Author: Qingcai Liu
Liu, Qingcai, 2024 Research on the construction and application of the self-management support model for older adults with intrinsic capacity decline based on mobile health technology, Flinders University, College of Nursing and Health Sciences
Terms of Use: This electronic version is (or will be) made publicly available by Flinders University in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. You may use this material for uses permitted under the Copyright Act 1968. If you are the owner of any included third party copyright material and/or you believe that any material has been made available without permission of the copyright owner please contact copyright@flinders.edu.au with the details.
Background：Strengthening and maintaining intrinsic capacity is the key to promoting healthy aging. Intervention measures to promote healthy aging have many starting points, which can improve its function by enhancing and maintaining intrinsic capacity. Based on high-tech mobile equipment, the medical service model can overcome the limit of time and space, and the online doctor-patient interaction, health management and chronic disease monitoring can be realized. This study aimed to establish a self-management model for the elderly with intrinsic capacity decline, and provide an effective intervention model for the elderly to prevent and manage the decline of internal ability, delay disability and promote healthy aging.
Aims:
(1) To understand the current self-management status and factors affecting self-management of older adults with intrinsic capacity decline in the community.
(2) To explore the self-management support needs of older people with intrinsic capacity decline and community workers based on mHealth technology.
(3) To construct the self-management support model based on mhealth technology for older people with intrinsic capacity decline in the community and to develop the self-management support system.
(4) To test the feasibility, acceptability and factors affecting the implementation of the self-management support model through a pre-experimental study, and test the initial application of the self-management support model using a pilot randomized controlled trial.
Methods:
(1) Using a cross-sectional survey method, a total of 382 older adults with intrinsic capacity decline were randomly selected from four communities in Changsha City, Hunan Province, and included in the questionnaire survey according to the inclusion and exclusion criteria. The assessment tools included the general demographic characteristics questionnaire, self-management ability scale, self-rated health, perceived social support scale, intrinsic capacity assessment tools, and the eHealth literacy scale, etc. Multivariate linear regression was used to analyse the status of self-management of older adults with intrinsic capacity decline in the community and factors affecting their self-management.
(2) The descriptive qualitative approach was used to conduct semi-structured in-depth interviews through purposive sampling with 12 older adults with intrinsic capacity decline, and 8 health professionals in a community health centre in Changsha City. Audio-recorded data were transcribed verbatim for data analysis. Thematic analysis was applied to analyze, interpret, and report findings on self-management support needs of older adults with intrinsic capacity decline and health professional’s recommendations.
(3) Based on factors affecting self-management explored in the previous period, and self-management needs of older adults and the recommendations of community health professionals, and the consideration of the WHO Guidelines for Integrated Care for Older People (ICOPE), Social Cognitive Theory, and Trans-theoretical Modeling Framework, the initial draft of a self-management support model was constructed. The model was modified and finalized by an expert panel using the nominal group technique. The self-management support system was developed based on self-management support model. and the system was tested by 256 cases for system functionality, interface, and security through the black-box test.
(4) The feasibility and acceptability of the self-management support model was analyzed through a pre-experimental study. Factors affecting older adults with intrinsic capacity decline in the community to use the self-management support model were explored from five aspects: personal characteristics of the users, external settings of the system, elements of the intervention, the intervention process, and internal settings of the system using the Consolidated framework for implementation research (CFIR) and the Technology Acceptance Model (TAM). The model was further optimized atter the pre-experimental study. In addition, the initial application effects of the self-management support model for older adults with intrinsic capacity decline in the community were evaluated through a pilot randomized controlled trial (RCT). A qualitative longitudinal study was also conducted alongside the RCT to analyze the process of self-management behavioral change in older people after receiving the intervention of the self-management support model.
Result:
(1) A total of 382 community-based older adults with intrinsic capacity decline who met the inclusion and exclusion criteria participated in this study. The overall level of self-management ability of older adults with intrinsic capacity decline was 67.05±12.53, which is a moderate level. Based on the multiple linear regression analysis results, factors affecting self-management ability in older adults with intrinsic capacity decline include age, intrinsic ability (dimensions of mental capacity, cognitive ability, vision, etc.), e-health literacy, and social support.
(2) The findings from the qualitative study using interviews reveal a total of 5 themes and 18 sub-themes that represent the self-management support needs of older adults with intrinsic capacity decline in the community. The 5 themes are described as: the demand for self-management support for older people in the community is urgent; m-health technology provides a new method of self-management support; existing mHealth services are relatively homogeneous in content and form and lack diversity; older people's needs for mHealth-based self-management support content and the design of the system for ageing; and concerns about the self-management support system. The study also identified four themes and 11 sub-themes from the interviews with health professionals form community health center. These themes are described as: existing self-management support services in the community are not comprehensive enough; self-management support based on m-health technology reduces the burden of health care workers; diversified content of self-management support services based on m-health technology is recommended; mobile devices need to consider the user's aging design needs; and concerns about self-management support systems; recommendations from participants indicate that mobile devices need to consider user compliance.
Conclusion:
(1) The current self-management status among older adults with intrinsic capacity decline in the community is not optimistic. Age, intrinsic capacity (mental capacity, cognitive capacity, and visual acuity), e-health literacy and social support influence self-management among older adults.
(2) Older people with intrinsic capacity decline in the community and health professionals in community health center agree on the importance of mHealth-based self-management. However, they also identified that existing mHealth services have many shortcomings in terms of content, form, and age-appropriate design. They made recommendations on the content and form of mHealth-based self-management support services and ageing-friendly design.
Keywords: Older adults; Intrinsic capacity decline; Community; Self-management support; Social cognition; Technology acceptance model; Trans-theoretical model
Subject: Health Service Management thesis
Thesis type: Doctor of Philosophy
Completed: 2024
School: College of Nursing and Health Sciences
Supervisor: Lily Xiao