Academic literature on the topic 'Sleep measurement tools'

Movement behaviours (physical activity, sedentary behaviour, and sleep) are important for the health and development of pre-school children (aged 3–4 years). There is limited qualitative research examining the acceptability and feasibility of tools used to assess movement behaviours in pre-schoolers. This study explored parental views on various measurement tools in three deprived areas in England, UK (West Yorkshire, County Durham and Northumberland). The study consisted of a demonstration of the different tools (accelerometers, a diary and a questionnaire), directly followed by focus group discussions. Three focus group discussions with a total of eleven parents and carers were transcribed verbatim and analysed using thematic analysis. Findings revealed four main themes: (1) importance of contextual information when using any measurement tool (e.g., child illness, capturing different routines); (2) practical issues associated with devices (e.g., aversion to devices being attached directly to the skin of their child; concern of larger devices during sleep time); (3) encouraging children to wear a device (e.g., making devices attractive to children—‘superpowers’); and (4) presentation of diaries and questionnaires (e.g., age-appropriate movement activities, preference for real-time recording over recall). Practical recommendations for the use of the tools to measure movement behaviours of pre-school children are provided.

Objectives:To collate and appraise the use of subjective measures to assess sleep in the intensive care unit (ICU).Design:A systematic search and critical review of the published literature.Data Sources:Medline, Scopus, and Cumulative Index to Nursing and Allied Health Literature were searched using combinations of the key words “Sleep,” “Critical Care,” “Intensive Care,” and “Sleep Disorders,” and this was complemented by hand searching the most recent systematic reviews on related topics.Study Eligibility Criteria:Papers were limited to non-gray English-language studies of the adult population, published in the last 10 years.Outcome Measures:Primary outcomes were the number and categorization of quantitative studies reporting measures of sleep, the number of participants for each data collection method, and a synthesis of related material to appraise the use of survey tools commonly used for sleep measurement in the ICU.Results:Thirty-eight papers reported quantitative empirical data collection on sleep, 17 of which used a primary method of subjective assessment of sleep by the patient or nurse. Thirteen methods of subjective sleep assessment were identified. Many of these tools lacked validity and reliability testing.Conclusions:Research using questionnaires to assess sleep is commonplace in light of practical barriers to polysomnography or other measures of sleep. A methodologically sound approach to tool development and testing is crucial to gather meaningful data, and this robust approach was lacking in many cases. Further research measuring sleep subjectively in ICU should use the Richards Campbell Sleep Questionnaire, and researchers should maintain a commitment to transparency in describing methods.

ObjectivesConsumer-grade smart devices are now commonly used by the public to measure waking activity and sleep. However, the ability of these devices to accurately measure sleep in clinical populations warrants more examination. The aim of the present study was to assess the accuracy of three consumer-grade sleep monitors compared with gold standard polysomnography (PSG).DesignA prospective cohort study was performed.SettingAdults undergoing PSG for investigation of a suspected sleep disorder.Participants54 sleep-clinic patients were assessed using three consumer-grade sleep monitors (Jawbone UP3, ResMed S+ and Beddit) in addition to PSG.OutcomesJawbone UP3, ResMed S+ and Beddit were compared with gold standard in-laboratory PSG on four major sleep parameters—total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO) and sleep efficiency (SE).ResultsThe accelerometer Jawbone UP3 was found to overestimate TST by 28 min (limits of agreement, LOA=−100.23 to 157.37), with reasonable agreement compared with gold standard for TST, WASO and SE. The doppler radar ResMed S+ device underestimated TST by 34 min (LOA=−257.06 to 188.34) and had poor absolute agreement compared with PSG for TST, SOL and SE. The mattress device, Beddit underestimated TST by 53 min (LOA=−238.79 to 132) on average and poor reliability compared with PSG for all measures except TST. High device synchronisation failure occurred, with 20% of recordings incomplete due to Bluetooth drop out and recording loss.ConclusionPoor to moderate agreement was found between PSG and each of the tested devices, however, Jawbone UP3 had relatively better absolute agreement than other devices in sleep measurements compared with PSG. Consumer grade devices assessed do not have strong enough agreement with gold standard measurement to replace clinical evaluation and PSG sleep testing. The models tested here have been superseded and newer models may have increase accuracy and thus potentially powerful patient engagement tools for long-term sleep measurement.

