Identification of diabetes self-management profiles in adults: a cluster analysis with selected self-reported outcomes
Switzerland, 2014 - 2015
Peytremann Bridevaux Isabelle
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May 05, 2020
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May 06, 2020
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Identification
Survey ID Number
10.16909-DATASET-19
Title
Identification of diabetes self-management profiles in adults: a cluster analysis with selected self-reported outcomes
Translated Title
L’identification de profils d'autogestion du diabète chez adulte : une analyse en cluster sur des données auto-reportées
Country
| Name | Country code |
|---|---|
| Switzerland | CHE |
Abstract
STUDY TYPE : Cross-sectional study using data from the 2014 follow-up of the CoDiab-VD cohort.
The current study describes diabetes self-management (DSM) profiles in adults using self-reported outcomes associated with the performance of diabetes care activities, and psychological adjustment to the condition. We used self-reported data from a community-based cohort of adults with diabetes (N= 316). We conducted clustering analysis on selected DSM self-reported outcomes (i.e., DSM behaviors, self-efficacy and perceived empowerment, diabetes distress and quality of life). We tested whether the clusters differed according to care delivery processes, socio-demographic and clinical variables. Clustering analysis revealed four distinct DSM profiles that combine high/low engagement in DSM, and good/poor psychological adjustment with the disease. The profiles are differently associated with variables of financial insecurity perceived, having an insulin treatment, having depression, and congruency of care received with the Chronic Care Model. The results could help health professionals gain a better understanding of the different realities of people living with diabetes, identify patients at risk of poor DSM-related outcomes, and lead to the development of profile-specific DSM interventions.
The current study describes diabetes self-management (DSM) profiles in adults using self-reported outcomes associated with the performance of diabetes care activities, and psychological adjustment to the condition. We used self-reported data from a community-based cohort of adults with diabetes (N= 316). We conducted clustering analysis on selected DSM self-reported outcomes (i.e., DSM behaviors, self-efficacy and perceived empowerment, diabetes distress and quality of life). We tested whether the clusters differed according to care delivery processes, socio-demographic and clinical variables. Clustering analysis revealed four distinct DSM profiles that combine high/low engagement in DSM, and good/poor psychological adjustment with the disease. The profiles are differently associated with variables of financial insecurity perceived, having an insulin treatment, having depression, and congruency of care received with the Chronic Care Model. The results could help health professionals gain a better understanding of the different realities of people living with diabetes, identify patients at risk of poor DSM-related outcomes, and lead to the development of profile-specific DSM interventions.
Kind of Data
Sample survey data [ssd] / Self-reported data collected from paper questionnaire
Unit of Analysis
The analysis unit is the individual.
Among the 519 participants recruited in 2011–2012, we sent the 2014 follow-up questionnaire to the 402 participants not lost to follow-up, and 339 participants returned the completed questionnaire (response rate 84.3%).
Among the 519 participants recruited in 2011–2012, we sent the 2014 follow-up questionnaire to the 402 participants not lost to follow-up, and 339 participants returned the completed questionnaire (response rate 84.3%).
Version
Version Description
Version 1.0
Version Date
2020-05-01
Scope
Keywords
| Keyword |
|---|
| Diabetes |
| self-management |
| clusters |
| health behaviors |
| self-efficacy |
| empowerment |
| diabetes distress |
| quality of life |
Coverage
Geographic Coverage
Canton of Vaud, Switzerland
Universe
Non-institutionalized adults with diabetes
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Peytremann Bridevaux Isabelle | Center for Primary Care and Public Health (Unisanté) |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Alexandre, Ketia | School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland | PhD Student |
| Vallet, Fanny | Faculté de Psychologie et des Sciences de l’Education, University of Geneva, Geneva, Switzerlan | Scientific associate |
| Betticher, Daniel | Faculté de Psychologie et des Sciences de l’Education, University of Geneva, Geneva, Switzerland | Study supervisor |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| The individuals with diabetes who participated in the 2014 follow-up of the CoDiab-VD cohort. | Answers to the self-assessed questionnaires | |
| Antille-Zuercher, Emilie | Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland | Management of the CoDiab-VD cohort and data management |
| Auxiliary staff | Enveloping of the documents and data entry |
Sampling
Sampling Procedure
Pharmacies accepting to participate in the study recruited individuals with diabetes during six-week periods in 2011-12 and 2017.
At time of the first recruitment in 2011-12, the number of survey participants were 519. From this baseline number, the participants of the 2014 follow-ups were 339. Among those participants, 316 had no missing data for the variables of interest and were included in the present study.
At time of the first recruitment in 2011-12, the number of survey participants were 519. From this baseline number, the participants of the 2014 follow-ups were 339. Among those participants, 316 had no missing data for the variables of interest and were included in the present study.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2014-10-01 | 2015-01-31 |
Data Collection Mode
Paper questionnaire, self-administered.
Data were self-reported by the participants, who filled in the questionnaire at home and sent it back to the investigators.
