Evidence map›Paper›PMID 35552722›Full record

ArticleJAMA network open2022

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors.

Aziz Mezlini, Allison Shapiro, Eric J Daza, Eamon Caddigan, Ernesto Ramirez, Tim Althoff, Luca Foschini

2 registry-linked trialsOpen access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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.

NCT06207929 completednot on this mapstarted 2024, after this paper: background citation

Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infections

TypeobservationalSponsorEvidation HealthRan2024 to 2024Enrolled18,157ConditionsInfluenza, Human, COVID-19, Influenza A, Influenza B
NCT06229444 nacompletednot on this mapstarted 2024, after this paper: background citation

Predict + Protect: A Randomized Controlled Trial Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to Influenza-like Illness (ILI)

TypeinterventionalSponsorEvidation HealthRan2024 to 2024Enrolled17,043ConditionsInfluenza, Influenza A, Influenza B, COVID-19ArmsILI Predictive Alerts, Reactive Content, and Proactive Content, ILI Predictive Alerts, Reactive Content, Proactive Content, No Intervention
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 10 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Article
  8. Epidemiologic and clinical updates on viral infections in Saudi Arabia.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024
    Article
  9. Article
  10. Physiology of sedentary behavior.Physiological reviews · 2023
    Review
  11. Article
  12. 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

7 authors at 2 institutions in 1 country.

Aziz MezliniEvidation Health Inc, Menlo Park, California.
Allison ShapiroEvidation Health Inc, Menlo Park, California.
Eric J DazaEvidation Health Inc, Menlo Park, California.
Eamon CaddiganEvidation Health Inc, Menlo Park, California.
Ernesto RamirezEvidation Health Inc, Menlo Park, California.
Tim AlthoffPaul G. Allen School of Computer Science & Engineering, University of Washington, Seattle.
Luca FoschiniEvidation Health Inc, Menlo Park, California.
Evidation Health (United States) · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented, and their public health burden is not accurately estimated. Objective: To estimate the true burden of influenza-like illness (ILI) in the US population using a surrogate measure of daily steps lost as measured by commercial wearable sensors. Design, Setting, and Participants: This cohort study modeled data from 15 122 US adults who reported ILI symptoms during the 2018-2019 influenza season (before the COVID-19 pandemic) and who had a sufficient density of wearable sensor data at symptom onset. Participants' minute-level step data as measured by commercial wearable sensors were collected from October 1, 2018, through June 30, 2019. Minute-level activity time series were transformed into day-level time series per user, indicating the total number of steps daily. Main Outcomes and Measures: The primary end point was the number of steps lost during the period of 4 days before symptom onset (the latent phase) through 11 days after symptom onset (the symptomatic phase). The association between covariates and steps lost during this interval was also examined. Results: Of the 15 122 participants in this study, 13 108 (86.7%) were women, and the median age was 32 years (IQR, 27-38 years). For their ILI event, 2836 of 15 080 participants (18.8%) sought medical attention, and only 61 (0.4%) were hospitalized. Over the course of an ILI lasting 10 days, the mean cumulative loss was 4437 steps (95% CI, 4143-4731 steps). After weighting, there was an estimated overall nationwide reduction in mobility equivalent to 255.2 billion steps (95% CI, 232.9-277.6 billion steps) lost because of ILI symptoms during the study period. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, 60.6% of this reduction in steps (154.6 billion steps [95% CI, 138.1-171.2 billion steps]) occurred among persons who sought no medical care. Age and educational level were positively associated with steps lost. Conclusions and Relevance: These findings suggest that most of the burden of ILI in this study would have been invisible to health care and public health reporting systems. This approach has applications for public health, health care, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes such as during long COVID-19.

Indexed as

COVID-19Influenza, HumanVirus DiseasesWearable Electronic DevicesAdultCohort StudiesFemaleHumansMalePandemicsPost-Acute COVID-19 Syndrome

Identifiers

PMID35552722
PMCPMC9099426
OpenAlexW4280595274

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 OpenQuestion graph.