Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Kirsten Grossmann; Martin Risch; Andjela Markovic; Stefanie Aeschbacher; Ornella C Weideli; Laura Velez; Marc Kovac; Fiona Pereira; Nadia Wohlwend; Corina Risch; Dorothea Hillmann; Thomas Lung; Harald Renz; Raphael Twerenbold; Martina Rothenbühler; Daniel Leibovitz; Vladimir Kovacevic; Paul Klaver; Timo B Brakenhoff; Billy Franks; Marianna Mitratza; George S Downward; Ariel Dowling; Santiago Montes; Duco Veen; Diederick E Grobbee; Maureen Cronin; David Conen; Brianna M Goodale; Lorenz Risch; on behalf of the COVID-19 remote early detection (COVID-RED) consortium

doi:10.1371/journal.pone.0292203

. 2024 Mar 6;19(3):e0292203. doi: 10.1371/journal.pone.0292203

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Kirsten Grossmann ^1,^2,^#, Martin Risch ^2,^3,^4,^#, Andjela Markovic ^5,^6,^7,^#, Stefanie Aeschbacher ⁸, Ornella C Weideli ^2,⁹, Laura Velez ², Marc Kovac ⁴, Fiona Pereira ¹⁰, Nadia Wohlwend ⁴, Corina Risch ⁴, Dorothea Hillmann ⁴, Thomas Lung ⁴, Harald Renz ¹¹, Raphael Twerenbold ^8,¹², Martina Rothenbühler ⁵, Daniel Leibovitz ⁵, Vladimir Kovacevic ⁵, Paul Klaver ¹³, Timo B Brakenhoff ¹³, Billy Franks ¹³, Marianna Mitratza ^14,¹⁵, George S Downward ^14,¹⁵, Ariel Dowling ¹⁶, Santiago Montes ¹⁷, Duco Veen ^18,¹⁹, Diederick E Grobbee ^14,^15,^‡, Maureen Cronin ^5,^‡, David Conen ^20,^‡, Brianna M Goodale ^5,^13,^‡, Lorenz Risch ^1,^2,^4,^21,^‡,^*; on behalf of the COVID-19 remote early detection (COVID-RED) consortium^¶

Editor: Ramada Rateb Khasawneh²²

¹Private University in the Principality of Liechtenstein (UFL), Triesen, Principality of Liechtenstein

²Dr Risch Medical Laboratory, Vaduz, Liechtenstein

³Central Laboratory, Kantonsspital Graubünden, Chur, Switzerland

⁴Dr Risch Medical Laboratory, Buchs, Switzerland

⁵Ava AG, Zürich, Switzerland

⁶Department of Psychology, University of Fribourg, Fribourg, Switzerland

⁷Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland

⁸Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland

⁹Soneva Fushi, Boduthakurufaanu Magu, Male, Maldives

¹⁰Department of Metabolism, Digestive Diseases and Reproduction, Imperial College London, South Kensington Campus, London, United Kingdom

¹¹Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Philipps University Marburg, Marburg, Germany

¹²Department of Cardiology and University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany

¹³Julius Clinical, Zeist, The Netherlands

¹⁴UMC Utrecht, Utrecht, The Netherlands

¹⁵Julius Global Health, the Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands

¹⁶Takeda Pharmaceuticals, Digital Clinical Devices, Cambridge, Massachusetts, United States of America

¹⁷Roche Diagnostics Nederland B.V., Almere, The Netherlands

¹⁸Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands

¹⁹Optentia Research Programme, North-West University, Potchefstroom, South Africa

²⁰Population Health Research Institute, McMaster University, Hamilton, Canada

²¹Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Inselspital, Bern, Switzerland

²²Yarmouk University, JORDAN

Competing Interests: The authors have read the journal’s policy and have the following competing interests: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. Andjela Markovic, Vladimir Kovacevic, Martina Rothenbühler, Brianna Goodale and Maureen Cronin are past employees of Ava AG. Brianna Goodale and Timo Brakenhoff are current employees of Julius Clinical BV. Billy Franks is a former employee of Julius Clinical BV and now an employee of Haleon. Paul Klaver and Duco Veen are former employees of Julius Clinical BV. Marianna Mitratza is a current employee of P95 CVBA. There are no patents, products in development or marketed products associated with this research to declare. These competing interests do not alter our adherence to PLOS ONE policies on sharing data and materials.

‡ DEG, MC, DC, BMG and LR also contributed equally to this work.

¶ Full list of group members of the COVID-RED consortium is provided in the acknowledgments.

^✉

* E-mail: lorenz.risch@ufli.li

Contributed equally.

Roles

Kirsten Grossmann: Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

Martin Risch: Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing

Andjela Markovic: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Stefanie Aeschbacher: Data curation, Investigation, Resources, Supervision, Writing – review & editing

Ornella C Weideli: Formal analysis, Investigation, Project administration, Resources, Validation, Writing – review & editing

Laura Velez: Writing – review & editing

Marc Kovac: Resources, Writing – review & editing

Fiona Pereira: Visualization, Writing – original draft, Writing – review & editing

Nadia Wohlwend: Formal analysis, Resources, Validation, Writing – review & editing

Corina Risch: Resources, Validation, Writing – review & editing

Dorothea Hillmann: Resources, Validation, Writing – review & editing

Thomas Lung: Project administration, Writing – review & editing

Harald Renz: Conceptualization, Project administration, Writing – review & editing

Raphael Twerenbold: Conceptualization, Supervision, Writing – review & editing

Martina Rothenbühler: Data curation, Formal analysis, Validation, Writing – review & editing

Daniel Leibovitz: Data curation, Validation, Writing – review & editing

Vladimir Kovacevic: Data curation, Validation, Writing – review & editing

Paul Klaver: Conceptualization, Methodology, Writing – review & editing

Timo B Brakenhoff: Conceptualization, Methodology, Writing – review & editing

Billy Franks: Conceptualization, Methodology, Writing – review & editing

Marianna Mitratza: Conceptualization, Investigation, Writing – review & editing

George S Downward: Conceptualization, Methodology, Writing – review & editing

Ariel Dowling: Conceptualization, Writing – review & editing

Santiago Montes: Conceptualization, Methodology, Project administration, Writing – review & editing

Duco Veen: Methodology, Validation, Writing – review & editing

Diederick E Grobbee: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

Maureen Cronin: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

David Conen: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

Brianna M Goodale: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

Lorenz Risch: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

Ramada Rateb Khasawneh: Editor

PMCID: PMC10917257 PMID: 38446766

Abstract

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.

