Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed

Abstract

While the negative association between physical activity and depression has been well established, it is unclear what precise characteristics of physical activity patterns explain this association. Complexity measures may identify previously unexplored aspects of objectively measured activity patterns, such as the extent to which individuals show repetitive periods of physical activity and the diversity in durations of such repetitive activity patterns. We compared the complexity levels of actigraphy data gathered over 4 weeks ($$\sim 40000$$ ∼ 40000 data points each) for every individual, from non-depressed ($$n=25$$ n = 25 ) and depressed ($$n=21$$ n = 21 ) groups using recurrence plots. Significantly lower levels of complexity were detected in the actigraphy data from the depressed group as compared to non-depressed controls, both in terms of lower mean durations of periods of recurrent physical activity and less diversity in the duration of these periods. Further, diagnosis of depression was not significantly associated with mean activity levels or measures of circadian rhythm stability, and predicted depression status better than these.

Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

“…In our findings, the magnitude of significant differences (effect size) in digital biomarkers between the depressed and non-depressed participants are interpreted as small to medium effect sizes (see Section 4.2). In line with our findings, medium effect sizes of significant differences in physical activity biomarkers have been reported in [51]. Our results suggest that digital biomarkers quantified from passively sensed smartphone and wearable data could be used to differentiate between the depressed and non-depressed to support current clinical care by recognising and monitoring depression without relying on the patient's ability to recall their behaviour and mood.…”

“…Previous work found statistically significant differences in mood and digital biomarkers when comparing depressed and non-depressed participants. More specifically, people with symptoms of depression showed significantly lower mood [35,37,38], reduced physical activity [35,51], longer sleep time [16], reduced location mobility [7] and increased phone usage [3,7] compared to people without symptoms of depression. In our findings, the magnitude of significant differences (effect size) in digital biomarkers between the depressed and non-depressed participants are interpreted as small to medium effect sizes (see Section 4.2).…”

Building and Realizing the Obstetrics and Gynecology Nursing Management Platform by Computer Information Technology under the Concept of Mindfulness Intervention

“…Relevant studies have shown that 82% of pregnant women have sleep quality problems during pregnancy, manifested as difficulty in falling asleep, insomnia, and an increased number of night awakenings. Sleep problems and negative emotions will not only affect the physical and mental health of pregnant women but also cause anxiety [ 4 ], depression [ 5 ], and other symptoms of pregnant women and have a continuous impact on the growth and development of the fetus. Severe sleep problems of pregnant women can cause undesirable consequences such as premature birth [ 6 , 7 ] and abortion [ 8 ] and bring heavy spiritual and economic burdens to pregnant women's families and society.…”

Multiscale entropy analysis of retinal signals reveals reduced complexity in a mouse model of Alzheimer’s disease

“…This theory postulates that, as stated by Goldberger et al 24 : 1) “the output of healthy systems (...) reveals a type of complex variability associated with long-range correlations”, and 2) “this type of multiscale, nonlinear complexity breaks down with aging and disease, reducing the adaptive capabilities of the individual”. Recent evidence supports this theory as, for instance, entropy measures of cerebral blood flow velocity decayed in altered states 27 , actigraphy data exhibited lower complexity in depressed subjects as compared to non-depressed controls 28 , and complexity of EEG signals from subjects with AD correlated well with performance in working memory tasks 29 . MSE analysis can have significant implications in biomedical research as several studies have demonstrated its application to detection of heartbeat irregularity 22 , 30 , postural control alterations 31 and postoperative neurocognitive dysfunction 32 , and diagnosis of depression 28 , Parkinson’s disease 33 , schizophrenia 34 , and AD 35 .…”