Skip to main content

Development of a behavior change intervention to improve physical activity adherence in individuals with metabolic syndrome using the behavior change wheel

Abstract

Background

Adherence to physical activity is inadequate in adults with metabolic syndrome. Adherence to physical activity recommendations is crucial and can result in improved health outcomes and reduced medical burdens. A comprehensive behavior change intervention, including identifying determinants of adherence to physical activity recommendations, intervention options, intervention content and implementation options, was imperative for enhancing physical activity adherence. The aim of the study is to develop an intervention to increase physical activity adherence among individuals with metabolic syndrome.

Methods

The study followed the eight steps of the Behavior Change Wheel guide, including defining the problem in behavioral terms (Step 1), selecting target behavior (Step 2), specifying target behavior (Step 3), identifying what needs to change (Step 4), identifying intervention functions (Step 5), identifying policy categories (Step 6), identifying behavior change techniques (Step 7), and determining model of delivery (Step 8). The semi-structured, in-depth interviews were employed to identify the determinants of adherence to physical activity among twenty-eight individuals with metabolic syndrome based on capability, opportunity, motivation and behavior model. Next, the intervention functions and policy categories were chosen to address these determinants. Finally, behavior change techniques were selected to assist in the delivery of the intervention functions and be translated into intervention content.

Results

Our study identified eighteen facilitators and fifteen barriers to physical activity adherence. It resulted in the selection of seven intervention functions and nineteen behavior change techniques for the intervention program. Then, the current study identified an app as the delivery mode. Finally, a behavioral change intervention was generated for individuals with metabolic syndrome to increase physical activity recommendation adherence.

Conclusions

The Behavior Change Wheel provided a systematic approach to designing a behavior change intervention, which helped improve the health outcomes and reduce medical burdens and economic burdens among individuals with metabolic syndrome. The findings suggested that potential intervention should pay special attention to increasing knowledge in metabolic syndrome, imparting skills of physical activity, offering a supportive environment, and providing suggestions on regular physical activity using the appropriate behavior change techniques. A feasibility study will be undertaken to assess the acceptability and effectiveness of the intervention program in the future.

Peer Review reports

Background

Metabolic syndrome is a worldwide medical and public health concern [1, 2]. It is a cluster of cardiovascular risk factors, not limited to increased waist circumference (WC), high systolic blood pressure (SBP) or diastolic blood pressure (DBP), high triglyceride (TG) levels, low high-density lipoprotein cholesterol (HDL-C), and elevated fasting blood glucose (FBG) [3]. Metabolic syndrome is increasing and is likely to reach epidemic proportions [4]. It has been estimated that global prevalence of metabolic syndrome is about 25% [5]. The prevalence of metabolic syndrome was 8.8% in 1991–1995, 29.3% in 2011–2015, and 31.1% in 2015–2017 in China [2, 6]. It is associated with negative outcomes, including a high risk with type 2 diabetes, cardiovascular disease, and all-cause mortality [7]. Additionally, the cost of the metabolic syndrome is in trillions, and forecasted to rise in the future [8]. In Germany, Spain and Italy, the health service cost of metabolic syndrome among patients with hypertension is €24,427, €1,900 and €4,877 million and expect to rise by 59%, 179% and 157% respectively by 2020 [9], which places heavy medical and economic burdens on individuals and the healthcare system. Thus, the management of metabolic syndrome is of paramount importance.

Physical activity has a substantial positive effect on metabolic syndrome [10]. Physical activity is defined as “any movement of the body produced by skeletal muscles that results in energy expenditure” [11]. It is imperative to sustain participation in physical activity since metabolic syndrome is a prevalent long-term condition, which requires substantial expenditure of effort and continuous perseverance [12]. However, during the process of participation, adherence to physical activity recommendations remains a great challenge among people with metabolic syndrome [13, 14]. In other words, the individuals' health-related behaviors (including taking medication, implementing lifestyle changes, etc.) are not completely consistent with the advice (prescriptions) provided by the health care providers [15]. Fappa et al., [16] found that individuals with metabolic syndrome may have poor metabolic syndrome parameters due to inadequate adherence to physical activity. Keller et al., [17] reported that adherence declined with an increase in the recommended frequency of exercise. Gallardo-Alfaro et al., [18] showed that the adherence to physical activity recommendations needed to be improved among people with metabolic syndrome. Chen et al., [19] reported that the physical activity level of individuals with metabolic syndrome was low. Therefore, it is essential to identify the facilitators and barriers to physical activity adherence and develop intervention strategies to improve it.

It has been found that most existing interventions to improve physical activity adherence have some effectiveness, but they tend to be poor in the application of theory [20,21,22], which may limit their success and lead to suboptimal adherence [19]. Theory-based intervention could enhance the effectiveness of behavior change components [23], as the relationships between constructs, that are predictive of behavior change, can be understood, translated into intervention content, and then examined for an explanation of how an intervention achieved, or did not, its desired outcome [24]. Thus, it is necessary to develop theoretically-informed intervention strategies to encourage persons to sustain physical activity and integrate them into their daily lives in China.

