scholarly journals Applying time series analyses on continuous accelerometry data – a clinical example in older adults with and without cognitive impairment

2020 ◽  
Author(s):  
Torsten Rackoll ◽  
Konrad Neumann ◽  
Sven Passmann ◽  
Ulrike Grittner ◽  
Nadine Külzow ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Stimulation ◽  
Memory Consolidation ◽  
Activity Patterns ◽  
Accelerometer Data ◽  
Activity Data ◽  
Link Type ◽  
Early Afternoon ◽  
The Impact

AbstractIntroductionCurrent analysis approaches of accelerometry data use sum score measures which do not provide insight in activity patterns over 24 hours, and thus do not adequately depict circadian activity patterns. Here, we used a functional approach to analyze accelerometer data that models activity pattern and circadian rhythm. As a test case, we demonstrated its application in patients with mild cognitive impairment (MCI) and age-matched healthy older volunteers (HOV). Moreover, we assessed the impact of chronotype on distribution of activity data.MethodsData of two studies were pooled for this analysis. Following baseline cognitive assessment participants were provided with accelerometers for seven consecutive days. A function on scalar regression (FoSR) approach was used to analyze 24 hours accelerometer data. In a second step, analyses were controlled for chronotype using the German version of the morningness-eveningness questionnaire (d-MEQ).ResultsInformation on 47 HOV (mean age 66 SD 6 years) and 13 patients with MCI (mean age 69, SD 8 years) were available for this analysis. MCI patients displayed slightly higher activity in the morning hours as compared to HOV (maximum relative activity at 7:35 am: 75.6%, 95% CI 2.6 to 200.4%, p = 0.031). After controlling for d-MEQ, disturbed activity patterns were found in MCI of intermediate or evening chronotype, compared to HOV, i.e., MCI presented with higher activities in the morning hours (peak at 8:40 am: 357.6%, 95% CI 92.9 to 985.1, p < 0.001) and early afternoon hours (peak at 1:40 pm: 401.8%, 95% CI 63.9 to 1436.4, p < 0.001).DiscussionUsing a novel approach of FoSR, we found timeframes with higher activity levels in MCI patients compared to HOV which were not evident if sum scores of amount of activity were used. In addition, we found that previously described activity patterns as a function of chronotype swere altered in MCI patients, possibly indicating that changes in circadian rhythmicity in neurodegenerative disease are detectable using easy-to-administer accelerometry.Clinical TrialsEffects of Brain Stimulation During Nocturnal Sleep on Memory Consolidation in Patients With Mild Cognitive Impairments, https://clinicaltrials.gov/ct2/show/NCT01782391?term=NCT01782391&rank=1,ClinicalTrial.gov identifier: NCT01782391Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment,https://clinicaltrials.gov/ct2/show/NCT01782365?term=NCT01782365&rank=1,ClinicalTrial.gov identifier: NCT01782365

scholarly journals Applying time series analyses on continuous accelerometry data—A clinical example in older adults with and without cognitive impairment

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251544
Author(s):  
Torsten Rackoll ◽  
Konrad Neumann ◽  
Sven Passmann ◽  
Ulrike Grittner ◽  
Nadine Külzow ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Stimulation ◽  
Memory Consolidation ◽  
Activity Patterns ◽  
Relative Activity ◽  
Lower Activity ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Link Type

Introduction Many clinical studies reporting accelerometry data use sum score measures such as percentage of time spent in moderate to vigorous activity which do not provide insight into differences in activity patterns over 24 hours, and thus do not adequately depict circadian activity patterns. Here, we present an improved functional data analysis approach to model activity patterns and circadian rhythms from accelerometer data. As a use case, we demonstrated its application in patients with mild cognitive impairment (MCI) and age-matched healthy older volunteers (HOV). Methods Data of two studies were pooled for this analysis. Following baseline cognitive assessment participants were provided with accelerometers for seven consecutive days. A function on scalar regression (FoSR) approach was used to analyze 24 hours accelerometer data. Results Information on 48 HOV (mean age 65 SD 6 years) and 18 patients with MCI (mean age 70, SD 8 years) were available for this analysis. MCI patients displayed slightly lower activity in the morning hours (minimum relative activity at 6:05 am: -41.3%, 95% CI -64.7 to -2.5%, p = 0.031) and in the evening (minimum relative activity at 21:40 am: -48.4%, 95% CI -68.5 to 15.4%, p = 0.001) as compared to HOV after adjusting for age and sex. Discussion Using a novel approach of FoSR, we found timeframes with lower activity levels in MCI patients compared to HOV which were not evident if sum scores of amount of activity were used, possibly indicating that changes in circadian rhythmicity in neurodegenerative disease are detectable using easy-to-administer accelerometry. Clinical trials Effects of Brain Stimulation During Nocturnal Sleep on Memory Consolidation in Patients With Mild Cognitive Impairments, ClinicalTrial.gov identifier: NCT01782391. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment, ClinicalTrial.gov identifier: NCT01782365.

