Feasibility and validity of accelerometer measurements to assess physical activity in toddlers

BACKGROUND

Accelerometers are considered to be the most promising tool for measuring physical activity (PA) in free-living young children. So far, no studies have examined the feasibility and validity of accelerometer measurements in children under 3 years of age. Therefore, the purpose of the present study was to examine the feasibility and validity of accelerometer measurements in toddlers (1- to 3-year olds).

METHODS

Forty-seven toddlers (25 boys; 20 ± 4 months) wore a GT1M ActiGraph accelerometer for 6 consecutive days and parental perceptions of the acceptability of wearing the monitor were assessed to examine feasibility. To investigate the validity of the ActiGraph and the predictive validity of three ActiGraph cut points, accelerometer measurements of 31 toddlers (17 boys; 20 ± 4 months) during free play at child care were compared to directly observed PA, using the Observational System for Recording Physical Activity in Children-Preschool (OSRAC-P). Validity was assessed using Pearson and Spearman correlations and predictive validity using area under the Receiver Operating Characteristic curve (ROC-AUC).

RESULTS

The feasibility examination indicated that accelerometer measurements of 30 toddlers (63.8%) could be included with a mean registration time of 564 ± 62 min during weekdays and 595 ± 83 min during weekend days. According to the parental reports, 83% perceived wearing the accelerometer as 'not unpleasant and not pleasant' and none as 'unpleasant'. The validity evaluation showed that mean ActiGraph activity counts were significantly and positively associated with mean OSRAC-P activity intensity (r = 0.66; p < 0.001; n = 31). Further, the correlation among the ActiGraph activity counts and the OSRAC-P activity intensity level during each observation interval was significantly positive (ρ = 0.52; p < 0.001; n = 4218). Finally, the three sedentary cut points exhibited poor to fair classification accuracy (ROC-AUC: 0.56 to 0.71) while the three light PA (ROC-AUC: 0.51 to 0.62) and the three moderate-to-vigorous PA cut points (ROC-AUC: 0.53 to 0.57) demonstrated poor classification accuracy with respect to detecting sedentary behavior, light PA and moderate-to-vigorous PA, respectively.

CONCLUSIONS

The present findings suggest that ActiGraph accelerometer measurements are feasible and valid for quantifying PA in toddlers. However, further research is needed to accurately identify PA intensities in toddlers using accelerometry.

Spectral analysis of accelerometry signals from a directed-routine for falls-risk estimation

Injurious falls are a prevalent and serious problem faced by a growing elderly population. Accurate assessment and long-term monitoring of falls-risk could prove useful in the prevention of falls, by identifying those at risk of falling early so targeted intervention may be prescribed. Previous studies have demonstrated the feasibility of using triaxial accelerometry to estimate the risk of a person falling in the near future, by characterizing their movement as they execute a restricted sequence of predefined movements in an unsupervised environment, termed a directed routine. This study presents an improvement on this previously published system, which relied explicitly on time-domain features extracted from the accelerometry signals. The proposed improvement incorporates features derived from spectral analysis of the same accelerometry signals; in particular the harmonic ratios between signal harmonics and the fundamental frequency component are used. Employing these additional frequency-domain features, in combination with the previously reported time-domain features, an increase in the observed correlation with the clinical gold-standard risk of falling, from = 0:81 to = 0:96, was achieved when using manually annotated event segmentation markers; using an automated algorithm to segment the signals gave corresponding results of = 0:73 and = 0:99, before and after the inclusion of spectral features. The strong correlation with falls-risk observed in this preliminary study further supports the feasibility of using an unsupervised assessment of falls-risk in the home environment.

Validation of the Taiwanese version of the Athens Insomnia Scale and assessment of insomnia in Taiwanese cancer patients

CONTEXT

It is well known that insomnia is highly prevalent in cancer patients. Although various studies have used the Athens Insomnia Scale (AIS) for insomnia assessment, it has never been applied to cancer patients with insomnia.

