Longitudinal StudiesCLIN-TIMENCT07812831
Clinical Time and Temporal Dynamics Study (CLIN-TIME)
An observational study of Longitudinal Studies and Prospective Studies, run by Truway Health, Inc. at 1 site in United States.
Trial summary
TH-CLINICAL-TIME is a prospective, longitudinal observational study evaluating how changes in clinical measurements over time may help researchers better understand patient health trajectories and clinically meaningful outcomes. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Longitudinal Studies, Prospective Studies, Biomarkers
- What is being tested?
- No Study-Assigned Intervention
- How many people
- 1,000 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2026to 1 Sep 2076
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population will consist of adults aged 18 years and older, including individuals with and without established medical conditions. Participants will be followed longitudinally to characterize individualized clinical trajectories and temporal dynamics using available clinical, physiologic, laboratory, imaging-derived, patient-reported, and remote or digital health information as permitted by the protocol. The population is intended to support evaluation of temporal biomarkers, longitudinal patterns, clinical trajectory features, and time-dependent associations with clinically meaningful outcomes.
Who may be able to join
- Adults aged 18 years or older.
- Able and willing to provide informed consent, when informed consent is required by the approved study protocol and applicable regulations.
- Willing and able to participate in longitudinal collection or evaluation of clinical, physiologic, laboratory, imaging-derived, patient-reported, or remote/digital health information as applicable to the study protocol.
- Ability to complete the protocol-defined follow-up schedule or provide sufficient longitudinal observations for analysis.
- Individuals with or without established medical conditions may be eligible, provided their participation is consistent with the study protocol.
- Availability of sufficient baseline information or measurements to establish an individualized reference state when required for the applicable analysis.
Who cannot join
- Inability to provide informed consent when consent is required and no legally authorized representative process is applicable.
- Inability to comply with protocol-defined data collection or follow-up requirements.
- Circumstances that, in the judgment of the investigator, would prevent reliable longitudinal data collection or interpretation.
- Enrollment in another research protocol that would materially interfere with participation or interpretation of the outcomes, when applicable.
- Any other exclusion criterion specifically identified in the IRB/ethics-approved protocol.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Prospective Evaluation of Temporal Clinical Dynamics, Physiologic Trajectories, and Time-Dependent Digital Biomarkers for Prediction of Clinically Meaningful Patient Outcomes
TH-CLINICAL-TIME is a prospective, longitudinal observational study evaluating how changes in clinical measurements over time may help researchers better understand patient health trajectories and clinically meaningful outcomes. The study will examine patterns such as changes from an individual's baseline, rate of change, persistence of abnormalities, variability, recurrence, and recovery following clinical events. Data may include routinely collected clinical measurements, physiologic observations, laboratory results, cardiac measurements, imaging-derived measurements, patient-reported information, and remote or digital health data when available and permitted by the study protocol. The study will investigate whether these temporal patterns provide additional information beyond individual measurements taken at a single point in time. The planned study horizon extends from 2026 through 2076, while individual participant follow-up will occur according to the approved study schedule, consent, and applicable requirements.
Read the detailed description
TH-CLINICAL-TIME is a prospective longitudinal observational research study designed to investigate the role of temporal dynamics in clinical medicine. The fundamental premise is that a clinical measurement may have different significance depending on its relationship to a patient's prior measurements, the direction and rate of change, the duration of an observed abnormality, the sequence of preceding or subsequent observations, and the patient's subsequent recovery or deterioration.
The study will establish longitudinal research datasets that permit evaluation of individual and population-level clinical trajectories. Depending on the participating cohort and available clinical data, observations may include vital signs, physiologic measurements, electrocardiographic information, cardiovascular measurements, point-of-care ultrasound or other imaging-derived measurements, laboratory results, symptoms, medications, procedures, healthcare encounters, patient-reported outcomes, and appropriately governed remote or digital health measurements.
A principal research objective is to determine whether time-dependent clinical features provide incremental information beyond conventional point-in-time measurements. Investigational temporal features may include baseline deviation, velocity of change, acceleration or deceleration, persistence, variability, recurrence, temporal sequence, time between clinically relevant observations, and recovery kinetics.
