Ongoing Consent in Longitudinal and Digital Health Studies: Best Practices

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-28

Quick Answer

• Ongoing consent in longitudinal and digital health studies requires planned re-consent checkpoints and dynamic consent models that adapt as research protocols evolve.
• Researchers should document consent decisions, track participant preferences, and build consent review into study milestones instead of treating consent as a one-time event.
• No single consent model fits all studies, and institutional policies and jurisdiction-specific requirements will shape which approach is appropriate.

Understanding Ongoing Consent in Longitudinal Research

Longitudinal studies follow participants over extended periods, often years or decades. Digital health research adds another layer of complexity because data collection can be continuous, passive, and involve multiple platforms. The consent process that works for a single-visit study does not automatically transfer to these longer timeframes.

The core problem is that consent given at enrollment may not reflect the actual research activities that occur later. Study protocols evolve, new research questions emerge, and data sharing requirements change. Participants may forget what they agreed to, and their preferences may shift over time. Researchers need structured approaches to maintain consent as the study evolves.

Ongoing consent is the practice of revisiting consent decisions at appropriate intervals or when study conditions change. It is distinct from initial consent because it acknowledges that consent is not a static event but a continuing relationship between researchers and participants.

The National Library of Medicine provides access to authoritative biomedical books and research-method references that cover consent practices in research settings. These resources can help researchers understand the ethical foundations and practical applications of ongoing consent.

Core Principles of Ongoing Consent

Consent as a Process

Consent in longitudinal research should be treated as a process instead of a single event. This means researchers plan for consent activities throughout the study lifecycle. The process includes initial consent, ongoing communication, and re-consent when needed.

The process approach recognizes that participants need ongoing information about how their data is used. It also creates opportunities for participants to ask questions and make informed decisions at different points in the study.

Respect for Participant Autonomy

Ongoing consent respects participant autonomy by giving them continued control over their participation. Participants should be able to withdraw or modify their consent as the study progresses. This is particularly important in digital health research where data collection may be less visible to participants.

Transparency About Data Use

Participants need clear information about how their data will be used, stored, and shared. This includes information about data management and sharing practices. The National Institutes of Health has established data management and sharing expectations that researchers should understand when planning their studies.

Transparency also means informing participants about any changes to the research protocol that might affect their willingness to participate.

Proportionality

The intensity of ongoing consent activities should be proportional to the risk and complexity of the study. A low-risk survey with minimal data collection may require less frequent re-consent than a study involving genetic data or continuous health monitoring.

Models of Ongoing Consent

Dynamic Consent

Dynamic consent is a model that uses digital tools to allow participants to manage their consent preferences over time. Participants can access a secure platform where they can view study updates, adjust their consent preferences, and communicate with the research team.

This model is particularly useful in digital health research because it aligns with the digital nature of the study. Participants can review new information and make decisions without needing in-person visits.

Dynamic consent requires a robust technical infrastructure and clear communication strategies. Researchers must ensure that participants understand how to use the platform and that the platform is accessible to all participants.

Tiered Consent

Tiered consent allows participants to choose the level of participation they are comfortable with. For example, a participant might consent to their data being used for the primary study but not for secondary research purposes.

This approach gives participants more control over their data and can be revisited as the study evolves. Researchers can ask participants to update their preferences at re-consent checkpoints.

Checkpoint-Based Re-Consent

Checkpoint-based re-consent involves scheduling specific points in the study where consent is revisited. These checkpoints might align with study milestones, such as the start of a new phase or the introduction of a new data collection method.

This approach is more structured than dynamic consent and may be easier to implement in studies with defined phases. It also provides a clear record of when consent was last confirmed.

Broad Consent

Broad consent is a model where participants agree to a range of future research uses at the time of enrollment. This approach is often used in biobank studies where samples may be used for many different research purposes over time.

Broad consent can reduce the need for re-consent, but it requires careful explanation at enrollment. Participants must understand the scope of the consent they are giving.

At a Glance

Practical Implementation of Ongoing Consent

Step 1: Assess the Study Design

Before deciding on a consent model, researchers should assess the study design. Consider the duration of the study, the types of data collected, the level of participant interaction, and the potential for future changes to the research plan.

This assessment should be documented and reviewed by the research team. It will inform the consent strategy and the resources needed to support ongoing consent.

Step 2: Select a Consent Model

Based on the assessment, select a consent model that fits the study. The model should be appropriate for the participant population and the research context. Consider the technical capabilities of the research team and the resources available for consent management.

Step 3: Build Consent into the Study Workflow

Consent should be integrated into the study workflow instead of treated as a separate activity. This means scheduling re-consent checkpoints, assigning staff responsibilities, and ensuring that consent activities are documented.

