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. 2026 Feb 7;27:92. doi: 10.1186/s12875-026-03216-6

Proactive health behaviour intervention strategies for older T2DM patients with multimorbidity: protocol for a cluster randomised controlled trial with hybrid type 2 effectiveness-implementation design

Yang Li 1,2,#, Qian Huang 1,2,#, Jianli Ge 1,2, Yue Liu 1,2, Shasha Geng 1,2, Qingqing Li 1,2, Xin Chen 1,2, Yingqian Zhu 1,2, Xiaotong Guo 1,2, Fengyi Lu 1,2, Ning Xu 1,2, Minggang Yu 1,3, Liang Zheng 4, Hua Jiang 1,2,✉
PMCID: PMC12977926  PMID: 41654714

Abstract

Background

The increasing prevalence of multimorbidity represents a significant public health challenge, particularly in an ageing global population. This is particularly evident in patients with type 2 diabetes mellitus (T2DM), yet the management of lifestyle factors remains suboptimal. The objective of this study was to evaluate the efficacy, feasibility and cost-effectiveness of proactive health behaviour intervention strategies based on digital health technology and the multi-theory model at the primary care level.

Methods

A cluster randomised controlled trial with a 24-month follow-up period will be conducted to evaluate the effectiveness of the proactive health behaviour intervention strategies. The strategies comprises three components, including free multi-sign monitoring bracelets and a mobile application, proactive health behaviour education delivered via short video format, and goal setting and support through shared decision-making. These elements can be readily integrated into routine outpatient practice. A minimum of 420 older T2DM patients with multimorbidity and exhibiting suboptimal proactive health behaviours will be recruited from 14 primary healthcare centres (PHCs) and randomly assigned on a PHC basis. The primary outcomes will be whether the participants have achieved at least one goal at six months, whether they have achieved glycemic control, and their cardiometabolic health scores at six months. The effectiveness of the intervention strategies will be assessed using the intention-to-treat dataset and mixed effect models. Additionally, implementation assessments and cost-effectiveness analyses will be conducted. Ethical approval for the protocol was obtained from the Ethics Committee of Shanghai East Hospital (No. 2024YS-083).

Discussion

This article presents the methodology of a pragmatic cluster randomised controlled trial utilising a hybrid effectiveness-implementation type 2 design. The objective is to assess the potential applicability of the proactive health behaviour intervention strategies in primary care for older T2DM patients with multimorbidity.

Trial registration

The study protocol has been registered prospectively in the Chinese Clinical Trial Registry (ChiCTR2400088374, assigned on 16/08/2024). Recruitment is ongoing at the time of manuscript submission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-026-03216-6.

Keywords: T2DM, Multimorbidity, Older people, Proactive health, Behaviour change, Primary care

Introduction

Background and rationale

As the global population continues to age, healthcare systems are confronted with considerable challenges in addressing the healthcare needs of an increasingly older population, particularly those with multimorbidity. The term ‘multimorbidity’ is used to describe patients who have been diagnosed with two or more chronic conditions [1]. These include hypertension, type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), cancer, mental illness and geriatric syndromes, which are not only heterogeneous but also complexly interrelated. This results in reduced physiological function, increased frailty and lower quality of life, as well as an increased risk of polypharmacy and disease burden, which may even result in disability or death [2–8]. A statistical analysis reveals that the average healthcare costs of patients with multimorbidity are 5.5 times higher than those of patients without multimorbidity, which undoubtedly places additional pressure on healthcare systems [9]. It is therefore of the utmost importance to enhance the healthcare management of the older population by focusing on the predominant multimorbidity patterns.

Cardiometabolic multimorbidity (CMM) is defined as the coexistence of two or more cardiometabolic diseases (CMDs), including hypertension, insulin resistance (IR), T2DM, hyperlipidemia, CVD, and stroke. In recent years, CMM has become the most prevalent multimorbidity pattern globally [10–13], accounting for over half of the disease burden in adults worldwide and exhibiting a tendency to increase with age [14, 15]. There are complex interactions and shared risk factors between CMDs [16, 17]. The synergistic effects of these conditions result in a significantly elevated mortality rate in patients with CMM in comparison to those with a single CMD [11]. Among all CMDs, T2DM plays a pivotal role. This is due to the complicated interplay between various modifiable and non-modifiable risk factors in the early stages, as well as its capacity to precipitate accelerated cardiovascular events [18, 19]. The majority of patients with T2DM suffer from multimorbidity, with approximately 40–50% of them having at least three other chronic diseases, particularly CMD or chronic kidney disease (CKD) [20–23]. The implementation of early intervention strategies targeting modified risk factors has the potential to mitigate the adverse effects on the cardiovascular and renal systems, thereby reducing the treatment burden and the risk of mortality in older T2DM patients with multimorbidity [24–26]. Nevertheless, approaches such as lifestyle interventions are not commonly adopted in clinical practice beyond the trial period, and their sustainability at the primary care level remains a challenge [27, 28].

Since 2009, the Chinese government has introduced the National Essential Public Health Service Package (NEPHSP), which requires primary healthcare centres (PHCs) to provide a defined set of minimum public health services for patients with hypertension and T2DM. The aforementioned services comprise screening, monitoring, routine follow-up, and health counselling for patients with hypertension and T2DM [29]. The aforementioned services are primarily provided by general practitioners (GPs) in PHCs. Notwithstanding augmented government funding and concomitant enhancements in management systems, extant evidence indicates that the management of hypertension and T2DM in PHCs remains a significant challenge [30, 31]. In particular, low levels of patient willingness to proactively use basic public health services and to follow health management advice, and the inability of untrained GPs to provide lifestyle counselling and support, have resulted in few T2DM patients being able to adopt and maintain healthy behaviours over time. This has resulted in the accumulation of comorbidities in the majority of patients. There is a high degree of concordance between T2DM and other CMDs in terms of both pathogenesis and management plans. Consequently, the benefits of lifestyle interventions are more pronounced in T2DM patients with ‘concordant comorbidities‘ [32]. Furthermore, evidence suggests that individuals with stable or well-controlled T2DM often lack awareness of lifestyle-related risks, partly because they may experience fewer ‘teachable moments’—defined as changes in life or health events that motivate learning and behavior change [33, 34]. For instance, patients with severe obesity who frequently experience weight-related distress are often more motivated to improve their lifestyle [35]. Consequently, T2DM patients who have other abnormal clinical indicators (e.g., elevated blood pressure or carotid plaque) or require medication adjustments for various reasons may be more willing to adopt proactive health behaviors [36]. For these reasons, older T2DM patients with multimorbidity were selected as the subjects of the study. These individuals are not only at higher risk of cardiovascular mortality and require more intensive lifestyle interventions, but they are also more likely to implement behavioural changes following the receipt of health education.