The study will investigate the concept of an individualized Clinical Temporal Phenotype, representing the longitudinal behavior of selected clinical variables rather than a single measurement. Individualized analyses may compare a participant's current observations with their own longitudinal baseline as well as with appropriate population reference distributions.
The research program may evaluate whether temporal patterns are associated with predefined clinical outcomes, including clinically meaningful deterioration, hospitalization, emergency evaluation, cardiovascular events, procedure-related outcomes, recovery characteristics, readmission, or other protocol-defined outcomes. Specific endpoints, populations, data sources, analytic methods, and follow-up schedules will be prespecified in the applicable study protocol and statistical analysis plan.
Where appropriate, longitudinal statistical and computational methods may be used to evaluate trajectories and dynamic prediction. These may include longitudinal regression, survival analysis, time-dependent modeling, recurrent-event analysis, joint longitudinal-survival models, dynamic risk prediction, and machine-learning methods. Any predictive models will be evaluated using appropriate temporal validation methods designed to prevent incorporation of future information into predictions of earlier events.
An additional research objective is to evaluate clinical lead time, defined as the interval between identification of a prespecified research signal and a subsequently documented clinical event or clinical recognition point. Research analyses may also evaluate whether recovery kinetics, recurrent deviations, or accelerating changes contain prognostic information.
Remote and digital data may be incorporated when specifically permitted by the study protocol, participant authorization, applicable privacy requirements, and institutional policies. Research-generated alerts or analytical signals will not independently constitute clinical diagnoses or treatment recommendations unless a separately authorized clinical-care process exists.
The study has a planned 50-year study-level horizon from 2026 through 2076 to permit investigation of long-term longitudinal patterns. The study horizon does not imply that every participant will be followed continuously for 50 years. Individual participation and follow-up duration will be governed by the approved protocol, informed consent or other applicable authorization, participant withdrawal, availability of follow-up data, and applicable institutional and regulatory requirements.
The study will employ coded research identifiers and appropriate data-governance procedures. Direct participant identifiers will be maintained separately from research datasets to the extent required by the applicable protocol and institutional policies. Research data will be subject to appropriate access controls, auditability, security safeguards, quality-control procedures, and retention and disposition requirements.
The study is observational and does not, by itself, assign participants to an experimental drug, biologic, or device intervention. Any clinical care received by participants remains under the direction of their treating healthcare professionals and is not altered solely by participation in the observational research unless separately specified in an approved protocol.
The overarching scientific objective is to determine whether clinical time itself can be characterized as a measurable dimension of patient state, and whether longitudinal trajectories, rather than isolated observations, can yield reproducible research biomarkers of clinical change, deterioration, and recovery.
- Conditions
- Longitudinal Studies, Prospective Studies, Biomarkers, Predictive Value of Tests, Telemedicine, Time of Death
- Being tested
- No Study-Assigned Intervention
- Keywords
- Clinical Temporal Dynamics, Clinical Time, Temporal Biomarkers, Longitudinal Clinical Data, Physiologic Trajectories, Temporal Phenotyping, Dynamic Risk Prediction, Clinical Trajectory, Individualized Baseline, Physiologic Velocity, Physiologic Acceleration, Recovery Kinetics, Temporal Digital Biomarkers, Longitudinal Monitoring, Remote Monitoring, Digital Health, Clinical Surveillance, Predictive Analytics, Time-Dependent Outcomes, Long-Term Clinical Outcomes
- Medical subject headings
- Sexual Behavior, Substance-Related Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Study-Assigned InterventionOther
This is a prospective observational study with no protocol-assigned drug, biologic, device, behavioral intervention, or other therapeutic intervention. Participants receive clinical care according to usual practice and applicable clinical decisions. The study observes and analyzes longitudinal clinical and physiologic data to characterize temporal dynamics and their association with clinically meaningful outcomes. Data collection may include routinely available clinical measurements, laboratory results, imaging-derived measurements, patient-reported information, and remote or digital health measurements when permitted by the protocol.
