BMC Geriatrics最新文献

Background: Stroke remains a leading cause of mortality and disability worldwide, with nutritional status emerging as a crucial yet underexplored risk factor in elderly populations. The Geriatric Nutritional Risk Index (GNRI) represents a valuable nutritional assessment tool specifically developed for geriatric populations. This study examined the association between GNRI and stroke prevalence among elderly individuals using nationally representative data.

Methods: This cross-sectional analysis utilized National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, including 16,092 participants aged ≥ 60 years. GNRI was calculated using serum albumin levels and body weight ratio, with participants categorized into quartiles. Stroke status was determined through self-reported physician diagnosis. Survey-weighted logistic regression models were constructed with progressive adjustments for demographic, lifestyle, and clinical factors.

Results: Among participants (mean age 70.0 years), 12.31% reported stroke history. GNRI demonstrated significant inverse association with stroke prevalence. Each one-standard-deviation increase in GNRI was associated with 12% lower stroke odds (odds ratio [OR]: 0.88; 95% CI: 0.83-0.93). Quartile analysis revealed progressively lower odds compared to the lowest quartile: Q2 (OR: 0.81; 95% CI: 0.69-0.95), Q3 (OR: 0.74; 95% CI: 0.63-0.87), and Q4 (OR: 0.77; 95% CI: 0.65-0.91) (P for trend < 0.05). Multiple analytical approaches consistently demonstrated a linear inverse association. Subgroup analyses revealed a stronger inverse association in females (OR: 0.96; 95% CI: 0.95-0.98) versus males (OR: 0.99; 95% CI: 0.97-1.00) and among current drinkers (OR: 0.96; 95% CI: 0.94-0.97).

Conclusions: Higher GNRI scores were significantly associated with lower stroke prevalence in elderly adults in a linear dose-response manner, with the association being particularly pronounced in females and current drinkers. These cross-sectional findings suggest that GNRI may be a useful nutritional risk screening tool in geriatric populations; however, prospective studies are needed to establish temporality and causality.

Background: Older adults with lower socioeconomic status (SES) are most affected by chronic diseases and in need of effective interventions to manage their conditions. Despite the proven benefits of mobile health (mHealth) in chronic disease management, the stimulation of mHealth adoption among people with lower SES is challenging. Such socioeconomic differences have not been investigated among older adults. The aims of this study were to identify factors associated with mHealth acceptance among older adults in the Netherlands with low and non-low SES, and to determine whether the influences of these factors differ between socioeconomic groups.

Methods: This cross-sectional study was performed using a questionnaire based on technology acceptance model (TAM) factors. The participants were aged ≥ 65 years, lived independently or in senior living facilities, and had no cognitive impairment. Associations between average TAM factor scores and respondents' intention to use mHealth applications were analyzed separately for low- and non-low-SES groups using controlled multivariable logistic regression. Models including interaction terms were then computed to investigate differences between groups.

Results: The sample comprised 360 respondents (mean age, 74.9 ± 7.0 years). Scores for the eight TAM factors were significantly lower in the low-SES group than in the non-low-SES group, indicating that it is more difficult to motivate the former to use mHealth. All factors except feelings of anxiety and social relationships were associated significantly with the use intention in the low-SES group, and all factors showed significant associations in the non-low SES group. The models including interaction terms showed that perceived usefulness, perceived ease of use, and service availability had significantly stronger relationships with the intention to use mHealth in the non-low SES group than in the low-SES group.

Conclusion: This study revealed differences in the associations of TAM factors with the intention to use mHealth applications between older adults with low and non-low SES. A stronger and more comprehensive approach is needed to stimulate mHealth adoption among low-SES older adults. Policy development with the consideration of specific TAM factors will increase mHealth adoption among older adults. To reduce health disparities, policies should be tailored to older adults' needs.