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Cognitive deficits in people who have recovered from COVID-19
relative to controls: An N=84,285 online study
Adam Hampshire1*, William Trender1, Samuel R Chamberlain2,3, Amy Jolly1, Jon E. Grant4,
Fiona Patrick5, Ndaba Mazibuko5, Steve Williams5, Joseph M Barnby5, Peter Hellyer1,5, Mitul
A Mehta5
Affiliations:
1Department of Brain Sciences, Imperial College London, UK
2Department of Psychiatry, University of Southampton, UK
3Department of Psychiatry, University of Cambridge, UK
4Department of Psychiatry, University of Chicago, USA
5Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
*Corresponding author. Dr Adam Hampshire Computational, Cognitive & Clinical
Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, E315A
Burlington Danes Building, Hammersmith Hospital, DuCane Road, London, W120NN UK
Tel: 0207 594 7993 a.hampshire@imperial.ac.uk
Keywords: COVID-19, cognition, attention, intelligence, big data, chronic symptoms
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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
Abstract
Case studies have revealed neurological problems in severely affected COVID-19 patients. However,
there is little information regarding the nature and broader prevalence of cognitive problems post-
infection or across the full spread of severity. We analysed cognitive test data from 84,285 Great
British Intelligence Test participants who completed a questionnaire regarding suspected and
biologically confirmed COVID-19 infection. People who had recovered, including those no longer
reporting symptoms, exhibited significant cognitive deficits when controlling for age, gender,
education level, income, racial-ethnic group and pre-existing medical disorders. They were of
substantial effect size for people who had been hospitalised, but also for mild but biologically
confirmed cases who reported no breathing difficulty. Finer grained analyses of performance support
the hypothesis that COVID-19 has a multi-system impact on human cognition.
Significance statement
There is evidence that COVID-19 may cause long term health changes past acute symptoms, termed
‘long COVID’. Our analyses of detailed cognitive assessment and questionnaire data from tens
thousands of datasets, collected in collaboration with BBC2 Horizon, align with the view that there
are chronic cognitive consequences of having COVID-19. Individuals who recovered from
suspected or confirmed COVID-19 perform worse on cognitive tests in multiple domains than would
be expected given their detailed age and demographic profiles. This deficit scales with symptom
severity and is evident amongst those without hospital treatment. These results should act as a clarion
call for more detailed research investigating the basis of cognitive deficits in people who have
survived SARS-COV-2 infection.
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Main Text
Introduction
There is growing evidence that individuals with severe COVID-19 disease can develop a range of
neurological complications1-3 including those arising from stroke4,5, encephalopathies6, inflammatory
syndrome4,7, microbleeds4 and autoimmune responses8. There are concerns regarding potential
neurological consequences due to sepsis, hypoxia and immune hyperstimulation4,9,10, with reports of
elevated cerebrospinal fluid autoantibodies in patients with neurological symptoms11, white matter
change in the brain2,12,13, and psychological and psychiatric consequences at the point of discharge14.
However, it is yet to be established whether COVID-19 infection is associated with cognitive
impairment at the population level; and if so, how this differs with respiratory symptom severity and
relatedly, hospitalisation status4,15. Measuring such associations is challenging. Longitudinal collection
of cognitive data from pre- to post-COVID is extremely problematic because infection is
unpredictable. Furthermore, it is important to include key minority sub-populations, for example,
older adults, racial-ethnic groups, and people with preexisting medical conditions16-18. This motivated
us to take a large-scale cross-sectional approach, whereby individuals who have recovered from
COVID-19 infection were compared to concurrently obtained controls while accounting for the
uneven sociodemographic distribution of virus prevalence and the associated population variability in
cognition. At the time of writing, we had collected comprehensive cognitive test and questionnaire
data from a very large cross-section of the general public, predominantly within the UK, as part of the
Great British Intelligence Test - a collaborative project with BBC2 Horizon. Due to the high visibility
of the study, this cohort spanned a broad age and demographic range. During May, at the peak of the
UK lockdown, we expanded the questionnaire (table S1) to include questions pertaining to the impact
of the pandemic, including suspected or confirmed COVID-19 illness, alongside details of symptom
persistence and severity, and relevant pre-existing medical conditions. We analysed data from 84,285
individuals (figure 1 & table S2) who completed the full extended questionnaire to determine whether
those who had recovered from COVID-19 showed objective cognitive deficits when performing tests
of semantic problem solving, spatial working memory, selective attention and emotional processing;
and whether the extent and/or nature of deficit related to severity of respiratory symptoms as gauged
by level of medical assistance.
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Results
Participants | Amongst 84,285 participants, 60 reported being put on a ventilator, a further 147 were
hospitalised without a ventilator, 176 required medical assistance at home for respiratory difficulties,
3466 had respiratory difficulties and received no medical assistance and 9201 reported being ill
without respiratory symptoms. Amongst these 361 reported having had a positive biological test,
including the majority of hospitalised cases. Full details of cohort age and sociodemographic
distributions are provided in supplementary tables 2a-i.
Figure 1 - COVID-19 illness in relation to cohort demographics
A | Distributions of people reporting having recovered from COVID-19 broken down according to the treatment
that they received for respiratory symptoms. Note, the broad and matched age distribution for all sub-groups. B |
People from a broad range of self-identified ethnic groups took part in this study.
Global cognitive deficits | Generalised linear modelling (GLM) was applied to determine whether
global cognitive scores covaried with respiratory COVID-19 symptom severity after factoring out age,
sex, handedness, first language, education level, country of residence, occupational status and
earnings. There was a significant main effect (F(5,84279)=11.848 p=1.76E-11), with increasing
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degrees of cognitive underperformance relative to controls dependent on level of medical assistance
received for COVID-19 respiratory symptoms (Figure 2a - table S3). People who had been
hospitalised showed large-medium scaled global performance deficits dependent on whether they
were (-0.57 standard deviations (SDs) N=60) vs. were not (-0.45SDs N=147) put onto a ventilator.
Those who remained at home (i.e., without inpatient support) showed small statistically significant
global performance deficits (assisted at home for respiratory difficulty -0.12 SD N=176; no medical
assistance but respiratory difficulty -0.10 SDs N=3466; ill without respiratory difficulty -0.04 SDs
N=9201).
Figure 2 - Cognitive deficits in people with suspected and confirmed Covid-19 illness
A | People who reported having recovered from COVID-19 performed worse in terms of global score. The scale
of this deficit increased with the level of treatment received for respiratory difficulty. B | The scale of the deficit
associated with COVID-19 was substantially greater than common pre-existing conditions that are associated with
vulnerability to the virus and cognitive problems.
