Exploring the relationship between self-management behaviour, family function and health information adoption behaviour in Chinese diabetic foot patients: a mixed-methods study protocol

Abstract

Introduction

Diabetic foot is a major burden and threat to individuals, families and society, making it imperative to promote good self-management behaviour. However, although nurses have provided these patients with excellent health knowledge, their self-management remains unsatisfactory. Although researches have shown that self-management requires family involvement, no research has been conducted in China on family function, specifically in the diabetic foot. Therefore, this study aimed to explore the relationship between self-management, family functioning, and health information adoption behaviour and explain the formation’s reason.

Method and analysis

We will conduct a mixed-methods study using an exploratory sequential study design in Zhejiang, China. In the first phase, cross-section research will be conducted using a convenient sampling strategy on 225 diabetic foot patients. SPSS V.26 was used for correlation and multiple stepwise regression analyses. Structural equation modelling will be performed by using AMOS V.24. The researchers will conduct a semistructured interview to collect qualitative data and use NVivo to analyse. Ultimately, we will ‘triangulate’ to integrate quantitative and qualitative data.

Ethics and dissemination

This study received ethical clearance from the Ethics Review Committee, the affiliated Sir Run Run Shaw Hospital of Medicine School, Zhejiang University (approval no: 2023-0145). All data collection processes will abide by health and safety measures required by the national government. Written informed consent will be obtained from all participants. The study will produce one paper that will be disseminated, to local stakeholders and participants, via local and international conferences and publications in peer-reviewed journals.

Correspondence to Leiwen Tang; [email protected]

STRENGTHS AND LIMITATIONS OF THIS STUDY

• The mixed-method allows for a comprehensive analysis of the situation of diabetic foot patients.

• This study is guided by an expert panel of experts in chronic care and qualitative research, whose insights are paramount in shaping the focus and design of the study.

• This is a single-centre investigation, thus limiting the generality of the findings.

Introduction

Diabetic foot¹ (DF) refers to the destruction of skin and deep tissue far from the ankle joint in patients with diabetes, often complicated with infection and arterial occlusion of the lower extremity of varying degrees, which involves muscle and bone tissue in severe cases. In recent years, the global prevalence rate has been about 6.3%.² In China, the annual incidence rate of DF is 8.1%, the annual recurrence rate is 31.6% and the annual mortality rate is 14.4%.³ It is the leading cause of disability and death in patients with diabetes, seriously affecting their quality of life. It is also a significant public health problem that burdens society heavily.

According to the International Diabetes Centre, good self-management behaviour in patients with diabetes can prevent 45%–85% of foot ulcers and avoid 85% of amputations. At the same time, 75%–80% of DF risk patients can prevent the occurrence of DF through foot self-care behaviour.⁴ Self-management behaviour⁵ refers to the behaviour that patients control and change their behaviours and living habits in the aspects of medication, daily life, diet, rehabilitation exercise and emotion to reduce the adverse effects of the disease and maintain and promote health. DF self-management behaviours⁶ include ensuring adequate nutrition, regular physical activity, appropriate medication use, foot care, regular monitoring of blood sugar and maintaining a healthy lifestyle. Proper self-management can effectively relieve skin colds, dry, cracking and numb skin of DF patients, stabilise blood sugar control levels,⁷ and significantly reduce anxiety and depression.⁸ And also can effectively reduce the recurrence.⁹ Therefore, good self-management behaviour is crucial for the occurrence and development of DF. However, a scoping review¹⁰ pointed that many people had knowledge of the various aspects of foot care but fewer practised proper foot care. Similarly, a survey¹¹ of self-management behaviours among 314 people with diabetes showed low scores, with an average of 11. In qualitative studies,^{12 13} researchers found that patients with diabetes and lower limb amputation profoundly lacked knowledge about diabetes self-management and foot and passive health-related behaviours. Due to their recurring condition, patients also had negative attitudes towards self-management.¹³ Therefore, further exploring the self-management behaviour of DF patients is urgently needed.

