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Pharmaceutical and biotech sponsors are increasingly interested in integrating Clinical Outcome Assessments (COAs) with Digital Health Technologies (DHTs) to generate richer, more continuous, and more patient-centred evidence. While this combination promises improved sensitivity, reduced recall bias, and more scalable data collection, it also introduces methodological, regulatory, and operational complexity that should not be underestimated. At its core, the integration of COAs and DHTs is not simply a technology upgrade—it is a redesign of how clinical outcomes are defined, captured, validated, and interpreted. Sponsors who approach this space strategically tend to achieve more robust datasets and fewer regulatory setbacks, while those who treat it as a plug-and-play solution often encounter inconsistencies, missing evidence, or endpoint misalignment. Below are the key considerations sponsors should evaluate before combining COAs and DHTs. 1. Endpoint Alignment and Conceptual Fit The first and most important question is whether the COA and DHT are measuring the same underlying clinical concept. COAs—such as patient-reported outcomes (PROs), clinician-reported outcomes (ClinROs), or performance outcomes (PerfOs)—are designed to capture subjective or semi-objective aspects of health. DHTs, including wearable sensors, smartphone apps, and connected devices, generate continuous or high-frequency objective data. The risk arises when sponsors assume these two data sources are interchangeable. For example, a COA measuring fatigue may not map cleanly to a DHT-derived step count or sleep duration. Without clear conceptual alignment, the integrated endpoint becomes difficult to interpret and may not be accepted by regulators. Sponsors should therefore ensure:
2. Validation of the Digital Measure Unlike traditional COAs, DHTs require additional layers of validation. This includes:
A key challenge is that validation is context-specific. A wearable device validated in a healthy population may not perform the same way in a patient population with mobility limitations. Sponsors should also consider whether the DHT is considered a fit-for-purpose tool within the regulatory framework of agencies such as the FDA or EMA. Lack of validation is one of the most common reasons integrated COA-DHT endpoints are rejected or downgraded in evidentiary strength. 3. Data Integrity and Missingness DHTs generate continuous data streams, but they are also highly susceptible to missingness due to:
When combined, these two data sources create complex missing-data patterns that are not easily handled by traditional statistical approaches. Sponsors should define:
Failure to address these issues early can undermine the credibility of the entire endpoint strategy. 4. Patient Burden and Engagement One of the main motivations for using DHTs is to reduce patient burden by passively collecting data. However, in practice, combining COAs and DHTs can sometimes increase burden rather than reduce it. Patients may be asked to:
If not carefully designed, this can lead to fatigue and reduced compliance, ultimately degrading data quality. Sponsors should prioritize:
5. Regulatory Strategy and Evidentiary Expectations Regulators are increasingly open to digital endpoints, but expectations remain stringent. The combination of COAs and DHTs must be justified in terms of:
A major pitfall is assuming that more data automatically equals better evidence. In reality, regulators prioritize clarity and consistency over complexity. Sponsors should engage early with regulatory agencies to align on:
6. Interoperability and Data Infrastructure COAs and DHTs often originate from different systems:
Without a unified infrastructure, sponsors risk:
7. Statistical Integration and Endpoint Hierarchy One of the most complex aspects of combining COAs and DHTs is determining how they are analyzed together. Options include:
Each approach has implications for power, interpretability, and regulatory acceptance. Sponsors must ensure that the statistical analysis plan clearly defines:
8. Governance, Change Control, and Vendor Management DHTs introduce ongoing operational complexity because devices, firmware, and algorithms may change during a trial. This creates risks for consistency and comparability across time. Sponsors should implement:
Without governance, even well-designed studies may suffer from inconsistencies that invalidate results. 9. Interpretation and Clinical Meaningfulness Ultimately, the goal of combining COAs and DHTs is to enhance the understanding of patient experience and clinical outcomes. However, interpretation remains a key challenge. For example:
Sponsors should predefine meaningful change thresholds and ensure alignment with clinical context, not just statistical significance. Where does the DHP-18 fit in? The Diabetes Health Profile-18 (DHP-18) is a validated, diabetes-specific patient-reported outcome measure (PROM) used to assess the psychological and behavioural impact of living with diabetes. It is designed for people with Type 1 and Type 2 diabetes and is typically used in clinical trials, observational studies, and health outcomes research. It was developed as a shortened version of the original DHP-1 and contains 18 items across three domains:
