Evidence map›Paper›PMID 35582635›Full record

ArticleIEEE internet of things journal2021

The Impact of Covid-19 on Smartphone Usage.

Tong Li, Mingyang Zhang, Yong Li, Eemil Lagerspetz, Sasu Tarkoma, Pan Hui

Open access · bronzeAbstract read
In one paragraph

Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.3field-weighted citation impact, top 7% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 51 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Mobile Device Usage before and during the COVID-19 Pandemic among Rural and Urban Adults.International journal of environmental research and public health · 2022
    Article
  11. Review
  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

6 authors at 3 institutions in 3 countries.

Tong LiSystem and Media LaboratoryDepartment of Computer Science and EngineeringHong Kong University of Science and Technology Hong Kong.ORCID https://orcid.org/0000-0002-4343-703X
Mingyang ZhangSystem and Media LaboratoryDepartment of Computer Science and EngineeringHong Kong University of Science and Technology Hong Kong.ORCID https://orcid.org/0000-0001-6517-2880
Yong LiBeijing National Research Center for Information Science and TechnologyDepartment of Electronic EngineeringTsinghua University Beijing 100084 China.ORCID https://orcid.org/0000-0001-5617-1659
Eemil LagerspetzDepartment of Computer ScienceUniversity of Helsinki 00100 Helsinki Finland.ORCID https://orcid.org/0000-0003-3875-8135
Sasu TarkomaDepartment of Computer ScienceUniversity of Helsinki 00100 Helsinki Finland.ORCID https://orcid.org/0000-0003-4220-3650
Pan HuiSystem and Media LaboratoryDepartment of Computer Science and EngineeringHong Kong University of Science and Technology Hong Kong.ORCID https://orcid.org/0000-0002-0848-2599
University of Helsinki · FIHong Kong University of Science and Technology · HKNational Engineering Research Center for Information Technology in Agriculture · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.

Indexed as

CorrelationsCovid-19outbreak stage inferencesmartphone usage

Identifiers

PMID35582635
PMCPMC8864954
OpenAlexW3153900319

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.