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Senff JR, Tack RWP, Tan BYQ, Prapiadou S, Kimball TN, Ng S, Duskin J, Shah-Ostrowski MJ, Nunley C, Brouwers HB, Chemali Z, Fricchione G, Tanzi RE, Pouwels K, Rosand J, Yechoor N, Anderson CD, Singh SD. Knowledge and practice of healthy behaviors for dementia and stroke prevention in a United States cohort. Sci Rep 2025; 15:15172. [PMID: 40307407 PMCID: PMC12044067 DOI: 10.1038/s41598-025-99246-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Accepted: 04/17/2025] [Indexed: 05/02/2025] Open
Abstract
At least 45% of dementia and 60% of stroke cases are due to modifiable risk factors and could in part be prevented through healthy behavior. This cross-sectional study clustered and characterized a U.S. cohort's knowledge and practice of healthy behavior associated with dementia and stroke. A total of 1,478 participants (mean age: 45.5 years, 51.8% female) were included. A hierarchical cluster analysis was performed to identify clusters based on the level of knowledge and practice of healthy behavior. We defined knowledge as recognizing eight modifiable risk factors (alcohol, diet, smoking, physical activity, sleep, stress, social relationships, and purpose in life) as important. We defined practice as complying with validated recommendations for each healthy behavior. Three clusters emerged: (I) high knowledge and poor practice (II) high knowledge and good practice, and (III) lower knowledge and poor practice. Participants in the high knowledge and good practice cluster were statistically significantly older, more educated, perceived fewer barriers (financial and time limitations), and more facilitators (motivation or knowing someone with dementia or stroke) compared to the other clusters. Our findings could assist in tailoring preventative strategies to enhance knowledge, translating knowledge into practice, and addressing particular facilitators and barriers per identified cluster.
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Affiliation(s)
- Jasper R Senff
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
- McCance Center for Brain Health, Massachusetts General Hospital, Harvard Medical School, Revolution Drive 399, Sommerville, MA, 02145, USA.
| | - Reinier W P Tack
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Benjamin Y Q Tan
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Neurology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Savvina Prapiadou
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Tamara N Kimball
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sharon Ng
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan Duskin
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Courtney Nunley
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
| | - H Bart Brouwers
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| | - Zeina Chemali
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Gregory Fricchione
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Benson-Henry Institute for Mind Body Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Rudolph E Tanzi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Koen Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan Rosand
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nirupama Yechoor
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Christopher D Anderson
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sanjula D Singh
- Brain Care Labs, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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Wang X, Jiang H, Zhao Z, Kevine NT, An B, Ping Z, Lin B, Zhang Z. Mediation Role of Behavioral Decision-Making Between Self-Efficacy and Self-Management Among Elderly Stroke Survivors in China: Cross-Sectional Study. Healthcare (Basel) 2025; 13:704. [PMID: 40218004 PMCID: PMC11988728 DOI: 10.3390/healthcare13070704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2025] [Revised: 03/17/2025] [Accepted: 03/19/2025] [Indexed: 04/14/2025] Open
Abstract
Background: Identifying the factors that impact self-management is crucial, as elderly stroke survivors frequently face challenges in self-management. Self-efficacy and behavioral decision-making are reported as influencing factors of self-management, but their relationship within the elderly population remains unconfirmed. This study aimed to explore whether self-efficacy impacts self-management through the mediating role of behavioral decision-making among elderly stroke survivors. Methods: A cross-sectional design and convenience sampling method were used in this study. A total of 291 elderly stroke survivors were recruited from a tertiary hospital in Henan Province, China, between March and July of 2024. Questionnaires were distributed to collect sociodemographic, self-efficacy, behavioral decision-making, and self-management data. A path analysis and correlation analysis were used to analyze the data. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Results: Elderly stroke survivors reported having a moderate level of self-management. There was a positive correlation between self-efficacy, behavioral decision-making, and self-management (all p < 0.01). The mediation model indicated that behavioral decision-making mediated the association of self-efficacy and self-management in the regression model (95% CI 0.03 to 0.14), and the effect value was 0.08. It was also confirmed that behavioral decision-making mediated the impact of self-efficacy and self-management, accounting for 25.81% of the total effect. Conclusion: Self-efficacy is not solely a key factor influencing self-management in elderly stroke survivors, but it also improves their self-management behaviors by facilitating behavioral decision-making. As a result, healthcare professionals should consider self-efficacy and behavioral decision-making as crucial elements for assessing elderly stroke survivors during discharge and follow-up.
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Affiliation(s)
- Xiaoxuan Wang
- Nursing and Health School, Zhengzhou University, Zhengzhou 450001, China; (X.W.); (N.T.K.)
| | - Hu Jiang
- Nursing and Health School, Zhengzhou University, Zhengzhou 450001, China; (X.W.); (N.T.K.)
| | - Zhixin Zhao
- Nursing and Health School, Zhengzhou University, Zhengzhou 450001, China; (X.W.); (N.T.K.)
| | | | - Baoxia An
- Henan Huaxian People Hospital, Anyang 456400, China
| | - Zhiguang Ping
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Beilei Lin
- Nursing and Health School, Zhengzhou University, Zhengzhou 450001, China; (X.W.); (N.T.K.)
| | - Zhenxiang Zhang
- Nursing and Health School, Zhengzhou University, Zhengzhou 450001, China; (X.W.); (N.T.K.)
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Sathirapanya C, Trijun J, Sathirapanya P. Integrating the Sufficiency Economy Royal Philosophy and Participatory Action Research Approach to Promote Self-Care for Stroke Prevention in Selected Communities of Southern Thailand. Healthcare (Basel) 2024; 12:1367. [PMID: 39057510 PMCID: PMC11275373 DOI: 10.3390/healthcare12141367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024] Open
Abstract
(1) Introduction: Effective control of stroke risk factors can reduce stroke incidence. Motivation for participatory action of community dwellers to practice self-care to modify stroke risk after providing them with knowledge of stroke risk factors is considered useful under a situation of limited healthcare resources. This study aimed to evaluate the outcomes of integrating the sufficiency economy philosophy (SEP), a royal economic philosophy in Thailand, and the participatory action research (PAR) approach on stroke risk factors control among selected communities. (2) Methods: Villagers who had medium to high stroke risk from two provinces with leading stroke incidences in southern Thailand were invited to participate in an eight-month SEP-PAR program conducted in 2019. Group meetings among the study participants, local healthcare providers, the researchers, and relevant stakeholders in the communities were held to co-design a health behaviors program targeting lower waist circumference (WC), body weight (BW), blood pressure (BP), fasting blood sugar, blood lipids, and smoking and alcohol consumption rates. Follow-up physical measurements and blood tests were compared with the baseline results for significant differences by descriptive statistics (p < 0.05) using the R program. (3) Results: Of 126 participants, 75.4% were female. Moderate and high stroke risk levels were found in 58.2% and 19.8%, respectively. Elevated baseline WC, BW, BP, and blood test results were found in 50-80% of the participants. The co-designed health behaviors in this study were dietary control, regular exercise, relieving psychological stress, and stopping smoking and alcohol consumption. Overall, the participants had significant adherence to the co-designed health behaviors. At the end of the program, the follow-up tests showed significant reductions in BW, BP, fasting blood sugar, and lipids, but not in WC. (4) Conclusions: A combined SEP and PAR approach was effective for stroke risk factors control among the community dwellers. Motivation for self-care is a significant strategic outcome expected of this approach. Longer follow-up studies in larger populations are needed.