OBJECTIVES

The purpose of this study was to establish the reliability and validity of the Taiwanese AIS version (AIS-T) and evaluate the severity of insomnia among cancer patients in Taiwan.

METHODS

Using a cross-sectional research design, 195 cancer patients (n=195) were recruited from outpatient oncology clinics.

RESULTS

Cronbach's alpha for internal consistency was 0.83, and the test-retest reliability was 0.94 over an interval of three days, based on a sample of 30 patients. Moreover, concurrent validity could be evaluated by significant correlations of the AIS-T with the Pittsburgh Sleep Quality Index-Taiwan form (PSQI-T) (r=0.82, P<0.001) and sleep efficiency measured by Actiwatch parameters (r=-0.54, P<0.001). Construct validity could be established by the Brief Fatigue Inventory-Taiwan form (r=0.56, P<0.001) and Medical Outcomes Study Short Form-36-Taiwanese version (physical component summary: r=-0.52, P<0.001; mental component summary: r=-0.53, P<0.001). The AIS-T could detect significant known-group validity from sleep quality (PSQI-T ≥ 5 or <5, respectively). The Actiwatch parameters are consistent with the results of the AIS-T, and both data sets indicate that patients experienced sleep disturbances. The prevalence of insomnia, as defined by the criteria of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, 4th ed., was 22.56%; 49.2% subjects had significant insomnia at the score ≥ 6 at AIS-T.

CONCLUSION

This study concludes that the AIS-T is a reliable and valid instrument for assessing insomnia among cancer patients in Taiwan.

Physical activity in U.S.: adults compliance with the Physical Activity Guidelines for Americans

BACKGROUND

To date, no study has objectively measured physical activity levels among U.S. adults according to the 2008 Physical Activity Guidelines for Americans (PAGA).

PURPOSE

The purpose of this study was to assess self-reported and objectively measured physical activity among U.S. adults according to the PAGA.

METHODS

Using data from the NHANES 2005-2006, the PAGA were assessed using three physical activity calculations: moderate plus vigorous physical activity ≥150 minutes/week (MVPA); moderate plus two instances of vigorous physical activity ≥150 minutes/week (M2VPA); and time spent above 3 METs ≥500 MET-minutes/week (METPA). Self-reported physical activity included leisure, transportation, and household activities. Objective activity was measured using Actigraph accelerometers that were worn for 7 consecutive days. Analyses were conducted in 2009-2010.

RESULTS

U.S. adults reported 324.5 ± 18.6 minutes/week (M ± SE) of moderate physical activity and 73.6 ± 3.9 minutes/week of vigorous physical activity, although accelerometry estimates were 45.1 ± 4.6 minutes/week of moderate physical activity and 18.6 ± 6.6 minutes/week of vigorous physical activity. The proportion of adults meeting the PAGA according to M2VPA was 62.0% for self-report and 9.6% for accelerometry.

CONCLUSIONS

According to the NHANES 2005-2006, fewer than 10% of U.S. adults met the PAGA according to accelerometry. However, physical activity estimates vary substantially depending on whether self-reported or measured via accelerometer.

Parent-child relationship of directly measured physical activity

BACKGROUND

Studies on parent-child correlations of physical activity have been mixed. Few studies have examined concurrent temporal patterns of physical activity and sedentary behaviors in parents and children using direct measures. The purpose of this study was to examine parent-child activity correlations by gender, day of week, and time of day, using accelerometers - a method for direct assessment of physical activity.

METHODS

Accelerometers were used to assess physical activity and sedentary time in 45 fathers, 45 mothers and their children (23 boys, 22 girls, mean age 9.9 years) over the course of 4 days (Thursday - Sunday). Participants were instructed to wear accelerometers for 24 hours per day. Data from accelerometers were aggregated into waking hours on weekdays and weekends (6:00 am to midnight) and weekday after-school hours (3:00 - 7:00 pm).