Each item is scored on a Likert scale and transformed into domain scores (0–100), where higher scores indicate worse outcomes / poorer health-related quality of life. The integration of Clinical Outcome Assessments (COAs) like the Diabetes Health Profile-18 (DHP-18) with Digital Health Technologies (DHTs) is reshaping how diabetes outcomes are measured in clinical research. Rather than treating these as separate data streams, modern trial design increasingly views them as complementary expressions of the same underlying disease state. Below is a concise summary of the key principles sponsors should consider when positioning the DHP-18 within a COA–DHT integrated strategy. 1. Start with the clinical concept, not the tools Successful integration begins by defining what is being measured (e.g., diabetes burden, treatment impact, daily functioning), rather than starting with instruments. The DHP-18 and DHTs are then selected as complementary ways to measure that single concept. 2. Use the DHP-18 as the patient-experience anchor The DHP-18 provides a validated measure of how patients feel and function, including psychological distress, activity barriers, and eating behaviour. In integrated models, it serves as the interpretive anchor against which digital signals are contextualised. 3. Predefine how COA and DHT relate Sponsors should explicitly define hypotheses linking DHP-18 domains to digital metrics (e.g., distress ↔ sleep disruption, activity barriers ↔ step counts). This prevents post-hoc interpretation and strengthens regulatory credibility. 4. Choose an integration model early Integration can take several forms:
5. Align timeframes between measures COAs like the DHP-18 are periodic, while DHTs are continuous. Sponsors must aggregate digital data into comparable time windows to ensure both reflect the same patient state. 6. Build a translation layer between domains A structured mapping is needed between DHP-18 domains and digital signals. For example:
7. Use the DHP-18 to validate digital endpoints Rather than replacing COAs, DHTs are strengthened by showing that digital patterns correspond to meaningful changes in DHP-18 scores. This anchors digital endpoints in patient-relevant outcomes. 8. Pre-specify statistical integration The analysis plan should define how COA and DHT data interact—whether through mixed models, latent constructs, or mediation analysis. Clear pre-specification is essential for regulatory acceptance. 9. Ensure operational interoperability Integration requires aligned systems, consistent identifiers, and harmonised data pipelines between eCOA platforms and DHT devices. Without this infrastructure, even well-designed endpoints can fail in execution. 10. Shift to a unified interpretation mindset Ultimately, the goal is not to compare COA versus DHT, but to interpret both as complementary signals of a single patient state. The DHP-18 represents lived experience, while DHTs represent continuous physiological and behavioural expression. Conclusion Combining COAs and DHTs offers a powerful opportunity to modernize clinical evidence generation, making it more continuous, patient-centred, and data-rich. However, success depends less on the technologies themselves and more on disciplined planning around validation, alignment, data integrity, and interpretation. Sponsors who treat COAs and DHTs as integrated components of a single measurement strategy—rather than separate datasets stitched together post hoc—are far more likely to generate evidence that is both scientifically robust and regulator-ready. The DHP-18 does not sit apart from digital health data—it sits at the centre of interpretation. In modern diabetes trials, it functions as the patient-experience anchor that gives meaning to continuous digital signals. When properly integrated with DHTs, it helps transform fragmented measurements into a coherent, clinically meaningful picture of disease impact.
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One of the major shifts in recent ADA/EASD guidance has been moving from a purely glucose-centric model to a person-centred care model. The consensus repeatedly emphasises that treatment decisions should be based not only on HbA1c and clinical risk factors but also on the individual's preferences, experiences, quality of life, and treatment burden.
The DHP-18 provides a practical mechanism for measuring exactly these patient-centred outcomes. 1. Supporting Person-Centred Care The ADA/EASD report advocates:
The DHP-18 measures:
These domains provide clinicians with structured insights into how diabetes affects daily life and can help guide more personalised treatment discussions. 2. Addressing Psychosocial Health The consensus report highlights the importance of:
The DHP-18's Psychological Distress domain can identify patients who may appear clinically stable but are struggling emotionally. For example, a patient with an acceptable HbA1c may report high levels of frustration, anxiety, or reduced confidence. This information can trigger additional support, education, or behavioural interventions. 3. Evaluating Outcomes Beyond HbA1c The ADA/EASD report increasingly recognises that success should not be measured solely by glycaemic control. Questions include:
The DHP-18 helps answer these questions by measuring outcomes that laboratory tests cannot capture. 4. Supporting Shared Decision-Making The consensus places shared decision-making at the centre of Type 2 diabetes management. The DHP-18 provides objective patient-reported data that can enrich conversations about:
Instead of focusing exclusively on HbA1c targets, clinicians can discuss outcomes that matter most to the patient. 5. Integration with Digital Health and Remote Care The ADA/EASD report recognises the growing role of:
These technologies generate large amounts of physiological data but often fail to capture the patient's lived experience. The DHP-18 can complement digital health platforms by adding the patient's voice to the clinical data stream. Together they provide:
6. Measuring the Real-World Impact of New Therapies Modern diabetes treatments such as:
The DHP-18 can provide evidence of these patient-relevant benefits in clinical practice and real-world evidence studies. For many years, diabetes management has been driven primarily by clinical measurements. Healthcare professionals rely on indicators such as HbA1c, blood glucose levels, blood pressure, lipid profiles, and body weight to assess disease control and guide treatment decisions. These measures remain essential because they provide objective evidence of how well diabetes is being managed and help predict the risk of long-term complications.