Behavioral science frameworks provide theory to help determine the potential impacts that support or disrupt initiation and maintenance of behavior change [25]. The Medical Research Council’s (MRC) framework on developing and evaluating complex interventions aims to help researchers adopt suitable methodologies [26]. Firstly, the behavior change wheel (BCW) framework was selected to guide the intervention development process for its ability to address the broad scope and incoherent definitions of theoretical constructs identified within existing theoretical frameworks and provides a systematic and transparent method for promoting behavior change [27, 28]. The BCW synthesized 19 behavior change frameworks and provided a three-stage intervention method (see Fig. 1): understanding the behavior (Stage 1), identifying intervention options (Stage 2) and identifying content and implementation options (Stage 3). The first stage involved four steps to understand the behavior: defining the problem in behavioral terms (Step1), selecting target behavior (Step2), specifying target behavior (Step3), and determining what needs to change (Step4). The second involved two steps (Step5 and Step6): identifying intervention functions (Step5) and policy categories (Step6). The third stage included two steps: identifying behavior change techniques (BCTs) (Step7) and model of delivery (Step8) [27]. In short, it consisted of three layers (see Fig. 2). At its hub is the capability, opportunity, motivation, and behavior (COM-B) model, which focuses on exploring determinants of target behavior [27]. Capability can be either ‘physical’ (having the physical skills, strength, or stamina) aspects required to perform the behaviour or ‘psychological’ (having the knowledge, psychological skills, strength, or stamina) aspects required to perform the behaviour. Opportunity can be ‘physical’ (what the environment allows or facilitates in terms of time, triggers, resources, locations, physical barriers, etc.) or ‘social’ (including interpersonal influences, social cues, and cultural norms). Motivation may be ‘reflective’ (involving self-conscious planning and evaluations) or ‘automatic’ (involving wants and needs, desires, impulses, and reflex responses). Additionally, the Theoretical Domains Framework (TDF) [29] has been added to the BCW to further unpack factors identified in the COM-B model into 14 theoretical domains. The second layer of the BCW is nine intervention functions, through which an intervention could modify behavior [27]. The third layer is seven policy categories, as high-level strategies, which help support the implementation of intervention functions [27]. Moreover, the BCTs from the version 1 of the BCT taxonomy (BCTTv1) are active ingredients and have been linked to the BCW to assist in delivery of intervention functions [30]. Additionally, the BCW provides theory-based linkages between COM-B components, intervention functions, BCTs, and policy categories [27]. The BCW has been widely applied to design behavior change interventions that target some health-related behaviors, such as eating habits [31], sedentary behavior [32], weight management [33] and physical activity behavior [34, 35]. Moreover, the intervention drawing on the BCW framework has showed benefits in improving the adherence to healthy eating, exercise, and body composition [36]. However, no known research has attempted to understand the physical activity behavior among individuals with metabolic syndrome using the BCW framework in China.

Fig. 1
figure 1

Stages involved in an intervention development using the BCW [27] (used with permission from authors)

Fig. 2
figure 2

The Behavior Change Wheel (used with permission from authors)

Therefore, the aim of this study was to systematically develop a comprehensive behavior change intervention to support adherence to physical activity in people with metabolic syndrome in China guided by the BCW framework. We hope that this study can be used by health care professionals when they plan to provide physical activity guidance for their clients, and ultimately improve the clinical health outcomes and quality of life of people with metabolic syndrome. Additionally, the methodology identified in the current study could provide references for other researchers developing behavior change interventions.

Methods

Based on the BCW framework, we developed a three-stage intervention that included eight steps [27].

Stage 1: Understanding the behavior

Step 1: Define the problem in behavioral terms

This step required researchers to formulate the problem in behavioral terms and assess two aspects: (i) who is involved in performing the behavior and (ii) what the behavior is [27]. Evidence on physical activity adherence was reviewed to identify the problem among people with metabolic syndrome. We searched Cochrane Library, Embase, Web of Science, PubMed, CINAHL, Chinese National Knowledge Infrastructure, Weipu and Wanfang for papers published through March 2021 using the following keywords: “metabolic syndrome”, “physical activity”, “exercise”, “movement”, “physical therapy”, “strength training”, “aerobic training”, “resistance training”, “weight training”, “physiotherapy”, “stretching exercise”, “kinesiotherapy” and “lifestyle”. Manually searching relevant papers for cited references was also conducted if necessary.

Step 2: Select target behavior

Step 2 involved determining the target behaviors that might solve the defined problems in Step 1. The final target behavior was selected based on four criteria from the BCW framework: (i) how much of an impact changing the behavior will have on the desired outcome, (ii) how likely it is that the behavior can be changed, (iii) how likely it is that the behavior will have a positive or negative impact on other, related behaviors, and (iv) how easy it will be to measure the behavior [27]. We carried out a literature search on physical activity management measures among people with metabolic syndrome to select potential target behaviors.

Step 3: Specify target behavior

The BCW guided us to specify the target behavior through six questions, including (i) who needs to perform the target behavior, (ii) what they need to do differently to achieve change it, (iii) where and (iv) when they do it, (v) how often, and (vi) with whom they do it [27]. To specify the target behavior, we reviewed existing literature on physical activity interventions for individuals with metabolic syndrome.

Step 4: What needs to change?

We performed a qualitative, descriptive study with a constructionist epistemology [37] that acknowledges that knowledge is constructed based on perception and experiences of individuals, and constructed via speech to understand the world [38] to explore determinants of physical activity adherence in this step, which included both barriers and facilitators. These determinants were then mapped into COM-B components and TDF domains. The study design was conducted following the Standards for Reporting Qualitative Research (SRQR) [39]. Three domains are included in the COM-B model: capability, opportunity, and motivation, which interact with one another to enable a behavior to occur. The TDF includes fourteen domains that can be condensed to fit the three constructions of the COM-B model, as follows: capability (knowledge, cognitive and interpersonal skills, memory, attention and decision processes, behavioral regulation, and physical skills), opportunity (social influences, environmental context, and resources), and motivation (reinforcement, optimism, emotions, social/professional role and identity, beliefs about capabilities, beliefs about consequences, goals, and intentions) [40].

Participants and settings

Individuals who have been diagnosed with metabolic syndrome according to the criteria proposed by the 2009 Joint Scientific Statement (harmonizing criteria) [3] and aged over 18 years were recruited. People who had severe diseases and could not communicate effectively due to oral diseases were excluded. Between May and August, 2021, two researchers recruited participants by distributing a recruitment advertisement. If individuals agreed to participate, they were given information about the study, and then they were asked to fill out a written informed consent form. We recruited participants with rich information through a purposive and criterion-based sampling method. Participants who met the criteria were selected by considering their representativeness of gender, age, education level, residence, income, and occupation to obtain rich information. Researchers conducted the study at a health promotion center of a general university hospital in Hangzhou, China.

Data collection

From May to August 2021, we conducted semi-structured, one-on-one interviews. An interview guideline (see Table 1) was developed based on the COM-B model and the TDF domains. The first author (CDD) interviewed people with metabolic syndrome in a quiet room. We recorded all interviews with the participants’ consent. The time of interviews ranged from 23 ~ 78 min. Data collection and analysis were conducted simultaneously. Researchers (CNQ and ZH) transcribed verbatim audio materials in Chinese, and researchers (CDD and ZH) coded the interviews within 24 h. Then, the next participant was interviewed. When there were no new themes occurring that meant thematic saturation, data collection could be stopped [41]. Additionally, we interviewed 3 additional people with metabolic syndrome to confirm that no new themes appeared.