Introduction

On March 11, 2020, the WHO declared the fast-spreading coronavirus disease (COVID-19) a global pandemic [1]. This novel viral disease was first detected in Wuhan, China, in December 2019 and is caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) [2]. Increasing knowledge about risk factors and symptoms, as well as the implementation of mass reverse transcription polymerase chain reaction (RT-PCR), serological tests, vaccines, and social restrictions have helped control its spread [3,4]. However, asymptomatic virus transmissions and emerging virus mutations pose ongoing challenges in dealing with the pandemic. Today, more than two years after the first case was detected, many countries worldwide continue to experience waves of rising infections, with numerous unknowns remaining in our understanding of SARS-CoV-2. In particular, consistent data about the role of sex in relation to COVID-19 are lacking [5,6]. Significant changes in physiological parameters such as breathing rate, heart rate, heart rate variability, and wrist skin temperature during a COVID-19 infection [7] raise the question about sex-specific differences within the trajectory of these parameters. A better understanding of sex-specific trajectories in physiological responses to the infection may support early detection and treatment of COVID-19.

A meta-analysis found that men with COVID-19 were globally almost three times more likely than women to be admitted to an intensive treatment unit [8]. Furthermore, the disease’s mortality rates were higher in men [9], potentially due to sex-specific differences in angiotensin-converting enzyme 2 (ACE2) expression [10,11]. On the other hand, women were found to more frequently experience persistent symptoms such as dyspnoea and fatigue several months after the acute phase of the illness [12]. The infection rates were similar between the sexes [8], although this observation may differ between countries [13]. Moreover, initial analyses of eumenorrheic women’s susceptibility to SARS-CoV-2 among a real-world sample are in line with previously shown immune function fluctuations across the menstrual cycle [14] and suggest increased susceptibility during the luteal phase [15]. Research on sex-specific differences in immune responses that underlie COVID-19 disease outcomes showed higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes in male patients, whereas female patients showed higher T cell activation during SARS-CoV-2 infection [16]. Also, higher levels of innate immune cytokines were associated with worse disease progression in female patients [16].

Previous studies have shown that direct-to-consumer and easy-to-use products with wide market availability, such as Fitbit [17], smartwatches [18], the Ava bracelet [7,19], and other wearable devices [20] could be used for surveillance of changes in physiological parameters to give the user an early warning before COVID-19 symptom occurrence [21] or during asymptomatic infection [22]. The COVI-GAPP study investigated the applicability of the Ava bracelet for pre-symptomatic detection of COVID-19 [23]. Developed as a fertility tracker, the bracelet measures physiological parameters, including wrist skin temperature, breathing rate, heart rate, heart rate variability, and skin perfusion [24]. The previously published interim analysis of the COVI-GAPP dataset demonstrated significant changes in skin temperature, breathing rate, heart rate, and heart rate variability during a COVID-19 infection [7]. These parameters were used to develop a machine learning (ML) algorithm for the detection of pre-symptomatic SARS-CoV-2 infection, which successfully detected 68% of COVID-19 cases up to two days before symptom onset. The algorithm is currently being tested and validated in a larger population with real-time access to the algorithm’s predictions [19].

The current work analyzed the same physiological parameters collected in the COVI-GAPP study to quantify sex-specific differences before, during, and after a COVID-19 infection. We examined differences in trajectories of physiological parameters over five defined phases (baseline, incubation, pre-symptomatic, symptomatic, and recovery) between female and male participants. Furthermore, we evaluated the performance of our ML algorithm for female and male participants separately with the goal of assessing and correcting a potential sex bias in its functionality. Finally, we examined sex differences in antibody levels following COVID-19 to gain additional insights into sex-specific immune responses.

Materials and methods

The current study was based on the COVI-GAPP research initiative and included continuous monitoring of biophysical signals by means of a wearable device, the Ava bracelet, coupled with periodic blood tests to assess SARS-CoV-2 antibody titres. Additionally, a ML algorithm was developed based on the COVI-GAPP data to aid in the early detection of COVID-19. This section provides an overview of the methodology employed to address the study’s three primary objectives: 1) investigation of sex differences in COVID-19-related physiological parameters; 2) evaluation of the sex-specific performance of a ML algorithm for early COVID-19 detection; and 3) analysis of sex differences in antibody titres following COVID-19.

Study design and participants

Since 2010, the observational population-based Genetic and Phenotypic Determinants of Blood Pressure and Other Cardiovascular Risk Factors (GAPP) study aims to better understand the development of cardiovascular risk factors in the general population of healthy adults aged 25 to 41 years [25]. From 2170 GAPP participants, 1163 individuals were enrolled in the COVI-GAPP study with inclusion and exclusion criteria published previously [23]. Data were collected from April 14, 2020, until January 31, 2022. The local ethics committee (KEK, Zürich, Switzerland) approved the study protocol, and written informed consent was obtained from each participant prior to enrolment (BASEC 2020–00786).

