The research has revealed that these devices may help in the identification of mild cognitive impairment (MCI) and Alzheimer’s disease dementia.
As part of the 12-week study, it was found that an iPhone, Apple Watch, iPad and the Beddit sleep monitoring device, in combination with digital apps may be able to differentiate people with MCI and mild Alzheimer’s disease dementia.
Evidation Health data scientist Nikki Marinsek said: “Over the past few years, we’ve seen how data and insights derived from wearables and mobile consumer devices have enabled people living with health conditions, along with their clinicians, to better monitor their health.
“We know that insights from smart devices and digital applications can lead to improved health outcomes, but we don’t yet know how those resources can be used to identify and accelerate diagnoses.”
Based on these initial findings, the companies plan to conduct further research to help identify people with neurodegenerative conditions earlier than ever before.
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Data obtained through the use of Apple’s personal digital devices suggest an ability to differentiate between individuals with the conditions and those without symptoms.
A total of 113 participants, aged 60 to 75 with and without mild cognitive impairment were studied in real-world settings to determine whether the devices, in combination with mobile applications, could help identify cognitive and behavioural differences.
Evidation could obtain study participants’ consent to collect and analyse 16 terabytes of data across a number of sources by establishing a secure study platform.
The app included psychomotor tasks, such as reading, typing and dragging one shape onto another or tapping a circle.
The results show that Apple devices and digital applications may have the potential to monitor symptoms of people diagnosed with MCI or dementia and detect cognitive changes that could be indicative of MCI.
These applications may also be capable of testing the efficacy of treatments and therapies and accelerate their development used in conjunction with traditional diagnostic tools to improve the accuracy of diagnoses.