Clinical Time and Temporal Dynamics Study (CLIN-TIME)

NCT07812831 | Enrolling By Invitation DMC

• 1 other identifier: TH-CLINICAL-TIME-2026-001
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Longitudinal Studies; Prospective Studies; Biomarkers; Predictive Value of Tests; Telemedicine; Time of Death; Time Perception; Time Reaction; Time Efficiency; Time Management; Population; Population Groups; Epidemic Disease; Pandemic; Sex Behavior; Sex Characteristics; Sex Hormone; Cardio-pulmonary; Surgical; Drugs of Abuse; Health Risks; Clinical Outcomes

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

Outcome Measures

Primary Outcomes (1)

• 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.

  At baseline and at 30 days, 90 days, 6 months, and 12 months after enrollment.

Secondary Outcomes (4)

• Within-Participant Temporal Trajectory Variability

  At 30 days, 90 days, 6 months, and 12 months after enrollment.

• Clinical Lead-Time to Prespecified Outcome

  From baseline through 12 months after enrollment.

• Calibration of Time-Dependent Clinical Prediction Models

  At 30 days, 90 days, 6 months, and 12 months after enrollment.

• Temporal Phenotype Stability

  At 90 days, 6 months, and 12 months after enrollment.

Study Arms (1)

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.

Other: No Study-Assigned Intervention

Interventions

No Study-Assigned Intervention OTHER

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.

Clinical Time Longitudinal Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Truway Health, Inc.: lead

Study Sites (1)

Truway Health, Inc.

New York, New York, 10016, United States

Location

Related Publications (6)

• 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.

  PMID: 32175520 BACKGROUND

• Chodankar 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.

  PMID: 38282631 BACKGROUND

• Holler 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.

  PMID: 37176726 BACKGROUND

• Carrasco-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.

  PMID: 37659105 BACKGROUND

• Pungitore 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.

  PMID: 37637723 BACKGROUND

• Bull 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.

  PMID: 32671229 BACKGROUND

MeSH Terms

Conditions

Sexual Behavior; Substance-Related Disorders

Condition Hierarchy (Ancestors)

Behavior; Chemically-Induced Disorders; Mental Disorders

Study Officials

• Gavin C Solomon, MD

  Truway Health, Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 1, 2026
• First Posted: September 10, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): September 1, 2076
• Study Completion (Estimated): September 1, 2076
• Last Updated: September 10, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR, ANALYTIC CODE
• Time Frame: Individual participant-level data (IPD) and supporting information will become available after publication of the primary study results and completion of data-quality, privacy, and governance review. Data will be available beginning at that time for qualified research purposes through a controlled-access process. The study protocol, statistical analysis plan, clinical study report, and analytic code will be made available when finalized and permitted by applicable policies and agreements. Access will continue for as long as the data and supporting materials remain available and appropriate for controlled research use, subject to applicable privacy, consent, regulatory, and institutional requirements.
• Access Criteria: De-identified individual participant-level data and applicable supporting information may be requested by qualified researchers for scientifically valid research purposes. Requests will be reviewed by the designated study research governance authority for scientific merit, feasibility, data availability, participant privacy, consistency with informed consent, and applicable regulatory and institutional requirements. Approved researchers will be required to execute an appropriate data-use agreement and comply with confidentiality, security, and permitted-use restrictions. Data will be provided through a controlled-access mechanism; direct participant identifiers will not be provided. Supporting materials may include the study protocol, statistical analysis plan, clinical study report, and analytic code, as available and permitted.

Locations

US (1)