However, while clinical measures are important, they do not provide a complete picture of a person's health. Diabetes is not simply a metabolic condition that can be fully understood through laboratory values. It is a lifelong condition that affects how people think, feel, behave, work, socialise, and manage their daily lives. As a result, understanding the patient's experience is increasingly recognised as a critical component of effective diabetes management. Patient feedback provides valuable insights into how individuals experience their condition, how treatments affect their daily lives, and whether healthcare interventions are delivering benefits that matter to patients. Without this information, healthcare providers may overlook important challenges that influence treatment success and long-term outcomes. Diabetes Is Managed Mostly Outside the Clinic One of the unique aspects of diabetes care is that most disease management takes place outside healthcare settings. Patients make countless decisions every day regarding medication, food choices, physical activity, glucose monitoring, and lifestyle behaviours. Healthcare professionals may see patients only a few times each year, yet patients live with diabetes every day. This means that successful diabetes management depends heavily on self-management. A treatment plan may look effective on paper, but if patients struggle to follow it, experience significant emotional burden, or feel overwhelmed by their condition, outcomes may suffer. Patient feedback helps healthcare providers understand what is happening between appointments. It provides information that cannot be captured through laboratory tests alone and offers a more complete understanding of the challenges patients face. Clinical Measures Tell Only Part of the Story HbA1c remains one of the most widely used indicators of diabetes control. It provides a valuable estimate of average blood glucose levels over the previous two to three months and is strongly associated with the risk of long-term complications. However, HbA1c does not tell us how a patient feels. Two individuals can achieve identical HbA1c results while having completely different experiences of living with diabetes. One patient may feel confident, motivated, and in control. They may successfully integrate diabetes management into their daily routine and experience little disruption to their quality of life. Another patient with the same HbA1c result may experience significant anxiety, frustration, social restrictions, or diabetes-related burnout. They may be achieving good glucose control but at considerable emotional and psychological cost. Without patient feedback, these differences may remain hidden. This highlights an important reality in modern healthcare: good clinical outcomes do not always mean good patient outcomes. The Hidden Burden of Diabetes Living with diabetes can place a significant emotional and psychological burden on individuals. Research has shown that severe diabetes distress affects approximately one in four people with Type 1 diabetes and one in five people with insulin-treated Type 2 diabetes. Many patients experience ongoing concerns about blood glucose levels, fear of complications, frustration with self-management requirements, and difficulties balancing diabetes with everyday life. The burden of diabetes extends beyond medical management. It can affect social activities, work performance, relationships, confidence, and overall wellbeing. Importantly, these challenges are not always visible during routine consultations. Patients may focus discussions on medication adjustments and blood test results while emotional concerns remain unspoken. Patient feedback provides a structured way to identify these issues and ensure they are considered during care planning. Why Patient Feedback Improves Outcomes Patient feedback is valuable because it provides information that can directly influence healthcare decisions and improve outcomes. First, it helps identify unmet needs. Patients may report difficulties that are not apparent through clinical measures, allowing healthcare professionals to provide targeted support. Second, patient feedback supports personalised care. No two people experience diabetes in exactly the same way. Understanding individual challenges allows treatment plans to be tailored to the specific needs of each patient. Third, patient feedback can improve patient engagement. When individuals feel that their experiences and concerns are valued, they are more likely to participate actively in their care and adhere to treatment recommendations. Research has consistently shown that patient-centred approaches are associated with improved adherence, better self-management behaviours, and improved health outcomes. In other words, understanding how patients experience their condition is not simply a matter of improving satisfaction. It can directly contribute to more effective diabetes management. Supporting Shared Decision-Making Modern healthcare increasingly emphasises shared decision-making, where patients and healthcare professionals work together to determine the most appropriate course of action. Effective shared decision-making requires more than clinical data. It requires an understanding of patient priorities, preferences, concerns, and goals. For example, one patient may prioritise reducing the burden of treatment, while another may focus on maintaining independence or improving quality of life. Patient