Although much research has figured out the key factors influencing self-management, emerging research also points to the role of healthcare information adoption.^{14 15} Health information adoption behaviour¹⁶ is a process in which participants select, evaluate and use health information based on the needs and goals of patients, ultimately influencing individual attitudes or changes in health behaviour. Previous studies have shown a relationship between health information adoption behaviour and self-management behaviour but usually focus on the impact of information quality, source and perceived usefulness on self-management behaviour. This behaviour has implications for the management of many chronic diseases. The perceived usefulness of health information is positively associated with SLE patients’ sleep quality.¹⁷ Good information quality effectively improves the quality of life and mental health in patients with chronic heart failure.¹⁸ Qualitative study¹⁹ suggests that people with diabetes with sufficient health information still have low levels of self-management due to a lack of perceived usefulness. Moreover, it positively affects anxiety, depression and compliance behaviour of patients with diabetes complications.²⁰On the other hand, research showed that the self-management level of patients with diabetes was the influencing factor in their use of health information,²¹ and can independently affect individuals’ willingness to adopt health information.²²

Another influential factor of self-management behaviour is family function. Family function²³ refers to the emotional exchange, communication and response ability to deal with external events among family members. Many studies have shown that family function is positively correlated with self-management behaviour. Meichun et al²⁴ investigated patients who had a stroke and showed that family function was the main factor affecting self-management behaviour. Cancan et al²⁵ studied haemodialysis patients and found that improving family intimacy and adaptability of patients could positively predict patients’ self-management behaviour. This result is consistent with the conclusion of Photharos.²⁶ The family function can not only directly affect self-management behaviour but also affect its influencing factors, such as self-efficacy and mental health. A survey of 1905 older people in China found that family function was positively correlated with self-efficacy, and the better the family function, the higher the self-efficacy.²⁷ The investigation of the psychological status and retrograde of patients who had a stroke in the Beijing-Tianjin-Hebei region found that their family function was moderate and negatively correlated with anxiety and depression.²⁸

Despite family function’s role in self-management, it has received little attention in the DF. Searching database, no articles focus on family function and DF, and fewer focus on diabetes. These articles^{29 30} found that the better the family function of patients with diebetes, the higher their self-management level and all dimensions of family function are positively correlated with self-management behaviour. In addition, the family function was positively correlated with diabetes burden and negatively correlated with mental health.³¹

In addition, the family function also acts as a mediator in many studies, which was consistent with social cognitive theory. Two researches^{32 33} found that family function was an intermediate variable between self-management and post-stroke depression in patients who had a stroke. In elderly patients with hypertension, function demonstrated mediation on self-management and well-being.³⁴

For young people in the USA, family function mediated the associations between neighbourhood conditions and children’s health,³⁵ as well as parental problem drinking and adolescent externalising behaviours.³⁶ However, as far as we know, no evidence illustrates whether family function mediated the effects of health information adoption behaviour on self-management among DF patients.

Furthermore, health information adoption behaviour and family function are related. Two large sample studies^{37 38} have shown that good family function helped patients access and use health information and directly impacted people’s intention to change their behaviour. Studies of dietary control in heart failure patients^{39 40} showed that positive family involvement and communication could effectively promote the adoption of a low-sodium diet, consistent with the Korean dietary cancer control survey.⁴¹ Similarly, older people were more likely to accept health messages when family members, especially younger people, were involved.⁴² On the other hand, there is no direct evidence that health information adoption behaviour affects family functioning, mostly regarding health behaviour change and health information literacy. In China, a survey on acute leukaemia⁴³ showed that health information utilisation improved family functioning in children. Meeting families’ specific health literacy needs can also support family function development and participation in disease management.⁴⁴

In conclusion, in summary, DF is a major burden and harms individuals, families and society, and promoting good self-management behaviours is essential. However, current research on DF self-management behaviours has mainly focused on the importance and improvement of adherence. Despite evidence of links between health information adoption behaviour, family functioning and self-management behaviour, research in the field of DF remains limited. Therefore, this study will explore the relationship between the three variables in the field of DF and the formation seasons.

Based on the discussion that is provided above, this study proposes four research hypotheses (figure 1):

H1: There is an interaction between health information adoption behaviour and the self-management behaviour of DF patients.

Figure 1. The theoretical model.

H2: Family function has a positive effect on the self-management behaviour of DF patients.