RESULTS

Across the 4 days, the mean minutes per day of moderate-to-vigorous physical activity (MVPA) for fathers was 30.0 (s.d. = 17.3), for mothers was 30.1 (s.d. = 20.1) and for children was 145.47 (s.d. = 51.64). Mothers' and fathers' minutes of MVPA and minutes of sedentary time were positively correlated with child physical activity and sedentary time (all ps < .05, with the exception of mothers' and children's sedentary time on weekdays from 6 am to 12 am). Multivariate linear regression analyses resulted in significant effects between parents and children for MVPA across all time segments. For sedentary activity, significant associations were observed only between father and child on the weekend. Sedentary activity of parents and children were not related for other time segments. Models examining the associations of one or two parents with high levels of MVPA or sedentary time indicated a dose response increase in child activity relative to parent.

CONCLUSIONS

Greater parental MVPA was associated with increased child MVPA. In addition, having two parents with higher levels of MVPA was associated with greater levels of activity in children. Sedentary time in children was not as strongly correlated with that of their parents. Findings lend support to the notion that to increase childhood activity levels it may be fruitful to improve physical activity among parents.

Mindfulness-based stress reduction versus pharmacotherapy for chronic primary insomnia: a randomized controlled clinical trial

OBJECTIVE

The aim of this study was to investigate the potential of mindfulness-based stress reduction (MBSR) as a treatment for chronic primary insomnia.

DESIGN

A randomized controlled trial was conducted.

SETTING

The study was conducted at a university health center.

PATIENTS

Thirty adults with primary chronic insomnia based on criteria of the Diagnostic and Statistical Manual of Mental Disorders, Text Revision, 4th Edition were randomized 2:1 to MBSR or pharmacotherapy (PCT).

INTERVENTIONS

Mindfulness-based stress reduction, a program of mindfulness meditation training consisting of eight weekly 2.5 hour classes and a daylong retreat, was provided, with ongoing home meditation practice expectations during three-month follow-up; PCT, consisting of three milligrams of eszopiclone (LUNESTA) nightly for eight weeks, followed by three months of use as needed. A 10-minute sleep hygiene presentation was included in both interventions.

MAIN OUTCOMES

The Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), sleep diaries, and wrist actigraphy were collected pretreatment, posttreatment (eight weeks), and at five months (self-reports only).

RESULTS

Between baseline and eight weeks, sleep onset latency (SOL) measured by actigraphy decreased 8.9 minutes in the MBSR arm (P < .05). Large, significant improvements were found on the ISI, PSQI, and diary-measured total sleep time, SOL, and sleep efficiency (P < .01, all) from baseline to five-month follow-up in the MBSR arm. Changes of comparable magnitude were found in the PCT arm. Twenty-seven of 30 patients completed their assigned treatment. This study provides initial evidence for the efficacy of MBSR as a viable treatment for chronic insomnia as measured by sleep diary, actigraphy, well-validated sleep scales, and measures of remission and clinical recovery.

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Semi-supervised event detection using higher order statistics for multidimensional time series accelerometer data

In this study, we target to automatically detect stereotypical behavioral patterns (stereotypy) and self-injurious behaviors (SIB) of Autistic children which can lead to critical damages or wounds as they tend to repeatedly harm oneself. Our custom designed accelerometer based wearable sensors are placed at wrists, ankles and upper body to detect stereotypy and SIB. The analysis was done on four children diagnosed with ASD who showed repeated behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. Our goal of detecting novel events relies on the fact that the limitation of training data and variability in the possible combination of signals and events also make it impossible to design a single algorithm to understand all events in natural setting. Therefore, a semi-supervised method to discover and track unknown events in a multidimensional sensor data rises as a very important topic in classification and detection problems. In this paper, we show how the Higher Order Statistics (HOS) features can be used to design dictionaries and to detect novel events in a multichannel time series data. We explain our methods to detect novel events in a multidimensional time series data and combine the proposed semi-supervised learning method to improve the adaptability of the system while maintaining comparable detection accuracy as the supervised method. We, compare our results to the supervised methods that we have previously developed and show that although semi-supervised method do not achieve better performance compared to supervised methods, it can efficiently find new events and anomalies in multidimensional time series data with similar performance of the supervised method. We show that our proposed method achieves recall rate of 93.3% compared to 94.1% for the supervised method studied earlier.

