Evidence map›Paper›PMID 40810942›Full record

Trial reportJAMA network open2025

Digital Biometric Measures in Long COVID: A Secondary Analysis of the STOP-PASC Randomized Clinical Trial.

Fatma Gunturkun, Haley Hedlin, Bren Botzheim, Yaowei Deng, Hector Bonilla, Prasanna Jagannathan, Tom C Quach, Sungbin Kim, Michelle Lin, Gerri O'Riordan and 7 more

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05576662 (Selective Trial Of Paxlovid for PASC), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05576662 phase2completednot on this map

Selective Trial Of Paxlovid for PASC (STOP-PASC): Randomized Double-Blind Placebo-Controlled Pilot Trial of Paxlovid for the Treatment of PASC

TypeinterventionalSponsorStanford UniversityRan2022 to 2023Enrolled168ConditionsPost-acute Sequelae of SARS-CoV-2 Infection, Long COVIDArmsNirmatrelvir, Placebo, Ritonavir
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Fatma GunturkunStanford Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California.
Haley HedlinStanford Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California.
Bren BotzheimStanford Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California.
Yaowei DengStanford Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California.
Hector BonillaDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Prasanna JagannathanDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Tom C QuachStanford University, Stanford, California.
Sungbin KimStanford University, Stanford, California.
Michelle LinDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Gerri O'RiordanAiCare Corporation, City of Industry, California.
Henry TzengAiCare Corporation, City of Industry, California.
Lukas AdamowiczAI/ML Quantitative Digital Sciences, Global Biometrics and Data Management, Pfizer Inc, Cambridge, Massachusetts.
Charmaine DemanueleAI/ML Quantitative Digital Sciences, Global Biometrics and Data Management, Pfizer Inc, Cambridge, Massachusetts.
Xuemei CaiBiomeasures, Endpoints and Study Technologies, Pfizer Research and Development, Pfizer Inc, Cambridge, Massachusetts.
Phillip C YangDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Upinder SinghDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Linda N GengDepartment of Medicine, Stanford University School of Medicine, Stanford, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Digital biometrics can be used to monitor disease, but there is limited research on their applications for assessing postacute sequelae of SARS-CoV-2 (PASC) or long COVID. Objective: To better understand digital biometric patterns in long COVID using wearable device technology and whether there are any differences between the nirmatrelvir-ritonavir and placebo-ritonavir intervention arms. Design, Setting, and Participants: This secondary analysis is an exploratory substudy of a placebo-controlled randomized clinical trial (Selective Trial of Paxlovid for PASC [STOP-PASC]) that was conducted from November 2022 to September 2023 at Stanford University. Trial participants were randomized, and a subset enrolled into the prespecified substudy. Intervention: Participants were randomized 2:1 to receive oral nirmatrelvir (300 mg) and ritonavir (100 mg) or placebo-ritonavir twice daily for 15 days. Substudy participants were provided with a smartwatch and asked to wear it for 24 hours a day, 7 days a week for the 15-week study. Main Outcomes and Measures: Mean changes in digital biometric measures in physical activity, heart rate, and oxygen saturation tracked by a smartwatch. Biometric measures were summarized by treatment arm for each of 5 different time frames: baseline, treatment period, and 3 subsequent time intervals. Summary measure trajectories were clustered and demographics and clinical characteristics were compared among clusters using absolute standardized differences expressed in units of SDs. Results: Of the 94 participants enrolled in the substudy, 50 (37 in nirmatrelvir-ritonavir and 13 in placebo-ritonavir) met the analysis eligibility criteria based on wear time and data completeness. These participants had a mean (SD) age of 42.7 (13.2) years and included 29 females (58.0%). Using mixed models for repeated measures, no significant differences were detected between the intervention arms in the change in biometric measures over time, consistent with the patient-reported outcomes in the STOP-PASC trial. In the overall substudy cohort, latent class mixed models and cluster analysis identified distinct longitudinal trajectories of long COVID over the 15-week study that tracked with different symptoms. Participants with lower daytime physical activity reported more severe fatigue (9 of 9 [100%] vs 21 of 23 [91.3%]; absolute standardized difference [ASD], 0.30), shortness of breath (9 of 9 [100%] vs 7 of 23 [30.4%]; ASD, 1.31), and cardiovascular symptoms (8 of 9 [88.9%] vs 12 of 23 [52.2%]; ASD, 0.70). Those with higher nighttime physical activity reported more gastrointestinal symptoms (2 of 3 [66.7%] vs 11 of 35 [31.4%]; ASD, 0.50). Additionally, participants with higher median daytime heart rates reported less fatigue (7 of 9 [77.8%] vs 39 of 40 [97.5%]; ASD, 0.63) and shortness of breath (3 of 9 [33.3%] vs 23 of 40 [57.5%]; ASD, 0.50) compared with those with lower heart rates. Conclusions and Relevance: This secondary analysis identified distinct longitudinal trajectories of physiological and behavioral digital biometric measures captured from wearable devices that reflect the heterogeneity and track with different symptoms of long COVID. Digital biometric measures from wearable devices have promising utility for long COVID research. Trial Registration: ClinicalTrials.gov Identifier: NCT05576662.

Indexed as

BiometryCOVID-19COVID-19 Drug TreatmentRitonavirWearable Electronic DevicesAdultFemaleHumansMaleMiddle AgedSARS-CoV-2Ritonavir

Identifiers

PMID40810942
PMCPMC12355292

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.