H3: There is the interaction between family function and health information adoption behaviour of DF patients.

H4: The influence of health information adoption behaviour on the self-management behaviour of DF patients can be exerted by influencing family function.

Aim and objectives

Study aims

This study aims to understand the current situation of DF patients’ self-management behaviour, health information adoption behaviour and family function, analyse the correlation among them and explore the mechanism of DF patients’ self-management behaviour, health information adoption behaviour and family function. In addition, semi-structured interviews are conducted to understand further the influence of health information adoption behaviour and family function on self-management behaviour and to further interpret the quantitative research model. The objective is to provide a basis for improving the self-management behaviour and health information adoption behaviour of DF patients.

Study objectives

• To describe the status quo of self-management behaviour, family functioning and health information adoption behaviour.

• To clarify the relationship between self-management behaviour, family function and health information adoption behaviour.

• To explore the feelings and experiences related to patients’ health information adoption behaviour and further explain its impact on self-management behaviour and family function.

Methods and analysis

Study design

We choose an explanatory sequential design in the mixed-methods study, which sequence is QUAN→qual, occurring in two distinct interactive phases. This design starts with collecting and analysing quantitative data, prioritising addressing the study’s questions. Four questionnaires/scales are used to gather quantitative data in the first phase. After statistically analysing these data, qualitative research is designed and conducted to explain the initial quantitative results (see figure 2). The study period is from 1 May 2023 to 1 May 2024.

Figure 2. Research flow diagram.

Phase 1: quantitative research

Using two questionnaires and two scales, we design this step as a population-based cross-sectional study to examine DF patients’ self-management behaviour, family function and health information adoption behaviour. Respectively, there are the ‘General Information Questionnaire’, ‘Health Information Adoption Behaviour Questionnaire’, ‘Self-management Behaviour Scale for DF patients’ and ‘Family Adaptability and Cohesion Evaluation Scale, Second Edition, Chinese version (FACESII-CV)’.

Study participates

Patients diagnosed with DF are admitted to Run Run Shaw Hospital, Zhejiang University School of Medicine.

Sampling size and sampling strategy

In this phase, we use convenience sampling for DF patients. There are nine demographic indicators, three dimensions of the ‘Self-management Behaviour Scale for DF patients’, six dimensions of the ‘Health Information Adoption Behaviour Questionnaire’ and two dimensions of the ‘Family Adaptability and Cohesion Evaluation Scale’. There are 20 variables in statistical analysis. According to Kendall’s sample estimation method, the sample size is 10–20 times the number of variables, namely 200–400. It is expanded by another 10%, 222–445 people, to reduce the error. As the sample size of the structural equation model is required to be more than 200, the sample size is determined to be 225 in this study.

Inclusion criteria

• Age ≥18 years old.

• Patients WHO meet the diagnostic criteria of diabetes established by WHO in 1999.

• Those who meet the diagnostic criteria of the International DF Working Group.

• Stable condition, clear consciousness, good language communication ability.

• Volunteer to participate in the study and sign the informed consent.

Exclusion criteria

• Impaired hearing and vision or unable to complete the study for other reasons.

• Vulnerable groups, including mental illness, cognitive impairment, critically ill patients, minors, pregnant women, illiterates, etc.

The operational definitions

DF self-management behaviour: This study refers to disease detection and control management, daily life management and foot care management in three dimensions, and the ‘Self-management Behaviour Scale for DF patients’ as a measuring tool.

Family function: This study refers to two dimensions of family function: family adaptability and family cohesion, and the ‘Family Adaptability and Cohesion Evaluation Scale’ is used as a measurement tool.

Health information adoption behaviour: This study refers to six dimensions of performance expectation, usability expectations, promotion condition, perceived risk, adoption willingness and behaviour change, and the ‘Health Information Adoption Behaviour Questionnaire’ is used as a measurement tool.

Study instruments

General information questionnaire: The researchers self-designed questionnaires containing information on gender, age, education level, marital status, occupation, place of residence, primary caregiver, previous illness and type of medication.