Data were self-reported by the participants, who filled in the questionnaire at home and sent it back to the investigators.
Questionnaires
Questionnaires
The 2014 annual questionnaire is composed of two parts: 1) the core questionnaire, which is quite similar for each annual CoDiab-VD questionnaire, and 2) a thematic module on psychosocial aspects of diabetes, which is specific to the 2014 questionnaire.
All variables described hereafter were collected in the 2014 questionnaire and used in the present study.
Variables used for the identification of diabetes self-management profiles:
Diabetes self-management behaviors (Summary of Diabetes Self-Care Activities: healthy eating,
physical activity, self-monitoring of blood glucose and foot care)
Self-efficacy (Stanford diabetes-specific questionnaire)
Empowerment (Diabetes Empowerment Scale)
Diabetes distress (Problem Areas In Diabetes (PAID instrument)
Diabetes-specific quality of life (Audit of Diabetes Dependent Quality of Life, ADDQoL)
Variables used for the comparison of diabetes self-management profiles:
Sociodemographic characteristics:
-Age
-Gender
-Household size
-Difficulty paying bills
-Employment
Clinical variables:
-Diabetes type
-Disease duration
-Antidiabetic treatment
-HbA1c knowledge
-Anthropometric values:
----Weight
----Height
-Smoking
-Depression screening (two validated questions for the screening of depression)
-Comorbidities:
----List of following chronic diseases: heart disease (heart failure, valve disease, heart muscle disease), chronic lung disease (asthma, chronic bronchitis, emphysema), osteoporosis, osteoarthritis or arthritis; cancer or malignancy or lymphoma (with the exception of skin cancer), gastric or duodenal ulcer, depression, Parkinson’s disease, hypertension, hyperlipidaemia, other chronic condition.
-Health literacy
Process of care indicators (whether the process was received during the past 12 months):
-Glycated haemoglobin (HbA1c) check
-Eye examination by ophthalmologist
-Urine test for microalbuminuria
-Diabetic foot examination
-Lipid profile
Outcomes of care indicators:
-Patient assessment of diabetes care, congruence of care with the chronic care model
-(Patient Assessment of Care for Chronic Conditions, PACIC)
All variables described hereafter were collected in the 2014 questionnaire and used in the present study.
Variables used for the identification of diabetes self-management profiles:
Diabetes self-management behaviors (Summary of Diabetes Self-Care Activities: healthy eating,
physical activity, self-monitoring of blood glucose and foot care)
Self-efficacy (Stanford diabetes-specific questionnaire)
Empowerment (Diabetes Empowerment Scale)
Diabetes distress (Problem Areas In Diabetes (PAID instrument)
Diabetes-specific quality of life (Audit of Diabetes Dependent Quality of Life, ADDQoL)
Variables used for the comparison of diabetes self-management profiles:
Sociodemographic characteristics:
-Age
-Gender
-Household size
-Difficulty paying bills
-Employment
Clinical variables:
-Diabetes type
-Disease duration
-Antidiabetic treatment
-HbA1c knowledge
-Anthropometric values:
----Weight
----Height
-Smoking
-Depression screening (two validated questions for the screening of depression)
-Comorbidities:
----List of following chronic diseases: heart disease (heart failure, valve disease, heart muscle disease), chronic lung disease (asthma, chronic bronchitis, emphysema), osteoporosis, osteoarthritis or arthritis; cancer or malignancy or lymphoma (with the exception of skin cancer), gastric or duodenal ulcer, depression, Parkinson’s disease, hypertension, hyperlipidaemia, other chronic condition.
-Health literacy
Process of care indicators (whether the process was received during the past 12 months):
-Glycated haemoglobin (HbA1c) check
-Eye examination by ophthalmologist
-Urine test for microalbuminuria
-Diabetic foot examination
-Lipid profile
Outcomes of care indicators:
-Patient assessment of diabetes care, congruence of care with the chronic care model
-(Patient Assessment of Care for Chronic Conditions, PACIC)
Data Processing
Data Editing
Data were checked first upon receipt of the questionnaires, for inconsistency (multiple check marks when only one allowed, incoherent numbers or between related questions); second, throughout the scanning process, for data entry; and last, by proofing the output Excel file obtained after the entry process.
OTHER PREOCESSING :
The clustering analysis was performed using self-reported data from the 316 participants who had no missing data on the variables used for the DSM profiles’ identification. First, we performed descriptive univariate analyses (i.e., frequency, percentages, mean scores and standard deviations). Successively, we converted variables’ scores used for cluster identification to standardized values since each instrument had different scales and units. These statistical procedures were applied on the average value of the diabetes distress variable (PAID-5, range 0 to 4), which was then rescaled for reporting to a range from 0 to 20 to ease the comparison with previous literature. To determine the number of clusters, we proceeded by visual examination of the dendrogram (agglomeration schedule) and looked at the stage of appearance of sudden large increase in the similarity measure between joined clusters. Then, to test the robustness of identified clusters, we compared two classification methods: agglomerative hierarchical procedure using Ward’s method and iterative partitioning using k-means method. We calculated a Cohen’s kappa coefficient to measure the level of agreement in the distribution of participants in the identified clusters between both methods. Finally, we tested whether the clusters differed according to socio-demographic, clinical and care delivery process variables using One-Way ANOVA for continuous variables and χ2 comparison for categorical variables. All analyses were conducted with SPSS statistical software 23.0.