Nevertheless, a multitude of challenges persist in the implementation of proactive health behaviour change counselling and guidance for older people to adopt healthy lifestyles within clinical settings [37]. The process of behavioural change in older people can be influenced by a number of factors, including oral health, sensory limitations, physical and functional status, general cognition, sleep disorders and social/environmental factors (such as economic status, cultural and religious beliefs) [38]. It is thus necessary to conduct a comprehensive assessment of these factors and develop individualised behaviour change goals through a process of shared decision-making. Furthermore, it is imperative to identify potential barriers to achieving lifestyle goals and to formulate suitable solutions. Moreover, long-term monitoring of behavioural objectives and revision of action plans are vital for the promotion and maintenance of health behaviour change in older T2DM patients with multimorbidity.

Objectives

In light of the above, we developed proactive health behaviour intervention strategies based on digital health technologies (such as wearable devices and a health management application) and the multi-theory model. Subsequently, a pragmatic cluster randomised controlled trial was planned to be conducted in the context of collaborating PHCs [39]. The objectives of this study were (1) to utilise the most straightforward approach to provide support and monitoring for proactive health behaviour intervention strategies, with the aim of promoting behaviour change and maintenance in older T2DM patients with multimorbidity (defined in this study as those with at least one of hypertension, hyperlipidemia or ischemic heart disease) and to assess the improvements in cardiometabolic health [2]. (2) to evaluate the implementation and determinants of our intervention strategies using implementation research frameworks, and (3) to explore the economics of intervention implemention at the primary care level through a two-year follow-up and cost-effectiveness analyses, with a view to guiding subsequent implementation optimisation and large-scale dissemination.

Methods

The study design, methods and results will be reported according to the Consolidated Standards of Reporting Trials (CONSORT) guidelines extension for cluster trials [40].

Study design

This study will employ a two-arm, pragmatic, cluster randomised controlled trial (CRT) with a hybrid effectiveness-implementation type 2 design [41–43]. This will facilitate an examination of both the efficacy of the intervention strategies and the factors influencing the implementation process. In particular, the CRT allows for an efficacious evaluation of proactive health behaviour intervention strategies based on digital health technologies and the multi-theory model in primary care. Qualitative and quantitative data will be employed to assess implementation outcomes and identify potential determinants of the implementation process in accordance with the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) model [44] and the Consolidated Framework of Implementation Research (CFIR) [45].

The components of proactive health behaviours, as defined in this study, include smoking, alcohol consumption, body mass index (BMI), physical activity and sleep. The data will be collected via structured questionnaires at both the baseline and follow-up points. The assessment of smoking will be conducted via a series of questions regarding the participant’s history of smoking and cessation practices within the past week, along with a detailed inquiry into the specific number of cigarettes consumed per day. The consumption of alcohol will be evaluated through questioning regarding the frequency of alcohol consumption in the past week and the specific type and quantity of alcohol consumed. The average daily alcohol intake will be calculated using the following formula: Alcohol intake (g) = alcohol content (%) * drinking volume (mL) * 0.8 (alcohol density, g/mL). Body mass index (BMI) will be calculated using the following formula: BMI = weight (kg) / height (m)². The level of physical activity will be estimated using the metabolic equivalent of tasks (METs) from different types of activity from the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Furthermore, the intervention group will utilise a multi-sign monitoring bracelet to document their daily step count, thereby facilitating the observation of their activity levels. Sleep quality will be evaluated using the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI). Moreover, the bracelet will be utilised by the intervention group to document their daily sleep status.

The concept of ideal proactive health behaviours can be defined as the attainment of optimal levels across all five components. These include never smoking or having quit smoking, never drinking or consuming alcohol in moderation (with an average daily alcohol intake of < 15 g), a BMI of ≥ 18.5 kg/m² and < 24 kg/m² [46], a cumulative METs from all activities per week equal to at least 150 min of moderate-intensity physical activity, and an average of 7–9 h of nighttime sleep per week.

The study was conducted in accordance with the ethical standards of the Declaration of Helsinki and approved by the Ethics Committee of Shanghai East Hospital (No. 2024YS-083). The study protocol was pre-registered with the Chinese Clinical Trial Registry (ChiCTR2400088374, URL: https://www.chictr.org.cn/showproj.html?proj=234309). The checklist was completed following the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline recommendations [47] (Supplementary Table 1).

Study location

The proactive health behaviour intervention strategies will be implemented through the integration of routine chronic disease management services at the primary care level. Participants will be recruited through GPs at each PHC. A total of 14 PHCs from three administrative districts (Pudong, Huangpu and Putuo) in Shanghai will be included in the study. A minimum of three GPs and 30 older T2DM patients with multimorbidity will be recruited from each PHC. This will result in a total of at least 420 participants. A 1:1 cluster randomisation will be performed at the PHC level, with participants assigned to either the control group (routine chronic disease management) or the intervention group (routine chronic disease management plus proactive health behaviour intervention strategies).

Enrollment and eligibility

Individual participants

GPs at each PHC will be primarily responsible for identifying potential T2DM participants from electronic health records and initiating recruitment during routine outpatient clinics, telephone and other appropriate channels. For patients who initially express interest in participating, the GPs will provide a detailed explanation of the study and record their personal information on screening forms. Subsequently, trained research assistants (RAs) from the study team will conduct the formal eligibility screening, obtain informed consent, and perform all scheduled data collection visits (baseline and follow-ups). The inclusion and exclusion criteria for participants are presented in Table 1.

Table 1.

