Changes in an older adult’s daily routine can be difficult to explain during an occasional appointment. Someone may be walking less, waking more often, or finding everyday tasks harder without knowing when the change began.

Digital phenotyping uses information from digital devices to help describe those patterns over time. It is being studied for early detection and clinical research, with potential applications in remote monitoring and personalized care. Its usefulness depends on the reliability of the measurements and how they are interpreted alongside the person’s circumstances.

What Information Does It Collect?

Digital phenotyping can involve passive measurements and active participation.

Passive data is collected without asking the person to complete a task each time. Depending on the device and permissions, this might include movement recorded by a wearable, travel patterns from a smartphone, or activity detected by sensors in the home.

Active data requires a response, such as completing a short memory task, recording a speech sample, or answering questions about sleep and mood. These responses can provide context that sensors cannot capture.

For example, a wearable might record less movement during the week. A brief check-in could reveal that the person has knee pain, is recovering from an illness, or has simply changed their routine. Interpreting the measurements alongside that information helps avoid assumptions.

How Digital Phenotyping Can Support Older Adults

Early Detection of Changes in Health

Digital phenotyping is being studied as a way to identify changes that could warrant earlier assessment. Researchers are examining whether patterns in movement, sleep, and other everyday activities relate to cognitive or functional difficulties.

A study published in JMIR Human Factors followed 37 older smartphone users for four weeks. It found associations between GPS-based mobility patterns and measures of cognition, everyday functioning, and mood. This suggests that movement data may contribute useful information, but the study did not establish a standalone diagnostic test or prove that monitoring improves outcomes. 

The potential use is to help identify a concern worth investigating. Spending less time outside, for example, could reflect a health problem, but it could also reflect poor weather or a change in transport. Clinical assessment remains necessary to understand the cause.

Clinical Trials

Digital phenotyping can give researchers a more detailed view of participants’ daily experiences between trial visits. Repeated measurements may help describe changes in activity or function over the course of a study.

For older participants, collecting some information at home may reduce travel demands. However, devices and tasks need to be manageable, including charging, setup, and troubleshooting.

Researchers also need to understand what a measurement means. An increase in recorded activity, for example, needs to be considered alongside symptoms, daily functioning, and other study findings before drawing conclusions about a treatment.

Our article on data platforms for clinical trials discusses how information from wearables, patient reports, and clinical systems can be brought together.

Remote Monitoring

Remote patient monitoring and digital phenotyping overlap, but they describe different things.

Remote patient monitoring allows care teams to review health information collected outside a clinical setting, such as blood pressure or glucose readings. Digital phenotyping focuses on patterns in behavior and functioning derived from digital data. Those patterns can become an additional source of information within a remote monitoring service.

For an older person managing a long-term condition, changes in daily activity could provide context alongside symptoms and physiological readings. The care team would still need to establish whether the change is clinically relevant and whether any response is needed.

Our article on remote patient monitoring software and AI explores the wider role of analytics and dashboards in reviewing information collected at home.

Care at Home

Home sensors can record aspects of daily routines without requiring someone to carry a phone or wear a device. Depending on the system, they may detect movement between rooms, door activity, or time spent in bed.

Research into in-home sensors for older adults has explored whether these measurements can help identify behavioral differences associated with cognitive impairment. These studies support further investigation, but do not establish that installing sensors will keep someone independent or prevent hospitalization. 

In a care setting, a change in routine could provide a reason to check in. It would need to be interpreted alongside what the person and their caregivers report. Visitors, shared living arrangements, and time away from home can all affect what household sensors record.

We discuss the broader use of wearables and remote monitoring to support aging at home in a related article.

Personalized Care

Repeated observations make it possible to examine changes against a person’s own usual pattern.

Consider two people with similar activity levels. One has maintained that level for years. The other was previously much more active and has recently reduced their walking. The same daily total could have a different meaning for each person.

That context may help a clinician decide what to ask about during a consultation. It can also inform discussions about whether an agreed care plan is manageable in daily life.

Personalization should include the person’s preferences. Someone may welcome activity tracking while declining location monitoring. Another person may prefer occasional questions to wearing a device throughout the day.

Best Practices for Digital Phenotyping in Older Adults

Use Measurements That Suit the Purpose

A device’s ability to record movement does not establish that it can identify cognitive decline or another health problem. The measurement needs to be evaluated for the particular question being asked and the people being monitored.

For older adults, this includes considering differences in mobility, device use, and daily routines. Results from younger, healthier participants may not apply to someone who uses a walking aid or needs help operating a device.

Interpret Changes in Context

A personal baseline can help explain what is usual for someone, but it is only part of the assessment. Medication changes, pain, travel, and changes in living arrangements can affect daily behavior.

