Characterization of Post-Viral Infection Behaviors Among Patients With Long COVID: Prospective, Observational, Longitudinal Cohort Analyses of Fitbit Data and Patient-Reported Outcomes
Bibliographic record
Abstract
BACKGROUND: Long COVID encompasses a range of health problems that can be highly debilitating. While some research has relied on self-reported measures of symptoms and functioning, few studies have characterized symptoms in relation to behaviors and physiology measured objectively through wearable devices. OBJECTIVE: The primary aim of this study was to identify longitudinal patterns in physical activity, physiology, and patient-reported outcomes (PROs) among patients with long COVID at a Federally Qualified Health Center in the United States. The secondary aim was to identify meaningful subgroups or phenotypes within this cohort and examine how PROs and symptoms overlay with physical activity characteristics. METHODS: This was a prospective, observational, longitudinal cohort study recruiting a subset of low-income patients enrolled in the Long COVID and Fatiguing Illness Recovery Program. From March 2022 to May 2023, a total of 172 patients with long COVID or myalgic encephalomyelitis/chronic fatigue syndrome were given Fitbit Charge 5 (Fitbit Inc) devices and instructed to wear them continuously for up to a year. Patients completed PRO questionnaires (PROMIS-29 [Patient-Reported Outcomes Measurement Information System-29] and symptom questionnaires, etc) at baseline, 3, and 6 months. Inclusion in longitudinal analysis required at least 20 hours of valid wear data per day for a minimum of 7 days. The World Health Organization guideline on moderate to vigorous physical activity (MVPA) was used to differentiate MPVA-active and MVPA-inactive patients. Linear mixed effects regression was performed to assess longitudinal associations between physical activity levels and PROs. RESULTS: Among 172 patients, 80.2% (n=138) were female, 75.6% (n=130) were White, 45.3% (n=78) were unemployed, and 94.8% (n=163) had estimated annual income below US $50,000. Of these patients, 82 (47.7%) met valid wear criteria, providing 50.5 days of Fitbit data on average. At baseline, MVPA-inactive patients (n=41) reported statistically more severe problems regarding physical function, fatigue, and dyspnea than MVPA-active patients (n=41) on both continuous and categorical scales, with P<.05 from both Student t tests (2-tailed) and chi-squared tests. Longitudinal analysis found that MVPA-inactive patients showed a decreased ability to participate in social roles (estimated group difference=-4.21 T-score points over 3 months, 95% CI -6.64 to -1.78, P<.001) and a higher intensity of sleep symptoms (estimated group difference=2.06 severity score points over 3 months, 95% CI 0.40 to 3.71, P=.02) over time. CONCLUSIONS: This study showed that patients with long COVID could remain MVPA-active despite experiencing symptoms. These findings provide insights into the relationship between PROs, physical activity, and long COVID, which suggests the importance of considering individual activity profiles when tailoring treatment plans, especially in a low-income population. The findings of this study should be interpreted as hypothesis-generating, considering its exploratory nature and limitations, including high attrition rates and missing data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".