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Affiliation(s)
- Chutarat Sathirapanya
- Department of Family Medicine and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
| | - Jamaree Trijun
- Department of Pharmacy, Khaochaison Hospital, Phatthalung Provincial Public Health Office, Khaochaison, Phatthalung 93130, Thailand;
| | - Pornchai Sathirapanya
- Department of Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
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Zhang M, Guo L, Namassevayam G, Wei M, Xie Y, Guo Y, Liu Y. Factors associated with health behaviours among stroke survivors: A mixed-methods study using COM-B model. J Clin Nurs 2024; 33:2138-2152. [PMID: 38590015 DOI: 10.1111/jocn.17103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/04/2024] [Accepted: 02/28/2024] [Indexed: 04/10/2024]
Abstract
AIMS To identify factors associated with health behaviours among stroke survivors, through a multi-centre study. DESIGN A sequential mixed methods design. METHODS In the quantitative research phase, a total of 350 participants were recruited through multi-stage sampling from December 2022 to June 2023. General information questionnaires, The Stroke Prevention Knowledge Questionnaire (SPKQ), Short Form Health Belief Model Scale (SF-HBMS), Health Promoting Lifestyle Profile (HPLPII), and the WHOQOL-BREF (World Health Organization Quality of Life Questionnaire, Brief Version) were distributed across five tertiary hospitals in Henan province, China. For the qualitative research component, semi-structured interviews were conducted to explore the barriers and facilitators of health behaviour. This study adheres to the GRAMMS guidelines. RESULTS A total of 315 participants (90.0%) completed the survey. Identified barriers to health behaviour included residing in rural areas, higher scores on the Charlson Comorbidity Index (CCI) and mRS, as well as lower scores on SPKQ, SF-HBMS and WHOQOL-BREF. Twenty-four individuals participated in qualitative interviews. Twenty-eight themes were identified and categorised by frequency, covering areas such as knowledge, skills, intentions, social influences, social/professional role and identity, environmental context and resources, beliefs about capabilities, beliefs about consequences and behavioural regulation. Both quantitative and qualitative data suggested that health behaviour among stroke survivors is at a moderate level, and the identified barrier factors can be mapped into the COM-B model (Capability, Opportunity, Motivation and Behaviour). CONCLUSION The study indicates that key barriers to health behaviour among stroke survivors align with the COM-B model. These identified factors should be carefully considered in the planning of future systematic interventions aimed at improving health behaviours among stroke survivors. PATIENT OR PUBLIC CONTRIBUTION Patients were invited to completed questionnaires in the study and semi-structured interviews. The investigators provided explanation of this study' content, purpose and addressed issues during the data collection.
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Affiliation(s)
- Mengyu Zhang
- College of Nursing, Zhengzhou University, Zhengzhou, China
| | - Lina Guo
- Department of Neurology, National Advanced Stroke Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Genoosha Namassevayam
- Department of Neurology, National Advanced Stroke Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Supplementary Health Sciences, Faculty of Health-Care Sciences, Eastern University, Trincomalee, Sri Lanka
| | - Miao Wei
- Department of Neurology, National Advanced Stroke Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - YuYing Xie
- College of Nursing, Zhengzhou University, Zhengzhou, China
| | - Yuanli Guo
- Department of Neurology, National Advanced Stroke Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanjin Liu
- Department of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Wang X, Zhang ZX, Lin BL, Jiang H, Wang W, Mei YX, Zhang C, Zhang Q, Chen SY. Mediation role of perceived social support between recurrence risk perception and health behaviour among patients with stroke in China: a cross-sectional study. BMJ Open 2024; 14:e079812. [PMID: 38355172 PMCID: PMC10868314 DOI: 10.1136/bmjopen-2023-079812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVES To examine whether patients who had a stroke with high recurrence risk perception would have healthier behaviour and to explore whether perceived social support would function as a mediator. DESIGN A cross-sectional study. SETTING The study was conducted in a public tertiary hospital in China. PARTICIPANTS A total of 254 patients with stroke were invited to participate, and 250 patients with stroke completed questionnaires validly. PRIMARY AND SECONDARY OUTCOME MEASURES Questionnaires were administered offline to collect data, consisting of four parts: general demographics and scales related to recurrence risk perception, perceived social support, and health behaviour. A path analysis and correlation analysis were used to analyse the data. RESULTS Out of 250 patients with stroke, 78.4% had moderately low health behaviour. The majority (70.8%) of these patients were elderly. High recurrence risk perception and high perceived social support were significantly associated with better health behaviour (all p<0.001). Perceived social support mediated the relationship between recurrence risk perception and health behaviour after controlling for age, gender, education and monthly income in the regression model (95% CI 0.263 to 0.460) and the effect value was 0.360. It was also confirmed that perceived social support had the highest mediation effect with a proportion of mediation up to 59.31%. CONCLUSIONS Recurrence risk perception and perceived social support were influential factors in promoting health behaviour. Moreover, the impact of recurrence risk perception on health behaviour was partially mediated by perceived social support. Therefore, to enhance the sustainability of health behaviour, it is crucial to inform patients with stroke about the risk of recurrence. Patients with more perception of recurrence risk can improve their recovery confidence and thus perceive more social support.
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Affiliation(s)
- Xiaoxuan Wang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Zhen-Xiang Zhang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Bei-Lei Lin
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Hu Jiang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Wenna Wang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Yong-Xia Mei
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Chunhui Zhang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Qiushi Zhang
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
| | - Su-Yan Chen
- Nursing and Health school, Zhengzhou University, Zhengzhou, China
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