Data collection

1. Ava bracelet

Physiological parameters of interest for this analysis were breathing rate, heart rate, heart rate variability, and wrist skin temperature. They were measured every 10 seconds by a wrist-worn bracelet while the user slept. If a minimum of 4 hours of relatively uninterrupted sleep is achieved, proprietary manufacturer algorithms are employed for pre-processing to eliminate artifacts, identify sleep stages, and provide the nightly physiological parameters. To mitigate potential fluctuations during transitions between wakefulness and sleep, the initial 90 and final 30 minutes of data from each night were excluded. Additionally, each physiological parameter underwent locally estimated scatterplot smoothing (LOESS) before analysis to reduce artificial fluctuations due to measurement errors, aligning with previously established best practices [26]. Further details on the applied data cleaning practices described by the manufacturer can be found in previous publications [7,27].

The CE-certified and FDA-cleared Ava Fertility Tracker (version 2.0; Ava AG, Switzerland) was originally built to detect ovulating women’s fertile days in real time with 90% accuracy [27–29]. The bracelet’s three sensors can track biophysical changes regardless of the wearer’s sex [7]. In the current study, they were used for detecting infection-based deviations from baseline parameters in both men and women (regardless of their menstruating status). In order to meet the European Union’s General Data Protection Regulation (GDPR) requirements on participant data, log-in procedure and data handling were performed with an anonymized email account. Participants synchronized their bracelets each morning upon waking to a complementary smartphone app. Participants were reviewed by a weekly compliance report showing synchronization rates. The study team contacted individuals to follow-up with log-in issues or operational challenges, therefore ensuring quality control.

In addition to automatically collected physiological data, participants also provided information in the complementary app about their daily alcohol, medication, and drug intake (for more information see Risch et al. [7]), as these substances can alter central nervous system functioning [30]. Furthermore, the app collected information about comorbidities that could potentially influence the physiological signals. Finally, the app provided a customized user functionality where participants reported COVID-19 symptoms in a daily diary. Participants were also able to see and monitor changes in their physiological parameters in the app.

2. SARS-CoV-2 antibody testing

SARS-CoV-2 antibody tests were performed by the medical laboratory Dr Risch Ostschweiz AG (Buchs SG, Switzerland) with an orthogonal test algorithm employing electrochemiluminescence (ECLIA) assays testing for pan-immunoglobulins directed against the N antigen (sensitivity of 96%, specificity of 99.9% for recognition of past SARS-CoV2 infection) and the receptor binding domain (RBD) of the SARS-CoV-2 spike protein (sensitivity of 97.6%, specificity of 99.8% for recognition of past SARS-CoV2 infection), as described by Schaffner et al. [31] and Weber et al. [32]. The enacted procedure ensures testing for actual SARS-CoV-2 infection independent of vaccine status. Baseline data were collected starting in April 2020 onwards (run 1; R1). Three follow-up blood samples (run 2, R2; run 3, R3; and run 4, R4) were collected within the scope of the study (Fig 1). The cut-off levels used for positive and negative values were ≥ 1.0 and ≤ 0.1, respectively. Values between 0.2–0.9 were considered as gray zone. Seroconversion was assumed if the first blood sample was negative for SARS-CoV-2 antibodies but a subsequent sample was positive. Follow-up calls with participants who tested positive were performed to discuss their symptoms and duration.

Fig 1 — The cut-off levels used for positive and negative values were ≥ 1.0 and ≤ 0.1, respectively. Values between 0.2–0.9 were considered as gray zone * Successful bracelet synchronization on more than 50% of days around symptom onset.

3. Questionnaires

When visiting the study centre for SARS-CoV-2 antibody tests, participants were asked to answer a questionnaire about their personal information (age, sex), smoking status (current, past, never), as well as symptoms and hospitalizations during COVID-19 infection. These visits occurred at approximately 6-month intervals across the duration of data collection. Body mass index (BMI) based on height and weight was calculated with data from the GAPP database.

Statistical analysis

Our primary objective was to examine sex differences in the trajectory of daily levels of the four physiological parameters across a SARS-CoV-2 infection (i.e., breathing rate in breaths per minute, skin temperature in degree Celsius, heart rate in beats per minute, heart rate variability). Heart rate variability was quantified as the ratio of low-frequency (0.04–0.15 Hz) to high-frequency (0.15–0.4 Hz) oscillations, as previously described [7]. Secondarily, we evaluated a machine learning algorithm designed for early detection of COVID-19 separately in male and female participants to examine potential sex biases in algorithm performance. Finally, we assessed sex-specific differences in antibody titres after SARS-CoV-2 infections. We processed and analysed all data using R (version 4.1.1) [33] and Python (version 3.6) [34].

1. Sex-specific differences in COVID-19 related physiological parameters

To examine the association between sex and physiological parameters during baseline, incubation, pre-symptomatic, symptomatic, and recovery phases of a COVID-19 infection, we applied multilevel linear mixed models with random intercepts and slopes including residual maximum likelihood estimation (REML) and Satterthwaite degrees of freedom. A multiplicative interaction term tested the association between sex and the infection phase. All signals measured more than 10 days before symptom onset via phone call confirmation with a study team member were categorized as occurring during the baseline period. The incubation period was defined as the time interval from 10 days up to 3 days before symptom onset. The pre-symptomatic period was defined as the two days before symptom onset, while the symptomatic period lasted from the day of symptom onset until the day symptoms ended. All signals measured after symptom end were categorized as occurring during the recovery period. We dummy-coded four variables to indicate the period within which the signal occurred, with the baseline serving as the reference period. Each of the four multilevel models was compared to the corresponding null model (i.e., an intercept-only model) by means of an ANOVA.

