Within five to 10 years, one in two older adults will use wearable devices daily to forecast life-threatening risks, predicts new research.
Researchers analysed wearable adoption trends in the US over the past decade and uncovered slower uptake among older generations.
The study identifies barriers to adoption and suggests that wearable manufacturers can better address senior-specific needs and provide user-friendly education on device usage.
ScienceSoft, which carried out the research, also explored how predictive algorithms can uncover complex health dependencies, referencing research demonstrating the use of machine learning in diagnosing geriatric conditions. Notably, machine learning algorithms have shown the highest accuracy in detecting atrial fibrillation (96.9 per cent), cardiovascular disease (96 per cent), Alzheimer’s disease (80–82 per cent), and diabetes (77 per cent).
Beyond the efficacy of wearables for preventive senior care, the study examines how these devices will reshape healthcare ecosystems.
In particular, ScienceSoft anticipates a reduction in the turnover rate among ambulance service providers due to a healthier workload distribution. Additionally, it highlights the emergence of a new niche for wearable tech manufacturers, driven by nearly 30 per cent of the US population being over the age of 55.