Health Information Adoption Behaviour Questionnaire⁴⁵: This questionnaire was designed by Pengfei. The scale contains six dimensions: performance expectation, usability expectations, promotion condition, perceived risk, adoption willingness and behaviour change. There are 19 items in the questionnaire, and Likert self-rated 5-point scale is adopted. The scores from ‘completely disagree’, ‘relatively disagree’, ‘uncertain’, ‘relatively agree’ and ‘fully agree’ are successively scored as 1, 2, 3, 4 and 5 points. The higher the score, the better the health information adoption behaviour. Cronbach’s α of this questionnaire is 0.912.

Self-management Behaviour Scale for DF patients⁴⁶: Gao developed this scale. The scale includes three dimensions: disease detection and control management (including six items), daily life management (including six items) and foot care management (including four items). Likert 4-level scoring method is adopted, with 4—‘always like this’, 3—‘often like this’, 2—‘rarely like this’ and 1—‘never like this’. The scale score range is 16–64 points; the higher the score, the better the self-management behaviour. The overall Cronbach’s α coefficient of the scale is 0.796.

Family Adaptability and Cohesion Evaluation Scale (FACESII-CV)⁴⁷: This scale was developed by Olsen⁴⁸ and adapted and revised by Phillips et al to evaluate family function regarding adaptability and cohesion. It includes 2 sub-scales, 16 items of intimacy (1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,30) and 14 items of adaptability (2,4,6,8,10,12,14,16,18,20,22,24,26,28). Each entry is rated on a 5-level scale: 1—no, 2—occasionally, 3—sometimes, 4—often, 5—always, where items (3,9,19,24,28,29) are negative integrals, other items are integrated positively, and the response to each item represents the degree to which the condition described in the item occurs in the family, the higher the score, the better the family closeness and adaptability. The Cronbach’s α of family closeness is 0.85, and the Cronbach’s α of family adaptability is 0.73.

Data collection

Ethics committee approval is required before investigation, and contact the hospital nursing department and the head of the department for consent and support. Master’s level nursing students are recruited as researchers for the study. The training is provided to the researchers over 1 week and includes: (A) a session to examine the questionnaire item by item, ensuring complete comprehension and discussing doubts and (B) each researcher performs a mock questionnaire collection. A consensus discussion selects a study committee to supervise the study process. The study committee comprises various independent members, including a geriatric chronic disease specialist, a qualitative research specialist and several research team members.

Before questionnaires are issued, the researchers introduce the purpose, significance and filling requirements to the subjects meeting the inclusion criteria. They ask them to complete the questionnaire in a secret form after obtaining their consent. The questionnaires are checked and recalled by the researcher on the spot. If there is any item that needs to be added, it will be filled in on the site. Data collection will run from May 2023 to October 2023.

Data analysis

SPSS V.22.0 and AMOS V.24.0 are used to analyse the data, and the significance level α=0.05. A p<0.05 is considered to be statistically significant. The specific analysis is as follows:

• Statistical description.

  • Described in frequency and percentage of patient’s gender, age, course of count data, education level and career.

  • Mean±SD was used to describe measurement data such as scores of each dimension of each questionnaire. If normality was not satisfied, median and quartile could be used.

• Statistical description.

  • Independent sample t-test or one-way variance is used to compare groups.

  • Pearson Correlation is used to analyse the relationships among self-management behaviour, family function and health information adoption behaviour.

  • Significant variables from demographic data, independent sample t-test or one-way analysis of variance, and statistically significant indicators from Pearson correlation analysis are included as independent variables in the regression. Multiple step-wise regression analysis determines the main influencing factors of self-management behaviour in DF patients.

  • AMOS V.24.0 is used to construct a structural equation model to explore the influence paths among variables.

Phase 2: qualitative research

The second phase of this study is a qualitative study, which aims to examine patients’ self-management behaviour through their health information adoption behaviour and family function. We aim to interpret quantitative results to understand better the underlying reasons for patient self-management.

Sampling size and sampling strategy

In the second phase, purposive sampling is used, selecting those scoring above 48 or below 32 on the ‘Self-management Behaviour Scale’ from the previous section. Generally, qualitative studies do not have specific provisions on sample size. Qualitative research sample size could not be determined in advance, but continuous sampling until the information is saturated. We conduct the interview guide based on the direct quantitative results.