2. Sex-specific differences in algorithm’s performance

The retrospective ML algorithm, developed as described in previous papers [7,19] aimed to detect a COVID-19 infection prior to symptom onset. The algorithm was designed to ingest trends in physiological signals across sets of days to detect deviations in these signals and predict a potential infection. The model was trained to predict infection two days and one day prior to symptom onset, as well as on the day of symptom onset. Here, we assessed the algorithm’s performance metrics separately for males and females to identify any potential sex bias in the model. Performance metrics were calculated per day in participants who tested positive, where days from -40 to -2 relative to the onset of the first symptoms were considered negative and days from -2 to day 0 as positive. In other words, positive predictions of the algorithm prior to 2 days before symptom onset were interpreted as false positives. The set of metrics selected for the evaluation of the algorithm included precision (the number of true positives divided by the sum of true positives and false positives), recall (the number of true positives divided by the sum of true positives and false negatives), and F-score (the harmonic mean of precision and recall).

3. Sex-specific differences in antibody titres of SARS-CoV-2 Nucleocapsid after COVID-19 infection

To gain a deeper understanding of sex-specific differences in the immune system’s reaction to the virus, antibody trajectories were monitored during the study period. Antibody titres reflected the concentration of antibodies in the blood that are specific to the SARS-CoV-2 virus. To enable a reliable comparison of antibody titres after a COVID-19 infection, antibody titres (values > 1.0) against the SARS-CoV-2 Nucleocapsid were compared between the sexes. Blood was collected four times over the course of the study with varying sample sizes (Fig 1). Normally distributed variables were compared using unpaired t-tests, and non-normally distributed variables were compared using Mann-Whitney U tests.

Results

Participants

A total of 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females) were enrolled in the study. During the study period, 127 participants (10.9%; [9.3,12.8]) contracted COVID-19. Eighty-two participants (mean age = 42.6 ± 5.3 years; 56 [68%] females) testing positive for SARS-CoV-2 had worn and synchronized their bracelet successfully on more than 50% of days around symptom onset (i.e., at least 20 days before and 20 days after symptom onset), thereby ensuring sufficient quality of data to be included in analyses. The number of days with successfully synchronized bracelet data did not differ (p = 0.967) between females (range 67 to 511 days; mean = 239.6 ± 71.8 days) and males (range 45 to 508 days; mean = 238.8 ± 86.4 days). With regards to the reported symptom duration, values for four participants (2 females) were missing and imputed based on the median across the sample.

Blood samples and questionnaire data were available from 1,144 participants. The mean age and BMI of these participants were 45 (± 5.5) and 24.7 (± 3.9), respectively. At baseline, male participants had significantly higher BMIs (26.17 ± 3.41) than female participants (23.70 ± 3.96; t(1079) = 10.71, p<0.001). They also reported significantly higher rates of hypertension (7.74%) than female participants (3.15%; X²(1) = 11.23, p<0.001). Analyses did not reveal any significant sex-based differences in smoking status, age, or hospitalization rate (Table 1).

Table 1. Sex differences in baseline characteristics.

Variables	Total n = 1,144	Male n = 478	Female n = 666	Test statistics	Significance (p value)
Smoking status, N (never: current: past smoker)	658: 167: 319	265: 68: 145	393: 99: 174	Χ2 (2) = 2.46	0.292
Hypertension, N (yes: no)	58: 1086	37: 441	21: 645	Χ2 (1) = 11.23	<0.001
Age, years (±SD)	43.99 (± 5.51)	44.3 (±5.35)	43.77 (±5.61)	t (1057) = 1.53	0.1449
BMI, kg/m2 (±SD)	24.72 (±3.94)	26.17 (±3.41)	23.7 (±3.96)	t (1079) = 10.71	<0.001
Hospitalization 01, N (yes: no)	0:10	0:4	0:6	Fisher’s exact test	1
Hospitalization 02, N (yes: no)	11:113	7:44	4:69	Χ2 (1) = 2.52	0.2047
Hospitalization 03, N (yes: no)	2:23	0:12	2:11	Χ2 (1) = 0.46	0.4973
Hospitalization 04, N (yes: no)	3:47	0:24	3:23	Χ2 (1) = 1.25	0.2625

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Baseline characteristics stratified according to sex were collected by questionnaires completed within the GAPP study. Information about hospitalization was collected four times (01–04) in the scope of the study centre visit for SARS-CoV-2 antibody tests. It was a part of the questionnaire for SARS-CoV-2 positive participants and represented a measure of disease severity. Data are presented as mean ± SD or number. The test statistic and the corresponding p-value are shown for the comparison between the female and male group for each variable.

Sex-specific differences in COVID-19 related physiological parameters

We show the trajectory of each of the four analysed physiological parameters during a SARS-CoV-2 infection separated by sex (Fig 2). The multilevel models revealed significant differences between male and female participants in all parameters during the symptomatic period (Table 2). We observed a larger increase in skin temperature, breathing rate, and heart rate, as well as a larger decrease in heart rate variability in males compared to females during this period. Moreover, male participants’ breathing rate and heart rate remained at significantly higher levels during the recovery period as compared to their female peers (Table 2). Each of the four models provided a significantly better fit to the data than the corresponding null model (p<0.0001).

Table 2. Results from multilevel linear mixed models showing the main effects of infection phase and sex as well as the interactions between the two with regards to changes in physiological signals.

