and digital trainings (F3,2021=51.41, P<.001, η2p=0.07) than the general population. With regard to digital trainings offered prior to and during the rehabilitation stay, the perceived usefulness of digital trainings on rehabilitation goals was associated with decreased symptoms of depression (β=–.14, P<.001), anxiety (β=–.12, P<.001), loneliness (β=–.18, P<.001), and stress postrehabilitation (β=–.19, P<.001). Participation in digital group therapy for depression was associated with an overall change in depression (F1,725=4.82, P=.03, η2p=0.01) and anxiety (F1,725=6.22, P=.01, η2p=0.01) from pre- to postrehabilitation. Conclusions This study validated the increased mental health constraints of psychosomatic rehabilitation patients in comparison to the general population and the effects of rehabilitation treatment. Digital rehabilitation components are promising tools that could prepare patients for their rehabilitation stay, could integrate well with face-to-face therapy during rehabilitation treatment, and could support aftercare. Trial Registration ClinicalTrials.gov NCT04453475; https://clinicaltrials.gov/ct2/show/NCT04453475 and ClinicalTrials.gov NCT03855735; https://clinicaltrials.gov/ct2/show/NCT03855735
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| 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 ».