comparing the long-term impact of covid-19 on the progression of alzheimer's disease in an elderly population with real-time monitoring as a prognosis

Background and aim: despite growing awareness of long covid and its neurological sequelae, the long-term influence of sars-cov-2 infection on alzheimer's progression remains poorly understood. this study aimed to compare the long-term impact of covid-19 on the progression of ad in elderly individuals, integrating real-time digital monitoring and biomarker analytics to establish predictive prognostic models.methods: a longitudinal cohort of 348 participants (age≥65 years) diagnosed with early-to-moderate ad was followed over four years (2021–2025). participants were divided into covid-19 positive (n=174) and covid-19 negative (n=174) groups, matched for age, sex, and comorbidities. serum inflammatory markers (il-6, tnf-α, crp), neurofilament light chain (nfl), and amyloid-β/tau ratios were analyzed biannually. machine learning algorithms were applied to identify prognostic patterns linking infection-related biomarkers with cognitive and functional decline.results: covid-19-positive ad patients showed a 29% faster rate of cognitive deterioration, indicating a strong association rather than a confirmed causal relationship—between prior sars-cov-2 infection and accelerated ad progression (p<0.01) and a 33% increase in nfl levels compared to controls. real-time monitoring detected significant fluctuations in sleep quality, gait stability, and heart rate variability within six months post-infection, which preceded measurable cognitive decline. predictive modeling achieved an accuracy of 87% in forecasting accelerated progression trajectories using combined digital and biochemical markers.conclusion: the incorporation of wearable and biochemical data into prognostic modeling marks a paradigm shift toward personalized, predictive neurology, enabling clinicians to anticipate and mitigate the long-term neurological consequences of covid-19 in the aging population.