COVIDWAVENCT04357834
WAVE. Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories
An observational study of COVID and COVID 19, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
Trial summary
The aim is to develop a wearable-based ICU (intensive care unit) prediction algorithm for inpatients contracted with SARS-CoV-2. Inpatients with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. Read more about this study
- Who is it for?
- Ages 18 to 120 years
- Conditions the study is for
- COVID, COVID 19, SARS-CoV 2
- What is being tested?
- Equipment with smartwatch throughout hospital stay on the general ward
- How many people
- 46 participants
- Where
- 1 siteSwitzerland
- Study dates
- 22 Oct 2020to 30 Jun 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 120 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with suspicion of COVID-19 or tested positive for SARS-CoV-2 that are or will be hospitalized on the general ward
Who may be able to join
- Informed consent as documented by signature
- Age >= 18 years
- Suspicion of COVID-19 or patient tested positive for SARS-CoV-2
- Hospitalisation on the general ward
Who cannot join
- Smartwatch cannot be attached around the wrist of the patient
- Direct transfer from the emergency department or external institution to ICU (i.e. no hospitalization on the general ward)
- Known allergies to components of the smartwatch
- Rejection of ICU transfer in the patient decree
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
Emergency Department, University Hospital Bern, Inselspital
Bern, 3010
About this study
Official title: Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories. The WAVE Study.
The aim is to develop a wearable-based ICU (intensive care unit) prediction algorithm for inpatients contracted with SARS-CoV-2. Inpatients with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participants will be equipped with a smartwatch, which gathers physiological data throughout hospitalisation.
Read the detailed description
The SARS-CoV-2 pandemic puts an unprecedented burden on the healthcare system, specifically its healthcare providers and the resource demands for intensive care units (ICUs). To support effective care despite large case numbers, hospital operations urgently need improved decision support in early identification of patients at risk of an acute COVID-19 deterioration that requires ICU.
The investigators aim at developing a wearable-based ICU algorithm for inpatients contracted with SARS-CoV-2. Inpatients on the general ward with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participant will be equipped with a smartwatch and wear the device throughout the hospital stay until the patient (1) is discharged home, (2) is transferred to the ICU, or (3) palliative care is initiated. The smartwatch collects several physiological parameters (e.g. heart rate, heart rate variability, respiration rate, oxygen saturation). The collected data will be used to develop an ICU prediction algorithm to detect patients at risk of an acute COVID-19 deterioration that requires ICU.
- Conditions
- COVID, COVID 19, SARS-CoV 2
- Being tested
- Equipment with smartwatch throughout hospital stay on the general ward
- Keywords
- Smartwatch, Intensive care, Intensive care unit, Prediction, Artificial intelligence, Clinical trajectories
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Equipment with smartwatch throughout hospital stay on the general wardOther
Participants with confirmed SARS-CoV-2 infection or suspicion of COVID-19 will be equipped with a smartwatch and wear the device throughout the hospital stay on the general ward.
| Study group | Role | What they receive |
|---|---|---|
| Smartwatch group | Not described Receives: Other: Equipment with smartwatch throughout hospital stay on the general ward |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of smartwatch data in predicting ICU requirement in COVID-19 contracted inpatients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
Accuracy of the WAVE-model will be assessed using physiological data recorded by the smartwatch (Garmin vivoactive 4) during hospitalization complemented by demographic and health-related patient-information and will be analysed using applied machine learning technology for ICU prediction.
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Secondary outcomes (20)
Diagnostic accuracy of routine physiological data in predicting ICU requirement in COVID-19 contracted in-patients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
Accuracy of the model will be assessed using physiological data routinely recorded during hospitalization and will be analysed using applied machine learning technology for ICU prediction.