	Skin temperature (degree Celsius)	Breathing rate (breaths per minute)	Heart rate (beats per minute)	Heart rate variability
Intercept	35.01 (<0.0001)	13.51 (<0.0001)	46.98 (<0.0001)	4.3 (<0.0001)
Infection phase
Baseline	Reference	Reference	Reference	Reference
Incubation	0.18 (0.15)	0.33 (0.13)	1.49 (0.12)	-0.25 (0.12)
Pre-symptomatic	0.23 (0.26)	0.71 (0.17)	1.26 (0.41)	-0.18 (0.42)
Symptomatic	0.74 (<0.0001)	2.93 (<0.0001)	6.88 (<0.0001)	-0.93 (<0.0001)
Recovery	0.22 (0.0006)	0.38 (0.004)	2.17 (0.003)	-0.28 (0.09)
Sex, female	0.45 (<0.0001)	0.91 (0.06)	4.96 (0.001)	-1.35 (<0.0001)
Interaction
Sex*Incubation	-0.02 (0.74)	-0.2 (0.11)	-0.36 (0.5)	0.09 (0.34)
Sex*Pre-symptomatic	-0.01 (0.92)	-0.26 (0.38)	0.07 (0.93)	0.04 (0.78)
Sex*Symptomatic	-0.28 (<0.0001)	-1.31 (<0.0001)	-3.09 (0.0001)	0.43 (<0.0001)
Sex*Recovery	-0.04 (0.23)	-0.25 (0.001)	-0.96 (0.02)	0.11 (0.25)

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Unstandardized beta coefficients are presented, with p-values in parentheses and in bold if lower than 0.05. Sex was coded such that positive coefficients represent larger values in females.

As a sensitivity analysis, we also tested potentially confounding variables as single terms in additional models to determine whether changes in physiological parameters occurred due to COVID-19 infection over and above changes associated with participant age, BMI, hypertension, medication, alcohol, and recreational drugs. These variables were selected based on previously reported associations with physiological signals [35]. Furthermore, we observed sex differences in BMI and hypertension in the current sample (Table 1) and, therefore, examined in the sensitivity analysis whether these effects can account for the sex differences found in the main analysis. Hypertension, medication and recreational drug intake were binary variables (i.e., yes/no), while alcohol intake was represented through four categories (i.e., none/1-2 drinks/3-4 drinks/5+ drinks with none as the reference category). As outlined in the main analysis, four multilevel models were computed (i.e., one for each physiological parameter) additionally including the described variables as fixed effects. In these models, the interactions between sex and phase of infection remained unchanged, indicating that they cannot be explained by the influence of the added variables (S1 Table).

Sex-specific differences in algorithm’s performance

Table 3 provides a by-sex breakdown of the algorithm’s performance. Sensitivity score can be found as the recall of the positive class (days with an existent SARS-CoV2 infection), while specificity is the recall of the negative class (days without a SARS-CoV2 infection). The algorithm showed the same precision (i.e., 92) when giving a SARS-CoV2 positive alert across participant sex. Cross-class recall was more balanced among females than males in our sample. Detecting 53% of SARS-CoV-2 positive days in females, the algorithm performed less well in males (26% of SARS-CoV2 positive cases detected).

Table 3. Performance metrics of the machine learning algorithm for female and male participants.

Participant Sex	Class	Precision	Recall	F-score
All	0	12.36	68.421	19.048
All	1	91.599	41.509	78.331
Female	0	12.977	60.69	20.859
Female	1	92.147	53.125	73.181
Male	0	10.811	80.0	15.385
Male	1	92.308	26.667	85.714

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Sensitivity score can be found as the recall of the positive class (i.e., days with an existent SARS-CoV2 infection), while specificity is the recall of the negative class (i.e., days without a SARS-CoV2 infection).

Sex-specific differences in antibody titres of SARS-CoV-2 Nucleocapsid after COVID-19 infection

Antibody titres of the female and male sub-groups were not significantly different across runs. Nucleocapsid antibody values in run 1 trended higher in female participants (Table 4).

Table 4. SARS-CoV-2 Nucleocapsid (N) antibody (AB) values stratified according to sex.

Variables	Male (n = 7)	Female (n = 7)	Test statistics	Significance (p value)
SARS-CoV-2 N AB run1	17.7 (5.8–83.5)	54.8 (5.7–135.1)	W = 13	0.14
	Male (n = 51)	Female (n = 68)
SARS-CoV-2 N AB run2	34.1 (1–183.7)	33.4 (1.7–212.2)	W = 1753	0.92
	Male (n = 62)	Female (n = 85)
SARS-CoV-2 N AB run3	40.05 (1–274)	29.7 (1.4–234.3)	W = 2772	0.59
	Male (n = 76)	Female (n = 102)
SARS-CoV-2 N AB run4	17.95 (1.2–221)	36.59 (1–266.4)	W = 4280	0.235

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Data are presented as median and interquartile range.

Discussion

The presented study examined sex-specific differences in physiological parameters among 82 individuals with a documented SARS-CoV-2 infection. We found that male participants experienced significantly larger increases in wrist skin temperature, breathing rate and heart rate as well as larger decreases in heart rate variability during the symptomatic period compared to females. In one of the first prospective cohort studies relying on wearable sensor technology to collect real-time continuous physiological signals, we provide evidence for sex-based differential physiological responses to COVID-19.

Considering the higher mortality and hospitalization rates observed in male COVID-19 patients [9], our findings may reflect sex-specific biological responses to the infection. In line with previous work [16], we did not observe any differences between the sexes with regard to antibody titers. However, Takahashi et al. [16] observed a stronger acute T-cell response in females as compared to male COVID-19 patients. The poorer T-cell response in men was associated with their worse disease progression. On the other hand, the authors measured higher levels of several pro-inflammatory innate immunity chemokines and cytokines in men as compared to women. They thus concluded that the early phase of COVID-19 is associated with key sex differences in immunological mechanisms potentially accounting for the differential disease progression between women and men.

