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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".