Breathing Easier: How Wearables Are Revolutionizing Patient-Reported Outcomes in Respiratory Disease
The rise of wearable technology is transforming how clinicians track chronic respiratory diseases like asthma and COPD (chronic obstructive pulmonary disease). Traditionally, managing these conditions has relied heavily on intermittent clinic visits and subjective symptom reports. But what if we could continuously monitor how patients breathe, move, and feel — right from their homes?
Enter wearables: smart devices that collect real-time physiological and behavioral data. These devices typically work in tandem with smartphone apps that prompt patients to complete patient-reported outcome (PRO) measures — allowing for integrated, real-time tracking of a full range of patient-relevant outcomes. When combined, these tools offer a powerful new lens for respiratory health.
Why PROs matter in respiratory disease
PROs are essential for understanding the true impact of respiratory disease on daily life. PRO measures like the Asthma Control Test (ACT), COPD Assessment Test (CAT), and modified Medical Research Council (mMRC) Dyspnea Scale help patients communicate their symptoms and limitations. Yet, these snapshots — typically completed during in-clinic visits — often miss the nuances of fluctuating symptoms and the effects of lifestyle or environment.
This is where wearables shine: they offer objective, continuous, real-world data that can complement traditional PROs — typically administered in-clinic on paper or electronically — by adding daily context and physiological insight to self-reported symptoms. By enabling patients to complete PRO measures remotely, often via smartphone apps, paired with real-time wearable data, we gain a fuller, more continuous picture of their health and functioning.
What wearables can measure
Modern wearables can track a range of data relevant to respiratory care, including:
- Physical activity (steps, walking time, exertion)
- Heart rate and heart rate variability
- Respiratory rate and breathing patterns
- Sleep quality and disruptions
- Environmental exposures (via linked apps or sensors)
While wearables provide continuous physiological data, PROs are typically captured via separate smartphone apps or digital platforms, where patients log symptoms, functioning, or side effects on a scheduled or event-triggered basis.
When patients report increased fatigue or shortness of breath, wearables can confirm whether activity levels dropped, sleep was disrupted, or physiologic stress markers changed — giving clinicians a fuller picture of disease impact and progression.
Applications in COPD and asthma
One of the most promising areas for wearables in respiratory care is pulmonary rehabilitation (PR). PR is a cornerstone therapy for COPD and increasingly recommended for severe asthma. However, adherence and engagement outside clinical settings can be challenging.
Wearables like Fitbit or Garmin devices are being used in PR programs to:
- Monitor daily activity levels
- Set and track exercise goals
- Deliver motivational feedback
- Correlate physical activity trends with PROs such as dyspnea and fatigue
Recent studies suggest that integrating wearables into PR not only boosts patient motivation but also correlates with improved self-reported symptoms and quality of life.
Another area of growth is early detection of exacerbations. New wearable patches and multi-sensor systems can detect subtle changes in respiratory rate, coughing, or oxygen saturation — sometimes days before a patient would seek help. When combined with self-reported symptoms like increased breathlessness or wheezing, these alerts could trigger early intervention and reduce hospitalizations.
Case in point: A digital lifeline for COPD patients
In one pilot program, COPD patients were equipped with a wearable sensor that tracked activity, respiratory patterns, and heart rate. They also submitted weekly symptom reports via an app. When wearable data indicated decreased activity and rising respiratory rate, and the patient-reported worsening breathlessness, clinicians were alerted and could intervene early — often adjusting treatment or scheduling a check-in before an exacerbation worsened.
This “digital safety net” approach is gaining traction as a way to personalize care and improve outcomes, especially in vulnerable or remote populations.
Challenges to widespread use
Despite their promise, wearables in respiratory care face several hurdles:
- Data integration: Many devices still don’t seamlessly connect with electronic health records (EHRs).
- Clinical validation: While feasibility is proven, more large-scale trials are needed to show that wearable-enhanced PRO monitoring improves long-term outcomes.
- Implementation: Providers may require training in how to teach their patients to utilize wearables and the associated smartphone apps that collect PRO data, meaning that time spent on these activities should be considered billable.
- Equity and access: Not all patients have smartphones, internet access, or feel comfortable using digital devices — particularly older adults, those in underserved or rural communities, and individuals facing technological or connectivity barriers.
- Privacy and regulation: Health data from consumer-grade devices must be handled securely, and many wearables are not yet classified as medical devices.
The road ahead
With increasing support from healthcare systems, regulators, and tech companies, the future looks bright for wearable-assisted respiratory care. Remote patient monitoring is now reimbursable in countries like the U.S., and smart integration with PRO tools is making these technologies more usable and impactful.