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Diagnostic accuracy of predicting hospital discharge without ICU admission in COVID-19 contracted in-patients quantified as area under the receiver operator characteristics curve
Accuracy of the model will be assessed using data on comorbidities, medication treatment during hospitalization and physiological data and will be analysed using casual machine-learning approaches
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of heart rate from baseline (hospitalization) to ICU admission
Heart rate will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S) and using routine medical monitors.
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of heart rate variability from baseline (hospitalization) to ICU admission
Heart rate variability will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of skin temperature from baseline (hospitalization) to ICU admission
Skin temperature will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of blood oxygen saturation from baseline (hospitalization) to ICU admission
Blood oxygen saturation will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S) and using routine medical monitors
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of respiration rate from baseline (hospitalization) to ICU admission
Respiration rate will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S) and using routine medical monitors
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of physical activity from baseline (hospitalization) to ICU admission
Physical activity will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of stress level from baseline (hospitalization) to ICU admission
Stress level will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of sleep pattern from baseline (hospitalization) to ICU admission
Sleep will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of steps per day from baseline (hospitalization) to ICU admission
Steps per day will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S)
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of systolic blood pressure from baseline (hospitalization) to ICU admission
Systolic blood pressure will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S) and using routine medical monitors
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of diastolic blood pressure from baseline (hospitalization) to ICU admission
Diastolic blood pressure will be recorded throughout the hospitalization using a smartwatch (Garmin vivoactive 4S) and using routine medical monitors
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of body temperature from baseline (hospitalization) to ICU admission
Body temperature will be recorded throughout the hospitalization using a routine medical thermometer
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of oxygen partial pressure (pO2) from baseline (hospitalization) to ICU admission
Oxygen partial pressure (pO2) will be routinely assessed during the hospitalization in arterial or venous blood gas analyses
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of CO2 partial pressure (pCO2) from baseline (hospitalization) to ICU admission
CO2 partial pressure (pCO2) will be routinely assessed during the hospitalization in arterial or venous blood gas analyses
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of blood pH from baseline (hospitalization) to ICU admission
Blood pH will be routinely assessed during the hospitalization in arterial or venous blood gas analyses
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of bicarbonate from baseline (hospitalization) to ICU admission
Bicarbonate will be routinely assessed during the hospitalization in arterial or venous blood gas analyses
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of base excess from baseline (hospitalization) to ICU admission
Base excess will be routinely assessed during the hospitalization in arterial or venous blood gas analyses
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of oxygen flow rate from baseline (hospitalization) to ICU admission
Oxygen flow rate will be routinely assessed during the hospitalization
Measured: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Apr 2020
- Study start22 Oct 2020
- Primary completion30 Jun 2021
- Study completion30 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for COVIDStudies of the same condition, open and under way.
- More trials run by Insel Gruppe AG, University Hospital BernThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 46 participants (actual)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- ETH Zurich
- Responsible party
- Sponsor
- Investigators
- Aristomenis Extradaktylos, Prof. MD, Study chair, University Hospital Bern - Department of Emergency Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04357834
- Sponsor's protocol ID
- WAVE
- Acronym
- WAVE
- Status last verified
- Aug 2021
- First submitted
- 17 Apr 2020
- Last updated
- 26 Aug 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Foll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Zuger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. JMIR Form Res. 2022 Jun 21;6(6):e35717. doi: 10.2196/35717.
PubMed 35613417
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories. The WAVE Study.
- ClinicalTrials.gov ID
- NCT04357834
- Acronym / sponsor ID
- WAVE / WAVE
- Conditions
- COVID, COVID 19, SARS-CoV 2
- Keywords
- Smartwatch, Intensive care, Intensive care unit, Prediction, Artificial intelligence, Clinical trajectories
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 46 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- ETH Zurich
- Study start
- 22 Oct 2020 (actual)
- Primary completion
- 30 Jun 2021 (actual)
- Study completion
- 30 Jun 2021 (actual)
- First posted
- 22 Apr 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The aim is to develop a wearable-based ICU (intensive care unit) prediction algorithm for inpatients contracted with SARS-CoV-2. Inpatients with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participants will be equipped with a smartwatch, which gathers physiological data throughout hospitalisation.