Step 4: Communicate with Participants

Ongoing communication is essential for maintaining consent. Participants should receive regular updates about the study and any changes that might affect their participation. This communication should be clear and accessible.

Step 5: Document Consent Activities

All consent activities should be documented. This includes the initial consent, any re-consent, and any communication with participants about consent. Documentation should be stored securely and be accessible to the research team.

Step 6: Review and Adjust

The consent process should be reviewed periodically to ensure it is working effectively. This review might include feedback from participants, staff, and the research team. Adjustments should be made as needed.

Records and Measurements for Consent Management

Consent Records

Consent records should include the consent form, the date of consent, the version of the consent form used, and any notes about the consent process. These records should be maintained for the duration of the study and in accordance with institutional policies.

Participant Tracking

Researchers should track participant consent status, including when consent was given, when it was last reviewed, and any changes to consent. This tracking can be done using a spreadsheet or a consent management system.

Communication Logs

Communication with participants about consent should be logged. This includes emails, phone calls, and any other interactions related to consent. These logs can help demonstrate that participants were informed about study changes.

Metrics for Consent Effectiveness

Researchers can measure the effectiveness of their consent process by tracking metrics such as the number of participants who withdraw consent, the number of participants who change their consent preferences, and the time between consent checkpoints.

These metrics can help identify issues with the consent process and guide improvements.

Common Failure Patterns in Ongoing Consent

Consent Fatigue

Participants may become fatigued by repeated consent requests. This can lead to participants ignoring consent communications or providing consent without fully understanding the information. Researchers should balance the need for ongoing consent with the burden on participants.

Incomplete Documentation

Consent documentation can become incomplete over time, especially in long studies. This can happen when staff change, when consent forms are updated, or when consent activities are not recorded. Incomplete documentation can create problems for the study and for participants.

Loss of Participant Contact

Participants in longitudinal studies may move, change contact information, or lose interest in the study. This can make it difficult to maintain ongoing consent. Researchers need strategies for maintaining contact with participants.

Protocol Drift

The study protocol may change over time, and the consent may not be updated to reflect these changes. This can mean that participants are not fully informed about the research they are participating in. Regular review of the consent against the protocol is needed.

Misunderstanding of Consent Scope

Participants may misunderstand the scope of their consent, particularly in studies with complex data use. This can lead to participants feeling that their trust has been violated. Clear communication and accessible consent materials can help address this.

Digital Health Research and Ongoing Consent

Passive Data Collection

Digital health research often involves passive data collection, where data is collected without active participant input. This can include data from wearable devices, smartphone apps, or electronic health records. Participants may not be aware of the extent of data collection.

Ongoing consent is particularly important in this context because participants need to be informed about the data being collected and how it is being used. The consent process should address the passive nature of data collection.

Data Sharing and Secondary Use

Digital health data may be shared with other researchers or used for secondary research. Participants need to be informed about these potential uses. The consent process should address data sharing and secondary use.

The National Institutes of Health has established data management and sharing expectations that researchers should consider when planning their studies. These expectations may affect the consent process.

Technology Changes

Digital health research often involves technology that changes over time. New devices, new apps, or new data collection methods may be introduced during the study. These changes may require re-consent if they affect the participant experience.

Participant Engagement

Digital health research can provide opportunities for participant engagement through apps or portals. These tools can be used to provide participants with information about the study and to facilitate ongoing consent.

Practical Implementation Steps

Step 1: Review the Study Protocol

Review the study protocol to identify points where consent may need to be revisited. This includes any planned changes to the study design, data collection, or data use.

Step 2: Develop a Consent Plan

Develop a consent plan that outlines the consent model, the re-consent checkpoints, and the communication strategies. This plan should be reviewed by the research team and, where applicable, by an institutional review board.

Step 3: Implement the Consent Plan

Implement the consent plan, ensuring that all staff are trained on the consent process and that the necessary infrastructure is in place.

Step 4: Monitor and Adjust

Monitor the consent process and make adjustments as needed. This includes tracking consent metrics, reviewing participant feedback, and updating the consent plan as the study evolves.

Records and Measurements for Consent Management

Consent Logs

Maintain a consent log that records the date of consent, the consent form version, and any re-consent activities. This log should be updated regularly and reviewed by the research team.

Participant Communication Logs

Keep a log of all communications with participants about consent. This includes emails, phone calls, and messages sent through a participant portal.

Withdrawal Tracking

Track participant withdrawals and the reasons for withdrawal. This information can help identify issues with the consent process or the study.

Audit Trails

Maintain audit trails for consent activities, particularly when using a digital consent platform. This can help demonstrate that consent was obtained and maintained appropriately.