Inclusion and exclusion criteria for individual participants

Inclusion criteria Exclusion criteria

(1) Aged ≥ 60 and ≤ 80 years;

(2) Diagnosed with T2DM and at least one of hypertension, hyperlipidaemia or ischaemic heart disease (our previous study demonstrated that these three CMDs are more prevalent in older T2DM patients, ranking in the top third of all comorbidities).

(3) Suboptimal proactive health behaviours, namely no more than four healthy lifestyles, including never having smoked or having quit smoking, never having consumed alcohol or moderately consuming alcohol (average alcohol intake < 15 g per day), a body mass index of ≥ 18.5 kg/m² and < 24 kg/m², a cumulative metabolic equivalent of tasks equivalent to at least 150 min of moderate-intensity physical activity per week, and an average night’s sleep of 7 to 9 h per week.

(4) Patients should be able to utilise smartphones and mobile phone applications with proficiency, either independently or with the assistance of a family member.

(5) Patients should be willing to participate in the study and sign the informed consent form.

(1) A history of a cardiovascular event in the previous year or hospitalisation for any reason in the previous six months;

(2) Comorbid unstable angina, severe arrhythmia, chronic heart failure (NYHA class > 3), uncontrolled hyperthyroidism, chronic renal insufficiency requiring maintenance haemodialysis, hepatic failure, active malignant neoplasm, neurological disorders leading to limb mobility disorders/dementia/aphasia, serious psychiatric disorders (such as bipolar disorder and schizophrenia), mental retardation, serious active autoimmune diseases, long-term use of hormones and/or immunosuppressants, biologically targeted therapies;

(3) Comorbid chronic respiratory diseases or history that accompanies the decline of lung function and may affect patients’ physical activity, such as acute exacerbation of chronic obstructive pulmonary disease, bronchial asthma, bronchiectasis, active lung cancer, active tuberculosis, severe thoracic or spinal deformity, diffuse lung disease (interstitial pneumonia, occupational lung disease, tuberculosis, etc.), and after lobar or one side of the total lung resection;

(4) Comorbid soft-tissue skin lesions, breaks in the skin, or abnormal blood supply (vascular occlusion or thrombosis, trauma) of both upper limbs may impede the wearing or measurement of the bracelet;

(5) Inability to correctly use the bracelet may result in inaccurate data collection.

(6) Inability to wear the bracelet for the requisite duration due to various reasons;

(7) Other conditions that the investigator deems unsuitable for enrolment.
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Individuals who meet the eligibility criteria and consent to participate in the study will be assigned a unique identification number and will undergo baseline data collection and blood sampling. The recruitment and eligibility screening process for the study commenced in August 2024 and will continue until November 2024. Participants will be followed for a period of 1–2 years after enrollment, dependent upon the number of participants who have maintained proactive health behaviours at the 12-month mark. Accordingly, the final anticipated follow-up will be extended to June 2027. The study process framework, including implementation assessments, is illustrated in Fig. 1.

Fig. 1.

Fig. 1

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Study flowchart

PHCs and gps

PHCs were selected and invited to participate in the study based on the number of older T2DM patients with multimorbidity served by each PHC and interest in the proactive health behaviour intervention strategies. In addition, facility conditions for implementing the interventions and availability of GPs willing to participate were also taken into account. Eligible PHCs were required to have at least three GPs willing to participate in this study.

Randomisation

To minimise the risk of cross-contamination between the intervention and control groups, cluster randomisation of PHCs is conducted by independent members of our research team who are not involved in data collection. First, a uniformly distributed random variable (temp) was generated using the pseudorandom number generator in STATA 18.0. The 14 PHCs were randomly ordered by temp and the natural numbers [1–14] were assigned to the PHCs according to their current order. A new random number variable (r) was then assigned to each PHC using the pseudorandom number generator. After sorting r again, the top 7 PHCs were assigned to the intervention group and the bottom half to the control group. This allocation method also facilitates access to uniform training and quality control through the implementation of the intervention strategies in each PHC.

Once the requisite number of participants has been enrolled and eligibility screened, the randomisation results will be notified to each PHC. In order to preclude the possibility of insufficient patient consent as a consequence of the randomisation results, and thus the necessity for supplementary recruitment, a 20% refusal rate was incorporated into the calculations. Consequently, the actual number of participants to be recruited was adjusted from the minimum of 30 per PHC to 36. A comprehensive understanding of the trial and their randomisation results will be provided to screened participants by GPs via offline counselling sessions. Unique codes will be assigned to participants who have signed informed consents by RAs.

Blinding

A blinded trial was not a viable option, given the intrinsic characteristics of the intervention strategies. It is acknowledged that both participants and GPs will be aware of the allocation. Accordingly, the blinding status will not be revealed during the data analysis.

Study interventions

Intervention group (proactive health behaviour intervention)

The intervention strategies were primarily based on the Multi-Theory Model (MTM) of health behavior change [48], a contemporary framework that integrates constructs from several established theories to explain both the initiation and the maintenance of behavior. The MTM was selected for its comprehensiveness and particular relevance to sustained lifestyle modification in chronic disease management. Its initiation model posits that behavior change starts with participatory dialogue (understanding the pros and cons), leading to the formation of behavioral confidence (self-efficacy in performing the behavior) and instigating changes in the physical and social environment. Correspondingly, its maintenance model emphasizes emotional transformation (regulating feelings towards the behavior), engaging in practice for change (habit formation), and utilizing a supportive social environment to sustain the new behavior. Our intervention components were explicitly designed to address these MTM constructs (see logic described below). The initiation model guides the early intervention phase (0–6 months) for participants establishing new behaviors, focusing on assessment and motivation. The maintenance model informs the later phase (12–24 months) for those sustaining behaviors, focusing on support and relapse prevention (Fig. 2).

Fig. 2.