The interview guide

We will develop the interview guide draft based on the literature review and discussion of the scientific research group. This draft will be further modified according to the results of phase one. Details are as follows:

• How do you feel about your current physical condition?

• How do you manage DF?

• How do you receive this health knowledge?

• What aspects of the family affect the acceptance and use of information?

• What view do family members have of diabetic foot?

• What impact do you think this knowledge has on your disease management?

• What is your ideal health information adoption and family function?

• Do you have any questions you would like to share?

Data collection and analysis

After the interview guide is determined, we conduct a preventive talk to help researchers modify and formulate the interview guide better. At the same time, timely find the problems in the interview and further change later. Establish a good relationship with the patients before the interview to ensure the smooth progress of the interview. The interview is conducted at the appropriate time for the patient to avoid interruption during the interview. Before the interview, the researchers explain the primary purpose of this study to respondents, the research content, and the privacy of personal information protection. After obtaining the interviewees’ consent, the interview is conducted at the agreed place. The environment is kept quiet and comfortable during the interview, and recording the interview content. Carefully observe the mood changes of the interviewees and record them. The interview lasts 30–45 min. Before the interview, through communication with the verbal consent of the subjects, and sign the informed consent. Each interviewee is numbered from N1 to Nx. Within 24 hours after the interview, transcribe and organise the content word by word, sentence by sentence and convert it into recorded content. Record the patient’s tone words and actions in the text during the transcription process.

Colaizzi’s seven-step analysis method was used to analyse the interview data:

• Carefully read all the interview materials to form a general understanding of the description of the research object.

• Extract meaningful statements that are consistent with the research question.

• Summarise and extract meaningful statements and code them.

• Summarise the encoded views, find common concepts or characteristics, and form themes, theme groups and categories.

• Relate the topic to the research object for a detailed description.

• State the essential structure of the phenomenon.

• We return the final analysis results to the research object to verify the authenticity of the content.

Trustworthiness of qualitative study

To have a rational understanding of the theoretical knowledge and practical experience of qualitative research, the researcher will thoroughly read works on qualitative research, such as ‘Qualitative Research in Nursing’. When designing an interview, consult experts to ensure the interview design is scientific and practical. Data analysis involves two individuals separately analysing data and comparing their findings. The researcher repeatedly analyses and compares the results with the original data. The researchers and members of the research group discuss and correct the analysis results to prevent the subjective bias of the researchers.

Result discussion: integration of quantitative and qualitative data

Integrating quantitative and qualitative data is the crux and can dramatically enhance the value of mixed-methods research. In this research, we adopt ‘Triangulation’ to analyse the quantitative and qualitative data and illustrate it by the communal display table. The triangulation steps⁴⁹ is as follows (figure 3).

Figure 3. Triangulation analysis.

• Sorting: Quantitative and qualitative data analyse separately and then sorted into thematic files related to our research questions.

• Convergence coding: The thematic files aim to compare quantitative and qualitative results and to judge their meaning and interpretation of the research question. This is accomplished using a convergence coding scheme that categorised thematic files into four categories, such as: agreement, partial agreement, silence (or lack of meaningful results) or disagreement. Two researchers conduct the convergence coding to reduce potential bias. Convergence coding is conducted throughout the study period on the preliminary quantitative and qualitative data, thus allowing for more focused data collection.

• Convergence assessment: Each part and the respective quantitative and qualitative results of the theoretical framework(s) are discussed by the study team. Particular attention is given to those sections where data seemed inconsistent (eg: if there were differences between thematic files linked) to the same research question). Where no agreement is reached, the original data are revisited to check for errors until all disagreements are resolved.

• Completeness assessment: Compare the nature and scope of the unique topic areas for each data source or method to enhance the completeness of the united set of findings and identify key differences in scope and/or coverage.

• Researcher comparison: At least two study team members participate in the data collection, analysis and triangulation phase(s) to reduce the impact of potential biases. The study team discusses all analyses. Specific attention is paid to disagreements and/or potential biases.

• Feedback: Feedback on the triangulated results to the research team and stakeholders for review and clarification. Report any differences stakeholders might have.

