BACKGROUND: Circadian and sleep disturbances are associated with increased risk of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Wearable activity trackers could provide a new approach in diagnosis and prevention.
OBJECTIVE: To evaluate sleep and circadian rhythm parameters, through wearable activity trackers, in MCI and AD patients as compared to controls, focusing on sex dissimilarities.
METHODS: Based on minute level data from consumer wearable devices, we analyzed actigraphic sleep parameters by applying an electromedical type I registered algorithm, and the corresponding circadian variables in 158 subjects: 86 females and 72 males (42 AD, 28 MCI, and 88 controls). Moreover, we used a confusion-matrix chart method to assess accuracy, precision, sensitivity, and specificity of two decision-tree models based on actigraphic data in predicting disease or health status.
RESULTS: Wake after sleep onset (WASO) was higher (p < 0.001) and sleep efficiency (SE) lower (p = 0.003) in MCI, and Sleep Regularity Index (SRI) was lower in AD patients compared to controls (p = 0.004). SE was lower in male AD compared to female AD (p = 0.038) and SRI lower in male AD compared to male controls (p = 0.008), male MCI (p = 0.047), but also female AD subjects (p = 0.046). Mesor was significantly lower in males in the overall population. Age reduced the dissimilarities for WASO and SE but demonstrated sex differences for amplitude (p = 0.009) in the overall population, controls (p = 0.005), and AD subjects (p = 0.034). The confusion-matrices showed good predictive power of actigraphic data.
CONCLUSION: Actigraphic data could help identify disease or health status. Sex (possibly gender) differences could impact on neurodegeneration and disease trajectory with potential clinical applications.