As clinicians and researchers continue to validate these tools, we can expect wearables — and the PRO data they pair with — to become a routine part of respiratory disease management. Smartphone apps are now central to this ecosystem, not just for data capture but for delivering care.
Streamlining Data Management and Improving Statistical Accuracy in Clinical Trials with AI
As clinical trials grow increasingly complex, the need for smarter, faster, and more efficient data processes and analysis is in demand. Artificial intelligence (AI) emerges as a powerful tool, especially in programming and data management. For clinical trial professionals, AI offers the promise of streamlining workflows, improving data quality, and reducing time to database lock.
The evolving role of AI in clinical data programming
AI is not replacing clinical programmers; it’s augmenting them. AI should be considered a tool to use within clinical trials, just as EDC and SAS are commonly used tools. Automation tools driven by machine learning can now handle routine, rules-based programming tasks such as edit check generation, derivation logic, and data transformation. This allows programmers to focus on more strategic activities like validating statistical code or optimizing data pipelines. AI needs the expertise of our clinical trial professionals.
Natural Language Processing (NLP) is also making great progress. For instance, NLP can interpret free-text protocol documents to auto-generate specifications, electronic case report form (eCRF) templates, or even suggest initial SDTM mappings, significantly reducing manual effort.
AI in data cleaning and quality oversight
Traditionally, data cleaning has been labor-intensive, with data managers manually reviewing queries, data listings, and edit checks across multiple data sources and systems. AI tools can now proactively flag anomalies or data trends that human review might miss, such as unexpected patterns in lab values, inconsistencies across visits, or possible fraudulent data across participants and sites.
Predictive models can help identify study participants at high risk of dropout or noncompliance, enabling earlier intervention. This not only improves data completeness but also enhances trial efficiency and participant retention. The effort and cost of replacing clinical trial participants is significant and felt across all stakeholders. Improving the patient’s experience would be a significant way to save time, money, and accelerating progress.
AI in statistical programming: From code automation to advanced insights
Statistical programming is central to clinical trial analysis from producing tables, listings, and figures (TLFs) to preparing submission-ready datasets. Traditionally reliant on manual coding in SAS or R, this work is now gaining speed, consistency, and quality through AI augmentation.
Where AI adds value in statistical programming
- Automated code generation: AI models trained on historical programming logic can produce initial SAS macros or R scripts for common TLFs and dataset derivations. These drafts accelerate development by up to 40–60%, freeing programmers and biostatisticians to focus on complex analyses and interpretation.
- Code review and validation: AI-assisted tools can scan code for logic errors, inefficiencies, redundant steps, and deviations from programming standards. Acting as a “second reviewer,” they flag potential issues early and suggest optimizations.
- Dynamic statistical modeling: AI algorithms can rapidly explore large trial datasets to detect subgroup effects, anomalies, or emerging trends. When guided by statistical oversight, these insights can refine analysis plans and support adaptive trial decisions.
The aim is not to replace human judgment, but to boost productivity, reproducibility, and the speed of insight generation, without compromising scientific rigor.
AI in biostatistics: Powering smarter, more adaptive clinical trials
Biostatistics remains the foundation of evidence generation in clinical trials, providing the methodological rigor to transform raw data into reliable conclusions. In the context of AI, biostatisticians play a dual role: safeguarding scientific validity while leveraging new computational tools to enhance insight generation. This requires a careful balance between deep domain knowledge and technical proficiency in emerging AI-driven methodologies. From applying knowledge graphs (KGs) to map complex biomedical relationships, to developing predictive models that anticipate trial outcomes, biostatistics is evolving into a more dynamic and interconnected discipline.
Where AI adds value in biostatistics
- Balanced expertise: Integrating statistical theory with AI/ML techniques to ensure robust, interpretable results.
- Knowledge graph applications: Using KGs to uncover hidden relationships between biomarkers, treatments, and outcomes, supporting hypothesis generation and trial design.
- Early prediction tools: Building predictive models for recruitment success, dropout risk, and endpoint achievement.
- Segmentation and personalization: Identifying patient subgroups most likely to benefit from a therapy, improving trial efficiency and precision medicine strategies.
- Support for registrational trials: Leveraging AI to optimize trial design, stratify patient populations, and run simulations that ensure the study is powered and structured for regulatory success.
Regulatory readiness and caution
Despite its promise, AI must be implemented thoughtfully. Regulatory agencies like the FDA are increasingly open to the use of advanced technologies but expect transparency, traceability, and validation. AI-based tools must be auditable and explainable, especially when used in clinical data workflows that feed into regulatory submissions.