Limitation

Although we have put much effort into the study again, some things could still be improved. First, this study is a single-centre study and does not involve patients in rural and remote areas, so further research is needed on the generality of the findings. Second, even if the most appropriate questionnaire has been selected, the number of entries is still high for older patients. This may cause them to resist and thus refuse to participate in the study.

Patient and public involvement

Patients and public involvement are an integrative part of the two phases. In the quantitative part, we cooperated with the scientific research group and clinical staff to confirm the research questions. Qualitative analysis will follow an iterative process. The interview guide was developed during the qualitative study based on the scientific research group’s discussion first. Then it will be modified according to the comments of the patients expected to participate in the study in the prevention talk. Two phases of data will be illustrated on the public platform and shared with all members involved.

Ethics and dissemination

The Ethics Review Committee has been approved by the Ethics Review Committee of Sir Run Run Shaw Medical College Affiliated Hospital of Zhejiang University (approval no: 2023-0145). This study does not involve vulnerable groups. The information table clearly defines the procedures, benefits, risks, hazards, discomfort and freedom to withdraw or terminate participation in the study. The content of this table will be explained before all data collection processes. Participants can communicate with the research team and the ethics review committee to clarify doubts. All quantity and quality phase participants will obtain written informed consent before participating. All data will be input and stored anonymously. The computer-stored data will be kept under password protection and accessible only to the research team. The results of this study will be disseminated through local and international conferences, as well as publications in peer-reviewed.

Thank you to the organisations and individuals who participated in the stakeholder consultation and initial open interviews. The findings from these discussions helped identify the research needs and frame the research questions.

Ethics statements

Patient consent for publication

Not applicable.

Footnote

Contributors XW wrote the manuscript and is leading the study; LT, PT, LL; YJ, YZ, JS and DDC supported the study design development and revised the manuscript critically. All authors read and approved the final manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

References

¹ Chinese Diabetes Society; Chinese Society of Infectious Diseases; Chinese Society for Tissue Repair and Regeneration. Chinese guideline on prevention and management of diabetic foot (2019 Edition). 2019; 11: 92–108.

² Zhang P, Lu J, Jing Y, et al. Global epidemiology of diabetic foot ulceration: a systematic review and meta-analysis Ann Med 2017; 49: 106–16. doi:10.1080/07853890.2016.1231932

³ Expert consensus on diabetic foot prevention and treatment under Multidisciplinary cooperation (2020 edition) full edition. Chinese J Burns 2020; 36: E01–E52.

⁴ Pocuis J, Li SMH, Janci MM, et al. Exploring diabetic foot exam performance in a specialty clinic. Clin Nurs Res 2017; 26: 82–92. doi:10.1177/1054773815596699

⁵ Lorig KR, Holman HR. Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med 2003; 26: 1–7. doi:10.1207/S15324796ABM2601_01

⁶ Mehravar F, Mansournia MA, Holakouie-Naieni K, et al. Associations between diabetes self-management and Microvascular complications in patients with type 2 diabetes. Epidemiol Health 2016; 38: e2016004. doi:10.4178/epih/e2016004

⁷ Jiang X, Bai G, Geng F, et al. Effects of Omaha system-based continuing care on self-efficacy and quality of life in diabetic foot patients. Modern J Integrat Tradit Chinese Western Med 2018; 27: 3979–3981+3986.

⁸ Qian S. Evaluation on effect of self-management intervention in patients with hypertension or diabetes mellitus. Southeast University.

⁹ van Netten JJ, Raspovic A, Lavery LA, et al. Prevention of foot ulcers in the at-risk patient with diabetes: a systematic review. Diabetes Metab Res Rev 2020; 36 Suppl 1: e3270. doi:10.1002/dmrr.3270

¹⁰ Manickum P, Mashamba-Thompson T, Naidoo R, et al. Knowledge and practice of diabetic foot care–A Scoping review. Diabetes Metab Syndr 2021; 15: 783–93. doi:10.1016/j.dsx.2021.03.030

¹¹ Li F, Gao Y, Xu Q, et al. Correlation between risk perception and Self⁃Management level of young and Middle⁃Aged patients with type 2 diabetes mellitus. Chinese Nurs Res 2023; 37: 590–5.