Fig. 2

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Initiation/maintenance model of proactive health behaviour change explained by the multi-theory model

Considering its practical feasibility and cost-effectiveness, the proactive health behaviour intervention strategies in this study are based on the contractual service relationship between GPs and older T2DM patients with multimorbidity in PHCs, with a greater emphasis on empowering patients and placing them at the centre of the behaviour change process. By distributing free multi-sign monitoring bracelets, short video and WeChat-based health education courses, and providing proactive health behaviour support services (including goal setting through shared decision-making, step-by-step goal review and feedback). It will improve participants’ knowledge, attitude and practice of proactive health behaviours at the cognitive and environmental levels, and ultimately contribute to their long-term maintenance. The logic of the intervention is grounded in the MTM and posits a sequential pathway to outcomes. The three components are designed to synergistically target modifiable factors at cognitive, behavioral, and environmental levels. (1) The multi-sign monitoring bracelets and mobile application provide continuous feedback, enhancing self-monitoring and behavioral confidence (a key MTM construct), which is hypothesized to directly influence the performance of proactive health behaviors. (2) The short video-based education aims to improve knowledge and outcome expectations (participatory dialogue in MTM), facilitating the initiation of behavior change. (3) Goal-setting and support through shared decision-making with GPs leverages the clinical relationship to provide tailored advice, address barriers, and foster emotional transformation and social support (environmental changes in MTM), which are crucial for both initiation and long-term maintenance. Together, these components are hypothesized to lead to improved proactive health behaviors (primary outcome: goal achievement), which in turn are expected to contribute to better glycemic control and cardiometabolic health scores (primary outcomes), as well as enhanced quality of life and self-efficacy (secondary outcomes), over the 6 to 24-month follow-up period.

Multi-sign monitoring bracelets and mobile application

Wearable devices represent a mature digital technology capable of continuous monitoring of patients, with the monitoring data displayed and easily interacted with through mobile applications. The multi-sign monitoring bracelet (Beijing Microchip Perception Technology) employed in this study is capable of monitoring multiple physiological parameters, including body temperature, pulse rate, respiration rate, blood oxygen concentration, physical activity, and sleep patterns. The trend of participants’ blood pressure can be adjusted by photoplethysmography (PPG), with the correction of the baseline ambulatory blood pressure monitoring data. The mobile phone application linked to the bracelet displays the trajectory of the aforementioned indicators and any anomalous values in central or discrete graphical interfaces. Furthermore, the application incorporates questionnaires that participants can complete independently, thus facilitating their self-assessment and partially achieving decentralisation of the trial. At baseline, RAs will provide instruction to participants and their family members on the utilisation of the bracelet and mobile application, thereby reducing the risk of dropout due to technical difficulties.

Proactive health behaviour education delivered via short video format

In China, a significant proportion of the older population is accustomed to engaging with short videos on mobile phone applications such as TikTok. Therefore, in comparison to offline or online training courses, which typically last approximately 40 min, the short-video format of health education courses is more readily accepted by older patients. The acceptance of the intervention strategies can be quantified through the analysis of background viewing data. The researchers created a series of short videos on a range of topics related to proactive health behaviours, including smoking, alcohol consumption, physical activity, weight, sleep, nutrition, communication with family members and GPs, and individual goal-setting for proactive health behaviours. Each topic of health education is comprised of three or four short videos, with an approximate duration of four minutes each. The short videos are delivered in a structured, phased sequence over four weeks: Week 1 focuses on the rationale for change (risks of smoking/alcohol); Week 2 covers actionable behaviors (physical activity, sleep); Week 3 addresses supportive skills (nutrition, communication); Week 4 prepares for goal-setting. Two or three videos are sent daily via the mobile application or WeChat, allowing for gradual learning and reflection.

Goal-setting and support for proactive health behaviours through shared decision-making

All goal-setting and subsequent support sessions are conducted by the participant’s own GP within the framework of their routine chronic disease management visits, ensuring the intervention is embedded in real-world primary care practice. Following the collection of baseline data on exposure to proactive health behaviours, participants will identify the components requiring change and commence the initial goal-setting process. Following a four-week interval of short video-based health education, both participants and doctors will identify the potential barriers and facilitators influencing behaviour change through communication and agree on individualised goals, including global goals and staged goals. The goal-setting process will be conducted in accordance with the 5 A model (Assess, Advise, Agree, Assist, Arrange) proposed by the U.S. Preventive Services Task Force (USPSTF) [49], as well as the Standards of Care in Diabetes recommended by the American Diabetes Association (ADA) [50]. Specific action plans, including detailed and quantifiable behavioural changes, will be negotiated upon goal-setting. Subsequently, the staged goals will be reassessed at the three-month follow-up visit, while the global goals will be evaluated at the six-month post-enrolment visit. GPs will provide their patients with the necessary guidance and support throughout the behavioural change process. In the event that behavioural change does not meet expectations at follow-up, a discussion will be held between participants and GPs regarding the suitability of the initial goal-setting and the necessity of critically employing, modifying, or abandoning the action plan. All participants in the intervention group will be required to sign a triplicate contract for proactive health behaviour change. One copy will be retained by the patient, one by the GP for goal review during follow-up, and one by the research team.

Control group (routine chronic disease management)

The control group will receive routine chronic disease management services from PHCs according to the service standards of NEPHSP. Such services will include routine follow-ups at three-month intervals, screening for comorbidities, and the provision of health recommendations. To ensure comparability with the intervention group, participants in the control group were also required to set behavioural change goals in accordance with suboptimal components at baseline and with the health recommendations provided by routine chronic disease management.

Concurrently, GPs in both groups will be asked to treat patients with T2DM, hypertension, hyperlipidemia or ischemic heart disease with medication in accordance with the Guideline for the Management of Diabetes Mellitus in the Elderly in China (2024 Edition) [51] in order to maintain as much consistency as possible in routine chronic disease management, with the exception of proactive health behaviour intervention strategies. There are no specific criteria for the cessation or modification of the assigned interventions.

Outcomes

Primary outcome

The primary outcome consists of three indicators: (1) the number of participants in the intervention group who achieved at least one of their proactive health behaviour goals at 6 months compared to the control group; (2) the number of participants who achieved glycaemic control (fasting plasma glucose (FPG) < 7.2 mmol/L and glycated hemoglobin (HbA1c) < 7.5%) at 6 months; and (3) the cardiometabolic health (CMH) scores at 6 months.