Given that the sex differences in physiological signals in our study are most pronounced during the symptomatic phase, we propose that they reflect the above-mentioned sex-specific immunological mechanisms [36]. Inflammatory markers (e.g., cytokines) have been shown to reflect disease severity in COVID-19 [37]. As the autonomic nervous system is known to modulate inflammation [38] and the examined physiological signals reflect the function of the autonomic nervous system [39], our findings suggest support for differential immunological responses to COVID-19 between the sexes.

Importantly, altered physiological signals such as decreased heart rate variability and increased skin temperature have been proposed as prognostic markers for several disorders, including cardiovascular disease [40] as well as infectious diseases like COVID-19 [18,41–43]. Modern wearable technology represents a unique and powerful framework to collect continuous real-time physiological data. The predictive value of physiological signals combined with the reliable history of measurements provided by wearables opens up new avenues to inform clinical actions and support future precision medicine approaches incorporating a variety of individual factors into clinical decisions (reviewed in Mitratza et al. [44]).

An important step towards precision medicine can be made by considering sex differences in modern digital health solutions. Historically, women have been underrepresented in clinical trials, leading to medical solutions focusing on men at the risk to women’s health [45]. Many diseases differ between female and male patients with regard to the prevalence, progression, or response to treatment [46]. For example, minor stroke is more often missed in female than male [47] patients, possibly due to definitions in clinical diagnosis reflecting typical manifestations in males [43]. More recently, a sex bias has been recognized in modern ML solutions that are often developed and trained on male data and thus result in better performance in men [48]. Therefore, in the presented work, we examined sex differences in the performance of our ML algorithm for early detection of COVID-19. The algorithm reached a higher sensitivity for female participants. We postulate this difference may be due to the larger sample size in the female group. However, the algorithm’s precision was the same in both groups, indicating that it is suitable for use in both men and women, as intended.

Limitations

While our study belongs to the first research to consider sex-based differences in COVID-19 detection using digital health, future work could continue to build upon our findings by examining the casual mechanism underlying differences between SARS-Cov-2 infected men and women. In particular, the inability to disentangle immunological versus menstrual-driven changes in physiological parameters among female participants limits our research’s generalizability. In menstruating women, a specific pattern has been recognized in the trajectory of physiological signals across the menstrual cycle, mirroring cycle-based shifts in sex hormones [27]. Particularly during the follicular phase of the menstrual cycle, decreased skin temperature, heart rate and breathing rate have been observed, while heart rate variability was increased. In contrast, the luteal phase was associated with increases in skin temperature, heart rate and breathing rate as well as decreases in heart rate variability, corresponding to the pattern found in COVID-19 patients. Sex differences in physiological signals measured in the current study may thus partly be due to hormonal impact. We cannot exclude such influence as we had limited information about female participants’ menstrual cycle or reproductive health (e.g., usage of hormonal birth control menopausal status). Future researchers may wish to record participants’ menstrual status and measure hormone levels directly, to probe the relationship between sex hormones and physiological differences.

Nevertheless, we believe that menses-driven changes in physiology do not adequately explain the sex differences in our results, as the dynamics of the observed physiological signals are in line with previous reports regarding COVID-19 and include increased skin temperature, heart rate and breathing rate as well as decreased heart rate variability during infection [20]. Additionally, the most pronounced sex differences in our study occurred during the symptomatic period, suggesting a disease-triggered disparity among males and females. Furthermore, hormonal influence offers a plausible explanation only in the first half of the menstrual cycle. The physiological changes observed in its second half could only amplify the trajectory found during COVID-19 in females and thus mask the sex differences in our study. Moreover, the magnitude of physiological changes during COVID-19’s symptomatic phase in the current study is, for all parameters, more than twice as large as the previously reported magnitude of changes across the menstrual cycle [27]. For example, we found that skin temperature increases by 0.7 degrees during the symptomatic phase of COVID-19, whereas this measure’s largest increase during the menstrual cycle is 0.2 degrees during the late luteal phase [27]. Of note, 30% (n = 17) of females in the sample were older than 45 years; peri- or post-menopausal, they were beyond natural reproductive age and thus unlikely to experience menses-modulating effects on their physiological parameters. Finally, we do not expect that the distribution of menstrual cycle phases follows a specific pattern for our participants (e.g., in complete synchronicity); rather, we expect each eumenorrheic woman to cycle on her own timeline and the alignment of menstrual phases between participants to occur at random. Taken together, we believe that the hormonal impact on our findings is minimal.

Another limitation important to note is the potential effect of recall bias on our findings. The COVID-19 symptom onset date was determined based on the participants’ retrospective reports, and the classification of the relevant infection periods (i.e., incubation, pre-symptomatic and symptomatic period) was based on this date. Therefore, an unreliable report would be associated with an inaccurate definition of the infection periods leading to shifts in trajectories of physiological signals. Furthermore, in the effort to smooth the data in the model, the abrupt changes in physiological signals after infection generated gradual alterations in the estimated trajectory. The deviations from the baseline during the first and last days may be reflective of such model artifacts (Fig 2). Finally, it is important to note that we did not adjust any parameters from our statistical tests to account for multiple testing. Therefore, we acknowledge chances for type 1 error in our findings. Nevertheless, we believe that our research provides important initial insights to be confirmed in future investigations. Furthermore, upcoming research should explore the mechanisms behind these sex differences, including the roles of sex hormones, genetic factors, and immune responses. Finally, the development of sex-specific treatment strategies, leveraging the insights gained from our study, holds potential for improved patient care and outcomes.

Conclusion

Our study demonstrates sex differences in physiological responses to COVID-19. The results highlight the importance of taking sex into account in medical treatment and care of COVID-19 patients, as well as when validating infection detection algorithms in digital health. Moreover, we reveal the potential of continuous real-time physiological signals as a clinical tool to inform future precision medicine approaches. Wearable technology, capable of providing a reliable history of measurements, can empower clinicians with invaluable insights into individual patient health, enabling more personalized and timely interventions that hold promise for improved patient outcomes in the fight against COVID-19 and beyond.