Relationship between cognitive deficits and positive biological test | The GLM was re-estimated
including confirmation of COVID-19 by biological test as a main effect (table S4). In proportion with
the number of UK confirmed cases, 361 people reported a positive biological test, including 87% of
the hospitalised with ventilator sub-group. There were significant main effects of positive test
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(F(1,84274)=21.624 p=3.32E-06 estimate=-0.33SDs) and respiratory severity (F(5,84274)=7.51
p=4.70E-07). Intriguingly though, the interaction was non-significant (F(4,84274)=0.97 p=0.420),
indicating a possible deficit for mild cases who were biologically confirmed as positive for COVID-
19. A further GLM restricted to those who reported no breathing difficulties (bio-positive=187 vs.
suspected=9014) confirmed this, with a robustly greater global performance deficit for positively
confirmed cases (t=-3.49 p<0.0001 estimate=-0.32SDs). Repeating the analysing for people who
reported staying at home with breathing difficulty but no assistance (bio-positive=84 suspected=3382)
showed a similar scaled deficit (t=-2.611 p=0.009 estimate=-0.36SDs). A larger relationship was
evident amongst cases who went to hospital but were not put on a ventilator (bio-positive=24 vs
suspected=123, t=-2.401 p=0.018 estimate=0.71SDs).
Ongoing symptoms and pre-existing conditions | One possibility was that the observed cognitive
deficits related to ongoing symptoms of COVID-19 infection, e.g., high temperature or respiratory
problems. Only a small proportion (0.76%) of participants reported having residual symptoms,
although this did include most (78%) of the ventilator group. When report of residual COVID-19
symptoms was included in the GLM (table S5), the main effect of respiratory severity was
undiminished (F(5,84278)=9.55 p=4.03E-09). The main effect of residual symptoms was formally
non-significant and of small effect size (F(1,84278)=3.82 p=0.051 estimate -0.10 SDs). Another
possibility was that the observed cognitive deficits had a basis in pre-existing conditions. When a
GLM was estimated including common pre-existing conditions (Fig. 2b - table S6), a number of them
showed the expected association with reduced cognitive performance. However, the statistical
significance and scale of the respiratory severity main effect remained approximately the same
(F(5,84270)=11.07 p=1.262E-10). Furthermore, the effect size for those who had been hospitalised
was of substantially greater scale than the other conditions examined.
Finer grained analysis of cognitive domains | Finally, the cognitive deficits were examined at a
finer grain. First, we applied principal component analysis (PCA) to the test summary scores,
producing three components. GLMs showed a robust main effect of respiratory symptom severity for
component 1 (Figure 3a - table S7), labelled Semantic Problem Solving (F(5,84279)=5.89 p=2E-05),
with significantly scaled deficits for the two hospitalised groups (no ventilator -0.28SDs, ventilator -
0.62SDs). Component 2, labelled Visual Attention, also showed a significant main effect
(F(5,84279)=7.46 p=2E-07). This reflected more graded deficits in attention scores, including
significantly scaled reductions in performance for the three groups who received medical assistance
(at home -0.22SDs, no ventilator -0.33SDs, ventilator -0.33SDs) and small but statistically significant
deficits for the milder groups (respiratory symptoms -0.07SDs, no respiratory symptoms -0.06SDs).
Component 3, labelled Spatial Working Memory, showed a threshold level main effect
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(F(5,84279)=2.23 p=0.049), with the deficit for the more severe hospital group being statistically non-
significant. Analysis of individual test scores (Figure 3b and table S8) further highlighted this broad
but variable profile of deficits across cognitive domains.
Figure 3 - Domain sensitivity of COVID-19 related cognitive deficits
A | The effect size of cognitive deficits varied across three cognitive domains, which were estimated by applying
principal component analysis with varimax rotation to the nine test summary scores. Semantic problem solving
was particularly reduced for people who had been put on a ventilator, but also showed a significant scaled
reduction for other people who required a hospital visit. Visual Attention showed similar scaled reductions in
performance for all groups who required medical assistance. Spatial working memory appeared not to be
significantly affected. B | The scale (SD units) of the cognitive deficit varied substantially across the nine tests.
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Discussion
Our analyses provide converging evidence to support the hypothesis that COVID-19 infection likely
has consequences for cognitive function that persist into the recovery phase. The observed deficits
varied in scale with respiratory symptom severity, related to positive biological verification of having
had the virus even amongst milder cases, could not be explained by differences in age, education or
other demographic and socioeconomic variables, remained in those who had no other residual
symptoms and was of greater scale than common pre-existing conditions that are associated with virus
susceptibility and cognitive problems.
The scale of the observed deficits was not insubstantial; the 0.57 SD global composite score reduction
for the hospitalised with ventilator sub-group was equivalent to the average 10-year decline in global
performance between the ages of 20 to 70 within this dataset. It was larger than the mean deficit of
512 people who indicated they had previously suffered a stroke (-0.40SDs) and the 1016 who reported
learning disabilities (-0.49SDs). For comparison, in a classic intelligence test, 0.57 SDs equates to an
8.5-point difference in IQ.
At a finer grain, the deficits were broad, affecting multiple cognitive domains. They also were more
pronounced for tests that assessed semantic problem solving and visual selective attention whilst
sparing tests of simpler functions such as emotional processing and working-memory span. Notably,
this profile cannot be explained by differences in the general sensitivity of our tests; e.g., Spatial Span
and Digit Span scores show robust age-related differences and sensitivity to some other neurologic
conditions. Instead, people who have recovered from COVID-19 infection show particularly
pronounced problems in multiple aspects higher cognitive or ‘executive’ function, an observation that
accords with preliminary reports of executive dysfunction in some patients at hospital discharge14, as
well as previous studies of ventilated patients with acute respiratory distress syndrome pre-
pandemic19.
Previous studies in hospitalised patients with respiratory disease not only demonstrate cognitive
deficits, but suggest these remain for some at a 5 year follow-up 19. Consequently, the observation of
post-infection deficits in the subgroup who were put on a ventilator was not surprising. Conversely,
the deficits in cases who were not put on a ventilator, particularly those who remained at home, was
unexpected. Although these deficits were on average of small scale for those who remained at home,
they were more substantial for people who had received positive confirmation of COVID-19
infection. One possibility is that these deficits in milder cases may reflect the lower grade
consequences of less severe hypoxia. However, as noted in the introduction, there have been case
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reports of other forms of neurological damage in COVID-19 survivors, including some for whom
such damage was the first detected symptom4. Accordingly, in the current study, bio-positive cases
who reported being ill with no breathing difficulties showed a 0.32SD magnitude cognitive deficit.
Based on this, we propose that a timely challenge is to cross-relate the multi-dimensional profile of
cognitive deficits observed here to imaging markers that can confirm and differentiate the underlying
neuropathologies of COVID-19. Indeed, some of the tests reported here now are being applied
alongside imaging in people recovering from severe illness with COVID-19 for that purpose.