Achievement of at least one of proactive health behaviour goals is defined as improvement in any of proactive health behaviour components for which a change goal was set and achievement of the target level during follow-up: change in smoking status from smoking to current abstinence, change in alcohol consumption from an average of ≥ 15 g per day to < 15 g per day, change in BMI from ≥ 24 kg/m2 to normal, physical activity achieved at a level of at least 150 min of moderate-intensity physical activity per week, and improvement in sleep quality or achievement of 7–9 h of sleep per night.

Although not a direct behavioral target, glycemic control (HbA1c < 7.5%) is included as a primary outcome because it is a key clinical endpoint that directly reflects the aggregate impact of lifestyle modifications on glucose metabolism and is strongly associated with long-term vascular complications in T2DM.

The CMH score refers to Wu’s study [52] and includes systolic blood pressure (SBP < 140 mmHg), diastolic blood pressure (DBP < 90 mmHg) [51], low-density lipoprotein cholesterol (LDL-C < 1.8 mmol/L), non-high-density lipoprotein cholesterol (non-HDL-C < 2.6 mmol/L), triglycerides (TG < 1.7 mmol/L) [53], and high-sensitivity C-reactive protein (hsCRP < 1 mg/L) [54]. Each indicator in the ideal range is assigned a score of 1, and the total CMH score ranges from 0 to 6.

Secondary outcome

Secondary outcome indicators include:

  1. The number of goals set by each participant at baseline, including motivation and confidence rankings for each goal (5-point Likert scale).

  2. The number of participants achieving all component change goals at 3 and 6 months.

  3. The number of participants achieving each of the five component goals at 3 and 6 months.

  4. Specific changes in each of the five proactive health behaviour components at 3 and 6 months, including reductions in cigarettes smoked, alcohol consumption, body mass index (BMI), and increases in physical activity (METs), sleep duration or quality (PSQI scores) from baseline.

  5. The number of ideal proactive health behaviour components at 3 and 6 months, with 1 point for each ideal component and a total score ranging from 0 to 5.

  6. Improvements in cardiometabolic health indicators at 3 and 6 months, including SBP, DBP, LDL-C, non-HDL-C, TG, and hsCRP.

  7. Improvements in quality of life and self-efficacy at 3 and 6 months. Health-related quality of life will be assessed using two complementary instruments: the Chinese version of the EQ-5D-5L and the 36-Item Short Form Survey (SF-36). The EQ-5D-5L is a preference-based measure whose utility scores (derived from the Chinese value set) are essential for calculating Quality-Adjusted Life Years in the planned cost-effectiveness analysis. The SF-36 is a health profile instrument that provides detailed scores across eight domains (e.g., physical function, mental health), offering a multidimensional view of well-being particularly relevant for older adults with multimorbidity. Both instruments are widely validated for use in chronic disease populations. Self-efficacy will be assessed by the Simplified Chinese Self-Efficacy for Managing Chronic Disease scale.

  8. The levels of all five proactive health behaviour components and the number of ideal components, as well as quality of life and self-efficacy at 12 and 24 months. Thus, the sustainability of the proactive health behaviour intervention strategies will be evaluated.

Data collection

Information pertinent to PHCs (e.g., number of residents in the area and number of patients with T2DM) and GPs (e.g., degree of education and duration of practice) has been collated during the recruitment period for PHCs and GPs.

Following the recruitment and eligibility screening of participants, those who meet the criteria and consent to participate in the study will be informed of the trial’s allocation results and commencement date via their GPs. It is anticipated that the baseline visit will commence at the participants’ next scheduled diabetes appointments. At the baseline visit, RAs will collect data pertaining to the socio-demographic characteristics of participants, including age, gender, marital status, and level of education. Additionally, they will gather information related to T2DM, such as the duration of the disease, the presence of complications, and the current medications being taken. They will also inquire about other medical conditions that may be present, such as hypertension, hyperlipidaemia, and ischaemic heart disease, as well as the medications used to treat these conditions. The data will be collected via either standardised electronic questionnaires or the electronic health record. Furthermore, the baseline survey will also evaluate elements related to outcome indicators and other functional status (Table 2). These the aforementioned procedures will be repeated at subsequent follow-ups. Trained RAs will be responsible for data collection and will ensure that the questionnaires are completed correctly and without omissions. All data will be collected and managed together based on the REDCap electronic data collection tool [55], with the exception of the multi-sign monitoring bracelet and APP data.

Table 2.

Measurements of the study

Outcome Measurement 0m 1m 3m 6m 12m 18m 24m
Characteristics Characteristics of PHCs PHCs' records √
Characteristics of GPs PHCs' records √
Sociodemographics PHCs' records √
T2DM and comorbidities PHCs' records √ √ √ √ √ √
Unrecognized CVD Early Diagnosis Questionnaire a[56] √ √ √ √ √ √
Functional status Anxiety GAD-7 √ √ √ √ √ √
Depression PHQ-9 √ √ √ √ √ √
Cognitive function SCD-Q9, MMSE √ √ √ √ √ √
Nutritional status MNA-SF √ √ √ √ √ √
Mobility 5-STS, 6MWT √ √ √ √ √ √
Proactive health behaviour Smoking status Structured questionnaire √ √ √ √ √ √ √
Alcohol consumption Structured questionnaire √ √ √ √ √ √ √
BMI Calculated by weight and height √ √ √ √ √ √ √
Physical activity IPAQ-SF √ √ √ √ √ √ √
Sleep PSQI, ISI √ √ √ √ √ √ √
Goal setting Behaviour change Structured questionnaire √
Primary outcome Achievement of goals Achievement of at least one goal √
Targeted glucose control FPG<7.2mmol/L & HbA1c<7.5% √ √ √ √ √
CMH score Number of ideal cardiometabolic health indicators b √ √ √ √ √
Secondary outcome Motivation and confidence 5-point Likert scale √ √ √ √ √ √
Change in proactive health behaviour components Achievement of all goals √ √ √ √ √
Achievement of each component goal √ √ √ √ √
Change in each component √ √ √ √ √
Number of ideal components √ √ √ √ √
Change in CMH indicators SBP, DBP, LDLC, Non-HDLC, TG, hsCRP √ √ √ √ √ √
Quality of life EQ-5D-5L, SF-36 √ √ √ √ √ √
Self-Efficacy Simplified Chinese Self-Efficacy for Managing Chronic Disease √ √ √ √ √ √
Cost effectiveness QALYs EQ-5D-5L √ √ √
Healthcare utilization PHCs' records √ √ √ √ √ √ √
Implementation costs Structured questionnaire & administration records √ √ √
Adverse events Adverse events PHCs' records & structured questionnaire √ √ √ √ √ √
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Abbreviations: PHC Primary healthcare center, GP General practitioner, T2DM Type 2 diabetes mellitus, CVD Cardiovascular disease, BMI Body mass index, IPAQ-SF International physical activity questionnaire-short form, PSQI Pittsburgh sleep quality index, ISI Insomnia Severity Index, GAD-7 Generalized Anxiety Disorder-7, PHQ-9 Patient Health Questionnaire-9, SCD-Q9 9-item Subjective Cognitive Decline Questionnaire, MMSE Mini-Mental State Examination, MNA-SF Mini Nutritional Assessment short-form, 5-STS 5-times Sit-to-Stand test, 6MWT 6-minute walk test, FPG Fasting plasma glucose, HbA1c Glycated hemoglobin, CMH Cardiometabolic health, SBP Systolic blood pressure, DBP Diastolic blood pressure, LDLC Low-density lipoprotein cholesterol, non-HDLC Non-high-density lipoprotein cholesterol, TG Triglycerides, hsCRP High-sensitivity C-reactive protein, SF-36 36-Item Short Form Survey, QALY Quality Adjustment Year of Life