feedback provides valuable information that supports these conversations and helps ensure that treatment decisions align with what matters most to the individual. This collaborative approach often leads to greater patient satisfaction and more sustainable treatment plans. The Growing Role of Patient Feedback in Digital Health The importance of patient feedback is becoming even more apparent as healthcare increasingly adopts digital health technologies. Continuous glucose monitors, mobile health applications, telemedicine services, and remote patient monitoring platforms are generating vast amounts of physiological data. These technologies provide valuable insights into glucose control and treatment patterns. However, digital health systems cannot directly measure emotional wellbeing, confidence, treatment burden, or quality of life. A patient may demonstrate stable glucose levels while simultaneously experiencing increasing anxiety, frustration, or burnout. Without patient feedback, these issues may remain invisible. Integrating patient-reported information into digital health programmes allows healthcare providers to combine objective clinical data with insights into the patient's lived experience. This creates a more comprehensive understanding of health and supports more proactive and personalised care. Measuring the Patient Experience As healthcare moves towards more patient-centred and value-based models of care, there is increasing recognition that patient experiences should be measured systematically rather than relying solely on informal conversations. Patient-reported outcome measures (PROMs) have been developed to capture information directly from patients about their health status, wellbeing, and quality of life. One example is the Diabetes Health Profile-18 (DHP-18)[1], a validated diabetes-specific patient-reported outcome measure designed to assess aspects of living with diabetes that traditional clinical indicators may not fully capture. The DHP-18 focuses on three important dimensions of the patient experience:
By providing structured information about these areas, the DHP-18 can help healthcare professionals gain a deeper understanding of how diabetes affects daily life and identify issues that may require additional support or intervention. Conclusion Effective diabetes management requires more than achieving target HbA1c levels and maintaining good blood glucose control. It requires understanding how diabetes affects the lives of the people who live with the condition every day. Patient feedback provides essential insights into emotional wellbeing, treatment burden, quality of life, and self-management challenges that cannot be captured through clinical measures alone. These insights support personalised care, improve patient engagement, strengthen shared decision-making, and contribute to better health outcomes. As healthcare continues to evolve towards patient-centred, digital, and value-based models of care, the importance of patient feedback will only continue to grow. Clinical measures will always remain fundamental to diabetes management, but they tell only part of the story. To truly understand treatment effectiveness and deliver care that matters, healthcare providers must also listen to the patient voice. Tools such as the DHP-18 provide one practical way of ensuring that this voice is measured, understood, and incorporated into care decisions.
Moving Beyond HbA1c: Why the DHP-18 is the Crucial Missing Link in Digital Health Clinical Trials9/6/2026 Traditional endpoints like blood glucose readings only tell half the story. To truly validate digital health technology in clinical trials, pharma leaders must quantify the daily psychological and behavioral burden on patients. For decades, the gold standard of success in diabetes care has been strictly biomedical. Clinical trial protocols have prioritised physical metrics: glycated haemoglobin (HbA1c) reductions, time-in-range (TIR) metrics, fasting plasma glucose, and lipid profiles. While these biometric data points are indispensable for determining physiological efficacy, they operate within a vacuum. They capture the physiological state of the disease, but completely bypass the human engine that drives those numbers: the patient’s daily behavior, cognitive load, and emotional coping mechanisms. In the rapidly evolving landscape of digital health technologies (DHTs)—including continuous glucose monitors (CGMs), smart insulin pens, digital therapeutics (DTx), and mobile health apps—this biomedical myopia is a significant liability. Digital health tools do not work like small-molecule drugs; they do not automatically alter biochemistry upon ingestion. Instead, they require active, sustained, and often exhausting behavioral interaction from the user. If a digital health solution demands too much cognitive energy, induces anxiety, or fails to account for the deeply ingrained behavioural barriers a patient faces, adoption plummets, adherence shatters, and clinical trial outcomes fail. To prove the true value of these innovations to regulators, payers, and clinicians, pharmaceutical and digital health innovators must measure the psychological infrastructure of diabetes management. The most effective, validated instrument for achieving this is the Diabetes Health Profile-18 (DHP-18)[1]. The Behavioral Realities of Diabetes Management Diabetes is arguably one of the most behaviorally demanding chronic conditions in modern medicine. Unlike conditions where management involves taking a single pill once a day, diabetes requires a relentless series of complex, high-stakes decisions every single waking hour. Patients must constantly calculate carbohydrate intake, judge physical activity levels, interpret fluctuating glucose trends, dose insulin, and anticipate hypoglycaemic episodes. This constant decision-making cycle does not occur in a psychological vacuum. It triggers a profound emotional toll known broadly as diabetes distress. When a digital health company introduces a new app or device designed to "streamline" this process, they are dropping their technology into an already overcrowded emotional ecosystem. If a clinical trial only measures whether a device can lower HbA1c over 12 weeks, it misses the critical operational variables that dictate long-term market success:
When digital health trials fail to track these human metrics, they risk misinterpreting their data. A drop in tool adherence during a trial might be blamed on a technical glitch or a flawed user interface, when the true culprit was unmeasured psychological distress or activity barriers induced by the intervention itself. Conversely, a tool that shows modest HbA1c improvements might actually be a massive breakthrough because it radically reduces the psychological burden of the disease—a benefit that payers and patients value immensely, but one that goes completely unrecorded without the proper Patient-Reported Outcome Measure (PROM). Anatomy of the DHP-18: A Specific, Validated Framework Generic health-related quality of life surveys (like the SF-36 or EQ-5D) are notoriously insensitive to the unique, highly specific challenges of living with diabetes. They ask broad questions about physical functioning or general mood that fail to capture the nuanced friction points of a diabetes management regimen. The DHP-18 was specifically designed to bridge this gap. Developed as a highly targeted, multi-attribute patient-reported outcome tool, it focuses squarely on the psychological and behavioural impact of living with diabetes. Rather than overwhelming busy trial participants with lengthy, exhausting questionnaires, the DHP-18 distils these complex human variables into 18 highly optimized questions across three distinct, clinically vital domains. 1. Psychological Distress This domain measures the emotional burden directly tied to diabetes and its management. It assesses feelings of dysphoric mood, anxiety, irritability, and vulnerability. In digital health trials, tracking psychological distress is vital for identifying whether an asset alleviates or worsens the user's mental load. For instance, if a digital therapeutic provides automated coaching, this domain registers whether those interventions offer reassurance or cause added performance anxiety. 2. Barriers to Activity Technology often promises freedom, but poorly designed hardware or rigid software can make patients feel restricted. This domain quantifies the perceived limitations that diabetes places on a patient’s lifestyle, social schedule, and physical activity. For digital health innovators developing wearable devices or continuous monitoring systems, showing a measurable reduction in activity barriers is a powerful proof point for product ergonomics and software integration. 3. Disinhibited Eating Food is the ultimate battleground in diabetes management. This domain dives into the behavioural and emotional triggers that lead to overeating, loss of dietary control, and responses to hypoglycaemic fears. Traditional biometric markers simply show the spike in blood sugar caused by disinhibited eating; the DHP-18 captures why it happened. For digital health solutions focused on nutritional coaching or metabolic health, moving the needle on this specific domain provides definitive proof of genuine behavioral modification. Why Pharma and Digital Health Executives Must Pivot Now The integration of the DHP-18 into clinical trial protocols is not just an academic exercise in patient centricity; it is a critical commercial and regulatory imperative for pharma and digital health executives. The digital health landscape is shifting from a market of unverified wellness apps to a highly regulated, value-driven clinical ecosystem. Survival in this new era requires sophisticated data strategies. Securing Regulatory and FDA Approvals Regulatory bodies, particularly the FDA and EMA, are placing unprecedented emphasis on patient-focused drug development (PFDD) and the integration of digital health technologies. Regulators openly acknowledge that traditional clinical endpoints do not tell the whole story of patient well-being. When evaluating software as a medical device (SaMD) or digital companion tools, regulators want to see rigorous, validated evidence that the technology improves how a patient feels, functions, or survives in their daily life. By utilizing the DHP-18, trial sponsors secure a validated, regulatory-compliant data set that demonstrates a quantifiable reduction in disease burden. This transforms a subjective claim ("our app is user-friendly") into an objective, statistically defensible endpoint (e.g. "our app demonstrated a 24% reduction in diabetes-specific psychological distress over 24 weeks"). Winning the Market Access and Payer Reimbursement Battle The ultimate hurdle for any digital health innovation is securing reimbursement from institutional payers and insurance providers. Payers are highly skeptical of digital