OTHER PREOCESSING :
The clustering analysis was performed using self-reported data from the 316 participants who had no missing data on the variables used for the DSM profiles’ identification. First, we performed descriptive univariate analyses (i.e., frequency, percentages, mean scores and standard deviations). Successively, we converted variables’ scores used for cluster identification to standardized values since each instrument had different scales and units. These statistical procedures were applied on the average value of the diabetes distress variable (PAID-5, range 0 to 4), which was then rescaled for reporting to a range from 0 to 20 to ease the comparison with previous literature. To determine the number of clusters, we proceeded by visual examination of the dendrogram (agglomeration schedule) and looked at the stage of appearance of sudden large increase in the similarity measure between joined clusters. Then, to test the robustness of identified clusters, we compared two classification methods: agglomerative hierarchical procedure using Ward’s method and iterative partitioning using k-means method. We calculated a Cohen’s kappa coefficient to measure the level of agreement in the distribution of participants in the identified clusters between both methods. Finally, we tested whether the clusters differed according to socio-demographic, clinical and care delivery process variables using One-Way ANOVA for continuous variables and χ2 comparison for categorical variables. All analyses were conducted with SPSS statistical software 23.0.
Access policy
Access authority
| Name | Affiliation | |
|---|---|---|
| Peytremann Bridevaux, Isabelle (Principal investigator) | Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland | isabelle.peytremann-bridevaux@unisante.ch |
| Racine, Céline (Repository manager) | Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland | dfri.data@unisante.ch |
Confidentiality
Access conditions
Licensed datasets, accessible under conditions.
Because of potentially identifying participants’ information, the dataset can only be accessed upon request through the data repository website.
To request access to licensed datasets, please register to the website to continue (https://data.unisante.ch/index.php/auth/register). Once your registration will be approved you must login and go to the "GET MICRODATA" tab and fill in the application form for access to the licensed dataset.
This form includes our Data access agreement and must be filled and submitted by the Lead Researcher. Lead Researcher refers to the person who serves as the main point of contact for all communications involving this agreement. Access to licensed datasets will only be granted when the Lead Researcher is an employee of a legally registered receiving agency (university, company, research centre, national or international organization, etc.) on behalf of which access to the data is requested. The Lead Researcher assumes all responsibility for compliance with all terms of this Data Access Agreement by employees of the receiving organization.
This request will be reviewed by the Primary Investigator, who may decide to approve the request, to deny access to the data, or to request additional information from the Lead Researcher. A signed copy of this request form may also be requested.
If special conditions apply to the transfer of the requested data (e.g. transfer under a contract between IUMSP and a third party), the references of the contract concerned or the document in which the conditions are specified must be referenced in the request form. The person who makes the request will still need to check the box confirming the acceptance of the terms of use, it being understood that the conditions written in the above mentioned contract will apply.
Because of potentially identifying participants’ information, the dataset can only be accessed upon request through the data repository website.
To request access to licensed datasets, please register to the website to continue (https://data.unisante.ch/index.php/auth/register). Once your registration will be approved you must login and go to the "GET MICRODATA" tab and fill in the application form for access to the licensed dataset.
This form includes our Data access agreement and must be filled and submitted by the Lead Researcher. Lead Researcher refers to the person who serves as the main point of contact for all communications involving this agreement. Access to licensed datasets will only be granted when the Lead Researcher is an employee of a legally registered receiving agency (university, company, research centre, national or international organization, etc.) on behalf of which access to the data is requested. The Lead Researcher assumes all responsibility for compliance with all terms of this Data Access Agreement by employees of the receiving organization.
This request will be reviewed by the Primary Investigator, who may decide to approve the request, to deny access to the data, or to request additional information from the Lead Researcher. A signed copy of this request form may also be requested.
If special conditions apply to the transfer of the requested data (e.g. transfer under a contract between IUMSP and a third party), the references of the contract concerned or the document in which the conditions are specified must be referenced in the request form. The person who makes the request will still need to check the box confirming the acceptance of the terms of use, it being understood that the conditions written in the above mentioned contract will apply.
Citation requirements
Alexandre K., Vallet F., Peytremann-Bridevaux I., and Desrichard O. Identification of diabetes self-management profiles in adults: a cluster analysis with selected self-reported outcomes. Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (May 2020), provided by the Unisanté Research Data Repository. DOI:10.16909/DATASET/19
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyright
(c) 2020, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland
Metadata production
DDI Document ID
10.16909-DATASET-19
Producers
| Name | Abbreviation | Role |
|---|---|---|
| Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland | Unisanté | Data publisher |
DDI Document version
Version 2.0 (may 2020)