An important consideration for any cross-group study is biased sampling. Crucially, our study
promotional material did not mention COVID-19. Instead, we raised the profile via a BBC2 Horizon
documentary plus news features stating that people could undertake a free online assessment to
identify their greatest cognitive strengths. This mitigated biased recruitment of people who suspected
that COVID-19 had affected their cognitive faculties. Including the questionnaire post assessment also
mitigated the potential for questionnaire items to bias expectations of poor self-performance due to
COVID-19.
Normal limitations pertaining to inferences about cause and effect from cross-sectional studies
apply3,20. One might posit that people with lower cognitive ability have higher risk of catching the
virus. We consider such a relationship plausible; however, it would not explain why the observed
deficits varied in scale with respiratory symptom severity. We also note that the large and
socioeconomically diverse nature of the cohort enabled us to include many potentially confounding
variables in our analysis. Nonetheless, we emphasise that longitudinal research, including follow-up
of this cohort, is required to further confirm the cognitive impact of COVID-19 infection and
determine deficit longevity as a function of respiratory symptom severity, and other symptoms. It also
is plausible that cognitive deficits associated with COVID-19 are no different to other respiratory
illnesses. The observation of significant cognitive deficit associated with positive biological
verification of having had COVID-19, i.e., relative to suspected COVID-19, goes some way to
mitigate this possibility. Further work is required to interrelate the deficits to underlying neurological
changes, and to disambiguate the associated pathological processes and cross compare to other
respiratory viruses. A fuller understanding of the marked deficits that our study shows will enable
better preparedness in the post-pandemic recovery challenges.
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Materials and Methods
Study promotion | The Great British Intelligence Test is an ongoing collaborative citizen
science project with BBC2 Horizon that launched in late December 2019. At the beginning of
January, articles promoting the study were placed on the Horizon homepage, BBC News
homepage and main BBC homepage, and circulated via news meta-apps. They remained in
prominent positions within the public eye throughout January. In May, aligned with report of
initial results considered of interest to the general public via a BBC2 Horizon documentary,
there was a further promotional push. This led to high recruitment in the months of January
and May, with lower, but still substantial recruitment between and after these dates.
Data collection | The study was promoted as a free way for people to test themselves in
order to find out what their greatest personal cognitive strengths were. It comprised a
sequence of nine tests from the broader library that is available via the Cognitron server
based on prior data showing that they can be used to measure distinct aspects of human
cognition, spanning planning/reasoning, working memory, attention and emotion processing
abilities, in a manner that is sensitive to population variables of interest whilst being robust
against the type of device that a person is tested on. In this respect, the battery of tests
should not be considered an IQ test in the classic sense, but instead, is intended to
differentiate aspects of cognitive ability on a finer grain. The tests also had been optimised
for application with older adults and people with mild cognitive and motor impairments.
All Cognitron tests were programmed in html5 with JavaScript by AH and WT. They were
hosted on a custom server system on the Amazon EC2 that can support diverse studies via
custom websites. The server system was specifically developed to handle spikey acquisition
profiles that are characteristic of main-stream media collaborative studies, fitting the number
of server instances in an automated manner to rapid changes in demand. Here, maximum
concurrent participants landing on the website information page was >36,000, with this
occurring at the point of the documentary airing on BBC2 in May.
After the nine cognitive tests, participants were presented with a detailed questionnaire with
items capturing a broad range of socio-demographic, economic, vocational and lifestyle
variables. During May, in response to the COVID-19 pandemic, the questionnaire was
extended to include items pertaining to the direct and indirect impact of the virus, along with
questions regarding common pre-existing medical conditions. At the time of writing, this had
been completed by 84,285 adults, predominantly within the UK. People under the age of 16
were not excluded. Instead, they were presented with an abbreviated questionnaire that did
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not include COVID-19 related items. This decision was made to help ensure accelerated
approval via the ethics board.
On completing the questionnaire, participants were provided with a summary report of their
performance relative to all other people who had undertaken each of the tests, which
highlighted the cognitive domains that they performed relatively highest on. This report was
used as a way to motivate people to take part in the study. The ordering of events as
outlined above was designed to mitigate biases. Specifically, the study did not advertise as
having a COVID-19 related questionnaire, avoiding biased sampling of people who were
concerned that the illness had reduced their cognitive functions. Furthermore, when filling
out the questionnaire, participants were yet to be shown how their performance compared to
the normative population, thereby avoiding biasing the questionnaire responses.
Data pre-processing | All processing and analysis steps were conducted in MATLAB by AH
with assistance from WT. Visualisation was conducted in R (v4.0.2) by JMB. Pre-processing
steps were as follows. Participants under 16 or who had not completed the extended
questionnaire were removed from the analysis. Each test was designed to produce one
primary accuracy-based performance measure (details of test designs are provided below).
Values more than 5 standard deviations from the mean were winsorised. Nuisance variables
were factored out by applying a generalised linear model and taking the standardised
residuals forwards for analysis relative to the variables of interest. This two-step approach
was chosen because it leverages the very large data when taking into account broadly
applicable nuisance variables such as age whilst ensuring that the model applied to examine
effects of interest had minimal possible complexity, thereby reducing any propensity for
overfit when contrasting between smaller sub-groups. Nuisance variables were age, sex,
racial-ethnicity, gender, handedness, first language (English vs other), country of residence
(UK vs other), education level, vocational status and annual earning. Age in years was taken
to the third order in the model to fit precisely the nonlinear age curves that are characteristic
of the tests.
Composite score estimation | Composite scores were extracted from the data in two
steps. First, an overall composite score was estimated across all nine tests by extracting the
first eigenvector. Then, principal component analysis was conducted with varimax rotation.
We conformed to the Kaiser convention of including components that had eigenvalues > 1,
producing 3 orthogonal components. Examination of the rotated component loading matrix
produced the expected easily interpretable solution. Specifically, the first component was
labelled ‘Semantic Problem Solving’ as the heaviest loadings for it were Rare Word
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Definitions, which involves assigning definitions to rare words, and Analogical Reasoning,
which requires the mapping of rules and relationships between different semantic contexts.
The second component was labelled ‘Visual Attention’ as the heaviest weightings were for
2D Mental Rotations followed by Target Detection, both of which require the rapid
processing of visual arrays that have complex combinations of features. The third
component was labelled ‘Spatial Working Memory’ as it comprised all of the spatial tests,
including the Spatial Span, which measures working memory capacity, and Tower of London
and Block Rearrange, which involve spatial problem solving.