a. The Early Diagnosis Questionnaire was a questionnaire on demographic characteristics and signs and symptoms suggestive of CVD (including coronary artery disease, atrial fibrillation and HF). It was developed from the Lifelines Cohort Study and participants with a score ≥24 need further investigations for CVD diagnosis

b. The ideal cardiometabolic health indicators were defined as SBP<140 mmHg, DBP<90 mmHg, LDLC<1.8 mmol/L, non-HDLC<2.6 mmol/L, TG<1.7 mmol/L, hsCRP<1mg/L

Quality control

A uniform training programme will be devised and implemented with the objective of providing comprehensive explanations of the entire workflow and considerations associated with the study. Following consultations and discussions with GPs and administrators from each PHC, any necessary adjustments will be made to the implementation strategy and the commencement date of each PHC will be determined in order to coordinate recruitment, eligibility screening, formal enrolment, course training and goal setting. During the study period, the research team will provide assistance and support for the implementation of the intervention strategies. Regular communication with GPs about progress and barriers to intervention implementation and areas for improvement will also be conducted via online meetings and offline discussions.

Statistical analyses

Sample size

The sample size was calculated using the prescribed formula for cluster randomised controlled trials [57].

graphic file with name d33e1331.gif

In this formula, n represents the sample size required for each group of matching individual randomised controlled trials, ICC denotes the intraclass correlation coefficient, and m signifies the average sample size of each cluster. In light of analogous studies [58], it can be inferred that the absolute difference in the anticipated achievement of at least one proactive health behaviour goal between the intervention and control groups should be 20% (the probability of behaviour change under routine chronic disease management is 20%). According to α = 0.05 and 1 - β = 0.8, the minimum sample size required for each group (n) is 79. If three GPs are to be recruited from each PHC, and 10 participants are to be recruited by each GP, with m = 30 and ICC = 0.04, the total number of samples (N) can be calculated as approximately 410 by taking a dropout rate of 20%. Therefore, with an average cluster size of 30 and 7 clusters in each group, this study requires approximately 420 participants to be enrolled from 14 PHCs.

Descriptive analyses

The baseline characteristics of participants in the intervention and control groups will be described, as will their proactive health behaviour goals (including motivation and confidence) and baseline levels of the five proactive health behaviour components. Depending on the results of normality tests, continuous variables will be expressed as either mean (standard deviation) or median (interquartile range), and compared using either the t-tests or the non-parametric tests. Categorical variables will be expressed as frequencies (percentages) and compared using the chi-squared test or Fisher’s exact test. Variables exhibiting a significant between-group difference (P ≤ 0.10) will be considered as potential confounders for subsequent analyses.

Primary or secondary outcome analyses

The efficacy of the intervention will be evaluated in accordance with the intention-to-treat principle.

The PHC clustering will be incorporated as a hierarchical variable in all models for outcome indicators, given its impact on GPs’ practice levels. In order to analyse the primary outcome, random effects logistic models will be employed to examine the odds ratios (ORs) of achieving at least one of the proactive health behaviour goals at 6 months for participants in the intervention and control groups (with individual differences considered as random effects), with adjustment for the potential confounders identified. Similarly, the secondary outcome will be analysed with the aforementioned covariates included in the primary analyses, employing the appropriate statistical models. These will be either linear mixed-effect models for continuous outcome variables or mixed-effect binomial regression models for ordinal outcome variables. The results will be expressed as effect values (95% confidence intervals) and relevant P values for all models.

Subgroup analyses

A series of subgroup analyses will be conducted, with a particular focus on the following variables: baseline age, gender, education level, baseline levels of different proactive health behaviour components, duration of T2DM, baseline HbA1c level, motivation and confidence in goal-setting. The objective of these subgroup analyses is to facilitate a more detailed comprehension of the variables influencing primary and secondary outcomes.

Sensitivity analyses

The missing data will be processed using multiple imputation by chained equations (MICE). In the event of discrepancies between the estimates derived from the multiple imputation dataset and those obtained from the completed dataset, alternative multiple imputation techniques will be employed to conduct sensitivity analyses and assess the robustness of estimation. Consequently, this process will yield multiple datasets and multiple models for each dataset. The appropriate combinations of results will be employed to generate parameter estimates (95% confidence intervals), standard errors, and P-values.

Implementation evaluation

The RE-AIM model will be employed to assess the reach, effectiveness, adoption, implementation and maintenance of the intervention, as detailed below.