Table 1 Interview schedule

Data analysis

The transcribed interview sessions were analyzed by the coders using a thematic analysis [42]. CDD and ZH independently read and reread the transcripts and interview notes to code inductively and then produce themes. A continuous analysis of the data and frequent discussions among the authors were done to refine and define the themes and subthemes. Two researchers categorized the specific themes into the most relevant domains (COM-B elements and TDF domains). Differences were discussed with the research team until a consensus was reached during inductive coding and deductive categorizing. When analyzing data, we wrote a reflective note to remain calm and objective and thus reduce the impact of any pre-existing notions. Additionally, in order to ensure trustworthiness, we enhanced the credibility, transferability, dependability and confirmability of the present study [43]. Credibility was ensured by our research team who discussed any differences in methodological issues and data analysis. Regarding transferability, this article described the participants’ characteristics, contexts and verbatim quotes to enable the reader to make judgments about the generalizability of the results. Dependability and confirmability were achieved by cross-checking transcripts by people who did not participate in the transcription process.

Stage 2: Identifying intervention options

Step 5: Identifying intervention functions

According to the BCW, the COM-B domains and TDF were connected to the intervention functions [27, 44]. The intervention functions included education, training, restriction, persuasion, incentivization, coercion, modeling, environmental restructuring, and enablement [27, 44]. Education refers to increasing understanding and knowledge of targeted behaviors [27]. Persuasion refers to using communication to induce positive or negative feelings or stimulate action [27]. Incentivization refers to creating an expectation of reward [27]. Coercion refers to creating an expectation of punishment or cost [27]. Training refers to imparting skills [27]. Restriction refers to using rules to reduce the opportunity to engage in the target behavior (or to increase the target behavior by reducing the opportunity to engage in competing behaviors) [27]. Environmental restructuring refers to changing the physical or social context [27]. Modelling refers to providing an example for people to aspire to or imitate [27]. Enablement refers to increasing means/reducing barriers to increase capability or opportunity [27]. According to BCW, intervention functions were selected on the basis of their affordability, practicability, effectiveness, acceptability, side-effects and safety, and equity (APEASE) [27]. Affordability refers to whether the cost of the proposed intervention is within budget [27]. Practicality refers to the extent to which an intervention is delivered as designed through the means intended to the target population [27]. Effectiveness refers to the effect size of the intervention in relation to the desired objectives in a real world context [27]. Cost-effectiveness refers to the ratio of effect (in a way that has to be defined, and taking account of differences in timescale between intervention delivery and intervention effect) to cost [27]. Acceptability refers to the extent to which an intervention is judged to be appropriate by relevant stakeholders (public, professional and political) [27]. Side effects/safety refers to whether the intervention has unwanted side effects or unintended consequences that need to be considered [27]. Equity refers to the extent to which an intervention may reduce or increase the disparities in standards of living, wellbeing or health between different sectors of society [27]. When there were different opinions on the selection of the intervention function, they were determined through group discussion. The research group members were PhD candidates or holders in nursing, had research interests in chronic disease nursing and had learning experience in behavioral science, which contributed to making this research more scientific and rigorous.

Step 6: Identifying policy categories

The step is to consider what policies would assist in the implementation of the identified intervention functions in Step 5. Policy categories included communication/marketing, guidelines, fiscal measures, regulation, legislation, environmental/social planning and service provision, which were determined using the APEASE criteria [27, 44]. Similarly, inconsistencies were resolved through group discussions.

Stage 3: Identifying content and implementation options

Step7: Identifying BCTs

We identified BCTs as intervention strategies for promoting the desired behavior. Using APEASE criteria, we selected the BCTs that were commonly used from the BCTTv1 for each IF [27, 30] by two researchers. Moreover, a comprehensive matrix was used to map the 59 BCTs to the TDF domains to identify any additional BCTs [27]. We resolved any disagreements within our research team through discussion.

Step8: Model of delivery

An intervention delivery model refers to the way in which it is administered [45]. Various delivery models must be considered before choosing the most appropriate one, including face-to-face, TV, apps, and cell phone message [27]. The modes of delivery for BCTs were assessed using the APEASE criterion [27]. In addition, the selection of delivery models could also consider similar research of physical activity interventions among people with metabolic syndrome. Inconsistencies were resolved by the research team through discussions.

Expert consultation

After all steps were completed, the key findings from each stage were synthesized. The intervention content and format were sent to 12 experts with different academic backgrounds through email, including advanced nursing practitioners, behavioral science experts, management scientists, and general physicians. All experts reviewed the intervention materials independently, and gave their feedback and comments (received via email after two weeks). We thoroughly reviewed and discussed each feedback and then revised the intervention content and format accordingly.

Ethical consideration

The Helsinki Declaration was complied with. The participating hospitals’ ethics committees approved this study (grant no. 20210220–32). All the participants signed free and informed consent forms prior to starting the research. Participants were informed that their data were confidential.

Results

Step 1: Define the problem in behavioral terms

Physical activity was one of primary interventions in the management of metabolic syndrome [46]. Several studies summarized that adherence to physical activity recommendations, such as moderate physical activity of at least 150 min per week, vigorous activity of at least 75 min per week, or a combination of both, and total leisure-time energy expenditure of over 300 metabolic equivalents of task (MET)·min/day, was not adequate among adults with metabolic syndrome [13, 16,17,18]. Physical inactivity was associated with an increased risk of serious complications while regular physical activity led to increased energy consumption and was related to reducing the risk of metabolic syndrome [47]. Therefore, we defined the problem as the inadequate adherence to physical activity recommendations.