Good interpretation allows for those explanations. It also recognizes when there is too little reliable information to draw a conclusion.

Keep Participation Manageable

Passive collection still involves effort. A wearable may need charging, an app may lose permissions, and a sensor may need maintenance.

Readable instructions, comfortable devices, and accessible support help people take part. Short active tasks should have a clear purpose. Older adults also vary in their familiarity with technology, so age alone is a poor guide to the support someone needs.

Make Consent Understandable

People should know what is being collected, who can access it, and what it will be used for. Location and household activity data can reveal personal routines even when no audio or video is recorded.

Consent discussions should explain the available choices, including how to pause or stop monitoring. Where cognitive impairment affects decision-making, the process needs appropriate support while respecting the person’s wishes and applicable consent requirements.

Connect Findings With Clinical Review

A monitoring result is useful only if someone can interpret it appropriately. Patients and caregivers need to understand who reviews the information and when.

Services should also make clear whether monitoring is periodic or continuous, and whether anyone responds outside normal working hours. A device that collects data throughout the day does not necessarily provide round-the-clock clinical supervision.

Common Issues and Limitations

Missing Data Can Be Mistaken for a Health Change

A low activity reading may reflect reduced movement, but it may also mean that a device was not worn. Missing smartphone data could result from a dead battery or a permissions change.

These gaps need to remain visible during interpretation. Before treating a change as a health concern, the person reviewing the data needs to check whether the device was collecting information as expected

Alerts Can Create Unnecessary Concern

An alert may identify a change that turns out to be harmless. A system can also fail to detect a genuine problem.

Frequent unnecessary alerts can create anxiety and extra work for care teams. Conversely, the absence of an alert should not be presented as proof that someone is well. Patients still need a way to report symptoms or concerns directly.

Findings May Not Apply to Every Older Adult

Results from people who already own smartphones and are comfortable using them may not represent people with limited connectivity, sensory impairments, or greater support needs.

Differences in language, disability, and living circumstances also affect how data should be interpreted. Our discussion of building AI that includes underserved patients explains why the people represented in development and testing matter.

Monitoring Can Feel Intrusive

A person may understand the purpose of monitoring and still find it uncomfortable. Family members may want more information than the person wishes to share.

Respecting those boundaries is part of providing care. Collecting only the information needed for an agreed purpose, and explaining access clearly, gives people a better basis for deciding whether to participate.

Measuring Change Does Not Prove Better Care

A system may detect a pattern accurately without improving a clinical decision or helping the person being monitored. Those benefits need to be evaluated separately.

For older adults, useful evaluation should consider whether monitoring leads to appropriate follow-up, whether its demands are acceptable, and whether it supports outcomes that matter to them. That might mean managing daily activities more comfortably or receiving help when their needs change.

Frequently Asked Questions

What is digital phenotyping in simple terms?

Digital phenotyping uses information from devices such as smartphones and wearables to understand how someone behaves and functions during daily life. Measurements collected over time can show patterns in movement, sleep, and other activities that may be relevant to their health.

Can digital phenotyping diagnose dementia?

Digital phenotyping should not be treated as a standalone diagnosis of dementia. Researchers are studying whether changes in everyday behavior can help identify people who need further assessment. A change in movement or routine can have several explanations, so clinical evaluation remains necessary.

How is digital phenotyping different from remote patient monitoring?

Remote patient monitoring is a way of collecting and reviewing health information outside a clinical setting. Digital phenotyping is an approach to understanding patterns in behavior and functioning through digital data. A remote monitoring service may use digital phenotyping alongside measurements such as blood pressure or glucose.

Does digital phenotyping require a smartphone?

Not always. Some approaches use wearables or sensors installed in the home. Others rely on smartphone data or ask participants to complete tasks on a phone or tablet. The equipment depends on what is being measured and what the person can comfortably use.

How can digital phenotyping support personalized care?

It can help care teams understand whether someone’s behavior has changed from their usual pattern. That information can support conversations about symptoms, daily activities, and care needs. Decisions still need to account for the person’s medical history, circumstances, and preferences.

Develop Your Digital Health Product With Estenda

If you are exploring digital phenotyping, Estenda can help you work through the research, software, and data requirements. Our Product Acceleration Framework guides product development with attention to security and compliance, alongside the needs of the people who will use it.

We support MedTech, life sciences, and healthcare organizations with research and strategy, custom software development, data analytics, AI/ML services, and implementation and support. For a digital phenotyping project, this can include connecting device data, developing patient applications, and building dashboards that help researchers and care teams interpret changes over time.

Our focus is on delivering useful value early, reducing development risk, and understanding how the product fits into patients’ lives and clinical workflows. Contact us today at info@estenda.com to learn more.