A 6-Month Evaluation of the Peer-Ceived Momentary Assessment Method in a Small Sample of Liver Transplant Patients and Their Support Persons: Longitudinal Observational Study
Notice bibliographique
Résumé
BACKGROUND: Patient-reported outcomes, including ecological momentary assessments (EMAs), are acquired from patients via repeated self-reports of their perceived momentary physical and emotional states before and after medical procedures. Patient-reported outcomes are used to measure health outcomes and quality of care. However, certain observable states or behaviors (eg, moods such as fatigue, hope, or medication adherence), or behaviors suggestive of health decline (eg, depression, cognitive decline), are not easily measured via self-reports in certain situations (eg, patients undergoing certain medical procedures, patients with dementia, and others). The peer-ceived momentary assessment (PeerMA) method involves support persons or peers (eg, family members and friends) to report their perception of a patient's subjective physical and emotional states and has been validated in healthy populations. OBJECTIVE: We examined the value of the PeerMA method in assessing the disease progression and recovery pathways of patients undergoing liver transplantation. Herein, the PeerMA method is operationalized via the patient's informal caregivers and the patient-based EMA, and wearable-based physical activity datasets from the patients. We report the feasibility results and human factors influencing the acceptance and reliability of the PeerMA method in a small study comprising 8 patients and support persons. METHODS: We conducted a longitudinal observational study of 6 months (autumn 2019 to spring 2020), collecting EMA/self-reports from 8 patients (at the liver transplant clinic at Stanford University Hospital, California) about their perceived levels of hope, sleep, fatigue, depression, and pain in addition to PeerMA-based reports of the same aspects from 7 caregivers. We collected physical activity records from 5 patients using a Fitbit bracelet. Participants completed pre- and poststudy surveys, contributing qualitative data. We implemented the PeerMA method using a smartphone app, making it easy to use by both patients and support persons. RESULTS: We collected 1142 patient-days and 976 support person-days. On average, each patient received 103 EMAs and responded to 64 (63%) of them, while support persons received 87 PeerMAs and responded to 64 (74%) of them. We report empirical evidence about the methodological feasibility of PeerMA, showing its dual and unique information streams unavailable by EMA alone. We show examples where support person assessments and physical activity data can inform health professionals about the actual state of a patient regarding outcomes such as hope, sleep quality, fatigue, pain, and depression. We discuss human factors influencing the acceptance of the method and make methodological recommendations. CONCLUSIONS: It is possible to leverage data acquired via the PeerMA method and a wearable activity monitor to complement EMA. The PeerMA method incorporates frequent observations from support persons in patients' daily lives, which can be compared and analyzed next to the patient's self-reports. Such data may help to study and assist patients during disease recovery, which is beneficial for patients recovering from an organ transplant.
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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 | 0,014 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».