Linear models | The overall summary score, three component scores and nine individual
test scores with nuisance variables factored out, were taken forwards for analysis with
general linear modelling. The first analysis examined differences in scores relative to people
who were not ill for those who reported that they believed they had recovered from the
COVID-19 illness. These were subdivided along an approximate severity scale into (i) those
who did not have trouble breathing, (ii) those who had breathing problems but received no
medical assistance, (ii) those who had breathing problems and received medical assistance
at home, (iv) those who were taken to hospital but were not put on a ventilator and (v) those
who were fitted with a ventilator. Further models were then run focused on the summary
score to examine if the observed deficits had a basis in other factors. These included as
additional factors in the GLM (i) positive confirmation of COVID-19 infection through a
biological test, (ii) people who reported residual COVID-19 symptoms, (and (iii) common pre-
existing medical conditions that affect the respiratory system or immune system and that are
associated with cognitive deficits. Further analyses that are not reported here include (iv)
pre-existing neurological conditions and (v) pre-existing psychiatric conditions. These are in
preparation for a further article, but we can report that inclusion of these variables does not
diminish the COVID-19 effects.
Task designs | The cognitive tests included in this study (and three more recently added
tests) can be viewed at https://gbit.cognitron.co.uk. In brief, the main study included nine
tests that based on previous analyses were known to be robust across devices, sensitive to
population variables of interest such as age, gender and education level, manageable for
older adults and patients with mild cognitive or motor deficits, and not so strongly correlated
as to measure just one overarching ability. Details of individual task designs are in
supplementary figures S1-9. Questionnaire items analysed in this study are outlined in
Supplementary Table S1
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Acknowledgements. Dr Hampshire is supported by the UK Dementia Research Institute and
Biomedical Research Centre at Imperial College London with technology development supported by
EU-CIG EC Marie-Curie CIG and NIHR grant II-LB-0715-20006. William Trender is supported by
the EPSRC Center for Doctoral Training in Neurotechnology. Dr Chamberlain’s role in this study was
funded by a Wellcome Trust Clinical Fellowship (Reference 110049/Z/15/Z). Joseph M Barnby is
supported by the UK Medical Research Council (MR/N013700/1) and King’s College London
member of the MRC Doctoral Training Partnership in Biomedical Sciences. Mitul Mehta is in part
supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South
London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are
those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health
and Social Care. We would like to acknowledge COST Action CA16207 ‘European Network for
Problematic Usage of the Internet’, supported by COST (European Cooperation in Science and
Technology); and the support of the National UK Research Network for Behavioural Addictions
(NUK-BA).
Competing Interests. The authors declare no conflict of interest exists.
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Supplementary Materials for
Cognitive deficits in people who have recovered from COVID-19
Adam Hampshire, William Trender, Samuel R Chamberlain, Amy Jolly, Jon E. Grant, Fiona
Patrick, Ndaba Mazibuko, Steve CR Williams, Joseph M Barnby, Peter Hellyer, Mitul A
Mehta
Correspondence to: a.hampshire@imperial.ac.uk
This PDF file includes:
Materials and Methods
Tables S1 to S8
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Materials and Methods
Study promotion
The Great British Intelligence Test is an ongoing collaborative citizen science project with
BBC2 Horizon that launched in late December 2019. At the beginning of January, articles
promoting the study were placed on the Horizon homepage, BBC News homepage and main
BBC homepage, and circulated via news meta-apps. They remained in prominent positions
within the public eye throughout January. In May, aligned with report of initial results
considered of interest to the general public via a BBC2 Horizon documentary, there was a
further promotional push. This led to high recruitment in the months of January and May,
with lower, but still substantial recruitment between and after these dates.
Data collection
The study was promoted as a free way for people to test themselves in order to find out what
their greatest personal cognitive strengths were. It comprised a sequence of nine tests from
the broader library that is available via the Cognitron server based on prior data showing that
they can be used to measure distinct aspects of human cognition, spanning
planning/reasoning, working memory, attention and emotion processing abilities, in a
manner that is sensitive to population variables of interest whilst being robust against the
type of device that a person is tested on. In this respect, the battery of tests should not be
considered an IQ test in the classic sense, but instead, is intended to differentiate aspects of
cognitive ability on a finer grain. The tests also had been optimised for application with older
adults and people with mild cognitive and motor impairments.
All Cognitron tests were programmed in html5 with JavaScript by AH and WT. They were
hosted on a custom server system on the Amazon EC2 that can support diverse studies via
custom websites. The server system was specifically developed to handle spikey acquisition
profiles that are characteristic of main-stream media collaborative studies, fitting the number
of server instances in an automated manner to rapid changes in demand. Here, maximum
concurrent participants landing on the website information page was >36,000, with this
occurring at the point of the documentary airing on BBC2 in May.
After the nine cognitive tests, participants were presented with a detailed questionnaire with
items capturing a broad range of socio-demographic, economic, vocational and lifestyle
variables. During May, in response to the COVID-19 pandemic, the questionnaire was
extended to include items pertaining to the direct and indirect impact of the virus, along with
questions regarding common pre-existing medical conditions. At the time of writing, this had
been completed by 84,285 adults, predominantly within the UK. People under the age of 16
were not excluded. Instead, they were presented with an abbreviated questionnaire that did
not include COVID-19 related items. This decision was made to help ensure accelerated
approval via the ethics board.
On completing the questionnaire, participants were provided with a summary report of their
performance relative to all other people who had undertaken each of the tests, which
highlighted the cognitive domains that they performed relatively highest on. This report was
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used as a way to motivate people to take part in the study. The ordering of events as
outlined above was designed to mitigate biases. Specifically, the study did not advertise as
having a COVID-19 related questionnaire, avoiding biased sampling of people who were
concerned that the illness had reduced their cognitive functions. Furthermore, when filling
out the questionnaire, participants were yet to be shown how their performance compared to
the normative population, thereby avoiding biasing the questionnaire responses.
Data pre-processing
All processing and analysis steps were conducted in MATLAB by AH with assistance from
WT. Visualisation was conducted in R (v4.0.2) by JMB. Pre-processing steps were as
follows. Participants under 16 or who had not completed the extended questionnaire were
removed from the analysis. Each test was designed to produce one primary accuracy-based
performance measure (details of test designs are provided below). Values more than 5
standard deviations from the mean were winsorised. Nuisance variables were factored out
by applying a generalised linear model and taking the standardised residuals forwards for
analysis relative to the variables of interest. This two-step approach was chosen because it
leverages the very large data when taking into account broadly applicable nuisance
variables such as age whilst ensuring that the model applied to examine effects of interest
had minimal possible complexity, thereby reducing any propensity for overfit when
contrasting between smaller sub-groups. Nuisance variables were age, sex, racial-ethnicity,
gender, handedness, first language (English vs other), country of residence (UK vs other),
education level, vocational status and annual earning. Age in years was taken to the third
order in the model to fit precisely the nonlinear age curves that are characteristic of the
tests.
