Alzheimer DiseaseEARSNCT04928690
Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping
An observational study of Alzheimer Disease and Alzheimer's Disease (Incl Subtypes), run by Novoic Limited.
Trial summary
The S22 study investigates, in a cross-sectional study, the ability of algorithms that analyse acoustic and linguistic patterns of spoken language to predict the presence of amyloid positivity in early stage Alzheimer's disease, specifically in Mild Cognitive Impairment (MCI) and cognitively normal (CN) cohorts; and… Read more about this study
- Who is it for?
- Ages 50 to 85 years
- Conditions the study is for
- Alzheimer Disease, Alzheimer's Disease (Incl Subtypes), Mild Cognitive Impairment
- How many people
- 140 participants
- Where
- No sites listed yet
- Study dates
- 20 Jun 2021to 30 Aug 2022
- How long will I take part?
- 2 Weeks
- 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 50 to 85 years
- All sexes
- Adult, Older adult
Study population: US and UK-based study population.
Who may be able to join
- Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 30 months at the time of consent for Arm 2 and Arm 4 participants (amyloid negative Arms).
- Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 60 months at the time of consent for Arm 1 and Arm 3 (amyloid positive Arms).
- Subjects must be aged 50-85 (inclusive).
- Subjects must have MMSE scores of 23-30 (inclusive) based on a test not older than 1 month at the time of the visit.
- Date of diagnosis (if applicable) maximum of five years prior to consent.
- Subjects' first language must be English.
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- Willing to participate in a study investigating speech and cognitive impairment.
- Able to provide valid informed consent.
- Able to use, or has a caregiver who is able to use a smartphone device.
- Has access to a smartphone device running an operation system of Android 6 or above; or iOS 10 or above.
- If taking part in the study through virtual visits, the following inclusion criteria also applies:
- Able to use, or has a caregiver who is able to use a personal computer, notebook or tablet.
- Has access to a personal computing device of that is running an operating system of macOS X with macOS 10.9 or later, or Windows 7 or above, or Ubuntu 12.04 or higher; OR has access internet browser software Internet Explorer version 11 or above; or Microsoft Edge version 12 or above, or Firefox version 27 or above, or Google Chrome version 30 or above, or Safari version 7 or above; AND capable of audio and video recording; AND able to connect to the internet.
Who cannot join
- Clinically significant unstable psychiatric illness in 6 months.
- Diagnosis of General Anxiety Disorder.
- Current, or history within the past 2 years of major depressive disorder diagnosis (according to DSM-5 criteria83); or psychiatric symptoms that, in the opinion of the investigator, could interfere with study procedures.
- History or presence of stroke within the past 2 years.
- Documented history of transient ischemic attack or unexplained loss of consciousness within the last 12 months.
- The participant is using drugs to treat symptoms related to AD, and the doses of these drugs were not stable for at least 8 weeks prior to consent.
- Participant is, or previously has been enrolled in the Sponsor's NOV-0100 or NOV-0110 studies.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping (EARS)
The S22 study investigates, in a cross-sectional study, the ability of algorithms that analyse acoustic and linguistic patterns of spoken language to predict the presence of amyloid positivity in early stage Alzheimer's disease, specifically in Mild Cognitive Impairment (MCI) and cognitively normal (CN) cohorts; and whether similar algorithms can predict cognitive functioning, in classifying MCI vs CN.
- Keywords
- Alzheimer's disease, Preclinical Alzheimer's disease, Prodromal Alzheimer's disease, Mild Cognitive Impairment, Normal Cognition, Amyloid, Speech, Acoustic, Language, Linguistic, Machine Learning, Artificial Intelligence
- Medical subject headings
- Alzheimer Disease, Cognitive Dysfunction, Speech, Language
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Arm 1: MCI amyloid positive | * Meet the National Institute of Aging - Alzheimer's Association (NIA-AA) core clinical criteria (2011) for MCI due to Alzheimer's * Positive amyloid PET or amyloid CSF status. * MMSE 23-30 (inclusive) | |
| Arm 2: MCI amyloid negative | * Non-AD Mild Cognitive Impairment (MCI) * Negative amyloid PET or amyloid CSF status. * MMSE 23-30 (inclusive) | |
| Arm 3: CN amyloid positive | * Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline * Positive amyloid PET or amyloid CSF status. * MMSE 26-30 (inclusive) | |
| Arm 4: CN amyloid negative | * Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline * Negative amyloid PET or amyloid CSF status. * MMSE 26-30 (inclusive) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms using speech recordings as input.