Step 2: Select target behavior

Several studies proposed the standards of physical activity for individuals with metabolic syndrome, including type, time and frequency of physical activity [4, 48, 49]. Two evidence recommended a minimum of 30 min of moderate-intensive physical activity at least five days a week for individuals with metabolic syndrome [50, 51]. An international panel recommended the standard of daily physical activity for metabolic syndrome individuals was 30 ~ 60 min [4]. Pattyn et al., [52] presented that at least 40 min of aerobic training at least twice a week was effective on cardiovascular risk factors related to the metabolic syndrome. Among these potential target behaviors, we intended to choose the behavior that met the four rating criteria [27] including (i) how much of an impact changing the behavior will have on the desired outcome, (ii) how likely it is that the behavior can be changed, (iii) how likely it is that the behavior will have a positive or negative impact on other, related behaviors, and (iv) how easy it will be to measure the behavior [27] as the target behavior. Furthermore, the formation of habits is crucial for adopting a new behavior, which takes two to eight months to accomplish [53]. Based on the recommendations of physical activity from existing literature, the four rating criteria, and the time of habit formation, achieving a minimum of 30 min of moderate-intensive physical activity at least five days a week for 24 weeks was selected as the target behavior.

Step 3: Specify target behavior

The specification of the target behavior is detailed in Table 2.

Table 2 Specifying the target behavior

Step 4: What needs to change?

We employed the COM-B model and the TDF to perform a behavioral diagnosis among 28 individuals with metabolic syndrome. Tables 3 and 4 present the sample demographics and the results of behavioral diagnosis, separately. Overall, 33 themes were identified through in-depth interviews with people with metabolic syndrome in our study (see Table 4). The following identified barriers need to be changed: perceived poor knowledge about the diagnosis of metabolic syndrome; absent knowledge about regular physical activity; lacking self-monitoring; fearing that physical activity would aggravate conditions; absence of physical activity skills; lacking time; lacking equipment and venue; perceived poor physical activity atmosphere; the influence of weather; perceiving physical activity as unimportant; not perceiving benefits of physical activity; low intention; having intention but lacking confidence; no goals; and being influenced by negative emotions.

Table 3 Demographics of the sample (n = 28)
Table 4 Behavioral analysis based on the TDF and the COM‐B model

Step 5: Identifying intervention functions

In the present study, seven out of the nine possible intervention functions were selected to tackle the identified barriers using APEASE criteria: education, enablement, training, environment restructuring, persuasion, modeling and incentivization. Restriction was not included because the study was not involved using the rules to improve physical activity adherence. Coercion was excluded as punishment or cost were not acceptable for people with metabolic syndrome.

Step 6: Identifying policy categories

As our study was intended to develop a behavior change intervention and was not involved with changing policy on physical activity based on the interview results, we did not address these policy categories and skipped this step.

Step7: Identifying BCTs

In our study, nineteen BCTs were identified based on the APEASE criteria, including: information about health consequences (5.1); prompts/cues (7.1); self-monitoring of behavior (2.3); goal setting (behavior) (1.1); demonstration of the behavior (6.1); instruction on how to perform a behavior (4.1); feedback on the behavior (2.2); behavioral practice/rehearsal (8.1); social support (practical) (3.2); restructuring the social environment (12.2); credible source (9.1); commitment (1.9); behavioral contract (1.8); goal setting (outcome) (1.3); action planning (1.4); review behavior goal(s) (1.5); reduce negative emotions (11.2); emotional consequences (5.6); and social support (emotional) (3.3) (see Table 5). Other BCTs were excluded because they were ineffective, unacceptable, impracticable, or too expensive. Specific reasons could be found in Table 5.

Table 5 Identification of the possible BCTs that could be used in the intervention

Step8: Model of delivery

Apps are increasingly showing great promise in increasing individual physical activity adherence [54, 55]. Especially during the COVID-19 pandemic, health related apps seem to be more able to meet individual health needs. Three systematic reviews summarized that the apps-based interventions were effective in increasing physical activity for longer than 3 months [54,55,56]. The results of systematic review and meta-analysis showed that the mobile app-assisted interventions effectively improved health outcomes, including weight, blood glucose and blood pressure [57, 58]. Additionally, the advantages of apps also include convenience and being inexpensive and automation, and they allow users to receive health services in any environment and at any time. Given these attractive features that met the APEASE criteria, researchers started to deliver physical activity interventions via apps [59, 60]. According to previous experience of physical activity interventions, the context of epidemic era, features of app, our research team chose app as model of delivery.

Expert consultation

Table 6 presents the mapping of the COM-B, TDF, barriers, intervention functions, BCTs, and potential intervention content. The main components constituted the intervention, including via the app (I) providing information on published research on the definition, negatives and physical activity-related knowledge of metabolic syndrome; (II) setting up reminders for individuals to record the type, time, frequency and (or) intensity of physical activity; (III) setting goals of physical activity; (IV) providing observable examples of individuals who perform physical activity properly; (V) providing video instruction on how to perform physical activity; (VI) providing suggestions on how to perform regular physical activity; (VII) presenting a speech from health care professionals to emphasize the benefits of physical activity for individuals with metabolic syndrome; (VIII) advising on the use of stress management skills, such as listening to music. Experts’ comments regarding the intervention content and format included:

  • “Encourage individuals to rehearse physical activity properly via the app.” should be revised as “repeat the physical activity according to your physical condition until you master it”.

  • “Inform the person of physical activity data” should be revised as “Inform the person of physical activity data and provide guidance”.

  • “Follow and record various experiences of successfully maintaining regular exercise” should be added.

  • “Record weight and WC every day” should be added.

  • “Provide illustrations of the energy expenditure of physical activity” should be added.

  • “Share own physical activity status with others via the app” should be added.

  • “Exercise” should be modified to “physical activity”.

  • “Set a weight loss goal as an outcome of regular physical activity.” should be revised as “Set a goal as an outcome of regular physical activity”.

  • “Set up reminders for individuals to record the type, time, frequency and (or) intensity of physical activity via the app.” should be revised as “At 21:00 every night, remind the individual to record physical activity status via the app”.

  • “Establish a contract with the individual to make sure to take regular physical activity via the app” may be not applicable in China.

Table 6 The intervention content identified based on BCTs

Experts suggested that in addition to the app, it is recommended to add other forms of intervention, such as mobile phone calls or telephone calls.