| Study group | Role | What they receive |
|---|---|---|
| Clinical Time Longitudinal Cohort | Participants enrolled in this prospective observational cohort will be followed longitudinally according to the approved study schedule. The cohort will be evaluated for temporal clinical dynamics, including changes from individualized baseline, rate and acceleration of change, persistence, variability, recurrence, temporal sequence, and recovery following clinically meaningful events. Analyses will integrate available longitudinal clinical, physiologic, laboratory, imaging-derived, patient-reported, and remote or digital health measurements when permitted by the study protocol. No experimental intervention is assigned by the study. Receives: Other: No Study-Assigned Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Time-Dependent Predictive Discrimination of Clinically Meaningful Patient Outcomes
Change in predictive discrimination of longitudinal temporal clinical features compared with point-in-time clinical measurements for prespecified clinically meaningful patient outcomes. Predictive discrimination will be quantified using the prespecified time-dependent area under the receiver operating characteristic curve (AUC) or concordance index (C-index), as applicable to the outcome and statistical analysis plan. Temporal features will include individualized baseline values and longitudinal rates of change.
Measured: At baseline and at 30 days, 90 days, 6 months, and 12 months after enrollment.
Secondary outcomes (4)
Within-Participant Temporal Trajectory Variability
Quantification of within-participant longitudinal variability in prespecified physiologic and clinical measurements using the coefficient of variation calculated from repeated assessments. The analysis will characterize individualized temporal trajectories while preserving participant-level longitudinal structure.
Measured: At 30 days, 90 days, 6 months, and 12 months after enrollment.
Clinical Lead-Time to Prespecified Outcome
Time in days between the first prespecified temporal signal meeting the protocol-defined detection threshold and the subsequent occurrence or clinical recognition of the corresponding prespecified clinically meaningful outcome. Lead-time will be calculated only for participants experiencing the applicable outcome during observation.
Measured: From baseline through 12 months after enrollment.
Calibration of Time-Dependent Clinical Prediction Models
Calibration of prespecified longitudinal clinical prediction models for clinically meaningful patient outcomes, assessed by the calibration slope and calibration-in-the-large at each scheduled assessment point. Model calibration will be evaluated according to the prespecified statistical analysis plan.
Measured: At 30 days, 90 days, 6 months, and 12 months after enrollment.
Temporal Phenotype Stability
Proportion of participants whose prespecified individualized temporal phenotype classification remains concordant across consecutive scheduled longitudinal assessments. Stability will be determined using the protocol-defined phenotype classification algorithm.
Measured: At 90 days, 6 months, and 12 months after enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Sep 2026
- Study start1 Sep 2026
- Primary completion1 Sep 2076 (estimated)
- Study completion1 Sep 2076 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Longitudinal StudiesStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Truway Health, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Gavin C Solomon, MD, Principal investigator, Truway Health, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07812831
- Sponsor's protocol ID
- TH-CLINICAL-TIME-2026-001
- Acronym
- CLIN-TIME
- Status last verified
- Sep 2026
- First submitted
- 1 Sep 2026
- Last updated
- 10 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant-level data (IPD) will be considered for sharing with qualified researchers following publication of the primary study results, subject to applicable informed consent, institutional policies, privacy requirements, data-use agreements, and regulatory requirements. Shared datasets will be de-identified or appropriately anonymized and will exclude direct participant identifiers. Requests will be evaluated based on scientific merit, data availability, participant privacy, and compatibility with the study consent and governance framework. Data may be provided through a controlled-access process rather than unrestricted public release.
Publications
Badawy R, Hameed F, Bataille L, Little MA, Claes K, Saria S, Cedarbaum JM, Stephenson D, Neville J, Maetzler W, Espay AJ, Bloem BR, Simuni T, Karlin DR. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digit Biomark. 2019 Oct 7;3(3):116-132. doi: 10.1159/000502951. eCollection 2019 Sep-Dec.
PubMed 32175520Chodankar D, Raval TK, Jeyaraj J. The role of remote data capture, wearables, and digital biomarkers in decentralized clinical trials. Perspect Clin Res. 2024 Jan-Mar;15(1):38-41. doi: 10.4103/picr.picr_219_22. Epub 2023 Oct 13.
PubMed 38282631Holler E, Chekani F, Ai J, Meng W, Khandker RK, Ben Miled Z, Owora A, Dexter P, Campbell N, Solid C, Boustani M. Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange. J Clin Med. 2023 May 5;12(9):3286. doi: 10.3390/jcm12093286.