Composite score estimation
Composite scores were extracted from the data in two steps. First, an overall composite
score was estimated across all nine tests by extracting the first eigenvector. Then, principal
component analysis was conducted with varimax rotation. We conformed to the Kaiser
convention of including components that had eigenvalues > 1, producing 3 orthogonal
components. Examination of the rotated component loading matrix produced the expected
easily interpretable solution. Specifically, the first component was labelled ‘Semantic
Problem Solving’ as the heaviest loadings for it were Rare Word Definitions, which involves
assigning definitions to rare words, and Analogical Reasoning, which requires the mapping
of rules and relationships between different semantic contexts. The second component was
labelled ‘Visual Attention’ as the heaviest weightings were for 2D Mental Rotations followed
by Target Detection, both of which require the rapid processing of visual arrays that have
complex combinations of features. The third component was labelled ‘Spatial Working
Memory’ as it comprised all of the spatial tests, including the Spatial Span, which measures
working memory capacity, and Tower of London and Block Rearrange, which involve spatial
problem solving.
Linear models
The overall summary score, three component scores and nine individual test scores with
nuisance variables factored out, were taken forwards for analysis with general linear
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modelling. The first analysis examined differences in scores relative to people who were not
ill for those who reported that they believed they had recovered from the COVID-19 illness.
These were subdivided along an approximate severity scale into (i) those who did not have
trouble breathing, (ii) those who had breathing problems but received no medical assistance,
(ii) those who had breathing problems and received medical assistance at home, (iv) those
who were taken to hospital but were not put on a ventilator and (v) those who were fitted with
a ventilator. Further models were then run focused on the summary score to examine if the
observed deficits had a basis in other factors. These included as additional factors in the
GLM (i) positive confirmation of COVID-19 infection through a biological test, (ii) people who
reported residual COVID-19 symptoms, (and (iii) common pre-existing medical conditions
that affect the respiratory system or immune system and that are associated with cognitive
deficits. Further analyses that are not reported here include (iv) pre-existing neurological
conditions and (v) pre-existing psychiatric conditions. These are in preparation for a further
article, but we can report that inclusion of these variables does not diminish the COVID-19
effects.
Test designs
The cognitive tests included in this study (and three more recently added tests) can be
viewed at https://gbit.cognitron.co.uk. In brief, the main study included nine tests that based
on previous analyses were known to be robust across devices, sensitive to population
variables of interest such as age, gender and education level, manageable for older adults
and patients with mild cognitive or motor deficits, and not so strongly correlated as to
measure just one overarching ability. Designs are in figures S1-9.
Figure S1 Block Rearrange
The Block Rearrange test measures spatial problem solving. The participant is presented
with a grid of coloured blocks on the left-hand side of the screen and on the right-hand side,
a black silhouette made up of a subset of the shapes on the left. The participant must make
the shape of the left-hand blocks match the silhouette on the right-hand side by removing
blocks. The blocks fall under gravity. The test comprises 15 trials of varying difficulty. The
difficulty is modulated by two factors; the number of blocks needed to remove and the
number of blocks that must fall in order to reach the target silhouette. Each trial is terminated
if, either the target silhouette is reached (correct trial) or an incorrect block is removed
(incorrect trial). The outcome measure is the total number of correct trials. Population mean
= 10.9 SD = 2.93.
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Figure S2 Tower of London
The Tower of London test measures spatial planning. It is a variant on the original Tower of
London Test (Shallice, 1982). The participant is shown two sets of three prongs with
coloured beads on them. The first set is the initial state and the second set is the target
state. The participant must work out the lowest number of moves it would take to transition
from the initial state to the target state. They must then input this number using an on-screen
number pad. This differs from the original test in that the participant is not allowed to move
the beads, all calculation and planning must be done in their head. This is to prevent correct
answers being reached through iterative error correction. The test consists of 10 trials of
variable difficulty. The difficulty is scaled using the number of beads and the convolutedness,
defined as the number of moves that must be made that do not place a bead in its final
target position. The outcome measure is the total number of correct trials. Population mean
= 6.57, SD = 2.62.
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Figure S3 Digit Span
Digit Span is a computerised variant on the verbal working memory component of the WAIS-
R intelligence test (Weschler, 1981). Participants view a sequence of digits that appear on
the screen one after another. Subsequently, they repeat the sequence of numbers by
entering them using an on-screen number pad. The difficulty is incremented using a ratchet
system, every time a sequence is recalled correctly, the length of the subsequent sequence
is incremented by one. The test is terminated when three consecutive mistakes are made on
a particular sequence length. The outcome measure is the maximum sequence length
correctly recalled. Minimum level = 2, maximum level = 20, ISI = 0ms, encoding time =
1000ms. Population mean = 6.98, SD = 1.58.
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Figure S4 Spatial Span
The Spatial Span test measures spatial short-term memory capacity. It is a variant on the
classic Corsi Block Tapping Test (Corsi, 1972). The participant is presented with a 4 x 4 grid,
onto which is displayed a sequence of squares in different positions in the grid. The
participant must then click the squares in the order that they were highlighted. The difficulty
is incremented using a ratchet system, every time a sequence is recalled correctly, the
length of the subsequent sequence is incremented by one. The test is terminated when three
consecutive mistakes are made on a particular sequence length. The outcome measure is
the maximum sequence length correctly recalled. Minimum level = 2, maximum level = 16,
ISI = 0ms, encoding time = 1500ms. Population mean = 6.10, SD = 1.23.
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Figure S5 Target Detection
The target detection test measures spatial visual attention. The participant is presented with
a target shape on the left of the screen and a probe area on the right side of the screen.
After 3000ms, the probe area begins to fill with shapes, the participant must identify and click
the target shape while ignoring the distractor shapes. Shapes are added every 1000ms and
a subset of the shapes in the probe area are removed every 1000ms. The trial runs for a
total of 120 addition/removal cycles. The target shape is included in the added shapes
pseudo randomly, at a frequency of 12 in 20 cycles. The outcome measure is the total
number of target shapes clicked. Population mean = 57.2, SD = 11.8.
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Figure S6 2D Mental Rotation
The 2D Mental rotation test measures the ability to spatially manipulate objects in mind
(Silverman et al., 2000). In this version of the test, a grid with coloured squares is presented
at the top of the screen, with a further four grids with coloured squares presented below (i.e.
probe grids). One of the four grids is identical to the target grid above but is rotated by either
90, 180 or 270 degrees whilst the other grids differ by five squares. To obtain maximum
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points, the participant must indicate which of the four grids is identical to target, solving as
many problems as possible within three minutes. For every correct response, the total score
increases by one. The outcome measure is the total score. Population mean = 26.8, SD =
8.35.
Figure S7 Analogical Reasoning
The Analogical Reasoning test measures semantic reasoning abilities. In this version of the
test, participants are presented with two written relationships that they must decide have the
same type of association or not (e.g. “Lion is to feline as cabbage is to vegetable”).
Participants must indicate their decision by selecting the True or False buttons presented
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below the written analogies. Analogies are varied across semantic distance to modulate
difficulty and associations types switch throughout the sequence of trials. To obtain
maximum points, participants must solve as many problems as possible within three
minutes. For every correct response, the total score increases by one. For every incorrect
response, the total score decreases by one. The outcome measure is the total score.