Discussion

The present study outlines a rigorous theory-based method to develop a complex intervention to increase physical activity adherence among people with metabolic syndrome in China. To date, this paper is the first to use the BCW in this context and population. The findings demonstrated that changing physical activity behaviors needs to consider various factors, including the capability, opportunity, and motivation of individuals and choose suitable BCTs to support identified intervention functions. Our study provided the opportunity for health care professionals to better understand multifactorial influences based on theory on physical activity adherence among individuals with metabolic syndrome. It also extended the use of the BCW framework for developing physical activity interventions to target behavioral barriers to physical activity adherence in this population.

To improve compliance with physical activity, interventions should leverage facilitators and overcome barriers. The study identified seven intervention functions to mainly tackle fifteen barriers according to the APEASE criteria. Furthermore, nineteen BCTs were selected to assist in the delivery of seven intervention functions and were then translated into potential intervention content.

We found that nearly all participants lacked knowledge about the diagnosis of metabolic syndrome from interview results. These findings were in line with a previous study that showed poor knowledge about the definition and diagnosis of metabolic syndrome among adults with metabolic syndrome [61]. The phenomenon may be attributable to the fact that metabolic syndrome is underdiagnosed and undertreated due to it being largely asymptomatic [62]. Thus, specific health education on the definition of metabolic syndrome should be provided. Additionally, due to the participants' inadequate knowledge about physical activity, it is essential to educate them on its benefits and teach them how to perform it [34]. Furthermore, lacking self-monitoring of physical activity behavior was a barrier to physical activity. As a component of the BCTs, self-monitoring was conducive to motivating individuals to engage in physical activities [63]. Hence, enabling individuals to write physical activity diaries and use pedometers may increase adherence to physical activity.

The present study also found that the adherence to physical activity may be increased through the restructuring social and physical environment. In the present study, suggesting users make friends with people who like physical activity was a way to restructure the social environment, through which, individuals were more likely to regard exercise as the new “normal” [64], thereby enhancing the enthusiasm for performing physical activity. In addition, in accordance with our results, the physical environmental barriers to undertaking physical activity were time, weather, and facilities among middle-aged and older adults [65, 66]. Hence, it is imperative to restructure the physical environment, for example, arranging time reasonably to help themselves integrate physical activity into their schedule and participating in physical activity with equipment and venues.

Our behavior analysis presented that some respondents had the intention to participate in physical activity but lacked confidence, which was important for successful physical activity adherence [14]. According to Zelle et al. [67], it was an effective approach to increase self‐efficacy through persuading individuals that they had the ability to conduct a behavior, and encouraging them to do so. For persons who did not perceiving benefits of physical activity, offering an opportunity to let them experience small accomplishments in their performance was also conducive to improving self-efficacy [68]. However, some participants had no intention to conduct physical activity, had no goals when undertaking physical activity and regarded it as unimportant. Lacking adequate understanding of the metabolic syndrome, individuals could be unaware of the presentation of the metabolic syndrome and their complication risks. Thus, health education targeting metabolic syndrome including the disease risk, the benefits of physical activity and setting goals should be enhanced by healthcare professionals. In addition, negative emotions affect physical activity. This aligns with the literature, which showed that people with anxiety and/or depression were characterized by sedentary and low levels of physical activity completion recommendations [69]. Using stress management skills, such as listening to music, could reduce stress [70] and then help enable individuals to do more physical activity [71].

In terms of the intervention functions, seven intervention functions, including education, persuasion, training, modelling, incentivization, environmental restructuring, and enablement, were identified as relevant for physical activity intervention. The study by Truelove et al., [59] selected six intervention functions (education, persuasion, incentivization, training, environment restructuring, and enablement) in a physical activity app intervention design, which was in accordance with our work. Moreover, this study identified nineteen potential BCTs from the qualitative data. These results are similar to the two studies [34, 35] in which twenty-one BCTs and fourteen BCTs were identified to promote physical activity behavior, separately. Most studies often used BCTs combinations to promote physical activity. Future research could examine which particular BCT or combinations of BCTs are most effective in changing the physical activity behavior among people with metabolic syndrome via the app.

Limitations

Although the study employed a strong theory to explore the influence mechanisms of action, our results must be interpreted cautiously with some limitations. First, all participants in the behavior analysis step were from Zhejiang Province in China. Therefore, the findings may be only applicable to people living with metabolic syndrome in China. Second, theme saturation was achieved, but given the disadvantages of theme saturation, we should interpret our findings with caution. Third, we did not identify all barriers and facilitators for increasing adherence to physical activity as we did not invite all key stakeholders in the present study, such as health care professionals, individuals’ relatives or friends. Fourth, it is essential to acknowledge the subjectivity of this analysis, as with many qualitative results, as well as concerns over external validity caused by a relatively small sample size. Fifth, a steering group was consulted only at certain steps, not all steps, which may lead to imperfect intervention design. Sixth, when selecting intervention functions and BCTs according to APEASE criteria, our research team did not also invite a multidisciplinary team. As a result, subjectivity existed. Seventh, our study did not focus primarily on changing policies, so we did not analyze policy categories. In future research, policy categories analysis is needed to help identify service provision, guidelines, environmental/social planning, and regulations for promoting behavioral change. Finally, when we applied the BCW, the intervention design process needs longer time. Therefore, efficiency of use was a potential problem.

Future research

With the guidance of the BCW framework, we have identified core ingredients that can be incorporated into the intervention design to facilitate adherence to physical activity. Subsequently, we will invite software engineers to design the app features based on the intervention content. In the future, a randomized controlled trial (RCT) evaluating the feasibility, effectiveness, and acceptability of the physical activity program will be needed. If effective, health care professionals could provide the intervention content for adults with metabolic syndrome to target barriers to physical activity and ultimately improve their health outcomes. Additionally, the intervention program could also be adapted for use in other health conditions where physical activity adherence needs to be addressed.

Conclusions

This study used a systematic approach to develop an intervention underpinned by the BCW theory to increase physical activity in adults living with metabolic syndrome in China, which may in turn improve the health outcomes for these individuals and reduce medical burden and economic burden. This study has identified nineteen BCTs, which can be used as active ingredients in intervention program of targeting behaviors determinants. Future studies should focus on whether the targeted intervention program enhances physical activity adherence and is accepted by metabolic syndrome individuals, ultimately to promote positive behavior change and improve health outcomes of individuals.  

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the risk of breaking anonymity being too high but are available from the corresponding author on reasonable request.