Supporting information

S1 Table. Results from multilevel linear mixed models showing the main effects of infection phase, sex, age, medication, drug and alcohol intake, BMI, and hypertension, as well as interactions between sex and infection phase with regards to changes in physiological signals.

(DOCX)

pone.0292203.s001.docx^{(27.8KB, docx)}

Acknowledgments

We thank the GAPP participants who enrolled in this study. Additionally, the authors thank the following for their contributions to the study: The local study team in Vaduz, FL, the different teams at the Dr Risch medical laboratories in Vaduz and Buchs, CH. We would also like to thank the Coobx AG in Balzers, FL, for the provision of 3D printed bracelet extensions for persons with large wrists. Addressing data protection issues, we acknowledge the substantial collaborative support of the Elleta AG as well as the national data protection agency in Liechtenstein. We thank the government of the Principality of Liechtenstein, the health ministers, and the Liechtenstein Office of Public Health for their support. Finally, our thanks are especially due to the Princely House of Liechtenstein, which gave decisive support that enabled the initiation of this project.

List of the members of the COVID-19 remote early detection (COVID-RED) consortium

* Lorenz Risch is lead author of this group (lorenz.risch@risch.ch)

First name	Surname	Organization
Andjela	Markovic	Ava AG, Switzerland
Maja	Rudinac	Ava AG, Switzerland
Maureen	Cronin	Ava AG, Switzerland
Vladimir	Kovacevic	Ava AG, Switzerland
Kirsten	Grossmann	Dr. Risch Anstalt, Liechtenstein
Lorenz	Risch*	Dr. Risch Anstalt, Liechtenstein
Martin	Risch	Dr. Risch Anstalt, Liechtenstein
Ornella	Weideli	Dr. Risch Anstalt, Liechtenstein
Billy	Franks	Julius Clinical, The Netherlands
Brianna	Goodale	Julius Clinical, The Netherlands
Ellen	Dutman	Julius Clinical, The Netherlands
Eric	Houtman	Julius Clinical, The Netherlands
Glenn	Van Wigcheren	Julius Clinical, The Netherlands
Hans	Van Dijk	Julius Clinical, The Netherlands
Ishak	Elmouhajir	Julius Clinical, The Netherlands
Jeffrey	Burggraaff	Julius Clinical, The Netherlands
Jon	Bouwman	Julius Clinical, The Netherlands
José	Broersen	Julius Clinical, The Netherlands
Jungyeon	Choi	Julius Clinical, The Netherlands
Kai	Hage	Julius Clinical, The Netherlands
Lotte	Smets	Julius Clinical, The Netherlands
Maartje	Hoffmann	Julius Clinical, The Netherlands
Marcel	van Willigen	Julius Clinical, The Netherlands
Marjolein	Jansen	Julius Clinical, The Netherlands
Myrna	Verhulst	Julius Clinical, The Netherlands
Niki	de Vink	Julius Clinical, The Netherlands
Paul	Klaver	Julius Clinical, The Netherlands
Pieter	van der Meer	Julius Clinical, The Netherlands
Tessa	Heikamp	Julius Clinical, The Netherlands
Timo	Brakenhoff	Julius Clinical, The Netherlands
Titia	Leurink	Julius Clinical, The Netherlands
Wendy	van Scherpenzeel	Julius Clinical, The Netherlands
Wout	Aarts	Julius Clinical, The Netherlands
Alison	Kuchta	Roche, The Netherlands
Christian	Simon	Roche, The Netherlands
Santiago	Montes	Roche, The Netherlands
Aren	Boogaard	Sanquin, The Netherlands
Florine	van Milligen	Sanquin, The Netherlands
Floris	Loeff	Sanquin, The Netherlands
Jim	Keijser	Sanquin, The Netherlands
Lea	Berkhout	Sanquin, The Netherlands
Maurice	Steenhuis	Sanquin, The Netherlands
Nadine	Commandeur	Sanquin, The Netherlands
Olvi	Christianawati	Sanquin, The Netherlands
Sofie	Keijzer	Sanquin, The Netherlands
Theo	Rispens	Sanquin, The Netherlands
Ariel	Dowling	Takeda, USA
Steve	Emby	Takeda, USA
Charisma	Hehakaya	University Medical Center Utrecht, The Netherlands
Daniel	Oberski	University Medical Center Utrecht, The Netherlands
George	Downward	University Medical Center Utrecht, The Netherlands
Gulseren	Yalvac	University Medical Center Utrecht, The Netherlands
Hans	Reitsma	University Medical Center Utrecht, The Netherlands
Janneke	Wijgert, van de	University Medical Center Utrecht, The Netherlands
Marianna	Mitratza	University Medical Center Utrecht, The Netherlands
Nathalie	Vigot	University Medical Center Utrecht, The Netherlands
Patricia	Bruijning	University Medical Center Utrecht, The Netherlands
Pieter	Stolk	University Medical Center Utrecht, The Netherlands
Rick	Grobbee	University Medical Center Utrecht, The Netherlands
Amos	Folarin	University College London, UK
Johann	Fevrier	University College London, UK
Pablo	Fernandez Medina	University College London, UK
Richard	Dobson	University College London, UK
Spiros	Denaxas	University College London, UK
Eskild	Fredslund	VIVE, Denmark
Jesper	Strømstad	VIVE, Denmark
Serkan	Korkmaz	VIVE, Denmark

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Data Availability

An anonymized version of the datasets generated in the COVID-RED trial as well as supporting documentation are publicly available through DataverseNL at the following link (doi:10.34894/FW9PO7). Further data that underlie the results reported in this paper were collected from study participants from the Principality of Liechtenstein, a very small country, where the risk of subject identification is increased due to the size of the population (less than 40’000 inhabitants). To respect data protection and to prevent the identification of participants, data access is restricted to researchers meeting the criteria for access to confidential data. Data are available from (contact: lorenz.risch@ufl.li, martin.risch@ksgr.ch, and david.conen@phri.ca). Further, the data underlying the results presented in the study are available from (Private University of the Principality of Liechtenstein, Institutional Review Board, 9495 Triesen; irb@ufl.li).