Clinical Gait assessment of older adults using open platform tools

Gait impairment is associated with increased falls risk. The gait of 321 community dwelling elderly adults was assessed using the TRIL Gait Analysis Platform (GAP), which was specially designed for ease of use in a research clinic setting by non-experts. The GAP featured body-worn kinematic sensors, a pressure sensitive electronic walkway, and two orthogonally mounted web cameras, and was developed using open platform tools. This flexible platform was applied to objectively measure gait parameters in different gait assessments. The results from the 6 meter walk assessment are presented here. In this assessment, participants were categorized by clinical falls history as 'fallers' or 'non-fallers'. Temporal and spatial gait parameters were examined. Significant differences in spatial parameters were observed when fallers and non-fallers were compared. Temporal parameters were found to differ, though not significantly.

Feasibility assessment of Doppler radar long-term physiological measurements

In this paper we examine the feasibility of applying doppler radar technique for a long-term health monitoring. Doppler radar was used to detect and eliminate periods of significant motion. This technique was verified using a human study on 17 subjects, and it was determined that for 15 out of 17 subjects there was no significant motion for over 85% of the measurement interval in supine positions. Majority of subjects exhibited significantly less motion in supine position, which is promising for sleep monitoring, and monitoring of hospitalized patients.

Development of an automated physical activity classification application for mobile phones

BACKGROUND

Physical activity classification is an objective approach to assess levels of physical activity, and indicates an individual's degree of functional ability. It is significant for a number of the disciplines, such as behavioural sciences, physiotherapy, etc. Accelerometry is found to be a practical and low cost method for activity classification that could provide an objective and efficient measurement of people's daily activities.

METHODS

This paper utilises a mobile phone with a built-in tri-axial accelerometer sensor to automatically classify normal physical activities. A rule-based activity classification model, which can recognise 4 common daily activities (lying, walking, sitting, and standing) and 6 transitions between postural orientations, is introduced here. In this model, three types of statuses (walking/ transition, lying, and sitting/standing) are first classified based on the kinetic energy and upright angle. Transitions are then separated from walking and assigned to the corresponding type using upright angle algorithm. To evaluate the performance of this developed application, a trial is designed with 8 healthy adult subjects, who are required to perform a 6-minute activity routine with an iPhone fixed at the waist position.

RESULTS

Based on the evaluation result, our application measures the length of time of each activity accurately and the achieved sensitivity of each activity classification exceeds 90% while the achieved specificity exceeds 96%. Meanwhile, regarding the transition identification, the sensitivities are high in stand-to-sit (80%) and low in sit-to-stand (56%).

Circadian rest-activity rhythm for maintenance of body shape

A recently developed wearable device has gained attention in the area of self-discipline for the prevention of lifestyle-related diseases. The present study aimed to clarify the relationship between circadian rhythm and body shape change using actigraphy. Using a body shape vector, we classified 24 women in their 40s and 50s into 3 groups with different body shape changes. A circadian rhythm experiment was conducted on weekdays for 1 week with 24 healthy women. Amounts of activity of the non-dominant wrist and trunk, subjective evaluation of sleep quality, and subjective state of activity were surveyed. In order to maintain a constant body shape throughout life, a less sedentary lifestyle with more trunk movement during the day, getting adequate sleep at night, and having a varied sleep-wake cycle may be important factors.