PubMed 37176726Carrasco-Ribelles LA, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violan C, Zabaleta-Del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J Am Med Inform Assoc. 2023 Nov 17;30(12):2072-2082. doi: 10.1093/jamia/ocad168.
PubMed 37659105Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. J Healthc Inform Res. 2023 Aug 14;7(3):313-331. doi: 10.1007/s41666-023-00143-4. eCollection 2023 Sep.
PubMed 37637723Bull LM, Lunt M, Martin GP, Hyrich K, Sergeant JC. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagn Progn Res. 2020 Jul 9;4:9. doi: 10.1186/s41512-020-00078-z. eCollection 2020.
PubMed 32671229
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Evaluation of Temporal Clinical Dynamics, Physiologic Trajectories, and Time-Dependent Digital Biomarkers for Prediction of Clinically Meaningful Patient Outcomes
- ClinicalTrials.gov ID
- NCT07812831
- Acronym / sponsor ID
- CLIN-TIME / TH-CLINICAL-TIME-2026-001
- Conditions
- Longitudinal Studies, Prospective Studies, Biomarkers, Predictive Value of Tests, Telemedicine, Time of Death
- Keywords
- Clinical Temporal Dynamics, Clinical Time, Temporal Biomarkers, Longitudinal Clinical Data, Physiologic Trajectories, Temporal Phenotyping, Dynamic Risk Prediction, Clinical Trajectory
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Truway Health, Inc.Industry (pharma, biotech, devices)
- Study start
- 1 Sep 2026 (actual)
- Primary completion
- 1 Sep 2076 (estimated)
- Study completion
- 1 Sep 2076 (estimated)
- First posted
- 10 Sep 2026 (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.
TH-CLINICAL-TIME is a prospective, longitudinal observational study evaluating how changes in clinical measurements over time may help researchers better understand patient health trajectories and clinically meaningful outcomes. The study will examine patterns such as changes from an individual's baseline, rate of change, persistence of abnormalities, variability, recurrence, and recovery following clinical events. Data may include routinely collected clinical measurements, physiologic observations, laboratory results, cardiac measurements, imaging-derived measurements, patient-reported information, and remote or digital health data when available and permitted by the study protocol. The study will investigate whether these temporal patterns provide additional information beyond individual measurements taken at a single point in time. The planned study horizon extends from 2026 through 2076, while individual participant follow-up will occur according to the approved study schedule, consent, and applicable requirements.
Detailed description
TH-CLINICAL-TIME is a prospective longitudinal observational research study designed to investigate the role of temporal dynamics in clinical medicine. The fundamental premise is that a clinical measurement may have different significance depending on its relationship to a patient's prior measurements, the direction and rate of change, the duration of an observed abnormality, the sequence of preceding or subsequent observations, and the patient's subsequent recovery or deterioration.
The study will establish longitudinal research datasets that permit evaluation of individual and population-level clinical trajectories. Depending on the participating cohort and available clinical data, observations may include vital signs, physiologic measurements, electrocardiographic information, cardiovascular measurements, point-of-care ultrasound or other imaging-derived measurements, laboratory results, symptoms, medications, procedures, healthcare encounters, patient-reported outcomes, and appropriately governed remote or digital health measurements.
A principal research objective is to determine whether time-dependent clinical features provide incremental information beyond conventional point-in-time measurements. Investigational temporal features may include baseline deviation, velocity of change, acceleration or deceleration, persistence, variability, recurrence, temporal sequence, time between clinically relevant observations, and recovery kinetics.
The study will investigate the concept of an individualized Clinical Temporal Phenotype, representing the longitudinal behavior of selected clinical variables rather than a single measurement. Individualized analyses may compare a participant's current observations with their own longitudinal baseline as well as with appropriate population reference distributions.
The research program may evaluate whether temporal patterns are associated with predefined clinical outcomes, including clinically meaningful deterioration, hospitalization, emergency evaluation, cardiovascular events, procedure-related outcomes, recovery characteristics, readmission, or other protocol-defined outcomes. Specific endpoints, populations, data sources, analytic methods, and follow-up schedules will be prespecified in the applicable study protocol and statistical analysis plan.