Abbreviations

APEASE:

Affordability, Practicability, Effectiveness and Cost-effectiveness, Acceptability, Side effects/safety and Equity

BCTs:

Behavior change techniques

BCTTv1:

Version 1 of the BCT taxonomy

BCW:

Behavior Change Wheel

CINAHL:

Cumulative Index of Nursing and Allied Health Literature

COM-B:

Capability, Opportunity, Motivation – Behavior

DBP:

Diastolic blood pressure

FBG:

Fasting blood glucose

HDL-C:

Low high-density lipoprotein cholesterol

RCTs:

Randomized controlled trails

SBP:

Systolic blood pressure

TDF:

Theoretical Domains Framework

TG:

Triglyceride

SRQR:

Standards for Reporting Qualitative Research

WC:

Waist circumference

References

  1. Fatahi A, Doosti-Irani A, Cheraghi Z. Prevalence and Incidence of Metabolic Syndrome in Iran: A Systematic Review and Meta-Analysis. Int J Prev Med. 2020;11:64.

    PubMed  PubMed Central  Article  Google Scholar 

  2. Yao F, Bo Y, Zhao L, Li Y, Ju L, Fang H, et al. Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017. Nutrients. 2021;13(12):4475.

    PubMed  PubMed Central  Article  Google Scholar 

  3. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5.

    CAS  PubMed  Article  Google Scholar 

  4. Pérez-Martínez P, Mikhailidis DP, Athyros VG, Bullo M, Couture P, Covas MI, et al. Lifestyle recommendations for the prevention and management of metabolic syndrome: an international panel recommendation. Nutr Rev. 2017;75(5):307–26.

    PubMed  PubMed Central  Article  Google Scholar 

  5. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20(2):12.

    PubMed  PubMed Central  Article  Google Scholar 

  6. Jhm A, Jlhm A, Pmw B, Klcm C, Pitd D, Eem E, et al. Prevalence of metabolic syndrome in Chinese women and men: a systematic review and meta-analysis of data from 734511 individuals - ScienceDirect. The Lancet. 2018;392(S1):S14.

    Google Scholar 

  7. Aguilar-Salinas CA, Rojas R, Gómez-Pérez FJ, Mehta R, Franco A, Olaiz G, et al. The metabolic syndrome: a concept hard to define. Arch Med Res. 2005;36(3):223–31.

    CAS  PubMed  Article  Google Scholar 

  8. Gallardo-Alfaro L, Bibiloni M, Mascaró CM, Montemayor S, Ruiz-Canela M, Salas-Salvadó J, et al. Leisure-Time Physical Activity, Sedentary Behaviour and Diet Quality are Associated with Metabolic Syndrome Severity: The PREDIMED-Plus Study. Nutrients. 2020;12(4):1013.

    CAS  PubMed Central  Article  Google Scholar 

  9. Scholze J, Alegria E, Ferri C, Langham S, Stevens W, Jeffries D, et al. Epidemiological and economic burden of metabolic syndrome and its consequences in patients with hypertension in Germany, Spain and Italy; a prevalence-based model. BMC Public Health. 2010;10:529.

    PubMed  PubMed Central  Article  Google Scholar 

  10. Myers J, Kokkinos P, Nyelin E. Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome. Nutrients. 2019;11(7):1652.

    CAS  PubMed Central  Article  Google Scholar 

  11. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. McAuley E, Blissmer B. Self-efficacy determinants and consequences of physical activity. Exerc Sport Sci Rev. 2000;28(2):85–8.

    CAS  PubMed  Google Scholar 

  13. Bianchi C, Penno G, Daniele G, Benzi L, Del PS, Miccoli R. Optimizing management of metabolic syndrome to reduce risk: focus on life-style. Intern Emerg Med. 2008;3(2):87–98.

    PubMed  Article  Google Scholar 

  14. Olson EA, Mullen SP, Raine LB, Kramer AF, Hillman CH, McAuley E. Integrated Social- and Neurocognitive Model of Physical Activity Behavior in Older Adults with Metabolic Disease. Ann Behav Med. 2017;51(2):272–81.

    PubMed  Article  Google Scholar 

  15. Organization WH. Adherence to longterm therapies: evidence for action. Geneva: World Health Organization; 2003.

  16. Fappa E, Yannakoulia M, Ioannidou M, Skoumas Y, Pitsavos C, Stefanadis C. Telephone counseling intervention improves dietary habits and metabolic parameters of patients with the metabolic syndrome: a randomized controlled trial. Rev Diabet Stud. 2012;9(1):36–45.

    PubMed  PubMed Central  Article  Google Scholar 

  17. Keller C, Treviño RP. Effects of two frequencies of walking on cardiovascular risk factor reduction in Mexican American women. Res Nurs Health. 2001;24(5):390–401.

    CAS  PubMed  Article  Google Scholar 

  18. Gallardo-Alfaro L, Bibiloni M, Mateos D, Ugarriza L, Tur JA. Leisure-Time Physical Activity and Metabolic Syndrome in Older Adults. Int J Environ Res Public Health. 2019;16(18):3358.

    CAS  PubMed Central  Article  Google Scholar 

  19. Chen D, Zhang H, Shao J, Tang L, Cui N, Wang X, et al. Determinants of adherence to diet and exercise behaviours among individuals with metabolic syndrome based on the Capability, Opportunity, Motivation, and Behaviour model: a cross-sectional study. Eur J Cardiovasc Nurs. 2022:zvac034. https://doi.org/10.1093/eurjcn/zvac034.

  20. Azar KM, Koliwad S, Poon T, Xiao L, Lv N, Griggs R, et al. The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial. J Med Internet res. 2016;18(5):e134.

    PubMed  PubMed Central  Article  Google Scholar 

  21. Everett E, Kane B, Yoo A, Dobs A, Mathioudakis N. A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial. J Med Internet Res. 2018;20(2):e72.

    PubMed  PubMed Central  Article  Google Scholar 

  22. Oh B, Cho B, Han MK, Choi H, Lee MN, Kang HC, et al. The Effectiveness of Mobile Phone-Based Care for Weight Control in Metabolic Syndrome Patients: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2015;3(3):e83.