The reach of the intervention in primary care will be evaluated in accordance with the characteristics of PHCs and GPs. At the patient level, the reach will be assessed in accordance with the number and characteristics of patients who meet the eligibility criteria.

The intervention’s effectiveness will be evaluated based on the primary and secondary outcomes outlined in the statistical analyses section.

The extent of intervention adoption will be gauged by the proportion of primary care settings and patients engaged in the study, as well as the representativeness of these participants.

The implementation level of the intervention will be assessed separately from the perspectives of GPs (delivery users, adherence) and patients (end users, dose received) in PHCs. A self-report checklist will be employed to evaluate the extent to which the intervention has been implemented in accordance with the researchers’ intentions (adherence) at each visit and at the six-month follow-up. The dose of the intervention received by participants will be evaluated upon completion of the short video-based education by means of a self-report questionnaire designed to examine their knowledge of proactive health behaviours.

The maintenance of the intervention will be assessed in terms of dropout rates and causes of participants during the follow-up period.

The factors influencing the adoption and implementation of the intervention will be evaluated via focus groups with GPs and participants in PHCs. Purposive sampling will be employed to ensure heterogeneity in terms of socio-demographics, knowledge and skills, and personal preferences. Once informed consent in writing and audio recording has been obtained, the interview dialogue will be recorded using recording devices, and participants will be encouraged to discuss the feasibility, acceptability, appropriateness, and outreach of the intervention. Transcripts will be coded and analysed using NVivo 18.0 to identify determinants to intervention implementation according to the CFIR model.

Cost-effectiveness analysis

Quality adjusted life years (QALYs) will be calculated by comparing the EQ-5D-5 L questionnaires at baseline and after 6 months [59]. In addition to the healthcare utilisation questionnaire, costs of visits to PHCs and hospitals will be extracted from participants’ electronic health records.

The cost-effectiveness of the intervention will be calculated using a discount rate of 5%. Accordingly, the incremental cost-effectiveness ratio (ICER) of the proactive health behaviour intervention will be calculated as the cost difference (∆C) divided by the effectiveness difference (∆E). The economic evaluation will be conducted according to the threshold recommended by the World Health Organization (1–3 times China’s real gross domestic product per capita) [60].

To assess the cost-effectiveness of the intervention, data will be collected over a two-year period. The calculation of QALYs will be based on a comparison of the EQ-5D-5 L questionnaire at baseline and after 24 months. Direct costs will be extracted from health care utilisation questionnaires and electronic health records. Indirect costs will be considered, including equipment/site and human capacity costs of the research team and PHCs, and development and maintenance costs of the wearable devices and APPs. The same 5% discount rate will be used for discounting.

Biological specimens collection and storage

During the study period, blood samples will be collected from participants for laboratory examinations and further analysis in the future, following the provision of informed consent All biological specimens will be processed and stored in accordance with the relevant specifications.

Patient and public involvement

Patients were not involved in the process of intervention strategy design and implementation protocol development. However, patient feedback will be collected through focus group interviews during the implementation process. This feedback will be used to improve the short-video based education courses and facilitate further implementation of intervention dissemination.

Discussion

This article outlines the methodology of a cluster randomised controlled trial that will assess the impact of an intervention strategy on proactive health behaviours and cardiometabolic health in older T2DM patients with multimorbidity. The intervention strategy is comprised of three components: free multi-sign monitoring bracelets and applications, short video-based education on proactive health behaviours, and goal-setting and support through shared decision-making. The intervention can be readily incorporated into the standard care provided in primary care settings. All specialist guidelines emphasise the effectiveness of lifestyle interventions for the prevention of CVD, particularly in high-risk populations, which include older patients with T2DM [51, 61, 62]. Nevertheless, achieving sustainable lifestyle changes for this population remains a significant challenge in routine practice [37, 63], and the evidence available at the primary care level is also limited [64–66]. In light of this, and with reference to previous studies [64–66], our objective is to minimise the time and resources required to deliver the lifestyle intervention so that it can be easily adopted by GPs and integrated into routine T2DM management.

Similarly, China’s PHCs have assumed a significant responsibility for the provision of healthcare services to older people. Nevertheless, the failure to promote and implement lifestyle interventions has resulted in an inability to reach the critical point for CVD prevention and control [67]. In this regard, the Chinese government has proposed a strategic plan to build a ‘healthy China’ and advocated ‘proactive health’ for the entire population [68], with the objective of further promoting residents’ health literacy and encouraging the adoption of healthy lifestyles. Prior research has indicated that the enhancement of health literacy for older adults based on health education represents a crucial element in the implementation of lifestyle interventions [69]. Moreover, the combination of digital health technology and group-based activities has been identified as the most effective approach. However, it should be noted that such resource-intensive interventions, which rely on extensive pre-training of health professionals, may not be sustainable in the long term [70]. A systematic review reveals that the majority of studies do not adequately address the adoption, implementation process and sustainability of lifestyle interventions [71]. This limits the wider application of intervention strategies. Consequently, there is a need to incorporate the characteristics of primary care organisations in local regions in order to provide effective proactive health behaviour interventions for local patients and to integrate them into the current health system for long-term sustainable implementation.

Despite the potential of digital health for chronic disease management in older adults [72–77], robust evidence on its effective and sustainable integration into resource-limited primary care routine remains scarce. Most research focuses on technological efficacy or short-term acceptability, lacking evaluation of GP-led, embedded interventions assessing long-term effectiveness, implementation, and cost-effectiveness in real-world settings [71]. This “practice-based evidence” gap hinders translation into routine care. Addressing this gap is a key strength of our study. Unlike research team-delivered interventions [78], our program is operationalized by GPs within their routine practice, using digital tools as a supportive “toolkit”. This practice-integrated design, evaluated through a hybrid type 2 framework [41–43], will simultaneously assess the strategy’s effectiveness and its implementation process, providing direct evidence for scalable integration into primary care. Secondly, the enrolment of individual participants prior to cluster randomisation serves to reduce the potential for selection bias [79]. A further strength of this study is that, in comparison with previous research [65], the effectiveness outcome encompasses not only the achievement of individualised proactive health behaviour goals but also improvements in glycemic control and other cardiometabolic indicators. This approach avoids the potential for subjective bias. The 24-month follow-up period is relatively limited, and a longer follow-up period would allow for a more comprehensive assessment of the sustainability of behavioural change and the long-term impact on cardiometabolic health. However, given the limited survival time of older adults, a 24-month follow-up period is deemed sufficient to assess the long-term maintenance of behaviour change. A further limitation of the study is that it required patients to complete a diary card integrated into the app (for reporting daily self-measurements of smoking, alcohol consumption, blood pressure, and blood glucose) and to wear the baralets for continuous monitoring of exercise and sleep over time. This is an onerous task for older patients, but it is also a valuable data source for evaluating intervention acceptance, dose, and adherence in this study.