A semi-supervised Hidden Markov model-based activity monitoring system

Most existing human activity classification systems require a large training dataset to construct statistical models for each activity of interest. This may be impractical in many cases. In this paper, we proposed a semi-supervised HMM based activity monitoring system, that adapts the HMM for a specific subject from a general model in order to alleviate the requirement of a large training data set. In addition, using two triaxial accelerometers, our system not only identifies simple events such as sitting, standing and walking, but also recognizes the behavior or a more complex activity by temporally linking the events together. Experimental results demonstrate the feasibility of our proposed system.

Gait assessment in Parkinson's disease patients through a network of wearable accelerometers in unsupervised environments

Parkinson's disease (PD) predominantly alters the motor performance of the affected individuals. In particular, the loss of dopaminergic neurons compromises the speed, the automaticity and fluidity of movements. As the disease evolves, PD patient's motion becomes slower and tremoric and the response to medication fluctuates along the day. In addition, the presence of involuntary movements deteriorates voluntary movement in advanced state of the disease. These changes in the motion can be detected by studying the variation of the signals recorded by accelerometers attached in the limbs and belt of the patients. The analysis of the most significant changes in these signals make possible to build an individualized motor profile of the disease, allowing doctors to personalize the medication intakes and consequently improving the response of the patient to the treatment. Several works have been done in a laboratory and supervised environments providing solid results; this work focused on the design of unsupervised method for the assessment of gait in PD patients. The development of a reliable quantitative tool for long-term monitoring of PD symptoms would allow the accurate detection of the clinical status during the different PD stages and the evaluation of motor complications. Besides, it would be very useful both for routine clinical care as well as for novel therapies testing.

Socializing by Day May Affect Performance by Night: Vulnerability to Sleep Deprivation is Differentially Mediated by Social Exposure in Extraverts vs Introverts

STUDY OBJECTIVES

to examine the effects of socially enriched versus socially impoverished environments on performance and alertness decline during sleep deprivation in extraverts versus introverts.

DESIGN

participants (n = 29 men, n = 19 women) were assigned to socially enriched (n = 24; 13 introverts, 11 extraverts) or socially impoverished (n = 24; 12 introverts, 12 extraverts) conditions (activities matched) for 12 hours (1000-2200) on Day 1 followed by 22 hours of sleep deprivation (2200-2000; 36 h awake total), monitored by actigraphy. The median split of volunteers' Eysenck Extraversion scores was used for extravert/introvert categorization. The Psychomotor Vigilance Task (PVT), modified Maintenance of Wakefulness Test (MWT), and Stanford Sleepiness Scale (SSS) were administered every 2 hours throughout. PVT speed, transformed lapses, modified MWT sleep-onset latency, and SSS were analyzed using mixed-model analyses of variance, with covariates of age and total actigraphic activity during enrichment or impoverishment.

SETTING

residential sleep/performance testing facility.

PARTICIPANTS

forty-eight healthy adults (aged 18-39).

INTERVENTIONS

Twelve hours of socially enriched or isolated environments in extraverts and introverts prior to sleep deprivation.

RESULTS

Social experience interacted with personality type to affect alertness and vigilance. Social enrichment, as compared with social impoverishment, was associated with more PVT lapses at 04:00 overall. Similarly, following social enrichment, PVT speed was significantly slower among extraverts than among introverts during sleep deprivation, but no personality-group differences emerged following social impoverishment. MWT sleep latency and SSS subjective sleepiness did not show significant personality or social-condition effects during sleep deprivation.

CONCLUSIONS

the effect of social exposure on vulnerability or resiliency to sleep deprivation was modulated by introversion and extraversion. Extraverts exposed to social environments were more vulnerable to subsequent sleep deprivation than were introverts.

Rest-activity disturbances in children with septo-optic dysplasia characterized by actigraphy and 24-hour plasma melatonin profiles

INTRODUCTION

A trial of melatonin treatment in children with septo-optic dysplasia (SOD) and sleep disruption is accepted clinical practice in many centers. However, no objective measurements of sleep/activity patterns with 24-h melatonin profiles have been published for these individuals, and the pathophysiological basis underlying sleep disorders in SOD remains largely unknown.