scholarly journals Transcranial stimulation enhances memory-relevant sleep oscillations and their functional coupling in mild cognitive impairment

2016 ◽  
Author(s):  
Julia Ladenbauer ◽  
Josef Ladenbauer ◽  
Nadine Külzow ◽  
Rebecca de Boor ◽  
Elena Avramova ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Memory Consolidation ◽  
Declarative Memory ◽  
Activity Patterns ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Sleep State ◽  
Sleep Physiology ◽  
Spindle Power

AbstractAlzheimer’s disease (AD) not only involves loss of memory functions but also prominent deterioration of sleep physiology, already evident in the stage ofmild cognitive impairment(MCI). Cortical slow oscillations (SO, 0.5-1 Hz) and thalamo-cortical spindle activity (12-15 Hz) during sleep, and their temporal coordination, are considered critical for memory formation. We investigated the potential of slow oscillatory transcranial direct current stimulation (so-tDCS), applied during a daytime nap in a sleep state-dependent manner, to modulate these activity patterns and sleep-related memory consolidation in 16 patients with MCI.Stimulation significantly increased overall SO and spindle power, amplified spindle power during SO up-phases, and led to stronger synchronization between SO and spindle power fluctuations in electroencephalographic recordings. Moreover, visual declarative memory was improved by so-tDCS compared to sham stimulation, associated with stronger synchronization. These findings indicate a well-tolerated therapeutic approach for disordered sleep physiology and deficits in memory consolidation in MCI patients.

scholarly journals Current State of Non-wearable Sensor Technologies for Monitoring Activity Patterns to Detect Symptoms of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rajaram Narasimhan ◽  
Muthukumaran G. ◽  
Charles McGlade
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Daily Activities ◽  
Wearable Sensor ◽  
Activity Data ◽  
Monitoring Activity

Mild cognitive impairment (MCI) could be a transitory stage to Alzheimer’s disease (AD) and underlines the importance of early detection of this stage. In MCI stage, though the older adults are not completely dependent on others for day-to-day tasks, mild impairments are seen in memory, attention, etc., subtly affecting their daily activities/routines. Smart sensing technologies, such as wearable and non-wearable sensors, coupled with advanced predictive modeling techniques enable daily activities/routines based early detection of MCI symptoms. Non-wearable sensors are less intrusive and can monitor activities at naturalistic environment with no interference to an individual’s daily routines. This review seeks to answer the following questions: (1) What is the evidence for use of non-wearable sensor technologies in early detection of MCI/AD utilizing daily activity data in an unobtrusive manner? (2) How are the machine learning methods being employed in analyzing activity data in this early detection approach? A systematic search was conducted in databases such as IEEE Explorer, PubMed, Science Direct, and Google Scholar for the papers published from inception till March 2019. All studies that fulfilled the following criteria were examined: a research goal of detecting/predicting MCI/AD, daily activities data to detect MCI/AD, noninvasive/non-wearable sensors for monitoring activity patterns, and machine learning techniques to create the prediction models. Out of 2165 papers retrieved, 12 papers were eligible for inclusion in this review. This review found a diverse selection of aspects such as sensors, activity domains/features, activity recognition methods, and abnormality detection methods. There is no conclusive evidence on superiority of one or more of these aspects over the others, especially on the activity feature that would be the best indicator of cognitive decline. Though all these studies demonstrate technological developments in this field, they all suggest it is far in the future it becomes an effective diagnostic tool in real-life clinical practice.