In conclusion, the findings of this study will provide valuable insights into the effectiveness, implementation, and cost-effectiveness of proactive health behaviour interventions for older T2DM patients with multimorbidity. The results will inform healthcare practitioners and policy makers of the potential benefits of promoting proactive health behaviour change as an efficient complement to chronic disease management services in primary care.

Trial status

The date of the protocol is 09/05/2024, with version 3.0. Recruitment and eligibility screening of participants commenced in August 2024 and is anticipated to be concluded by October 2024. Formal enrollment and baseline data collection is expected to be completed by December 2024, after which follow-up and data analysis will be conducted and results reported in other separate publications.

Supplementary Information

Supplementary Material 1. (124KB, doc)

Acknowledgements

The research team would like to acknowledge the contributions of the following individuals and their teams of GPs for their assistance with the research: Aiqing Fan (Sunqiao Community Health Service Center, Pudong New Area, Shanghai), Hao Huang (Jinyang Community Health Service Center, Pudong New Area, Shanghai), Li Liu (Huamu Community Health Service Center, Pudong New Area, Shanghai), Shengbing Zhang (Weifang Community Health Service Center, Pudong New Area, Shanghai), Shijun Wang (Huinan Community Health Service Center, Pudong New Area, Shanghai), Shirong Chen (Yingbo Community Health Service Center, Pudong New Area, Shanghai), Tianying Wang (Sanlin Kangde Community Health Service Center, Pudong New Area, Shanghai), Weiguo Zhou (Sanlin Community Health Service Center, Pudong New Area, Shanghai), Xiaohua Lu (Lujiazui Community Health Service Center, Pudong New Area, Shanghai), Yajun Cao (Beicai Community Health Service Center, Pudong New Area, Shanghai), Yaling Li (Dongming Community Health Service Center, Pudong New Area, Shanghai), Ying Jin (Dapuqiao Community Health Service Center, Huangpu District, Shanghai), Yingxue Hua (Xinchang Community Health Service Center, Pudong New Area, Shanghai), Zheng Ye (Changfeng Community Health Service Center, Putuo District, Shanghai).

Abbreviations

ADA

American Diabetes Association

BMI

body mass index

CFIR

Consolidated Framework of Implementation Research

CKD

chronic kidney disease

CMD

cardiometabolic disease

CMH

cardiometabolic health

CMM

cardiometabolic multimorbidity

CONSORT

Consolidated Standards of Reporting Trials

CRT

cluster randomised controlled trial

CVD

cardiovascular disease

DBP

diastolic blood pressure

FPG

fasting plasma glucose

GP

general practitioners

HbA1c

glycated hemoglobin

hsCRP

high-sensitivity C-reactive protein

ICC

intraclass correlation coefficient

ICER

incremental cost-effectiveness ratio

IPAQ-SF

International Physical Activity Questionnaire-Short Form

IR

insulin resistance

ISI

Insomnia Severity Index

LDL-C

low-density lipoprotein cholesterol

METs

metabolic equivalent of tasks

MICE

multiple imputation by chained equations

NEPHSP

National Essential Public Health Service Package

non-HDL-C

non-high-density lipoprotein cholesterol

T2DM

type 2 diabetes mellitus

OR

odds ratio

PHC

primary healthcare centre

PPG

photoplethysmography

PSQI

Pittsburgh Sleep Quality Index

QALY

quality adjusted life year

RA

Research assistant

RE-AIM

Reach, Effectiveness, Adoption, Implementation, and Maintenance

SBP

Systolic blood pressure

SPIRIT

Standard Protocol Items: Recommendations for Interventional Trials\

TG

Triglycerides

USPSTF

U.S. Preventive Services Task Force

Authors’ contributions

HJ conceived the study and applied for funding. The study design was developed by HJ, YL and QH. JG, YL, SG, QL, XC, YZ, XG, FL, NX and MY participated in the planning, coordination and management of data collection. YL and LZ contributed to the statistical design of the study. All authors provided critical feedback on the design and implementation of the study. YL and QH drafted the manuscript and HJ revised it. All authors have approved the final version of the manuscript for publication.

Funding

This study was funded by the Special Project of Clinical Research in Health Industry of Shanghai Municipal Health Commission (202140248) and the National Key Research and Development Program of China (2022YFC3601504) to Hua Jiang.

Data availability

The research team will have exclusive rights to all data. The dataset, statistical code and final protocol of the study will be made available to interested parties on reasonable request to the corresponding author.

Declarations

Ethics approval and consent to participate

Approval for this study was granted by the Ethics Committee of Shanghai East Hospital (Approval No. 2024YS-083) on 10/05/2024. Written informed consent will be obtained from all participants, and permission will be sought to collect biological samples for storage and further research. Should participants wish to withdraw from the trial, they will be asked whether they consent to the utilisation of their data for subsequent analyses. In the event of any material alterations being made to the initial approval document, these will be submitted by the principal investigator for approval to the ethics committee. Following the requisite approval, the revised protocol will be disseminated to all pertinent stakeholders, and updated in the Chinese Clinical Trial Registry.

Consent for publication

The findings of this study will be disseminated in peer-reviewed journals for the benefit of professional practitioners, without any individual data or confidential information being disclosed.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yang Li and Qian Huang contributed equally to this work and share first authorship.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (124KB, doc)

Data Availability Statement

The research team will have exclusive rights to all data. The dataset, statistical code and final protocol of the study will be made available to interested parties on reasonable request to the corresponding author.

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