Population mean = 24.1, SD = 11.5.
Figure S8 Rare Word Definitions
In this test, individuals are assessed on their ability to identify the correct definitions of a
words. Participants are presented with a word accompanied by four descriptive statements.
They must decide which of the four statements provides the correct definition of the word.
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Words vary based on their frequency of use in English written language, resulting in rare and
commonly used words to be presented. For each word, the participant has twenty seconds
to choose a definition. To obtain maximum points, participants must answer 21 word-
definitions correctly. For every correct response, the total score increases by one point. The
outcome measure is the total score. Population mean = 16.7, SD = 2.84.
Figure S9 Face Emotional Discrimination
This test measures an individual's ability to identify and discern between emotions.
Participants are presented with pictures of two people, each expressing a particular emotion
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(e.g. happy, neutral, angry, scared). They must decide if the emotions expressed by each
person are the same or different. Trials vary based on the emotions used as well as whether
individuals have congruent vs. incongruent emotional expressions. To obtain maximum
points, participants must complete 50 trials as accurately as possible. For every correct
answer, the total score increases by one point. The outcome measure is total score.
Population mean = 42.6, SD = 3.50.
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Table S1 | Questionnaire items analysed in this study
Q1. How old are you?
Response via number stepper
Q2. Sex
Male
Female
Other
Q3. Are you left or right handed?
Left-handed
Right-handed
Ambidextrous
Q4. First Language
English
Other
Q5. To ensure we have a representative sample of the population, please indicate your
ethnicity?
American Hispanic
East Asian
Indian, South Asian or South-East Asian
North African
Rom, Sinti or Bedouin
Sub-Saharan African or Afro-American
West-cCntral Asian
White European or North American
Mixed ethnicity
Unknown
Q6. Country of residence
UK
Other
Q7. What is your level of education
No schooling
Primary/Elementary school
Secondary school/High school diploma
University degree
PhD
Q8. What is your occupational status?
Disabled/Not applicable/Sheltered employment
Homemaker
Retired
Student
Unemployed/Looking for work
Worker
Q9. How much do you earn?
notworking
prefer not to say
£0-10K
£10-20K
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£20-30K
£30-40K
£40-50K
£50-60K
£60-70K
£70-80K
£80-90K
£90-100K
>100K
Q10. Have you had, or suspect you have had symptoms of COVID-19?
No
Yes, but the symptoms have passed
Yes, currently experiencing symptoms
Q11. Have you had a positive test for COVID-19?
No/don’t know/awaiting test results
Yes
Q12. Did you experience breathing difficulties?
No
Yes
Q13. What happened as a result of your breathing difficulties?
I was not ill
I was ill but had no respiratory symptoms
I stayed at home
I stayed at home and needed medical assistance (e.g. called 999)
I went to hospital, but was not put on a ventilator (see above for definition)
I went to hospital and was put on a ventilator (breathing tube and mechanical
assistance for breathing)
Q14. Have you been diagnosed with any of the following conditions?
Lung conditions (e.g. asthma, emphysema or bronchitis)
Heart disease
Chronic kidney disease
Liver disease (e.g. hepatitis)
Diabetes
High blood pressure
Irregular heartbeat (atrial fibrillation)
Problems with your spleen (e.g. sickle cell disease, or if you have had your spleen
removed)
A weakened immune system as the result of a condition such as HIV or AIDS, or
medicines such as steroid tablets or chemotherapy
None of the above
NB – this is a non-exhaustive list focused on the questionnaire items as they were analysed
for this study. Some items (e.g., language and country of residence) were collapsed from a
larger response set due to sampling being very sparse outside of the UK.
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Table S2a | age distribution and severity counts per age
Count
AGE
Was not ill
Ill but had no
respiratory
symptoms
Respiratory
symptoms, no
assistance at home
Respiratory
symptoms, medical
assistance at home
Went to hospital but
was not put on a
ventilator
Went to hospital
and was put on a
ventilator
16
783
132
29
0
0
2
17
667
99
30
0
1
0
18
680
111
22
0
4
1
19
657
101
37
1
1
0
20
645
117
38
5
0
2
21
696
101
41
0
0
3
22
721
147
34
1
3
4
23
860
121
41
2
1
1
24
833
124
39
3
1
1
25
961
154
62
4
1
0
26
1047
176
55
2
1
2
27
1101
190
86
3
4
2
28
1195
178
85
3
2
1
29
1215
209
72
5
2
1
30
1269
193
73
1
2
3
31
1186
176
80
7
5
1
32
1272
175
82
2
4
1
33
1151
171
67
4
4
0
34
1058
171
80
3
2
1
35
1046
172
74
6
1
1
36
1160
153
69
3
4
2
37
1009
180
63
3
3
1
38
1105
194
84
6
2
0
39
1321
204
97
4
5
0
40
1280
211
77
4
6
4
41
1191
185
82
6
5
0
42
1231
198
59
4
2
1
43
1181
179
71
4
1
1
44
1231
148
74
5
2
2
45
1427
218
82
7
1
1
46
1338
220
92
8
3
1
47
1413
207
69
11
3
0
48
1518
180
92
10
3
3
49
1610
215
78
2
4
1
50
1633
239
87
8
5
0
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51
1458
193
91
8
3
3
52
1554
201
81
1
6
0
53
1583
212
86
5
6
1
54
1672
196
82
1
4
3
55
1723
212
90
3
1
2
56
1606
183
68
3
6
1
57
1592
175
73
2
2
0
58
1558
184
68
3
3
0
59
1550
181
58
1
7
0
60
1575
175
62
0
1
0
61
1460
121
48
1
3
0
62
1455
155
46
0
1
0
63
1423
144
43
1
3
1
64
1367
132
40
1
0
1
65
1294
142
45
1
2
1
66
1221
100
28
0
1
0
67
1161
91
22
2
2
0
68
1010
74
32
0
0
0
69
901
60
21
2
1
0
70
890
65
17
2
2
0
71
739
50
9
0
0
0
72
798
36
10
1
0
0
73
679
48
11
0
0
1
74
398
23
11
0
0
0
75
379
24
5
0
1
0
76
295
10
2
0
1
0
77
243
16
2
1
1
0
78
182
5
3
0
0
0
79
161
11
0
0
0
0
80
127
6
4
0
0
0
81
97
2
1
0
0
0
82
100
8
1
0
2
0
83
76
2
0
0
0
0
84
42
4
0
0
0
0
85
51
0
1
0
0
0
86+
124
11
2
0
0
2
Table S2b | Ethnic groups within the GBIT cohort
Whole cohort
Extended questionnaire
N
%
N
%
Ethnicity
9599
2.5
479
0.6
American Hispanic
14128
3.7
891
1.1
East Asian
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14685
3.9
2181
2.6
Indian, South Asian or South-East
Asian
9935
2.6
1882
2.2
Mixed ethnicity
1091
0.3
133
0.2
North African
585
0.2
55
0.1
Rom, Sinti or Bedouin
2314
0.6
253
0.3
Sub-saharan African or Afro-
american
5533
1.5
953
1.1
Unknown
1757
0.5
257
