ScienceSoft analyzed 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.
The focal point of the study is the growing role of machine learning in wearables. Peer-reviewed research from PubMed, MDPI, ScienceDirect, and other leading sources for scientific and medical research proves that monitoring vital signs, such as heart rate, can support the early detection of health risks and potentially reduce reliance on traditional in-person medical examinations. ScienceSoft explores how predictive algorithms can uncover complex health dependencies and references 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%), cardiovascular disease (96%), Alzheimer’s disease (80–82%), and diabetes (77%).
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 (36% in 2022) due to a healthier workload distribution. Additionally, it highlights the emergence of a new niche for wearable tech manufacturers, driven by nearly 30% of the US population being over the age of 55.