Where appropriate, longitudinal statistical and computational methods may be used to evaluate trajectories and dynamic prediction. These may include longitudinal regression, survival analysis, time-dependent modeling, recurrent-event analysis, joint longitudinal-survival models, dynamic risk prediction, and machine-learning methods. Any predictive models will be evaluated using appropriate temporal validation methods designed to prevent incorporation of future information into predictions of earlier events.
An additional research objective is to evaluate clinical lead time, defined as the interval between identification of a prespecified research signal and a subsequently documented clinical event or clinical recognition point. Research analyses may also evaluate whether recovery kinetics, recurrent deviations, or accelerating changes contain prognostic information.
Remote and digital data may be incorporated when specifically permitted by the study protocol, participant authorization, applicable privacy requirements, and institutional policies. Research-generated alerts or analytical signals will not independently constitute clinical diagnoses or treatment recommendations unless a separately authorized clinical-care process exists.
The study has a planned 50-year study-level horizon from 2026 through 2076 to permit investigation of long-term longitudinal patterns. The study horizon does not imply that every participant will be followed continuously for 50 years. Individual participation and follow-up duration will be governed by the approved protocol, informed consent or other applicable authorization, participant withdrawal, availability of follow-up data, and applicable institutional and regulatory requirements.
The study will employ coded research identifiers and appropriate data-governance procedures. Direct participant identifiers will be maintained separately from research datasets to the extent required by the applicable protocol and institutional policies. Research data will be subject to appropriate access controls, auditability, security safeguards, quality-control procedures, and retention and disposition requirements.
The study is observational and does not, by itself, assign participants to an experimental drug, biologic, or device intervention. Any clinical care received by participants remains under the direction of their treating healthcare professionals and is not altered solely by participation in the observational research unless separately specified in an approved protocol.
The overarching scientific objective is to determine whether clinical time itself can be characterized as a measurable dimension of patient state, and whether longitudinal trajectories, rather than isolated observations, can yield reproducible research biomarkers of clinical change, deterioration, and recovery.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Clinical Time Longitudinal Cohort | – | Other: No Study-Assigned InterventionParticipants enrolled in this prospective observational cohort will be followed longitudinally according to the approved study schedule. The cohort will be evaluated for temporal clinical dynamics, including changes from individualized baseline, rate and acceleration of change, persistence, variability, recurrence, temporal sequence, and recovery following clinically meaningful events. Analyses will integrate available longitudinal clinical, physiologic, laboratory, imaging-derived, patient-reported, and remote or digital health measurements when permitted by the study protocol. No experimental intervention is assigned by the study. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adults aged 18 years and older, including individuals with and without established medical conditions. Participants will be followed longitudinally to characterize individualized clinical trajectories and temporal dynamics using available clinical, physiologic, laboratory, imaging-derived, patient-reported, and remote or digital health information as permitted by the protocol. The population is intended to support evaluation of temporal biomarkers, longitudinal patterns, clinical trajectory features, and time-dependent associations with clinically meaningful outcomes.
Key inclusion criteria (6 listed)
- Adults aged 18 years or older.
- Able and willing to provide informed consent, when informed consent is required by the approved study protocol and applicable regulations.
- Willing and able to participate in longitudinal collection or evaluation of clinical, physiologic, laboratory, imaging-derived, patient-reported, or remote/digital health information as applicable to the study protocol.
- Ability to complete the protocol-defined follow-up schedule or provide sufficient longitudinal observations for analysis.
- Individuals with or without established medical conditions may be eligible, provided their participation is consistent with the study protocol.
Key exclusion criteria (5 listed)
- Inability to provide informed consent when consent is required and no legally authorized representative process is applicable.
- Inability to comply with protocol-defined data collection or follow-up requirements.
- Circumstances that, in the judgment of the investigator, would prevent reliable longitudinal data collection or interpretation.
- Enrollment in another research protocol that would materially interfere with participation or interpretation of the outcomes, when applicable.
- Any other exclusion criterion specifically identified in the IRB/ethics-approved protocol.
