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.
Sex‐differences in neural responses to experimental pain in Alzheimer's disease