Funding Statement

This work has received support from the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, the Hanela Foundation in Switzerland, and the Innovative Medicines Initiative (IMI) 2 Joint Undertaking under grant agreement No 101005177. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0292203.r001

Decision Letter 0

Ramada Rateb Khasawneh

9 Oct 2023

PONE-D-23-28587Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)PLOS ONE

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Additional Editor Comments:

the paper is a good paper, but may I ask you to submit the row data

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Reviewers' comments:

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Overall, the study is very well organised and provides a clear and comprehensive overview of the background, research gap, and objectives of the study. The results are clearly stated and discussed. However, I have added a few suggestive comments along with the original manuscript file to be addressed for clarity. I suggested few grammatical and sentence composition suggestions as well using tracked changes, the authors can approve if they find appealing. Please refer to the original file for the comments.

Reviewer #2: 1. I would suggest removing funding sources from the Abstract section.

2. Table 4, first row, looking at the mean and SD of 32.66 (±34.74), clearly it doesn't follow the Gaussian distribution. Consider representing the data in median and IQR as the others.

Reviewer #3: The article followed the right research and Public ethics. But my question: Is the underlying cardiovascular conditions of the patients play a role in generating Sex-specific differences in physiological parameters relating to the SARS-CoV-2 infections

among a research participants? I need some explanation on this

**********

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Reviewer #1: No

Reviewer #2: Yes: Dr. Abdurrahman Ahmad El-fulaty

Reviewer #3: Yes: Emmanuel Adamolekun

**********

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Attachment

Submitted filename: Manuscript_sex_differences_health_parameters_20230910-20231002-AA.docx

pone.0292203.s002.docx^{(162.1KB, docx)}

PLoS One. 2024 Mar 6;19(3):e0292203. doi: 10.1371/journal.pone.0292203.r002

Author response to Decision Letter 0

18 Dec 2023

See document 'Response to Reviewers_20231123' for our answers to the reviewers.

Attachment

Submitted filename: Response to Reviewers_20231123.docx

pone.0292203.s003.docx^{(67.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0292203.r003

Decision Letter 1

Ramada Rateb Khasawneh

5 Jan 2024

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

PONE-D-23-28587R1

Dear Dr. Risch,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Ramada Rateb Khasawneh

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Nice Paper ......Good Luck

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Dr. Abdurrahman Ahmad El-fulaty

**********

PLoS One. doi: 10.1371/journal.pone.0292203.r004

Acceptance letter

Ramada Rateb Khasawneh

14 Feb 2024

PONE-D-23-28587R1

PLOS ONE

Dear Dr. Risch,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

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If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ramada Rateb Khasawneh

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

(DOCX)

pone.0292203.s001.docx^{(27.8KB, docx)}

Attachment

Submitted filename: Manuscript_sex_differences_health_parameters_20230910-20231002-AA.docx

pone.0292203.s002.docx^{(162.1KB, docx)}

Attachment

Submitted filename: Response to Reviewers_20231123.docx

pone.0292203.s003.docx^{(67.4KB, docx)}

Data Availability Statement

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PERMALINK

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Kirsten Grossmann

Martin Risch

Andjela Markovic

Stefanie Aeschbacher

Ornella C Weideli

Laura Velez

Marc Kovac

Fiona Pereira

Nadia Wohlwend

Corina Risch

Dorothea Hillmann

Thomas Lung

Harald Renz

Raphael Twerenbold

Martina Rothenbühler

Daniel Leibovitz

Vladimir Kovacevic

Paul Klaver

Timo B Brakenhoff

Billy Franks

Marianna Mitratza

George S Downward

Ariel Dowling

Santiago Montes

Duco Veen

Diederick E Grobbee

Maureen Cronin

David Conen

Brianna M Goodale

Lorenz Risch

Roles

Abstract

Introduction

Materials and methods

Study design and participants

Data collection

1. Ava bracelet

2. SARS-CoV-2 antibody testing

Fig 1. Study flow chart of the 1,163 participants that are enrolled in the COVI-GAPP study.

3. Questionnaires

Statistical analysis

1. Sex-specific differences in COVID-19 related physiological parameters

2. Sex-specific differences in algorithm’s performance

3. Sex-specific differences in antibody titres of SARS-CoV-2 Nucleocapsid after COVID-19 infection

Results

Participants

Table 1. Sex differences in baseline characteristics.

Sex-specific differences in COVID-19 related physiological parameters

Fig 2. Trajectory of the four analysed physiological parameters across the course of a confirmed COVID-19 infection centred around participant-reported symptom onset.

Table 2. Results from multilevel linear mixed models showing the main effects of infection phase and sex as well as the interactions between the two with regards to changes in physiological signals.

Sex-specific differences in algorithm’s performance

Table 3. Performance metrics of the machine learning algorithm for female and male participants.

Sex-specific differences in antibody titres of SARS-CoV-2 Nucleocapsid after COVID-19 infection

Table 4. SARS-CoV-2 Nucleocapsid (N) antibody (AB) values stratified according to sex.

Discussion

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Ramada Rateb Khasawneh

Roles

Author response to Decision Letter 0

Decision Letter 1

Ramada Rateb Khasawneh

Roles

Acceptance letter

Ramada Rateb Khasawneh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES