Better Risk Perception Via Patient Similarity to Control Hyperglycemia and Sustained by Telemonitoring
Better Risk Perception Via Patient Similarity to Control Hyperglycemia and Sustained by Telemonitoring
Background: Diabetes significantly raises the likelihood of complications, thereby increasing the risk of diabetes-related mortality, particularly due to vascular complications. It is vital to address this rising trend of mortality, by enhancing awareness of diabetes complications to improve risk perception and ultimately reduce mortality rates. Managing diabetes effectively requires interventions addressing both risk communication and monitoring, helping patients better understand and make informed decisions about their health.
Objectives: The primary aim is to evaluate and compare the effectiveness of combined risk communication session using an AI module (PERDICT.AI) and home-based diabetes monitoring (PTEC-DM) versus a standalone risk communication session in improving health outcomes (risk perception, medication adherence, self-care activities and glycaemic control) among poorly controlled diabetes patients. Secondary aims are to explore participants' views and experiences of risk communication session using PERDICT.AI, PTEC-DM and usual care and clinician' views on utility of the new approach to improve risk perception.
Methods: A mixed-method study design will be employed to conduct a multi-arm randomized controlled trial across four of the SingHealth Polyclinics cluster (Pasir Ris, Eunos, Sengkang, Tampines North). Patient participants will be randomly allocated in a 1:1:1 ratio to one of the three arms. Arm 1 will receive risk communication session using PERDICT.AI and home-based diabetes monitoring using PTEC-DM alongside usual care. Arm 2 participants will undergo a standalone risk communication session using PERDICT.AI with usual care while arm 3 will serve as the control group with usual care. A total of 360 (120 in each group) participants will be enrolled by simple randomization. Eligible patient must be of age between 36 and 65 years with HbA1c >8.0% within the last 6 months.
Significance of the study: Findings from the study may add evidence to the scientific knowledge of using these approaches to improve risk perception and recommend development of similar interventions.
Diabetes has emerged as a significant public health concern globally, and Singapore is no exception. As of 2022, 8.5% of the adults in Singapore is affected by diabetes and the number is expected to reach 1 million by 2050, making it imperative to address the associated challenges. The economic implications of diabetes extend beyond healthcare costs, impacting productivity and quality of life. The total cost among the working-age population with diabetes - direct and indirect costs included - is expected to rise from USD 787 million (USD 5,646 per person) in 2010 to USD 1,867 million in 2050 (USD 7,791 per person).
In addition, diabetes poses a substantial risk of complications that can adversely impact various organ systems. Complications such as cardiovascular diseases, neuropathy, and retinopathy pose severe threats to the health of individuals with poorly managed diabetes. A study on global trend of diabetes mortality revealed a concerning global increase in diabetes-related mortality, particularly due to vascular complications, posing a significant challenge to diabetes management. To address the rising trends of mortality, it is crucial to enhance awareness of diabetes complications to improve risk perception and ultimately reduce mortality rates.
Perceived risk of diabetes complications can impact patient behavior, influencing adherence to treatment plans and lifestyle changes. Individuals with a higher risk perception may be more likely to engage in proactive management, leading to better health outcomes and potentially reducing mortality rates associated with diabetes complications. On the other hand, individuals with poorer risk perception may neglect necessary precautions, leading to suboptimal disease management and an increased likelihood of complications, potentially impacting mortality rates.
A systematic review on risk perceptions of diabetes complications highlights a concerning lack of awareness regarding the risk of diabetes related complications among individuals with type 2 diabetes mellitus (T2DM). Similarly, research studies on diabetes complications risk awareness, particularly in Singapore, revealed knowledge gaps among adults. Despite the significant impact on quality of life, later-stage T2DM and its complications were perceived as slowly progressing and not immediately life-threatening. Hence, for poorly controlled diabetes patients, effective communication regarding the risks of complications is paramount.
Weaver et al defined risk communication as "the effective and accurate exchange of information about health risks and hazards" so as to "advance risk awareness and understanding and promote health-protective behaviors". Enhancing risk communication not only promotes informed decision-making but also advances early intervention and preventive measures. Furthermore, Hashim J et al emphasized the importance of considering social and cultural factors in the development of effective interventions among adults with elevated risk perception yet do not engage in preventive actions. The study also suggested that diverse perspectives concerning the benefits and weaknesses related to preventive measures can impact the long-term sustainability of these behaviors.
Risk communication interventions have been developed for patients with T2DM to improve their risk perceptions and health actions. These interventional studies explore different methods to communicate diabetes complication risks to those with T2DM. interventions include range of innovative risk communication methods like visual aids, general nudges, digital tool for personalized risk information and family support through WeChat. While such interventions contribute to valuable insights, there are some limitations with these tools like limited long-term impact, technology adoption challenges. Addressing these drawbacks with an integrated approach could enhance the robustness and applicability of the findings in diverse healthcare settings.
PERDICT.AI based counselling
An AI-enabled similarity-based model, named PERDICT.AI (Personalised Diabetes Counselling Tool using Artificial Intelligence) was developed by a team of primary care physicians and computer scientists in Singapore to help physicians communicate risks to patients with diabetes mellitus. The tool ranks a patients' HbA1c levels with similar patients (or peers) from a de-identified database, showing how prevalent diabetes complications are based on HbA1c severity. This is referred to as "peer-comparison" and the tool underwent revisions following feedback from primary care physicians to enhance its usefulness in risk communication.
Based on Health Belief Model (HBM), a risk communication intervention, was developed for Primary care Physicians (PCPs) to counsel patients with T2DM on their glycemic control and the complications that could arise, and to recommend ways to improve glycemic control and prevent complications (or further complications). This will be supported by information from PERDICT.AI.
Risk communication using PERDICT.AI dynamically communicates an individual's glycaemic control, offering a comparative ranking among peers to enhance motivation and awareness. Furthermore, it assesses the risk of potential complications comparing with peer data with exemplary cases to underscore the consequences of suboptimal management. In addition, it will generate personalized recommendations including medication adjustment and personalized health plans.
Diabetes management often requires consistent encouragement and guidance, which a static risk communication tool may not deliver. In addition, passive receipt of information might not motivate patients to actively take part in diabetes management. Such lack of engagement could lead to reduced adherence to recommended strategies, limiting the tool's overall impact. This is evident from the @RISK study, where the improved risk perception observed initially at 2 weeks dissipated by the 12th week, highlighting a temporal limitation in sustaining positive outcomes. Although participants in the intervention arm reported higher satisfaction with risk communication, this did not translate into sustained improvements. This underscores the need for an integrated approach to sustain positive outcomes beyond short-term.
Integration with telemonitoring system
Sustaining improved risk perception over an extended period can be achieved through telemonitoring. By utilizing telemonitoring technology, healthcare providers can maintain a consistent connection with patients, offering real-time insights into their health status. Additionally, telemonitoring facilitates continuous education and support, thereby contributing to the long-term sustainability of improved risk perception and can significantly enhance diabetes management and prevent complications.
The Primary Tech-Enhanced Care (PTEC) programme focuses on encouraging patients to manage chronic conditions at home through user-friendly kits. The Home Diabetes Monitoring programme (PTEC-DM) enables home-based glucose and blood pressure monitoring once a week using a Bluetooth enabled device. These reading will be securely transmitted to the study team via the app and managed appropriately through teleconsultation. Additionally, participants will receive health nudges, encouragements, and reminders through in-app messages to support their well-being.
The integration of PTEC-DM with the risk communication using PERDICT.AI capitalizes on the strengths of human interaction and adaptability, contributing to a more holistic and patient-centred diabetes management approach. Such combined approach addresses both monitoring and guidance, contributing to enhanced patient understanding and informed decision-making. Hence this study is designed with the following objectives, adopting a multi-site, multi-arm randomized controlled trial design.
Objectives
Primary objective:
i. To assess the effectiveness of the risk communication using an AI enabled tool (PERDICT.AI) in improving risk perception score, quality of life and health outcomes (medication adherence and selfcare activities and glycemic control) among poorly controlled diabetes patients ii. To determine the effectiveness of a combination of risk communication session using PERDICT.AI and telemonitoring (PTEC-DM) in improving risk perception, quality of life and health outcomes
Secondary objectives:
iii. To compare the impact of the two approaches in improving risk perception, quality of life and health outcomes among poorly controlled diabetes patients iv. To assess the cost-effectiveness of the advanced care by comparing the incremental costs and health outcomes v. To explore participants' views and experiences of risk communication session using PERDICT.AI, PTEC-DM and usual care vi. To explore clinician' views on utility of the new approach to improve risk perception
Hypothesis:
• There will be improvement in patients' risk perception score and health outcomes (glycemic control and self-care activities) after the intervention.
Materials and methods
Study setting The study will be conducted at 4 polyclinics from a primary care clinic cluster taking care of more than 200,000 residents with diabetes in the Eastern region of Singapore.
Study design Sequential explanatory mixed-method study
Quantitative: Multi-arm randomized controlled trial (RCT) at four polyclinics which includes SingHealth Polyclinics at Pasir Ris, Tampines North, Eunos and Sengkang.
Qualitative: In-depth interview among the study participants' and clinician, who are integral part of the study team delivering interventions.
Quantitative: Multi-arm RCT This RCT involves three arms, incorporating a combination of interventions and standard care, as outlined below.
Arm 1: Advanced care with risk communication using an AI enabled tool (PERDICT.AI) + home-based monitoring using PTEC DM (main intervention arm) Arm 2: Usual care + risk communication using an AI enabled tool (PERDICT.AI) Arm 3: Usual care All groups will also receive a diabetes pamphlet.
Randomization Patient participants from each study site will be randomly allocated in a 1:1:1 ratio to one of the above-mentioned arms in an open-label fashion, using computer-generated random numbers for simple randomization of subjects. The nature of the intervention makes impossible to blind patients and research team to participant allocation. The randomization sequence is written and kept in an opaque sealed envelope, which will be labelled with a serial number. The study team will open the sealed envelope once the patient has consented to participate and then will be assigned to the study arms accordingly. All participants will receive a diabetes pamphlet ('Pamphlet - Taking Control of Diabetes').
Inclusion Criteria:
Exclusion Criteria: