Mood Monitoring Over One Year for People With Chronic Obstructive Pulmonary Disease Using a Mobile Health System: Retrospective Analysis of a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Comorbid anxiety and depression can add to the complexity of managing treatment for people living with chronic obstructive pulmonary disease (COPD). Monitoring mood has the potential to identify individuals who might benefit from additional support and treatment. OBJECTIVE: We used data from the sElf-management anD support proGrammE (EDGE) trial to examine: (1) the extent to which the mood-monitoring components of a mobile health system for patients with COPD were used by participants; (2) the levels of anxiety and depression symptoms among study participants; (3) the extent to which videos providing advice about coping with low mood were viewed; and (4) the characteristics of participants with differing levels of mood and utilization of mood monitoring. METHODS: A total of 107 men and women with a clinical diagnosis of COPD, aged ≥40 years old, were recruited to the intervention arm of the EDGE trial. Participants were invited to complete the Patient Health Questionnaire-8 and the Generalized Anxiety Disorder-7 test every four weeks using a tablet computer. Mood disturbance based on these measures was defined as a score ≥5 on either scale. Participants reporting a mood disturbance were automatically directed (signposted) to a stress or mood management video. Study outcomes included measures of health status, respiratory quality of life, and symptoms of anxiety and depression. RESULTS: Overall, 94 (87.9%) participants completed the 12-month study. A total of 80 participants entered at least one response each month for at least ten months. On average, 16 participants (range 8-38 participants) entered ≥2 responses each month. Of all the participants, 47 (50%) gave responses indicating a mood disturbance. Participants with a mood disturbance score for both scales (n=47) compared with those without (n=20) had lower health status (P=.008), lower quality of life (P=.009), and greater anxiety (P<.001) and increased depression symptoms (P<.001). Videos were viewed by 64 (68%) people over 12 months. Of the 220 viewing visualizations, 70 (34.7%) began after being signposted. Participants signposted to the stress management video (100%; IQR 23.3-100%) watched a greater proportion of it compared to those not signposted (38.4%; IQR 16.0-68.1%; P=.03), whereas duration of viewing was not significantly different for the mood management video. CONCLUSIONS: Monitoring of anxiety and depression symptoms for people with COPD is feasible. More than half of trial participants reported scores indicating a mood disturbance during the study. Signposting participants to an advisory video when reporting increased symptoms of a mood disturbance resulted in a longer view-time for the stress management video. The opportunity to elicit measures of mood regularly as part of a health monitoring system could contribute to better care for people with COPD.
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 | 0,019 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».