¹² Zhu X, Lee M, Chew EA, et al. When nothing happens, nobody is afraid!” beliefs and perceptions around Self‐Care and Health‐Seeking Behaviours: voices of patients living with diabetic lower extremity amputation in primary care. Int Wound J 2021; 18: 850–61. doi:10.1111/iwj.13587

¹³ Oni D. Foot self-care experiences among patients with diabetes: A systematic review of qualitative studies. Wound Manag Prev 2020; 66: 16–25. doi:10.25270/wmp.2020.4.1625

¹⁴ Han Y, Jiang B, Guo R. Factors affecting public adoption of COVID-19 prevention and treatment information during an Infodemic: cross-sectional survey study. J Med Internet Res 2021; 23: e23097. doi:10.2196/23097

¹⁵ Zhu Y, Jiang H, Zhou Z. Information adoption behavior in online Healthcare communities from the perspective of personality traits. Front Psychol 2022; 13. doi:10.3389/fpsyg.2022.973522

¹⁶ Song X, Wang P. Research on concept and effective factors of information adoption behavior. Informat Sci 2010; 28: 760–2.

¹⁷ Wang J. The Relationship between Health Literacy and Family Care, Social Support and Sleep Quality in Patients with systemic lupus erythematosus, thesis. Anhui Medical University, 2021.

¹⁸ Mack C, Terhorst Y, Stephan M, et al. Help in a heartbeat?: a systematic evaluation of mobile health applications (Apps) for coronary heart disease. Int J Environ Res Public Health 2021; 18: 10323. doi:10.3390/ijerph181910323

¹⁹ Zhu X, Lee ES, Lim PXH, et al. Exploring barriers and Enablers of Self‐Management Behaviours in patients with diabetic foot ulcers: A qualitative study from the perceptions of patients, Caregivers, and healthcare professionals in primary care. Int Wound J 27, 2023. doi:10.1111/iwj.14153

²⁰ Wang Z, Zhou J, Bai J, et al. Investigation of medical delays in the elderly with diabetic foot ulcers. Geriatrics & Health Care 2019; 25: 387–9.

²¹ Huang X, Li Y, Li L, et al. Survey the current state about information acquisition and Internet utilization of diabetic patients in six provinces of China. Chinese J Health Educat 2014; 30: 20–2.

²² Mo X, Deng Z. Analysis of the influence of health self-efficacy on health information adoption via SNS. Chinese J Health Stat 2015; 32: 753–7.

²³ Olson DH. Circumplex model of marital and family systems. Journal of Family Therapy 2000; 22: 144–67. doi:10.1111/1467-6427.00144

²⁴ Shu M, Hong X, Xu J, et al. The impact of family function on self-management behavior and recurrence in stroke patients. J Nurs Sci 2019; 34: 75–8.

²⁵ Chen C, Li H, Liu H, et al. A study on the relationship among self-management behavior, family function, locus of control among maintenance Hemodialysis patient. Chinese J Health Educat 2020; 36: 1053–7.

²⁶ Photharos N, Wacharasin C, Duongpaeng S. Model of self-management behavior in people experiencing early stage chronic kidney disease. PAC RIM INT J NURS RES 2018; 22: 360–71.

²⁷ Li F, Wang X, Wang J, et al. Correlation between family function and self-efficacy among elderly adults in home-based setting in Guiyang city. Chinese J Modern Med 2018; 28: 53–7.

²⁸ Li N, Wu L, Liu Z. Relationship between anxiety, depression and family function in elderly stroke patients with disability in Beijing-Tianjin-Hebei region. Chinese J Gerontol 2021; 41: 4401–4.

²⁹ Cárdenas L, Vallbona C, Baker S, et al. Adult onset diabetes mellitus: Glycemic control and family function. Am J Med Sci 1987; 293: 28–33. doi:10.1097/00000441-198701000-00007

³⁰ Pamungkas RA, Chamroonsawasdi K. Family functional-based coaching program on healthy behavior for Glycemic control among Indonesian communities: A quasi-experimental study. Oman Med J 2020; 35: e173. doi:10.5001/omj.2020.115

³¹ Bennich BB, Munch L, Egerod I, et al. Patient assessment of family function, Glycemic control and quality of life in adult patients with type 2 diabetes and incipient complications. Can J Diabetes 2019; 43: 193–200. doi:10.1016/j.jcjd.2018.09.002

³² Liu H, Zhao Q, Wang K, et al. Study on mediating role of family function between activity of daily living and post - stroke depression among stroke patients. Chinese Nurs Res 2019; 33: 3350–5.