What’s next?
As AI becomes more embedded in clinical trial ecosystems, we can expect increased integration with EDC systems, CDISC standards, and statistical programming tools. The goal isn’t to eliminate human oversight but to enhance it, allowing clinical data professionals to make faster, better-informed decisions.
Final takeaways
AI is reshaping programming and data management in clinical trials. For clinical trial professionals, now is the time to become familiar with these tools, understand their capabilities and limitations, and engage with cross-functional teams to ensure responsible and impactful implementation. Ultimately our goal is to shorten drug development timelines and improve patient outcomes. With AI, we can be part of the solution to provide improved treatments for patients.
Interested in learning more?
Join Steven Thacker, Sheree King, Kunal Sanghavi, and Juan Pablo Garcia Martinez for their upcoming webinar, “How AI Enhances Biometrics Services: Streamlining Data Management and Improving Statistical Accuracy in Clinical Trials” on Thursday, August 28 at 10 am ET:
Improving Efficiency in Oncology Dose-Escalation Trials: A Cautious Bayesian Approach
In the dynamic world of oncology drug development, the complexity of dose-finding studies increases substantially when multiple disease types are evaluated within a single trial. The heterogeneity between cancer types poses a critical challenge: how can we design efficient dose-escalation procedures that account for patient differences across indications particularly when one indication recruits more quickly than the other?
A new approach, cautious iBOIN (ciBOIN), offers a compelling answer. Built on the foundation of the Bayesian Optimal Interval (BOIN) design and its variant with informative priors (iBOIN), ciBOIN introduces a prudent method for borrowing strength from common cancer types that recruit faster to rarer types with slower recruitment while maintaining separate maximum tolerated dose (MTD) estimation for each cancer type.
The dose-escalation dilemma in multi-cohort trials
Traditional dose-escalation designs often face a trade-off between safety and efficiency. When trials pool data across disease types, they risk obscuring differences in toxicity profiles.
On the other hand, treating each type entirely independently can lead to missed opportunities to leverage valuable information.
Enter ciBOIN: A pragmatic compromise
The ciBOIN method was developed as a compromise between pooling disease types and separate dose-escalation. It allows dose-escalation decisions in the slower-recruiting disease type to be cautiously influenced by data from the faster-recruiting one. The design is particularly appealing in trials where each disease type may require a distinct MTD estimation due to differing patient profiles.
Through extensive simulations, ciBOIN was compared against separate dose-escalation using BOIN over a range of scenarios. The assessed scenarios and results can be classified in three categories:
- Same toxicity in both disease types: ciBOIN leads to similar or slightly better MTD detection rates with less patients overdosed and a lower DLT rate compared to a separate dose-escalation.
- Higher toxicity in the common disease type: ciBOIN underestimates the MTD for the rare type but achieves improved safety, reducing the number of patients exposed to overly toxic doses and lowering the overall dose-limiting toxicity (DLT) rate compared to a separate dose-escalation.
- Higher toxicity in the rare disease type: Here, ciBOIN again underestimates the MTD a bit, this time in the common disease type, but again with reduced overdosing rates.
Overall, ciBOIN results in smaller trial sizes. The highest reduction (~3 patients) with ciBOIN compared to separate dose escalation was observed in the highest dose-toxicity profile.
A balanced path forward
The findings support ciBOIN as a viable compromise between full pooling and strict separation. It ensures that dose recommendations are never too aggressive, thereby safeguarding patient safety while still achieving gains in operational efficiency.
Notably, ciBOIN enables a nuanced strategy: one that adapts to the heterogeneity of real-world oncology trials without overcomplicating implementation. For sponsors and statisticians navigating increasingly complex pipelines, this approach may offer a timely and practical innovation.
Looking ahead
As oncology trials continue to evolve toward platform and umbrella designs, methods like ciBOIN will be instrumental in ensuring both flexibility and rigor. Future work may explore extending the framework to accommodate more than two cohorts or using other approaches than BOIN and iBOIN.
Ultimately, ciBOIN exemplifies how thoughtful design choices, informed by Bayesian thinking and tempered by clinical caution, can help meet the dual mandate of safety and speed in early-phase drug development.
Interested in learning more?
Martin Kappler, along with Yuan Ji from the University of Chicago, will present “ciBOIN — A Bayesian-Informed Dose-Escalation Design for Multi-Cohort Oncology Trials with Potentially Varying Maximum Tolerated Doses” at the 46th Annual Conference of the International Society for Clinical Biostatistics (ISCB) on August 24–28, 2025, in Basel, Switzerland.