Comprehending Nutrition and Lifestyle Behaviors of People with Metabolic Syndrome: A Focus Group Study.
Muhammad Daniel Azlan MahadzirKia Fatt QuekAmutha RamadasPublished in: Healthcare (Basel, Switzerland) (2022)
Demographically and socio-culturally appropriate care is critical for empowering people with metabolic syndrome (MetS) to self-manage their condition. This focus group study aimed to explore the understanding of nutrition and lifestyle behaviors (NLBs) of Malaysians with MetS. Adults with MetS (N = 21) participated in four focus groups at a university's research clinic in Malaysia. A thematic framework analysis approach was applied to the focus group data using an initial coding framework developed from the Health Belief Model. Six main themes were identified on perceived motivations, barriers, and threats toward healthy NLBs. Motivations to adopt healthy NLBs were body image, personal experience of adverse complications, and family and social support. The perception that healthcare is a business model, the idea that changes in NLBs are difficult and expensive, and cultural influence on food intake were identified as barriers to healthy NLBs. Inadequate knowledge of MetS was identified as a subtheme in this study. Health education and health promotion activities that aim to modify the NLBs of people with MetS should consider the community's perception of motivation and barriers to change. Addressing these aspects in the development of programs can potentially increase program adoption and adherence, ensuring the success of community-based lifestyle interventions.
Keyphrases
- healthcare
- metabolic syndrome
- social support
- physical activity
- health promotion
- depressive symptoms
- public health
- mental health
- insulin resistance
- uric acid
- quality improvement
- cardiovascular disease
- weight loss
- electronic health record
- cardiovascular risk factors
- health information
- palliative care
- primary care
- type diabetes
- adipose tissue
- emergency department
- pain management
- risk factors
- artificial intelligence
- big data
- social media
- machine learning
- adverse drug