    PubMed  PubMed Central  Article  Google Scholar 

  23. Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care. 2005;14(1):26–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Keller C, Fleury J, Sidani S, Ainsworth B. Fidelity to Theory in PA Intervention Research. West J Nurs Res. 2009;31(3):289–311.

    PubMed  Article  Google Scholar 

  25. Stanton-Fay SH, Hamilton K, Chadwick PM, Lorencatto F, Gianfrancesco C, de Zoysa N, Coates E, Cooke D, McBain H, Heller SR, et al. The DAFNEplus programme for sustained type 1 diabetes self management: Intervention development using the Behaviour Change Wheel. Diabet Med. 2021;38(5):e14548.

    PubMed  Article  Google Scholar 

  26. Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021;374:n2061.

    PubMed  PubMed Central  Article  Google Scholar 

  27. Michie SALWR. The Behaviour Change Wheel—a guide to designing interventions. Great Britain: Silverback; 2014.

    Google Scholar 

  28. Stacey FG, James EL, Chapman K, Courneya KS, Lubans DR. A systematic review and meta-analysis of social cognitive theory-based physical activity and/or nutrition behavior change interventions for cancer survivors. J Cancer Surviv. 2015;9(2):305–38.

    PubMed  Article  Google Scholar 

  29. Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7:37.

    PubMed  PubMed Central  Article  Google Scholar 

  30. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81–95.

    PubMed  Article  Google Scholar 

  31. Rohde A, Duensing A, Dawczynski C, Godemann J, Lorkowski S, Brombach C. An App to Improve Eating Habits of Adolescents and Young Adults (Challenge to Go): Systematic Development of a Theory-Based and Target Group-Adapted Mobile App Intervention. JMIR Mhealth Uhealth. 2019;7(8):e11575.

    PubMed  PubMed Central  Article  Google Scholar 

  32. Huang Y, Benford S, Price D, Patel R, Li B, Ivanov A, Blake H. Using Internet of Things to Reduce Office Workers’ Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach. JMIR Mhealth Uhealth. 2020;8(7):e17914.

    PubMed  PubMed Central  Article  Google Scholar 

  33. Curtis KE, Lahiri S, Brown KE. Targeting Parents for Childhood Weight Management: Development of a Theory-Driven and User-Centered Healthy Eating App. JMIR Mhealth Uhealth. 2015;3(2):e69.

    PubMed  PubMed Central  Article  Google Scholar 

  34. Wang H, Blake H, Chattopadhyay K. Development of a School-Based Intervention to Increase Physical Activity Levels Among Chinese Children: A Systematic Iterative Process Based on Behavior Change Wheel and Theoretical Domains Framework. Front Public Health. 2021;9:610245.

    PubMed  PubMed Central  Article  Google Scholar 

  35. Mabweazara SZ, Leach LL, Ley C. Development of a context-sensitive physical activity intervention for persons living with HIV and AIDS of low socioeconomic status using the behaviour change wheel. BMC Public Health. 2019;19(1):774.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Martín-Payo R, Papín-Cano C, Fernández-Raigada RI, Santos-Granda MI, Cuesta M, González-Méndez X. Motiva.DM2 project. A pilot behavioral intervention on diet and exercise for individuals with type 2 diabetes mellitus. Diabetes Res Clin Pract. 2021;171:108579.

    PubMed  Article  Google Scholar 

  37. Sandelowski M. What’s in a name? Qualitative description revisited. Res Nurs Health. 2010;33(1):77–84.

    PubMed  Article  Google Scholar 

  38. CW. Introducing Qualitative Research in Psychology. (3rd ed.). England: Open University Press; 2013.

  39. O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014;89(9):1245–51.

    PubMed  Article  Google Scholar 

  40. Cane J, Connor DO, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7(1):37.

    PubMed  PubMed Central  Article  Google Scholar 

  41. Francis JJ, Johnston M, Robertson C, Glidewell L, Entwistle V, Eccles MP, et al. What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol Health. 2010;25(10):1229–45.

    PubMed  Article  Google Scholar 

  42. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

    Article  Google Scholar 

  43. Lincoln YS, Guba EG. But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Directions for Program Evaluation. 1986;1986(30):73–84.

    Article  Google Scholar 

  44. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.

    PubMed  PubMed Central  Article  Google Scholar 

  45. Davidson KW, Goldstein M, Kaplan RM, Kaufmann PG, Knatterud GL, Orleans CT, et al. Evidence-based behavioral medicine: what is it and how do we achieve it? Ann Behav Med. 2003;26(3):161–71.

    PubMed  Article  Google Scholar 

  46. Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26(4):364–73.

    PubMed  Article  Google Scholar 

  47. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–9.

    PubMed  PubMed Central  Article  Google Scholar 

  48. Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB, et al. Behavioral Counseling Interventions to Promote a Healthy Diet and Physical Activity for Cardiovascular Disease Prevention in Adults with Cardiovascular Risk Factors: US Preventive Services Task Force Recommendation Statement. JAMA. 2020;324(20):2069–75.

    PubMed  Article  Google Scholar 

  49. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, Van Pelt RE, Wang H, Eckel RH. The metabolic syndrome. Endocr Rev. 2008;29(7):777–822.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52.

    PubMed  Article  Google Scholar 

  51. Thompson PD, Buchner D, Pina IL, Balady GJ, Williams MA, Marcus BH, et al. Exercise and physical activity in the prevention and treatment of atherosclerotic cardiovascular disease: a statement from the Council on Clinical Cardiology (Subcommittee on Exercise, Rehabilitation, and Prevention) and the Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity). Circulation. 2003;107(24):3109–16.

    PubMed  Article  Google Scholar 

  52. Pattyn N, Cornelissen VA, Eshghi SR, Vanhees L. The effect of exercise on the cardiovascular risk factors constituting the metabolic syndrome: a meta-analysis of controlled trials. Sports Med. 2013;43(2):121–33.

    PubMed  Article  Google Scholar 

  53. Lally P, Jaarsveld C, Potts H, Wardle J. How are habits formed: Modelling habit formation in the real world. Eur J Soc Psychol. 2010;40(6):998–1009.

    Article  Google Scholar 

  54. Coughlin SS, Whitehead M, Sheats JQ, Mastromonico J, Smith S. A Review of Smartphone Applications for Promoting Physical Activity. Jacobs J Community Med. 2016;2(1):021.