Common Failure Patterns

Failure to Re-Consent

A common failure is failing to re-consent participants when the study changes. This can happen when the study team is not aware of the need for re-consent or when the consent process is not well integrated into the study workflow.

Inadequate Communication

Another failure is inadequate communication with participants about consent. This can happen when communication is not planned or when participants are not given enough information to make informed decisions.

Poor Documentation

Poor documentation of consent activities can create problems for the study. This can happen when consent records are not maintained or when the consent process is not documented.

Lack of Participant Engagement

If participants are not engaged in the consent process, they may not understand their rights or the study. This can lead to participants withdrawing from the study or being unhappy with the research.

Limitations of Ongoing Consent

Participant Burden

Ongoing consent can place a burden on participants, especially if they are asked to review consent materials frequently. This burden can lead to consent fatigue and reduced engagement.

Resource Requirements

Ongoing consent requires resources, including staff time, technology, and communication materials. This can be a challenge for studies with limited resources.

Complexity

Ongoing consent can be complex to manage, especially in studies with multiple data types or multiple research uses. This complexity can lead to errors or confusion.

Jurisdiction-Specific Requirements

Consent requirements can vary by jurisdiction. Researchers need to be aware of the requirements in their jurisdiction and ensure that their consent process complies with these requirements.

Safety and Regulatory Context

Institutional Review Boards

Institutional review boards play a role in reviewing consent processes and ensuring that they are appropriate for the study. Researchers should work with their institutional review board to ensure that their consent plan meets the requirements.

Data Management and Sharing Policies

The National Institutes of Health has established data management and sharing policies that researchers should be aware of. These policies may affect the consent process, particularly regarding data sharing and secondary use.

Professional Escalation Criteria

Researchers should escalate consent issues to their institutional review board or other appropriate authority when they are uncertain about the consent process or when they identify a potential issue. This includes situations where participants may not have been adequately informed or where the consent process may not be compliant with requirements.

Building a Consent Decision Matrix for Protocol Changes

A recurring challenge in longitudinal and digital health studies is deciding whether a protocol change requires re-consent, a simple notification, or no participant contact at all. Researchers often default to either over-consenting, which creates consent fatigue, or under-consenting, which risks violating participant trust. A structured decision matrix resolves this ambiguity by creating a consistent, defensible process for evaluating every protocol modification.

The Three-Tier Classification System

The matrix classifies protocol changes into three tiers based on their potential impact on participant welfare, data use, and participant expectations. Each tier triggers a different consent action.

Tier 1: No Consent Action Required

Tier 1 changes do not affect participant experience, data use, or risk profile. Examples include administrative corrections to internal documents, minor staffing changes, or updates to data storage locations that do not change security protections. These changes require documentation in the study log but no participant contact.

Tier 2: Notification Without Re-Consent

Tier 2 changes are informational but do not alter the fundamental scope of participation. Examples include adding a new data analysis method that uses already collected data within the original consent scope, extending the study duration by a few months, or adding a new research team member. Participants should receive a notification through the study portal, email, or regular newsletter. The notification should explain the change and remind participants that they can withdraw or modify consent at any time.

Tier 3: Full Re-Consent Required

Tier 3 changes alter the core elements of what participants agreed to. Examples include collecting a new type of sensitive data, sharing data with a new external organization, using data for a purpose outside the original scope, or extending the study significantly beyond the stated timeframe. These changes require formal re-consent using the full consent process, including a revised consent form and an opportunity for participants to ask questions.

Building the Decision Matrix

The matrix should be created before the study begins and reviewed by the institutional review board. It should include the following columns for each potential change type:

The matrix should be specific to each study. A change that is Tier 2 in one study may be Tier 3 in another, depending on the sensitivity of the data and the participant population.

Applying the Matrix in Practice

When a protocol change is proposed, the research team should follow a structured workflow.

Step 1: Document the Proposed Change

Record the nature of the change, the reason for the change, and the date it was proposed. This documentation should be stored in the study master file.

Step 2: Classify the Change

Use the decision matrix to classify the change as Tier 1, Tier 2, or Tier 3. If the change does not fit neatly into an existing category, the team should discuss it and document the reasoning for the classification.

Step 3: Escalate Uncertain Cases

If the team is uncertain about the classification, the change should be escalated to the institutional review board or a designated ethics advisor. The escalation should include the proposed change, the team's assessment, and the specific question about consent requirements.

Step 4: Execute the Consent Action

For Tier 2 changes, send the notification and log it. For Tier 3 changes, prepare the new consent form, contact participants, and document the re-consent process.

