Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis
Haiyan Qu,1 Richard M Shewchuk,1 Joshua Richman,2 Lynn J Andreae,3 Monika M Safford4 1Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham (UAB), Birmingham, AL, USA; 2Department of Surgery, School of Medicine, UAB, Birmingham, AL, USA; 3Dep...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-05-01
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| Series: | Risk Management and Healthcare Policy |
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| Online Access: | https://www.dovepress.com/identifying-patient-profiles-for-developing-tailored-diabetes-self-man-peer-reviewed-fulltext-article-RMHP |
| _version_ | 1828346089226371072 |
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| author | Qu H Shewchuk RM Richman J Andreae LJ Safford MM |
| author_facet | Qu H Shewchuk RM Richman J Andreae LJ Safford MM |
| author_sort | Qu H |
| collection | DOAJ |
| description | Haiyan Qu,1 Richard M Shewchuk,1 Joshua Richman,2 Lynn J Andreae,3 Monika M Safford4 1Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham (UAB), Birmingham, AL, USA; 2Department of Surgery, School of Medicine, UAB, Birmingham, AL, USA; 3Department of Medicine, School of Medicine, UAB, Birmingham, AL, USA; 4Department of Medicine, Weill Cornell Medical College, New York, NY, USACorrespondence: Haiyan Qu, Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, SHPB 580D, 1716 9th Ave. South, Birmingham, AL, 35294-1212, USA, Email hyqu@uab.eduPurpose: Interventions that are tailored to the specific psychosocial needs of people with diabetes may be more effective than a “one size fits all” approach. The purpose of this study is to identify patient profiles with distinct characteristics to inform the development of tailored interventions.Methods: A latent class cluster analysis was conducted with data from the ENCOURAGE trial based on participant responses to 6 baseline psychosocial measures, including trust in physicians, perceived discrimination, perceived efficacy in patient–physician interactions, social support, patient activation, and diabetes distress. The trial’s primary outcomes were hemoglobin A1c, body mass index, systolic blood pressure, low-density lipoprotein cholesterol, and quality of life; secondary outcomes were diabetes distress and patient engagement.Results: Three classes of participants were identified: Class 1 (n = 72) had high trust, activation, perceived efficacy and social support; low diabetes distress; and good glycemic control (7.1 ± 1.3%). Class 2 (n = 178) had moderate values in all measures with higher baseline A1c (8.1 ± 2.1%). Class 3 (n = 155) had high diabetes distress; low trust, patient engagement, and perceived efficacy; with similar baseline A1c (8.2 ± 2.1%) as Class 2. Intervention effects differed for these 3 classes.Conclusion: Three distinct subpopulations, which exhibited different responses to the ENCOURAGE intervention, were identified based on baseline characteristics. These groups could be used as intervention targets. Future studies can determine whether these approaches can be used to target scarce resources efficiently and effectively in real-world settings to maximize the impact of interventions on population health, especially in impoverished communities.Keywords: latent class cluster analysis, patient-centered care, diabetes, self-management, trust in physicians |
| first_indexed | 2024-04-14T00:21:05Z |
| format | Article |
| id | doaj.art-bb0c181fc82549758c9a62b63bfa08d9 |
| institution | Directory Open Access Journal |
| issn | 1179-1594 |
| language | English |
| last_indexed | 2024-04-14T00:21:05Z |
| publishDate | 2022-05-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Risk Management and Healthcare Policy |
| spelling | doaj.art-bb0c181fc82549758c9a62b63bfa08d92022-12-22T02:22:56ZengDove Medical PressRisk Management and Healthcare Policy1179-15942022-05-01Volume 151055106375281Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster AnalysisQu HShewchuk RMRichman JAndreae LJSafford MMHaiyan Qu,1 Richard M Shewchuk,1 Joshua Richman,2 Lynn J Andreae,3 Monika M Safford4 1Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham (UAB), Birmingham, AL, USA; 2Department of Surgery, School of Medicine, UAB, Birmingham, AL, USA; 3Department of Medicine, School of Medicine, UAB, Birmingham, AL, USA; 4Department of Medicine, Weill Cornell Medical College, New York, NY, USACorrespondence: Haiyan Qu, Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, SHPB 580D, 1716 9th Ave. South, Birmingham, AL, 35294-1212, USA, Email hyqu@uab.eduPurpose: Interventions that are tailored to the specific psychosocial needs of people with diabetes may be more effective than a “one size fits all” approach. The purpose of this study is to identify patient profiles with distinct characteristics to inform the development of tailored interventions.Methods: A latent class cluster analysis was conducted with data from the ENCOURAGE trial based on participant responses to 6 baseline psychosocial measures, including trust in physicians, perceived discrimination, perceived efficacy in patient–physician interactions, social support, patient activation, and diabetes distress. The trial’s primary outcomes were hemoglobin A1c, body mass index, systolic blood pressure, low-density lipoprotein cholesterol, and quality of life; secondary outcomes were diabetes distress and patient engagement.Results: Three classes of participants were identified: Class 1 (n = 72) had high trust, activation, perceived efficacy and social support; low diabetes distress; and good glycemic control (7.1 ± 1.3%). Class 2 (n = 178) had moderate values in all measures with higher baseline A1c (8.1 ± 2.1%). Class 3 (n = 155) had high diabetes distress; low trust, patient engagement, and perceived efficacy; with similar baseline A1c (8.2 ± 2.1%) as Class 2. Intervention effects differed for these 3 classes.Conclusion: Three distinct subpopulations, which exhibited different responses to the ENCOURAGE intervention, were identified based on baseline characteristics. These groups could be used as intervention targets. Future studies can determine whether these approaches can be used to target scarce resources efficiently and effectively in real-world settings to maximize the impact of interventions on population health, especially in impoverished communities.Keywords: latent class cluster analysis, patient-centered care, diabetes, self-management, trust in physicianshttps://www.dovepress.com/identifying-patient-profiles-for-developing-tailored-diabetes-self-man-peer-reviewed-fulltext-article-RMHPlatent class cluster analysispatient-centered carediabetesself-managementtrust in physicians |
| spellingShingle | Qu H Shewchuk RM Richman J Andreae LJ Safford MM Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis Risk Management and Healthcare Policy latent class cluster analysis patient-centered care diabetes self-management trust in physicians |
| title | Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis |
| title_full | Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis |
| title_fullStr | Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis |
| title_full_unstemmed | Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis |
| title_short | Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis |
| title_sort | identifying patient profiles for developing tailored diabetes self management interventions a latent class cluster analysis |
| topic | latent class cluster analysis patient-centered care diabetes self-management trust in physicians |
| url | https://www.dovepress.com/identifying-patient-profiles-for-developing-tailored-diabetes-self-man-peer-reviewed-fulltext-article-RMHP |
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