    PubMed  PubMed Central  Google Scholar 

  55. Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, et al. Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. J Med Internet Res. 2019;21(3):e12053.

    PubMed  PubMed Central  Article  Google Scholar 

  56. Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016;13(1):127.

    PubMed  PubMed Central  Article  Google Scholar 

  57. Liu K, Xie Z, Or CK. Effectiveness of Mobile App-Assisted Self-Care Interventions for Improving Patient Outcomes in Type 2 Diabetes and/or Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials. JMIR Mhealth Uhealth. 2020;8(8):e15779.

    PubMed  PubMed Central  Article  Google Scholar 

  58. Flores MG, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile Phone Apps to Promote Weight Loss and Increase Physical Activity: A Systematic Review and Meta-Analysis. J Med Internet Res. 2015;17(11):e253.

    Article  Google Scholar 

  59. Truelove S, Vanderloo LM, Tucker P, Di Sebastiano KM, Faulkner G. The use of the behaviour change wheel in the development of ParticipACTION’s physical activity app. Prev Med Rep. 2020;20:101224.

    PubMed  PubMed Central  Article  Google Scholar 

  60. Smith R, Michalopoulou M, Reid H, Riches SP, Wango YN, Kenworthy Y, et al. Applying the behaviour change wheel to develop a smartphone application “stay-active” to increase physical activity in women with gestational diabetes. BMC Pregnancy Childbirth. 2022;22(1):253.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Wang Q, Chair SY, Wong EM, Taylor-Piliae RE, Qiu X, Li XM. Metabolic Syndrome Knowledge among Adults with Cardiometabolic Risk Factors: A Cross-Sectional Study. Int J Environ Res Public Health. 2019;16(1):159.

    PubMed Central  Article  Google Scholar 

  62. Fujiyoshi A, Murad MH, Luna M, Rosario A, Ali S, Paniagua D, et al. Metabolic syndrome and its components are underdiagnosed in cardiology clinics. J Eval Clin Pract. 2011;17(1):78–83.

    PubMed  Article  Google Scholar 

  63. Kanejima Y, Kitamura M, Izawa KP. Self-monitoring to increase physical activity in patients with cardiovascular disease: a systematic review and meta-analysis. Aging Clin Exp Res. 2019;31(2):163–73.

    PubMed  Article  Google Scholar 

  64. Clarke AL, Jhamb M, Bennett PN. Barriers and facilitators for engagement and implementation of exercise in end-stage kidney disease: Future theory-based interventions using the Behavior Change Wheel. Semin Dial. 2019;32(4):308–19.

    PubMed  Article  Google Scholar 

  65. Spiteri K, Broom D, Bekhet AH, de Caro JX, Laventure B, Grafton K. Barriers and Motivators of Physical Activity Participation in Middle-aged and Older-adults - A Systematic Review. J Aging Phys Act. 2019;27(4):929–44.

    PubMed  Article  Google Scholar 

  66. Justine M, Azizan A, Hassan V, Salleh Z, Manaf H. Barriers to participation in physical activity and exercise among middle-aged and elderly individuals. Singapore Med J. 2013;54(10):581–6.

    PubMed  Article  Google Scholar 

  67. Zelle DM, Corpeleijn E, Klaassen G, Schutte E, Navis G, Bakker SJL. Fear of Movement and Low Self-Efficacy Are Important Barriers in Physical Activity after Renal Transplantation. PLoS One. 2016;11(2):e147609.

    Google Scholar 

  68. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215.

    CAS  PubMed  Article  Google Scholar 

  69. Helgadóttir B, Forsell Y, Ekblom Ö. Physical activity patterns of people affected by depressive and anxiety disorders as measured by accelerometers: a cross-sectional study. PLoS One. 2015;10(1):e115894.

    Article  Google Scholar 

  70. Linnemann A, Ditzen B, Strahler J, Doerr JM, Nater UM. Music listening as a means of stress reduction in daily life. Psychoneuroendocrino. 2015;60:82–90.

    Article  Google Scholar 

  71. Schultchen D, Reichenberger J, Mittl T, Weh T, Smyth JM, Blechert J, et al. Bidirectional relationship of stress and affect with physical activity and healthy eating. Br J Health Psychol. 2019;24(2):315–33.

    PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We would like to extend our sincere appreciation to the participating hospital. Finally, the author(s) would like to thank all metabolic syndrome individuals who participated in this study.

Funding

This study was supported by research grants from the Zhejiang province medical technology project ( WKJ-ZJ-1925) in 2019, the National Social Science Fund of China (20BGL275), the National Natural Science Foundation of China (72004193), Yuan Nei Ren Cai Xiang Mu of Guizhou Provincial People's Hospital(2022-18), Shanghai Jiao Tong University School of Medicine-Nursing Development Program, and Shanghai Sailing Program (21YF1422400).

Author information

Authors and Affiliations

Authors

Contributions

Dandan Chen: Conceptualization, methodology, formal analysis, writing-original draft, writing-review & editing. Hui Zhang: Conceptualization, formal analysis, writing-review & editing. Nianqi Cui and Feng Song: Conceptualization, methodology, formal analysis. Jing Shao: Conceptualization, revising the article. Jingjie Wu: Methodology, writing-review & editing. Leiwen Tang: Methodology, writing-review & editing. Pingping Guo: Data curation. Na Liu and Xiyi Wang: Data curation. Zhihong Ye: The conception and design of the study, validation, supervision, formal analysis, writing—review & editing. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Zhihong Ye.

Ethics declarations

Ethics approval and consent to participate

The Declaration of Helsinki was complied. Ethics for the study was approved by the ethics committee of the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (grant no. 20210220–32). All participants signed free and informed consent forms prior to starting the research.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, D., Zhang, H., Cui, N. et al. Development of a behavior change intervention to improve physical activity adherence in individuals with metabolic syndrome using the behavior change wheel. BMC Public Health 22, 1740 (2022). https://doi.org/10.1186/s12889-022-14129-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-14129-1

Keywords

  • Metabolic syndrome
  • Physical activity adherence
  • Behavioral Change Wheel
  • Mobile health