From consumer tracking to clinical infrastructure: what the next phase of wearables means for healthcare

Wearables have been part of healthcare for decades, from ambulatory cardiac monitoring such as the Holter monitor, while fitness trackers and smartwatches have brought routine self-monitoring into everyday life for many of us. What has changed is not their existence, but the range of physiological data they can now capture, the quality of that data, and the seriousness with which health systems are beginning to use them.

Wearables have moved beyond simple activity tracking, measuring steps, sleep, and heart rate towards continuous physiological observation. They are increasingly used to estimate heart rate variability, respiratory rate, oxygen saturation, temperature, rhythm irregularity, and other markers relevant to prevention, long-term condition management, and recovery. We are also seeing progress not only in physical sensing, but in biochemical sensing, hybrid multimodal sensing, applied to early-warning systems, and more continuous monitoring of chronic disease.

Healthcare still relies heavily on snapshots, with episodic condition-monitoring appointments and patient self-reports in between, shaped by a patient's memory, timing, and what a patient is able to notice or describe. Continuous data changes that. It offers a view of what is happening between appointments, in daily life, and over time. In practical terms, that means services can start to work with trajectories rather than fragments of information.

The policy context has caught up with that shift. England's 10 Year Health Plan explicitly places wearables within the future model of care, stating that by 2035 wearables will be standard in preventative, chronic and post-acute NHS treatment, that all NHS patients will have access to these technologies, and that devices will be provided free in areas where health need and deprivation are highest. The plan places wearables within a wider set of shifts: from hospital to community, analogue to digital, and sickness to prevention. This frames wearables not as optional innovation, but as enabling service transformation and part of future clinical infrastructure.

These devices do not only collect more data: continuous monitoring can reveal clinically relevant patterns that conventional service models either miss or detect too late.

A large Nature Medicine study using long-term commercial wearable sleep data linked to electronic health records found that sleep stages, sleep duration, and sleep regularity were associated with chronic disease incidence, higher odds of obesity, hypertension, major depressive disorder, and generalized anxiety disorder. The point is not that wearables replace diagnosis. It is that longer-term continuous measurement can make patterns visible at a scale and over a duration that conventional studies and routine services are rarely set up to capture.

Prevention often remains vague in policy language. Continuous monitoring starts to make it more operational. If a system can identify deterioration earlier, see instability sooner, or detect patterns that precede diagnosis, then prevention becomes less abstract and more achievable.

Oncology

In oncology, wearable monitoring is increasingly being explored as a way of understanding treatment tolerance, toxicity, fatigue, and functional decline during chemotherapy. A recent systematic review in The Oncologist found that wearable devices are being used in oncology for risk and outcomes prediction, treatment monitoring, and rehabilitation planning, using real-world physiological and activity data to support a more continuous view of how patients are actually coping during treatment. An NIHR study in Greater Manchester is also evaluating whether continuous vital-sign monitoring during cancer treatment is feasible, acceptable, and clinically useful.

Wearables are also being used in cancer follow-up and survivorship. In breast cancer-related lymphoedema, the L-Dex U400 uses bioimpedance spectroscopy to support earlier detection of unilateral lymphoedema after treatment. The value of this is that fluid changes may be detected earlier than would be possible through visual assessment alone, creating the possibility of earlier intervention.

Reproductive and women's health

In reproductive and women's health, we are moving beyond cycle logging into continuous physiological monitoring. Wearable devices such as the Ava bracelet measure multiple physiological parameters during sleep, including temperature and cardiovascular signals, to help identify the fertile window and ovulation timing in real time.

Pregnancy monitoring is another area of rapid development. Bloomlife has developed a wearable for remote monitoring for high-risk pregnancies, built around maternal oversight and home monitoring, with the aim of improving patient safety and clinical oversight outside conventional care settings. More broadly, remote monitoring is becoming a more established model in high-risk pregnancy care.