METHODS

We studied six children with rest-activity disturbances and SOD. All wore an Actiwatch-Mini (a noninvasive method of detecting and recording movement intensity) for 2 wk and were admitted to hospital for a 24-h period during which hourly measurements of serum melatonin were taken. Sleep data were analyzed in conjunction with a detailed sleep diary. Ethical approval was obtained for these studies.

RESULTS

Two children produced virtually no melatonin throughout the 24-h period of measurement and had fragmented sleep patterns with no evidence of a non-24-h sleep-wake disorder or delayed sleep-phase disorder. One child had a normal melatonin profile despite actigraphy showing an arrhythmic sleep pattern. The remaining three children had fragmented sleep, with two having normal melatonin profiles and one having a modest increase in daytime melatonin concentrations, making the timing of dim-light melatonin onset difficult to discern.

CONCLUSIONS

There is considerable variation in timing and amount of melatonin secretion in these children. Surprisingly, none of the children had either actigraphic or melatonin profile evidence of a non-24-h sleep-wake disorder or delayed sleep-phase disorder. Understanding the heterogeneous nature of underlying sleep disorders in this group of children is important and has implications for their management.

Real-life walking impairment in multiple sclerosis: preliminary comparison of four methods for processing accelerometry data

This study further validates accelerometers as a measure of walking impairment in persons with multiple sclerosis. We examined total movement counts and three novel methods of processing accelerometer data (i.e. standard deviation, approximate entropy and detrended fluctuation analysis) for quantifying real-life walking impairment in this population. A total of 70 individuals with a definite diagnosis of multiple sclerosis completed a battery of patient-rated measures of walking impairment and then wore an ActiGraph accelerometer for 7 days. The data were analyzed using multivariate analysis of variance and bivariate correlation analysis. The results indicated that total daily movement counts and standard deviation of daily movement counts differed between groups of persons with mild, moderate, and severe self-reported disability status and who were independently ambulatory or ambulatory with assistance. Those two metrics for the accelerometer data further demonstrated strong correlations with patient-rated measures of walking impairment. By comparison, there were smaller and often non-significant differences in approximate entropy and detrended fluctuation analysis metrics for the accelerometer data as a function of disability and ambulatory status, and only moderate correlations with patient-rated measures of walking impairment. The results confirm that the metric of total daily movement counts correlates with level of disability, ambulatory status, and patient reports of walking impairment in persons with multiple sclerosis. We further demonstrate that variability, indexed by the standard deviation of daily movement counts, correlates with multiple sclerosis-related disability, ambulatory status, and self-reported walking impairment. Such results provide preliminary evidence that variability in accelerometer counts is not simply noise and may provide important information about multiple sclerosis-related walking impairment.

Marital/cohabitation status and history in relation to sleep in midlife women

STUDY OBJECTIVES

To examine whether current and/or history of marital/cohabitation status are associated with sleep, independent of demographic and general health risk factors.

DESIGN

Longitudinal, observational study of women, with sleep measured via multi-night in-home polysomnography and up to 35 nights of actigraphy.

SETTING

Participants' homes.

PARTICIPANTS

Caucasian (n = 170), African American (n = 138), and Chinese women (n = 59); mean age 51 years.

INTERVENTIONS

None.

MEASUREMENTS

Sleep quality was assessed via questionnaire. Sleep duration, continuity, and architecture were calculated using in-home polysomnography (PSG). Sleep continuity was also assessed by actigraphy. Categories of marital/cohabiting status or changes in status were inclusive of women who were legally married or living as married as well as transitions into or out of those partnership categories.

RESULTS

Partnered (married or cohabiting) women at the time of the sleep study had better sleep quality and PSG and actigraphy-assessed sleep continuity than unpartnered women; however, with covariate adjustment, most of these associations became non-significant. Analyses of women's relationship histories over the 6-8 years prior to the sleep study showed advantages in sleep for women who were consistently partnered versus women who were unpartnered throughout this interval, or those who had lost or gained a partner over that time course. These results persisted after adjusting for potential confounders.