0.3
West-central Asian
321399
84.4
77504
91.6
White European or North American
Table S2c | Sex
Female 46449
Male 37478
Other 358
Table S2d | Handedness
Ambidextrous
2164
Left-handed
9089
Right-handed
73032
Table S2e | First language
English
79283
Other
5002
Table S2f | country of residence
UK 77729
Other 6556
Table S2g | Education level
208 01 No schooling
1730 02 Primary/Elementary school
30229 03 Secondary school/High school diploma
48745 04 University degree
3373 05 PhD
Table S2h | Occupational status
878 Disabled/Not applicable/Sheltered employment
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2727 Homemaker
16503 Retired
6487 Student
2654 Unemployed/Looking for work
54653 Worker
383 Unknown
Table S2i | Earnings
29632 notworking
1990 prefer not to say
828 £0-10K
8962 £10-20K
11423 £20-30K
10180 £30-40K
7188 £40-50K
4265 £50-60K
2514 £60-70K
1818 £70-80K
1143 £80-90K
1107 £90-100K
3235 >100K
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Table S3 | General linear model of global task performance vs. respiratory severity
ANOVA
SumSq
DF
MeanSq
F
pValue
Respiratory
severity
90.888
5
18.178
11.848
1.76E-11
Error
1.29E+05
84279
1.5343
t
p
Estimate
N
SE
0.0000
1.0000
0.0000
71235
0.0000
Was not ill
-
2.8974
0.0038
-0.0398
9201
0.0137
Ill but had no respiratory symptoms
-
4.5170
0.0000
-0.0973
3466
0.0215
Respiratory symptoms, no assistance at home
-
1.2977
0.1944
-0.1213
176
0.0935
Respiratory symptoms, medical assistance at
home
-
4.4023
0.0000
-0.4502
147
0.1023
Went to hospital but was not put on a ventilator
-
3.5358
0.0004
-0.5656
60
0.1600
Went to hospital and was put on a ventilator
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Table S4a | Positive COVID-19 biological test rates
No/awaiting
results
Yes
Percent positive
71235
0
0.0
No ill
9014
187
2.0
Ill but had no respiratory symptoms
3382
84
2.4
Respiratory symptoms, no assistance at home
162
14
8.0
Respiratory symptoms, medical assistance at
home
123
24
16.3
Went to hospital but was not put on a ventilator
8
52
86.7
Went to hospital and was put on a ventilator
Table S4b | General linear model including positive COVID-19 biological test as a
factor
ANOVA
SumSq
DF
MeanSq
F
pValue
57.58
5.00
11.52
7.51
<0.0001
respiratory severity
33.17
1.00
33.17
21.62
<0.0001
positive test
5.97
4.00
1.49
0.97
0.4205
interaction
129270.00
84274.00
1.53
Error
Estimates
t
P
Estimate
SE
0a
0a
0a
0a
Negative
-4.65
0.0000
-0.33
0.07
Positive
0a
0a
0a
0a
Was not ill
-2.40
0.0166
-0.03
0.01
Ill but had no respiratory symptoms
-4.13
0.0000
-0.09
0.02
Respiratory symptoms, no assistance at home
-1.02
0.3099
-0.10
0.09
Respiratory symptoms, medical assistance at
home
-3.85
0.0001
-0.40
0.10
Went to hospital but was not put on a ventilator
-1.63
0.1024
-0.28
0.17
Went to hospital and was put on a ventilator
Table S4c | Contrasting positive COVID-19 biological test within select groups
Estimate SE tStat pValue N group
-0.32 0.091 -3.49 0.0005 187 Ill but had no respiratory symptoms
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-0.36 0.14 -2.61 0.0091 84 Respiratory symptoms, no assistance at
home
Table S5a | Residual symptom rates
Symptoms still
No
Yes
%
71235
0
0
Was not ill
8856
345
4
Ill but had no respiratory symptoms
3271
195
6
Respiratory symptoms, no assistance at home
160
16
9
Respiratory symptoms, medical assistance at
home
129
18
12
Went to hospital but was not put on a ventilator
13
47
78
Went to hospital and was put on a ventilator
Table S5b | General linear model including residual symptoms as a factor
ANOVA
SumSq
DF
MeanSq
F
pValue
73.25
5
14.65
9.55
<0.0001
Respiratory severity
5.86
1
5.86
3.82
0.0507
Symptoms still?
129300.00
84278
1.53
Error
Estimates
t
p
Estimate
SE
0.00
NA
0
0
Was not ill
-2.59
0.0096
-0.0359
0.01
Ill but had no respiratory symptoms
-4.21
0.0000
-0.0916
0.02
Respiratory symptoms, no assistance at home
-1.20
0.2315
-0.112
0.09
Respiratory symptoms, medical assistance at
home
-4.27
0.0000
-0.4377
0.10
Went to hospital but was not put on a ventilator
-2.94
0.0033
-0.4853
0.17
Went to hospital and was put on a ventilator
-1.95
0.0507
-0.1026
0.05
Still have some symptoms at the moment
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Table S6 | GLM including pre-existing conditions
Incidence
2289
Weakened immune system
619
Kidney disease
3128
Diabetes
2385
Heart disease
822
High blood pressure
202
Heart problems
426
Liver disease
9115
Lung conditions
167
Spleen/sickle cell
ANOVA
SumSq
DF
MeanSq
F
pValue
Estimate
17.18
1
17.18
11.20
0.0008
-0.090
Weakened immune
system
1.11
1
1.11
0.72
0.3946
-0.044
Kidney disease
29.69
1
29.69
19.36
0.0000
-0.104
Diabetes
16.74
1
16.74
10.91
0.0010
-0.087
Heart disease
0.11
1
0.11
0.07
0.7927
-0.014
High blood pressure
0.84
1
0.84
0.55
0.4595
-0.067
Heart problems
8.56
1
8.56
5.59
0.0181
-0.145
Liver disease
2.20
1
2.20
1.44
0.2310
-0.017
Lung conditions
1.14
1
1.14
0.75
0.3880
0.080
Spleen/sickle cell
84.84
5
16.97
11.07
0.0000
Respiratory severity
129220.00
84270
1.53
Error
Estimate
SE
tStat
pValue
0a
0a
0a
na
Was not ill
-0.04
0.01
-3.07
0.002
Ill but had no respiratory symptoms
-0.09
0.02
-4.39
0.000
Respiratory symptoms, no assistance at home
-0.11
0.09
-1.22
0.224
Respiratory symptoms, medical assistance at
home
-0.44
0.10
-4.26
0.000
Went to hospital but was not put on a
ventilator
-0.52
0.16
-3.21
0.001
Went to hospital and was put on a ventilator
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Table S7a | Test scores correlation matrix
Rare word definition
Tower of London
Block rearrange
Digit span
Emotional face
discrimination
Mental rotation
Spatial span
Target detection
Analogical reasoning
0.17
0.14
0.19
0.14
0.09
0.09
0.08
0.37
Rare word definition
0.17
0.31
0.14
0.11
0.09
0.20
0.09
0.22
Tower of London
0.14
0.31
0.11
0.09
0.15
0.20
0.11
0.20
Block rearrange
0.19
0.14
0.11
0.08
0.10
0.18
0.08
0.26
Digit span
0.14
0.11
0.09
0.08
0.01
0.06
0.05
0.11
Emotional face
discrimination
0.09
0.09
0.15
0.10
0.01
0.20
0.22
0.24
Mental rotation
0.09
0.20
0.20
0.18
0.06
0.20
0.12
0.19
Spatial span
0.08
0.09
0.11
0.08
0.05
0.22
0.12
0.13
Target detection
0.37
0.22
0.20
0.26
0.11
0.24
0.19
0.13
Analogical reasoning
Tests included in the battery were selected based on deliverability via Internet browsers and
phones, sensitivity to population variables of interest such as age, and tendency to measure
different as opposed to a single common aspect of cognitive ability.
