A smartwatch can count steps, estimate active minutes, track heart rate, and summarize sleep. For patients, that feedback can be motivating. For clinicians, it offers a glimpse into the thousands of hours between medical appointments.
The central challenge is no longer whether wearables can collect data. They can. The harder question is whether healthcare can interpret those data in a way that is clinically useful, operationally manageable, and meaningful to the patient.
The evidence is not the problem
In 2022, Ferguson and colleagues published a large systematic review in The Lancet Digital Health examining wearable activity trackers and their effect on physical activity and health outcomes. The findings were clear: wearable activity trackers can increase physical activity, particularly when combined with feedback, behavioral support, and broader intervention design.
That matters clinically because physical activity is not simply a wellness preference. It is a major determinant of cardiometabolic health, functional capacity, and long-term risk.
But the evidence also points to a limitation. A wearable device may support behavior change, but the device itself is rarely the complete intervention. The science is not the bottleneck. The system is.
Remote patient monitoring: what clinical wearable programs get right, and where they fall short
Consumer wearables have normalized the idea that daily behavior can be measured. Patients who once had little objective sense of their movement can now see steps, exercise minutes, trends, streaks, and reminders in real time.
Remote patient monitoring programs are genuinely trying to bridge wearables and clinical care. They have helped create reimbursement pathways and workflow concepts. But measuring movement is not the same as managing movement.
A step count may tell us that a patient moved. It does not tell us whether that change was meaningful from baseline.
A step count does not tell us whether the change was sustained, whether it reflected improved fitness, whether symptoms limited progress, or whether the information should change a clinical conversation. Wearables identify the visibility problem. They do not, by themselves, build the workflow, interpretation layer, or accountability structure needed to act on that information.
The proof of concept and its limits
If wearables represent the visibility layer, cardiac rehabilitation represents the proof of concept for structured exercise-based care.
Cardiac rehabilitation shows what physical activity can look like when it is embedded in a clinical model: supervised, measured, progressive, behaviorally supported, and connected to risk-factor management. It is one of the clearest examples of exercise becoming part of healthcare rather than remaining advice given at the end of a visit.
It also reaches only a fraction of the people who could benefit. Many patients with cardiovascular risk, cardiometabolic disease, or low functional capacity are not eligible for traditional cardiac rehabilitation. Others are eligible but do not attend, do not complete, or lose support once the program ends.
This reflects how healthcare has historically designed exercise: as a referral pathway for selected patients rather than a scalable clinical behavior-management system. Wearables may help extend visibility beyond formal programs, but visibility without interpretation does not solve the clinical problem.
The nuance most platforms miss
Access to wearable data is not the same as physical activity management. The key concept is baseline-referenced change.
A 68-year-old patient recovering from a cardiac event who increases from 1,500 to 5,500 steps per day is not the same clinical situation as a 52-year-old patient with hypertension whose step count falls from 9,000 to 5,000 steps per day. The same number can mean progress, decline, compensation, or noise depending on the patient's baseline and clinical context.
Clinicians do not need every data point a wearable can generate. They need to know whether the patient is improving, plateauing, declining, disengaging, or developing a pattern that should prompt a different conversation.
This is the gap that neither consumer wellness platforms nor traditional episodic care fully occupy: the space between activity advice and clinical infrastructure, between a recommendation to move more and a managed clinical behavior.
What closing the gap requires
Making wearable activity data clinically meaningful requires capabilities that healthcare still lacks at scale.
- A clinically relevant measurement framework. Steps, active minutes, heart rate, sedentary time, and exercise intensity can all be useful, but they should not be treated as interchangeable.
- Baseline-referenced interpretation. The same activity number can mean different things in different patients. Clinical usefulness depends on understanding change from the patient's starting point.
- Workflow integration for clinicians. Most clinicians cannot review months of raw wearable data during a short appointment. Information must be summarized in a way that is concise, interpretable, and connected to a reasonable next step.
- Behavioral support when progress stalls. Wearables can show that activity declined, but they usually cannot explain why. A clinically serious model needs a way to identify and respond to disengagement.
None of these requirements are technologically impossible. They are operationally underdeveloped.
Why this moment matters
Two converging forces are creating an unusual window for progress. First, cardiometabolic prevention is becoming more clinically and economically important. Payers and health systems increasingly need prevention models that can be measured, managed, and scaled. The widespread adoption of GLP-1 medications has created a new urgency around lifestyle co-intervention.
Second, wearable adoption has created a new data layer outside the clinic. Patients are already generating information about movement, activity, sleep, and physiology. The question is whether healthcare can convert that information into something more useful than a graph inside a consumer app.
The missing piece is not the evidence or the technology. The missing piece is the clinical architecture that connects the two, and makes physical activity visible, interpretable, and accountable within the care system.
Frequently asked questions
Are wearable devices clinically useful?
Wearable devices can be clinically useful when they measure something relevant, produce reliable information, and support an appropriate clinical conversation or action.
Why are step counts not enough?
Step counts are easy to understand, but they do not fully describe physical activity, exercise intensity, sedentary time, symptoms, or cardiorespiratory fitness.
What is baseline-referenced change?
Baseline-referenced change means interpreting activity based on where the patient started rather than relying only on a universal target. The same number can signal improvement for one patient and deterioration for another.
Why have wearables not been fully integrated into clinical care?
Wearables generate large amounts of data, but most healthcare workflows are not designed to interpret continuous consumer-device information. Clinicians need concise, validated, context-aware summaries rather than raw data streams.
Can wearable devices replace physicians or cardiac rehabilitation?
No. Wearables should not be treated as replacements for medical care, cardiac rehabilitation, or professional clinical judgment.
What do payers and health systems need from wearable data?
Payers and health systems need clinically meaningful measures, clear patient selection, workflow fit, evidence of sustained behavior change, and a plausible link to better care delivery or reduced avoidable utilization.
References
- Ferguson T, Olds T, Curtis R, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digital Health. 2022;4(8):e615-e626.
- Hughes A, Shandhi MMH, Master H, Dunn J, Brittain E. Wearable devices in cardiovascular medicine. Circulation Research. 2023;132(5):652-670.
- Biswas A, Oh PI, Faulkner GE, et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162(2):123-132.
- Tang MSS, Moore K, McGavigan A, Clark RA, Ganesan AN. Effectiveness of wearable trackers on physical activity in healthy adults: systematic review and meta-analysis of randomized controlled trials. JMIR mHealth and uHealth. 2020;8(7):e15576.
- Alter DA, et al. The implementation of a value-based learning health system for preventative care in Ontario, Canada. Am J Cardiovasc Dis. 2023.
- Alter DA. From insight to infrastructure: managing cardiorespiratory fitness. J Am Coll Cardiol. 2026.