Characterizing Digital Mindfulness Intervention Utilization and Weekly Assessments: A Secondary Analysis of a Randomized Controlled Trial of Caregivers of Persons Living with Dementia (Preprint)
Notice bibliographique
Résumé
Abstract Background Caregivers of persons living with dementia are at increased risk of reporting high stress. Mindfulness-based interventions (MBIs) teach caregivers mindfulness skills and are effective at reducing stress. Digital MBIs are a feasible way to improve access to MBIs for caregivers of persons living with dementia. Yet, caregiver improvement with digital MBI utilization is less defined in the literature. Objective The goal of this secondary data analysis was to characterize weekly mindfulness and stress ratings among caregivers of persons living with dementia and to examine how digital MBI utilization impacted these ratings throughout 12 weeks of a feasibility trial. Methods Participants were eligible for this secondary analysis if they were randomized to the digital MBI condition (Healthy Minds Program for Caregivers [HMP-C], n=46) and completed weekly ratings over the 12-week trial. At baseline and at the end of each week of the trial, participants rated their mindfulness and stress in the past week from 0 to 10. Weekly HMP-C utilization was defined as the time spent using HMP-C in the week prior to the weekly ratings. Descriptive statistics and visualizations were used to characterize mindfulness and stress ratings. Generalized linear mixed models were used to estimate the effect of mindfulness on stress and the effect of HMP-C utilization on stress and mindfulness throughout the trial (α=.05). Results Baseline mindfulness and stress ratings were 4 (IQR 3-6, range 0‐8) and 7 (IQR 6.5-8, range 4‐10), respectively. Stress had the greatest decrease between baseline and week 3 (−2 points on average), whereas mindfulness had the greatest increase between baseline and week 4 (+2.5 points on average). There was a significant fixed effect of baseline mindfulness on baseline stress (β=.5, P <.001), with a significant interaction between mindfulness and study week (β=.05, P =.001), suggesting that this relationship was attenuated over time. There was variability in baseline stress (τ 00 =2.04) and the relationship between mindfulness and stress (τ ₁₁ =0.08), with a high correlation (ρ 01 =0.86), suggesting that those with high baseline stress benefited most from increases in mindfulness. For every 10 minutes of HMP-C utilization between baseline and week 1, mindfulness and stress ratings were 0.14 points higher ( P <.001) and 0.14 points lower ( P <.001), respectively. Despite a significant interaction between HMP-C utilization and study week in both models, the effect size was small (mindfulness: β=.02, P <.001; stress: β=.01, P =.013), suggesting that this relationship was sustained over time. Conclusions Mindfulness and stress changed mostly during the first 3 to 4 weeks of the trial. During this time of mindfulness skill acquisition, mindfulness and stress were most significantly negatively associated, especially among those with high baseline stress. The consistent relationship between HMP-C utilization and mindfulness and stress suggests that continued use of HMP-C may be helpful for skill maintenance.
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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 | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».