Detailed description
The SARS-CoV-2 pandemic puts an unprecedented burden on the healthcare system, specifically its healthcare providers and the resource demands for intensive care units (ICUs). To support effective care despite large case numbers, hospital operations urgently need improved decision support in early identification of patients at risk of an acute COVID-19 deterioration that requires ICU.
The investigators aim at developing a wearable-based ICU algorithm for inpatients contracted with SARS-CoV-2. Inpatients on the general ward with suspicion of COVID-19 or with confirmed SARS-CoV-2 infection will be included. The participant will be equipped with a smartwatch and wear the device throughout the hospital stay until the patient (1) is discharged home, (2) is transferred to the ICU, or (3) palliative care is initiated. The smartwatch collects several physiological parameters (e.g. heart rate, heart rate variability, respiration rate, oxygen saturation). The collected data will be used to develop an ICU prediction algorithm to detect patients at risk of an acute COVID-19 deterioration that requires ICU.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Smartwatch group | – | Other: Equipment with smartwatch throughout hospital stay on the general ward |
Who may qualify
- Ages
- 18 to 120 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with suspicion of COVID-19 or tested positive for SARS-CoV-2 that are or will be hospitalized on the general ward
Key inclusion criteria (4 listed)
- Informed consent as documented by signature
- Age >= 18 years
- Suspicion of COVID-19 or patient tested positive for SARS-CoV-2
- Hospitalisation on the general ward
Key exclusion criteria (4 listed)
- Smartwatch cannot be attached around the wrist of the patient
- Direct transfer from the emergency department or external institution to ICU (i.e. no hospitalization on the general ward)
- Known allergies to components of the smartwatch
- Rejection of ICU transfer in the patient decree
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Equipment with smartwatch throughout hospital stay on the general wardParticipants with confirmed SARS-CoV-2 infection or suspicion of COVID-19 will be equipped with a smartwatch and wear the device throughout the hospital stay on the general ward. | Other | Smartwatch group |
Primary outcome
Diagnostic accuracy of smartwatch data in predicting ICU requirement in COVID-19 contracted inpatients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Accuracy of the WAVE-model will be assessed using physiological data recorded by the smartwatch (Garmin vivoactive 4) during hospitalization complemented by demographic and health-related patient-information and will be analysed using applied machine learning technology for ICU prediction.
20 secondary outcomes
Diagnostic accuracy of routine physiological data in predicting ICU requirement in COVID-19 contracted in-patients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85).
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Diagnostic accuracy of predicting hospital discharge without ICU admission in COVID-19 contracted in-patients quantified as area under the receiver operator characteristics curve
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of heart rate from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of heart rate variability from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of skin temperature from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of blood oxygen saturation from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of respiration rate from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of physical activity from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of stress level from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of sleep pattern from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of steps per day from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of systolic blood pressure from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of diastolic blood pressure from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of body temperature from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of oxygen partial pressure (pO2) from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of CO2 partial pressure (pCO2) from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of blood pH from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of bicarbonate from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of base excess from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Change of oxygen flow rate from baseline (hospitalization) to ICU admission
Time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Aug 2021.
- Derived from this study: PubMed 35613417Foll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Zuger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. JMIR Form Res. 2022 Jun 21;6(6):e35717. doi: 10.2196/35717.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Aristomenis Extradaktylos, Prof. MD study chair
University Hospital Bern - Department of Emergency Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID and COVID 19, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of smartwatch data in predicting ICU requirement in COVID-19 contracted inpatients quantified as the area under the receiver operator characteristics curve (AUC ROC > 0.85). (time frame: until hospital discharge, transfer to ICU or palliative care is initiated (expected to be on average after 7-30 days)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04357834, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Aug 2021; AzTrials last refreshed its copy on 8 Nov 2026.