health tools that promise abstract long-term savings based on short-term biomedical fluctuations. They demand proof of value. If your digital health tool can show that it directly mitigates barriers to activity or curbs disinhibited eating patterns, you are offering payers data that correlates with long-term lifestyle compliance, reduced emergency visits for severe hypoglycaemia, and better overall resource utilization. The DHP-18 provides the exact health economics and outcomes research (HEOR) metrics required to build an unassailable value dossier for market access teams. Optimizing Trial Design and Accelerating Retention Patient retention is a notorious bottleneck in clinical trials, especially in digital health studies where "app abandonment" rates can skyrocket within the first 30 days. Patients routinely drop out of trials if the technology feels intrusive, confusing, or emotionally draining. Deploying the DHP-18 at intervals throughout a trial acts as an early warning system for participant burnout. If data trends reveal a sharp spike in psychological distress or activity barriers for example by week four, trial operations teams can proactively flag usability issues or adjust support protocols before those distressed patients withdraw from the study entirely. Protecting patient retention saves millions in trial delays. Implementing the DHP-18: A Blueprint for Trial Designers Integrating a new PROM into an upcoming clinical trial requires deliberate planning. To extract maximum strategic value from the DHP-18, clinical operations and digital product teams should follow a structured deployment model. Strategic Protocol Integration The DHP-18 must be integrated into the trial protocol as a formal secondary or exploratory endpoint from day one. It should be scheduled at logical intervals that match the expected behavioral adoption curve of the technology. For example:
Native Digital Delivery Because you are validating digital health technologies, the method of collecting PROM data should match the medium of the intervention. Delivering paper questionnaires to patients using an advanced digital therapeutic creates unnecessary friction. Sponsors should integrate the DHP-18 directly into the study’s Electronic Patient-Reported Outcome (ePRO) platform or embed it natively within the user interface of the digital app being tested. This digital-native approach ensures higher completion rates, eliminates transcription errors, and allows for clean, automated data aggregation. Advanced Data Cross-Correlation The true magic happens when correlating the DHP-18 domain scores directly with passive device telemetry data. By pairing subjective survey responses with objective platform metrics, data scientists can unlock deep insights into patient behavior:
This multi-dimensional understanding allows product development teams to make precision adjustments to algorithms, user journeys, and feature sets to optimize both clinical safety and long-term user retention. Conclusion: The New Currency of Diabetes Innovation The digital health revolution has reached a point of critical maturity. The market is no longer impressed by sleek interfaces or basic connectivity. The innovations that survive the next decade will be those that prove they can seamlessly weave themselves into the messy, complex, and emotionally challenging realities of a patient's daily routine without adding to their burden. Relying entirely on traditional clinical endpoints like blood glucose readings is a legacy strategy that tells only half the story. It treats the patient as a passive biological vessel rather than an active behavioural agent. To truly validate digital health technology, pharma and digital health innovators must elevate human metrics to the exact same level of scientific rigor as biochemical markers. By embedding the DHP-18 into clinical trial designs, sponsors gain access to a validated, 3-domain perspective on psychological distress, activity barriers, and eating behaviors. This tool transforms abstract patient experiences into hard, actionable data. It provides the precise evidence needed to satisfy stringent regulatory bodies, win over skeptical insurance payers, and create life-changing digital solutions that patients will actually stick with for the long haul. If you want to validate your next diabetes innovation, stop looking exclusively at the blood data. Start measuring the human experience behind the numbers. Reference
The DHP-18 is a copyrighted instrument. To incorporate it as a primary or secondary endpoint in your project, you must obtain a license through Oxford University Innovation. Licensing, linguistic translation and digital migration approvals can be requested through Oxford University Innovation’s click-to-licence portal. |
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June 2026
AuthorHello and welcome to my blog. I am an independent researcher living in Oxfordshire, providing consultancy and training in health-related research including, health related quality of life research, patient reported outcome measures (PROMs), psychology of living with diabetes, qualitative research, patient experience, cognitive interviews, research training. I obtained a PhD in Psychology (Without corrections) at Imperial College, University of London and have a BA (Hons) in Psychology. I am a chartered psychologist and Associate Fellow of the British Psychological Society. 
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