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 |
|---|---|---|
| No Study-Assigned InterventionThis is a prospective observational study with no protocol-assigned drug, biologic, device, behavioral intervention, or other therapeutic intervention. Participants receive clinical care according to usual practice and applicable clinical decisions. The study observes and analyzes longitudinal clinical and physiologic data to characterize temporal dynamics and their association with clinically meaningful outcomes. Data collection may include routinely available clinical measurements, laboratory results, imaging-derived measurements, patient-reported information, and remote or digital health measurements when permitted by the protocol. | Other | Clinical Time Longitudinal Cohort |
Primary outcome
Time-Dependent Predictive Discrimination of Clinically Meaningful Patient Outcomes
Time frame: At baseline and at 30 days, 90 days, 6 months, and 12 months after enrollment.
Change in predictive discrimination of longitudinal temporal clinical features compared with point-in-time clinical measurements for prespecified clinically meaningful patient outcomes. Predictive discrimination will be quantified using the prespecified time-dependent area under the receiver operating characteristic curve (AUC) or concordance index (C-index), as applicable to the outcome and statistical analysis plan. Temporal features will include individualized baseline values and longitudinal rates of change.
4 secondary outcomes
Within-Participant Temporal Trajectory Variability
Time frame: At 30 days, 90 days, 6 months, and 12 months after enrollment.
Clinical Lead-Time to Prespecified Outcome
Time frame: From baseline through 12 months after enrollment.
Calibration of Time-Dependent Clinical Prediction Models
Time frame: At 30 days, 90 days, 6 months, and 12 months after enrollment.
Temporal Phenotype Stability
Time frame: At 90 days, 6 months, and 12 months after enrollment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2026.
- Background: PubMed 32175520Badawy R, Hameed F, Bataille L, Little MA, Claes K, Saria S, Cedarbaum JM, Stephenson D, Neville J, Maetzler W, Espay AJ, Bloem BR, Simuni T, Karlin DR. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digit Biomark. 2019 Oct 7;3(3):116-132. doi: 10.1159/000502951. eCollection 2019 Sep-Dec.
- Background: PubMed 38282631Chodankar D, Raval TK, Jeyaraj J. The role of remote data capture, wearables, and digital biomarkers in decentralized clinical trials. Perspect Clin Res. 2024 Jan-Mar;15(1):38-41. doi: 10.4103/picr.picr_219_22. Epub 2023 Oct 13.
- Background: PubMed 37176726Holler E, Chekani F, Ai J, Meng W, Khandker RK, Ben Miled Z, Owora A, Dexter P, Campbell N, Solid C, Boustani M. Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange. J Clin Med. 2023 May 5;12(9):3286. doi: 10.3390/jcm12093286.
- Background: PubMed 37659105Carrasco-Ribelles LA, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violan C, Zabaleta-Del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J Am Med Inform Assoc. 2023 Nov 17;30(12):2072-2082. doi: 10.1093/jamia/ocad168.
- Background: PubMed 37637723Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. J Healthc Inform Res. 2023 Aug 14;7(3):313-331. doi: 10.1007/s41666-023-00143-4. eCollection 2023 Sep.
- Background: PubMed 32671229Bull LM, Lunt M, Martin GP, Hyrich K, Sergeant JC. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagn Progn Res. 2020 Jul 9;4:9. doi: 10.1186/s41512-020-00078-z. eCollection 2020.
- Individual participant dataSharing planned: Yes. Individual participant-level data (IPD) will be considered for sharing with qualified researchers following publication of the primary study results, subject to applicable informed consent, institutional policies, privacy requirements, data-use agreements, and regulatory requirements. Shared datasets will be de-identified or appropriately anonymized and will exclude direct participant identifiers. Requests will be evaluated based on scientific merit, data availability, participant privacy, and compatibility with the study consent and governance framework. Data may be provided through a controlled-access process rather than unrestricted public release.
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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.
Gavin C Solomon, MD principal investigator
Truway Health, Inc.
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 Longitudinal Studies and Prospective Studies, run by Truway Health, Inc. at 1 site in United States.
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 6 inclusion and 5 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: enrolling by invitation, as of the sponsor's last update on 10 Sep 2026. 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 United States. 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?
This study is still under way. Results are not available in the source record yet.
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?
Time-Dependent Predictive Discrimination of Clinically Meaningful Patient Outcomes (time frame: At baseline and at 30 days, 90 days, 6 months, and 12 months after enrollment.). 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 NCT07812831, which also lists 6 linked publications.
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 10 Sep 2026.