³³ Yang L, Zhao Q, Liu H, et al. Family functioning mediates the relationship between activities of daily living and Poststroke depression. Nurs Res 2021; 70: 51–7. doi:10.1097/NNR.0000000000000472

³⁴ Zhang X, Zheng Y, Qiu C, et al. Well-being mediates the effects of social support and family function on self-management in elderly patients with hypertension. Psychol Health Med 2020; 25: 559–71. doi:10.1080/13548506.2019.1687919

³⁵ Fan Y, Chen Q. Family functioning as a mediator between neighborhood conditions and children’s health: evidence from a national survey in the United States. Soc Sci Med 2012; 74: 1939–47. doi:10.1016/j.socscimed.2012.01.039

³⁶ Finan LJ, Schulz J, Gordon MS, et al. Parental problem drinking and adolescent Externalizing behaviors: the mediating role of family functioning. J Adolesc 2015; 43: 100–10. doi:10.1016/j.adolescence.2015.05.001

³⁷ Zhang W, Liu Y, Zhao Y, et al. Effects of family function and community health service on health literacy of infectious diseases among community residents. J Nurs Adm 2022; 22: 331–5. doi:10.1186/s12875-021-01444-6

³⁸ Carter SR, Moles R, White L, et al. Medication information seeking behavior of patients who use multiple medicines: how does it affect adherence. Patient Educ Couns 2013; 92: 74–80. doi:10.1016/j.pec.2013.01.019

³⁹ Dunbar SB, Clark PC, Stamp KD, et al. Family partnership and education interventions to reduce dietary sodium by patients with heart failure differ by family functioning. Heart & Lung 2016; 45: 311–8. doi:10.1016/j.hrtlng.2016.04.001

⁴⁰ Lennie TA, Chung ML, Moser DK. What should we tell patients with heart failure about sodium restriction and how should we counsel them Curr Heart Fail Rep 2013; 10: 219–26. doi:10.1007/s11897-013-0145-9

⁴¹ Lee MK. Caregiving strain, family functioning, and effort to change diet for patients with gastrointestinal cancer: A cross-sectional descriptive study. Eur J Oncol Nurs 2023; 62: S1462-3889(22)00172-7. doi:10.1016/j.ejon.2022.102264

⁴² Luijkx K, Peek S, Wouters E. Grandma, you should do it—it’s cool” older adults and the role of family members in their acceptance of technology. Int J Environ Res Public Health 2015; 12: 15470–85. doi:10.3390/ijerph121214999

⁴³ Tian Y, Ren X, Chen J. Informational and emotional support for family of children with acute leukemia: effects on family function. J Nurs Sci 2012; 27: 76–8.

⁴⁴ Cheung W, Davey J, St John W, et al. Health literacy of mothers Accessing child development services: a model of information use. Aust J Prim Health 2016; 22: 497–504. doi:10.1071/PY15021

⁴⁵ Li P. Research On Users’ Health Information Adoption And Sharing Willingness Based On Social Question-And-Answer Community. ChongQing Medical University, 2021.

⁴⁶ Gao N, Liu Y. Development of self management behavior scale for patients with diabetic foot. Chinese Nurs Res 2016; 30: 1173–80.

⁴⁷ Phillips M, Qijie S, Yanping Z, et al. Preliminary evaluation of Chinese version of FACES II and FES: comparison of normal families and families of schizophrenic patients. Chinese Mental Health J 1991; 5: 198–202.

⁴⁸ Olson DHL. Family inventories: Inventories used in a national survey of families across the family life cycle. Family Social Science, University of Minnesota, 1985.

⁴⁹ de Haan M, van Eijk-Hustings Y, Vrijhoef HJ. Using mixed methods in health services research: A review of the literature and case study. J Health Serv Res Policy 2021; 26: 141–7. doi:10.1177/1355819620955223