Measured: baseline
Secondary outcomes (16)
The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Measured: baseline
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Measured: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Measured: baseline
The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Measured: baseline
The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Measured: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Measured: baseline
The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Measured: baseline
The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Measured: baseline
The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Measured: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Measured: baseline
The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Measured: baseline
The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Measured: baseline
The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Measured: baseline
The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Measured: baseline
The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Measured: baseline
For each classifier/regressor in outcome 1-16, the correlation between the AUC/CIA and each age group, gender and speech-to-reverberation modulation energy ratio group, as measured by the Kendall rank correlation coefficient.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2021
- Study start20 Jun 2021 (estimated)
- Primary completion15 Aug 2021 (estimated)
- Study completion30 Aug 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Alzheimer DiseaseStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 140 participants (estimated)
- Target duration
- 2 Weeks
Sponsor and investigators
- Lead sponsor
- Novoic Limited
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Emil Fristed, MSc, Principal investigator, Novoic Ltd
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04928690
- Sponsor's protocol ID
- NOV-0120
- Acronym
- EARS
- Status last verified
- Jun 2021
- First submitted
- 14 Jun 2021
- Last updated
- 16 Jun 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 16 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping (EARS)
- ClinicalTrials.gov ID
- NCT04928690
- Acronym / sponsor ID
- EARS / NOV-0120
- Keywords
- Alzheimer's disease, Preclinical Alzheimer's disease, Prodromal Alzheimer's disease, Mild Cognitive Impairment, Normal Cognition, Amyloid, Speech, Acoustic
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 140 participants (estimated)
- Lead sponsor
- Novoic LimitedIndustry (pharma, biotech, devices)
- Study start
- 20 Jun 2021 (estimated)
- Primary completion
- 15 Aug 2021 (estimated)
- Study completion
- 30 Aug 2022 (estimated)
- First posted
- 16 Jun 2021 (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 S22 study investigates, in a cross-sectional study, the ability of algorithms that analyse acoustic and linguistic patterns of spoken language to predict the presence of amyloid positivity in early stage Alzheimer's disease, specifically in Mild Cognitive Impairment (MCI) and cognitively normal (CN) cohorts; and whether similar algorithms can predict cognitive functioning, in classifying MCI vs CN.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Target follow-up
- 2 Weeks
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Arm 1: MCI amyloid positive | – | –* Meet the National Institute of Aging - Alzheimer's Association (NIA-AA) core clinical criteria (2011) for MCI due to Alzheimer's * Positive amyloid PET or amyloid CSF status. * MMSE 23-30 (inclusive) |
| Arm 2: MCI amyloid negative | – | –* Non-AD Mild Cognitive Impairment (MCI) * Negative amyloid PET or amyloid CSF status. * MMSE 23-30 (inclusive) |
| Arm 3: CN amyloid positive | – | –* Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline * Positive amyloid PET or amyloid CSF status. * MMSE 26-30 (inclusive) |
| Arm 4: CN amyloid negative | – | –* Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline * Negative amyloid PET or amyloid CSF status. * MMSE 26-30 (inclusive) |
Who may qualify
- Ages
- 50 to 85 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- US and UK-based study population.
Key inclusion criteria (13 listed)
- Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 30 months at the time of consent for Arm 2 and Arm 4 participants (amyloid negative Arms).
- Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 60 months at the time of consent for Arm 1 and Arm 3 (amyloid positive Arms).
- Subjects must be aged 50-85 (inclusive).
- Subjects must have MMSE scores of 23-30 (inclusive) based on a test not older than 1 month at the time of the visit.
- Date of diagnosis (if applicable) maximum of five years prior to consent.
Key exclusion criteria (7 listed)
- Clinically significant unstable psychiatric illness in 6 months.
- Diagnosis of General Anxiety Disorder.
- Current, or history within the past 2 years of major depressive disorder diagnosis (according to DSM-5 criteria83); or psychiatric symptoms that, in the opinion of the investigator, could interfere with study procedures.
- History or presence of stroke within the past 2 years.
- Documented history of transient ischemic attack or unexplained loss of consciousness within the last 12 months.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms using speech recordings as input.
Time frame: baseline
16 secondary outcomes
The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Time frame: baseline
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Time frame: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
Time frame: baseline
The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Time frame: baseline
The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Time frame: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Time frame: baseline
The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
Time frame: baseline
The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Time frame: baseline
The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Time frame: baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Time frame: baseline
The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
Time frame: baseline
The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Time frame: baseline
The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Time frame: baseline
The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Time frame: baseline
The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
Time frame: baseline
For each classifier/regressor in outcome 1-16, the correlation between the AUC/CIA and each age group, gender and speech-to-reverberation modulation energy ratio group, as measured by the Kendall rank correlation coefficient.
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jun 2021.
- Individual participant dataSharing planned: Undecided
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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.
Emil Fristed, MSc principal investigator
Novoic Ltd
Questions about this trial
What is this clinical trial studying?
An observational study of Alzheimer Disease and Alzheimer's Disease (Incl Subtypes), run by Novoic Limited.
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 13 inclusion and 7 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: status not confirmed, as of the sponsor's last update on 16 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Head of Clinical Operations (07849522891, marton@novoic.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms using speech recordings as input. (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 16 Jun 2021.