Protective preparation: a process central to family caregivers of persons with mild cognitive impairment

2017 ◽  
Vol 30 (3) ◽  
pp. 375-384 ◽  
Author(s):  
Ching-Lin Wang ◽  
Li-Min Kuo ◽  
Yi-Chen Chiu ◽  
Hsiu-Li Huang ◽  
Huei-Ling Huang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Family Caregivers ◽  
Caregiver Burden ◽  
Family Caregiving ◽  
Healthcare Providers ◽  
Theory Approach ◽  
Face To Face ◽  
The Family ◽  
The Impact

ABSTRACTBackground:To develop a theoretical model explaining the longitudinal changes in the caregiving process for family caregivers of persons with mild cognitive impairment (MCI) in Taiwan.Methods:A longitudinal, grounded theory approach using in-depth face-to-face interviews and an open-ended interview guide. We conducted 42 interviews over a two-year period; each participant was interviewed at least once every six months. All participants were interviewed in their home. The participants total of 13 family caregivers of persons with MCI.Results:One core theme emerged: “protective preparation.” This reflected the family caregiving process of preparation for a further decline in cognitive function, and protection from the impact of low self-esteem, accidents, and symptoms of comorbidities for the family member with MCI. Protective preparation contained three components: ambivalent normalization, vigilant preparation, and protective management.Conclusions:Interventions to help family caregivers manage the changes in persons with MCI can reduce caregiver burden. Our findings could provide a knowledge base for use by healthcare providers to develop and implement strategies to reduce caregiver burden for family caregivers of persons with MCI.

scholarly journals Cognitive Outcomes during COVID-19 confiNemeNt in Elderly and their Caregivers using Technologies for DEMentia (CONNECT DEM): study protocol (Preprint)

2020 ◽  
Author(s):  
Jessica Marian Goodman-Casanova ◽  
Elena Dura-Perez ◽  
Gloria Guerrero-Pertiñez ◽  
Pilar Barnestein-Fonseca ◽  
Jose Guzman-Parra ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Social Isolation ◽  
Community Dwelling ◽  
Mild Dementia ◽  
Care Services ◽  
Health And Social Care ◽  
The Impact ◽  
Community Dwelling Older Adults

BACKGROUND Coronavirus disease 2019 has forced worldwide the implementation of unprecedented restrictions to control its rapid spread and mitigate its impact. The Spanish government has enforced social distancing, quarantine and home confinement. This restriction of daily life activities and separation from loved ones may lead to social isolation and loneliness with health-related consequences in community-dwelling older adults with mild cognitive impairment or mild dementia and their caregivers. Additionally, an inadequate access to healthcare and social support services may aggravate chronic conditions. Technology home-based interventions emerge for combating social isolation and loneliness preventing the risk of viral exposure. OBJECTIVE The aim of this cohort study is to explore, analyze and determine the impact of social isolation on: 1) cognition, quality of life, mood, technophilia and perceived stress of community-dwelling older adults with mild cognitive impairment or mild dementia, and on caregiver burden; 2) health and social care services access and utilization, and 3) cognitive, social and entertainment use of ICTs. METHODS This study will be conducted in the Spanish region of Andalucía (Málaga). In total 200 dyads, consisting of a person with mild cognitive impairment or mild dementia (PMCI/MD) and their informal caregiver will be contacted by telephone. Potential respondents will be participants of the SMART 4 MD (N=100) and TV-AssistDem (N=100) clinical trials. RESULTS The change in means in the variables will be analyzed comparing baseline results in the previous studies with those during and after confinement using the ANOVA test of repeated measures or the non-parametric Friedman test if appropriate. The performance of a multivariate analysis of variance (ANCOVA) to introduce possible covariates will also be contemplated. A 95% confidence level will be used. CONCLUSIONS If the hypothesis is proven, these findings will demonstrate the negative impact of social isolation due to the COVID-19 confinement on cognition, quality of life, mood, and perceived stress of community-dwelling older adults with mild cognitive impairment and mild dementia, the impact on technophilia, caregiver burden, and health and social care services access and utilization; and the cognitive, social and entertainment use of ICTs during the COVID-19 confinement and afterwards. CLINICALTRIAL NCT: 04385797

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