Biochemical sensing

Wearable sweat sensors are becoming sophisticated non-invasive biochemical sensing systems, measuring metabolites such as cortisol, glucose and lactate, electrolytes including sodium and potassium, and skin temperature in real time through wearable microfluidic and biochemical patches. These platforms are still emerging, but the clinical significance is huge and points towards a future where continuous biochemical sensing could make it possible to detect physiological stress, dehydration, metabolic instability, inflammation, or treatment-related deterioration earlier than is possible through intermittent testing alone, supporting more timely intervention between appointments rather than after problems become clinically obvious.

Drug development and clinical trials

Wearables are also becoming more important in drug development which opens the door to richer digital biomarkers and continuous endpoints in clinical trials, rather than relying solely on intermittent clinic visits and snapshot assessments. In practice, that means wearables are becoming relevant not only to care delivery, but also to how evidence itself is generated.

We are already seeing wearables and patient-generated data being used as part of routine care. The REMORA programme led by the University of Manchester is designed to collect daily rheumatoid arthritis symptoms through a smartphone app and integrate those data into NHS electronic patient records, so they can be used in outpatient consultations. The programme aims to improve consultations by giving both clinicians and patients detailed information on how patients have felt in the weeks and months between appointments.

At an individual level, continuous data can support earlier recognition of deterioration, a more accurate picture of symptoms and recovery, and more tailored intervention. At a service level, it challenges the logic of pathways built around occasional contact.

All of this creates real opportunities. Continuous physiological data can support earlier intervention, more tailored support, better long-term condition management, stronger prevention, and more care delivered beyond hospital walls. It may also help shift healthcare away from occasional contact and towards a model that is more responsive to what is happening in people's actual lives.

The opportunity that patient-generated health data brings is substantial, but so are the challenges.

1. Care integration

The first is care integration. How are wearables integrated into services, how is data interpreted and used clinically, and how ready are organisations to make use of this volume of data? If data is continuous but the service model remains episodic, there is a mismatch. Data sitting in a separate app, a commercial dashboard, or a PDF report is not integrated care. The key issue is whether that information can be used meaningfully and safely inside clinical workflow. Adding a wearable to an unchanged pathway does not automatically improve care. Wearables can generate more data, but without clear clinical responsibility, organisations risk more alerts without the capacity to respond.

2. Service redesign

The second is service redesign. Continuous monitoring does not fit neatly inside services built around intermittent review. If health systems want wearables to support prevention and remote care at scale, then appointment logic, escalation routes, staffing assumptions, and digital support models will need to change. The NHS plan itself implies a move towards new care models, but delivery depends on far more than device availability.

3. Digital equity

The third is digital equity. The promise of wearables is often described in universal terms, but access and benefit are not universal. Device cost, connectivity, digital literacy, language, disability access, trust, and ease of use all shape who can participate. Good Things Foundation says 8.5 million adults in the UK lack the most basic digital skills. The NHS plan's commitment to free provision in high-need areas is important, but hardware alone will not solve the wider participation problem.

4. Data governance and safety

The fourth is data governance and safety. Increased data brings increased risk to the security of that data. We learned only this past week what that risk looks like. UK Biobank data had been advertised for sale on Alibaba platforms in China. While the dataset did not include names or contact details, it did include sensitive health-related variables. The point is broader than one breach. As health data become more detailed, more continuous, and more valuable, governance has to become more robust. Trust is part of the infrastructure too.

Wearables have already moved beyond consumer tracking. They are becoming part of how health systems think about monitoring, prevention, and care beyond the clinic. The policy direction is explicit as the evidence base gets stronger. The question now is not whether the technology is advancing, but whether services are prepared to absorb what that advancement demands. Without integration, service redesign, digital inclusion, and public trust, wearables risk becoming another layer of data without consequence. With them, they could support a more anticipatory, responsive, and personalised model of care. The difference will not be made by the device. It will be made by the system built around it.

---

If you would like to discuss the implications for your organisation, I welcome that conversation. Email us at hello@equitihealth.co.uk

Shoshana Bloom is Founder and Principal Consultant at Equiti Health Ltd, specialising in digital health transformation and AI governance.