Step 5: Update the Matrix

After the change is processed, review the matrix to see if it needs to be updated to cover similar future changes. This keeps the matrix relevant as the study evolves.

Common Failure Patterns in Consent Decision-Making

Failure to Use the Matrix

Some teams create a matrix but do not use it consistently. This happens when the matrix is not integrated into the protocol change process or when staff are not trained on its use. The matrix should be referenced in the study standard operating procedures.

Overly Broad Tier 3 Classification

Some teams classify too many changes as Tier 3, leading to unnecessary re-consent and participant burden. This often happens when the team is risk-averse or when the matrix is not specific enough. The matrix should define clear thresholds for each tier.

Under-Classification of Sensitive Changes

The opposite failure is classifying a Tier 3 change as Tier 2 or Tier 1. This can happen when the team does not fully understand the implications of the change or when the change is subtle. The matrix should include examples of changes that are likely to be Tier 3.

Inconsistent Application Across the Team

Different staff members may classify the same change differently. This can be addressed by training and by having a designated person review all classifications.

Records for the Decision Framework

The decision framework requires specific records to be effective.

Change Log

The change log records every proposed protocol change, the date, the classification, and the consent action taken. This log provides an audit trail for the study.

Classification Justification Notes

For each change, the team should document the reasoning for the classification. This is particularly important for changes that are not clearly Tier 1, Tier 2, or Tier 3.

Notification Log

The notification log records all Tier 2 notifications, including the date, the method of communication, and the content of the notification.

Re-Consent Log

The re-consent log records all Tier 3 re-consent activities, including the date, the consent form version, and the outcome.

Professional Escalation Criteria

The research team should escalate to the institutional review board when any of the following occur:

• The team cannot agree on the classification of a change.
• The change involves a data type or data use that was not anticipated in the original protocol.
• The change affects a vulnerable participant population.
• The team identifies that a previous change was misclassified and participants were not properly informed.

The [<a href="#ref-1">1</a>] Committee on Publication Ethics core practices provide context for handling ethical issues in research, and the [<a href="#ref-2">2</a>] NIH Grants and Funding resources can help researchers understand the expectations for grant-funded studies. The [<a href="#ref-3">3</a>] NIH Data Management and Sharing Policy also provides context for how data sharing changes should be handled in the consent process.

Integration with the Study Workflow

The decision matrix should be integrated into the study workflow from the start. It should be referenced in the study protocol, the standard operating procedures, and the staff training materials. The matrix should be reviewed at each study milestone to ensure it remains current.

The matrix also supports the reporting requirements of the [<a href="#ref-4">4</a>] EQUATOR Network guidelines, which emphasize transparent reporting of research methods. By documenting the consent decision process, the study team can demonstrate that consent was maintained appropriately throughout the study.

Frequently Asked Questions

What is the difference between initial consent and ongoing consent?

Initial consent is obtained at the start of a study and covers the planned research activities. Ongoing consent is the process of revisiting consent as the study evolves, which may include re-consent at checkpoints or using a dynamic consent model.

When should re-consent be obtained?

Re-consent should be obtained when there are significant changes to the study, such as new data collection methods, new data uses, or changes to the study protocol. Re-consent may also be obtained at regular intervals, such as annually.

What is dynamic consent?

Dynamic consent is a model that uses technology to allow participants to manage their consent over time. Participants can access a platform to view study information, adjust their preferences, and receive updates.

How can researchers maintain participant engagement in the consent process?

Researchers can maintain engagement by communicating regularly with participants, providing clear and accessible information, and using tools such as consent portals. It is also important to minimize the burden on participants.

What are the challenges of ongoing consent in digital health research?

Challenges include managing passive data collection, addressing data sharing and secondary use, and keeping up with changes in technology. These challenges require careful planning and communication.

How should consent be documented?

Consent should be documented with the consent form, the date of consent, the version of the consent form, and any re-consent activities. This documentation should be maintained for the duration of the study.

What should researchers do if a participant wants to withdraw consent?

Researchers should respect the participant's decision and follow the study's procedures for withdrawal. This may include stopping data collection and ensuring that the participant's data is handled according to their wishes.

Where can I find more information about consent in research?

The National Library of Medicine provides access to authoritative biomedical books and research-method references that can help researchers understand consent practices. The National Institutes of Health also provides information about data management and sharing policies that may be relevant.

Related Articles

• Cross-Sectional vs. Longitudinal Studies: Choosing the Right Design for Your Research Question
• Open Science Practices: A Guide for Researchers
• The Role of the Research Ethics Committee: What Researchers Need to Know
• Understanding Internal Validity in Research: Threats and Mitigation Strategies
• Conducting a Study: A Practical Checklist for Life Science Researchers

References and Further Reading

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.