CONCLUSIONS

The stable presence of a partner is an independent correlate of better sleep quality and continuity in women.

Ambulatory monitoring of sleep disorders

BACKGROUND

Behavioural and functional activity monitoring has a long history in sleep research. The term "Actigraphy" refers to methods using computerized wristwatch-size devices (generally placed on the wrist, but also on the ankle or trunk) to record the movement it undergoes. Collected data are displayed on a computer and analyzed for change in rhythm parameters that in turn provide an estimate on wake-sleep parameters (such as total sleep time, percent of time spent asleep, total wake time, percent of time spent awake and the number of awakenings). Actigraphy provides a useful, cost-effective, non-invasive and portable method for assessing specific sleep disorders. The present review is an amalgam of current knowledge with proposed clinical application and for research of actigraph.

CONCLUSION

Actigraphy cannot stand alone as a diagnostic tool for all clinical groups. Particularly so with those diagnosed with sleep disorders with significant motility or long catatonic periods of wakefulness during sleep.

Preliminary results on the effect of sensor position on unobtrusive rollover detection for sleep monitoring in smart homes

Older adults experience increased sleep movement disorders and sleep fragmentation, and these are associated with serious health consequences such as falls. Monitoring sleep fragmentation and restlessness in older adults can reveal information about their daily and long-term health status. Long-term home monitoring is only realistic within the contact of unobtrusive, non-contact sensors. This paper presents exploratory work using the pressure sensor array as an instrument for rollover detection. The sensor output is used to calculate a center of gravity signal, from which five features are extracted. These features are used in a decision tree to classify detected movements in two categories; rollovers and other movements. Rollovers were detected with a sensitivity and specificity of 82% and 100% respectively, and a Mathew's correlation coefficient of 0.86 when data from all sensor positions were included. Intrapositional and interpositional effects of movements on sensors placed throughout the bed are described.

Subjective perception of sleep, but not its objective quality, is associated with immediate postpartum mood disturbances in healthy women

STUDY OBJECTIVES

This study investigated whether there was a relationship between disrupted sleep and postpartum mood disturbances in women during the week after delivery.

DESIGN

Sleep and mood were measured during the third trimester (Time-1) and one week postpartum (Time-2) in a 2-stage longitudinal design.

SETTING

Participants were recruited from an antenatal clinic in a regional Melbourne hospital.

PARTICIPANTS

Forty-four healthy women at low risk for postpartum depression.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Objective sleep was measured by actigraphy and subjective sleep by the Pittsburgh Sleep Quality Index; mood was assessed by the Depression Anxiety Stress Scale, the Hospital Anxiety Depression Scale, and the Positive and Negative Affect Schedule. Sleep and mood questionnaires were administered at Time-1 and Time-2. Wrist actigraphy was collected for one week at both times. After delivery, both objective and subjective nighttime sleep significantly worsened with decreased total sleep time and sleep efficiency, while daytime napping behavior significantly increased. On average, mood improved across all scales after delivery, although 45.95% of the sample experienced deterioration of mood. Regression analyses showed little relationship between Time-1 and Time-2 objective nighttime sleep, and postpartum mood. Variables that related to both Time-1 and Time-2 subjective perception of sleep, including subjective nighttime sleep, sleep-related daytime dysfunction, and daytime napping behavior, were significant predictors of postpartum mood.

CONCLUSIONS

The perception of poor sleep, and the conscious awareness of its impact during wake-time, might share a stronger relationship with the occurrence of immediate postpartum mood disturbances than actual sleep quality and quantity.

Sleep disordered breathing, daytime symptoms, and functional performance in stable heart failure

STUDY OBJECTIVES

To evaluate characteristics of sleep disordered breathing (SDB); clinical and demographic correlates of SDB; and the extent to which SDB explains functional performance and symptoms in stable heart failure patients receiving care in structured HF disease management programs.