Table S7b | Rotated PCA loadings for 9 tests
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Verbal
problem
solving
Attention
Spatial
working
memory
Analogical reasoning
0.6
0.3
0.1
Rare word definition
0.6
0.1
0.1
Digit span
0.3
0.1
0.1
Tower of London
0.2
0.1
0.5
Block rearrange
0.1
0.2
0.5
Spatial span
0.1
0.3
0.3
Target detection
0.1
0.3
0.1
Mental rotation
0.1
0.7
0.0
Emotional face discrimination
0.2
0.0
0.1
Three simple and interpretable components are evident for the varimax rotated factor model.
The Emotional Discrimination test measures an ability that has little overlap with the other 8
tests.
Table S7c | Rotated component loadings for 8 tests
Verbal
problem
solving
Attention
Spatial
working
memory
Digit span
0.2
0.0
0.2
Rare word definition
0.8
0.1
0.1
Analogical reasoning
0.4
0.3
0.2
Target detection
0.1
0.3
0.1
Mental rotation
0.0
0.7
0.0
Spatial span
0.1
0.3
0.3
Tower of London
0.1
0.1
0.6
Block rearrange
0.1
0.2
0.5
After removing the Emotional Discriminations test, three components are still evident, and
the varimax rotated solution remains similar to the 9 test PCA.
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Table S7d | GLMs of component scores vs respiratory severity measures
ANOVAs
SumSq
DF
MeanSq
F
pValue
COMPONENT 1
Respiratory
severity
46
5
9.25
5.89
<0.0001
Error
132350
84279
1.57
COMPONENT 2
Respiratory
severity
71
5
14.29
7.46
<0.0001
Error
161390
84279
1.92
COMPONENT 3
Respiratory
severity
25
5
4.93
2.23
0.0489
Error
186830
84279
2.22
Estimate
SE
tStat
pValue
COMPONENT 1
0.00
0.00
0.21
0.83
(Intercept)
0a
0a
0a
na
Was not ill
0.02
0.01
1.30
0.19
Ill but had no respiratory symptoms
-0.04
0.02
-2.05
0.04
Respiratory symptoms, no assistance at home
-0.09
0.09
-0.95
0.34
Respiratory symptoms, medical assistance at home
-0.28
0.10
-2.74
0.01
Went to hospital but was not put on a ventilator
-0.62
0.16
-3.84
0.00
Went to hospital and was put on a ventilator
COMPONENT 2
0.01
0.01
2.05
0.04
(Intercept)
0a
0a
0a
na
Was not ill
-0.06
0.02
-3.90
0.00
Ill but had no respiratory symptoms
-0.07
0.02
-2.85
0.00
Respiratory symptoms, no assistance at home
-0.22
0.10
-2.15
0.03
Respiratory symptoms, medical assistance at home
-0.33
0.11
-2.92
0.00
Went to hospital but was not put on a ventilator
-0.33
0.18
-1.86
0.06
Went to hospital and was put on a ventilator
COMPONENT 3
0.01
0.01
1.15
0.25
(Intercept)
0a
0a
0a
na
Was not ill
-0.04
0.02
-2.51
0.01
Ill but had no respiratory symptoms
-0.04
0.03
-1.54
0.12
Respiratory symptoms, no assistance at home
0.07
0.11
0.62
0.54
Respiratory symptoms, medical assistance at home
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-0.15
0.12
-1.22
0.22
Went to hospital but was not put on a ventilator
-0.21
0.19
-1.10
0.27
Went to hospital and was put on a ventilator
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Table S8 | Analysis of individual test summary scores relative to respiratory severity
Rare word
definitions
Analogical
reasoning
Target Detection
Tower of
London
Mental rotation
Block rearrange
Spatial span
Emotional
discrimination
Digit span
ANOVA P
<0.0001
<0.0001
0.0003
0.0089
<0.0001
0.0082
0.0006
0.0006
0.0297
Task coefficients relative to control
Not ill
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
No respiratory symptoms
0.00
-0.02
-0.02
-0.03
-0.04
-0.04
-0.04
0.04
0.01
No assistance at home
-0.04
-0.06
-0.04
-0.03
-0.04
-0.02
-0.06
-0.02
-0.04
Assistance at home
-0.09
-0.03
-0.13
-0.01
-0.15
0.04
-0.10
-0.07
-0.02
Hospital, no ventilator
-0.22
-0.38
-0.16
-0.11
-0.23
-0.15
-0.06
-0.18
0.00
Hospital with ventilator
-0.53
-0.42
-0.35
-0.29
-0.24
-0.17
-0.10
-0.01
0.32
Task p relative to control
Not ill
1
1
1
1
1
1
1
1
1
No respiratory symptoms
0.7502
0.0804
0.0423
0.0175
0.0003
0.0028
0.0018
0.0003
0.3511
No assistance at home
0.0178
0.0009
0.0158
0.0684
0.0117
0.2917
0.0011
0.2006
0.0325
Assistance at home
0.2260
0.6961
0.0812
0.9244
0.0420
0.5875
0.1919
0.3868
0.8456
Hospital, no ventilator
0.0069
<0.0001
0.0541
0.1252
0.0043
0.0567
0.4369
0.0275
0.9224
Hospital with ventilator
<0.0001
0.0011
0.0056
0.0262
0.0596
0.1361
0.4245
0.9025
0.0103
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