DESIGN

Cross-sectional, observational study.

SETTING

Structured heart failure disease management programs.

PARTICIPANTS

170 stable chronic heart failure patients (mean age = 60.3 +/- 16.8 years; n = 60 [35%] female; n = 50 [29%] African American; left ventricular ejection fraction mean = 32 +/- 14.6).

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Full polysomnography was obtained for one night on participants in their homes. Participants completed the 6-minute walk, 3 days of actigraphy, MOS-SF 36, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Multi-Dimensional Assessment of Fatigue Scale, and the Centers for the Epidemiological Studies of Depression Scale. Fifty-one percent had significant SDB; Sixteen (9%) of the total sample had central sleep apnea. Severe SDB was associated with a 4-fold increase in the likelihood of poor self-reported physical function (OR = 4.15, 95%CI = 1.19-14.57) and CSA was associated with low levels of daytime mobility (OR = 4.09, 95%CI = 1.23-13.62) after controlling for clinical and demographic variables. There were no statistically significant relationships between SDB and daytime symptoms or self-reported sleep, despite poorer objective sleep quality in patients with SDB.

CONCLUSIONS

Severe SDB is associated with poor physical function in patients with stable HF but not with daytime symptoms or self-reported sleep, despite poorer objective sleep quality in patients with SDB.

Sleep actigraphy pattern and behavioral/emotional difficulties in kindergarten children: association with hypothalamic-pituitary-adrenocortical (HPA) activity

BACKGROUND

Various studies of adult endocrinology and sleep show close connections between poor sleep quality, deterioration of the HPA axis and negative psychological characteristics. However, the extent to which these associations may have already emerged and developed in childhood remains unclear.

METHODS

A total of 82 preschoolers (age 4.91+/-0.48) underwent activity monitoring for seven consecutive days and nights, wearing a digital movement-measuring instrument. Additionally, on the first and on the last morning of sleep registration, the activity of the HPA axis was assessed via the amount of cortisol in the saliva. Psychological and behavioral assessments were also made.

RESULTS

Three sub-groups of good (22%), normal (58.5%) and poor (19.5%) sleepers were distinguished. Poor sleep patterns were associated with higher HPA activity and with behavioral/emotional difficulties.

CONCLUSIONS

The interplay between unfavorable sleep patterns, deterioration of the HPA axis and behavioral/emotional difficulties is already apparent in pre-school children.

Characterization of activity epochs in actimetric registries for infantile colic diagnosis: Identification and feature extraction based on wavelets and symbolic dynamics

The diagnosis and therapy planning of high prevalence pathologies such as infantile colic can be substantially improved by statistical signal processing of activity/rest registries. Assuming that colic episodes are associated to activity episodes, diagnosis aid systems should be based on preprocessing techniques able to separate real activity from rest epochs, and feature extraction methods to identify meaningful indices with diagnostic capabilities. In this paper, we propose a two step diagnosis aid methodology for infantile colic in children below 3 months old. Identification of activity periods is performed by means of a wavelet based activity filter which does not depend on the acquisition device (as so far proposed methods do). In addition, symbolic dynamic analysis is used for extraction of discriminative indices from the activity time series. Results on real data yielded 100% sensitivity and 80% specificity in a study group composed of 46 cases and 10 control subjects.

Statistical characterization of actigraphy data during sleep and wakefulness states

Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire week. In this paper the actigraphy data is described by a weighted mixture of two distributions where the weight evolves along the day according to the patient circadian cycle. Thus, one of the distributions is mainly associated with the wakefulness state while the other is associated with the sleep state. Actigraphy data, acquired from 20 healthy patients and manually segmented by trained technicians, is used to characterize the acceleration magnitude during sleep and wakefulness states. Several mixture combinations are tested and statistically validated with conformity measures. It is shown that both distributions can co-exist at a certain time with varying importance along the circadian cycle.