Objective The objective of this study was to develop patient-reported outcome measures for sleep dysfunction and sleepiness in multiple sclerosis (MS), since there are currently no MS-specific measurement tools for these clinically important entities. Methods Items were generated from semi-structured interviews followed by cognitive debrief. A 42-item pool was administered to patients with MS at three neuroscience centres in the UK. Comparator scales were co-administered. Constructs were validated by Rasch analysis, guided by initial exploratory factor analysis. Results There were two supraordinate qualitative themes of diurnal sleepiness and non-restorative nocturnal sleep. Rasch analysis on 722 records produced three scales, which corresponded to diurnal sleepiness, non-restorative nocturnal sleep and fragmented nocturnal sleep. All had excellent fit parameters, were unidimensional and were free from differential item functioning. A summed raw score cut-point of 31/48 in the Diurnal Sleepiness Scale equated to the standard cut-point of 10 on the Epworth Sleepiness Scale (ESS). Conclusion Three high-quality measurement scales were developed, and together they compose the Neurological Sleep Index for MS (NSI-MS). The Diurnal Sleepiness Scale might provide an alternative to the ESS. The Non-Restorative Nocturnal Sleep Scale and the Fragmented Nocturnal Sleep Scale appear to be the only such measures for use in MS.

Purpose:To assess measurement sensitivity and diagnostic characteristics of athlete-monitoring tools to identify performance change.Methods:Fourteen nationally competitive swimmers (11 male, 3 female; age 21.2 ± 3.2 y) recorded daily monitoring over 15 mo. The self-report group (n = 7) reported general health, energy levels, motivation, stress, recovery, soreness, and wellness. The combined group (n = 7) recorded sleep quality, perceived fatigue, total quality recovery (TQR), and heart-rate variability. The week-to-week change in mean weekly values was presented as coefficient of variance (CV%). Reliability was assessed on 3 occasions and expressed as the typical error CV%. Week-to-week change was divided by the reliability of each measure to calculate the signal-to-noise ratio. The diagnostic characteristics for both groups were assessed with receiver-operating-curve analysis, where area under the curve (AUC), Youden index, sensitivity, and specificity of measures were reported. A minimum AUC of .70 and lower confidence interval (CI) >.50 classified a “good” diagnostic tool to assess performance change.Results:Week-to-week variability was greater than reliability for soreness (3.1), general health (3.0), wellness% (2.0), motivation (1.6), sleep (2.6), TQR (1.8), fatigue (1.4), R-R interval (2.5), and LnRMSSD:RR (1.3). Only general health was a “good” diagnostic tool to assess decreased performance (AUC –.70, 95% CI, .61–.80).Conclusion:Many monitoring variables are sensitive to changes in fitness and fatigue. However, no single monitoring variable could discriminate performance change. As such the use of a multidimensional system that may be able to better account for variations in fitness and fatigue should be considered.

Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.

Data at the world health organization shows that around more than 100 million people worldwide suffer from Obstructive Sleep Apnea (OSA) and most sufferers go undetected. The purpose of this study is to develop an apnea monitor that can detect the symptoms of Obstructive Sleep Apnea using the Telemedicine Smartphone System (TmSS). The contribution of this study is the generation of telemedicine systems in sleep apnea monitoring devices. So that the monitoring tools can be effective and efficient when used, then the Obstructive Sleep Apnea monitoring tool is made with the TmSS system that can transmit data values wirelessly and in real-time. This tool uses a flex sensor to detect patient breathing. The output generated by the sensor is then conditioned on an analog signal conditioner (PSA) circuit. The signal output from the PSA is processed on the ATMega 328 microcontroller to get the respiration value which is then sent via Bluetooth HC-05 and displayed on an android smartphone device which will also display a warning notification in the event of stopping breathing (apnea). Based on the results of the measurement of respiration values compared with standard equipment produces the highest error value of 6.98% and the lowest of 0.00%. The tool can send data respiration values using the TmSS system properly. This tool can be implemented to detect the symptoms of Obstructive Sleep Apnea.

Purpose: To examine the measurement reliability and sensitivity of common athlete monitoring tools in professional Australian Football players. Methods: Test–retest reliability (noise) and weekly variation (signal) data were collected from 42 professional Australian footballers from 1 club during a competition season. Perceptual wellness was measured via questionnaires completed before main training sessions (48, 72, and 96 h postmatch), with players providing a rating (1–5 Likert scale) regarding their muscle soreness, sleep quality, fatigue level, stress, and motivation. Eccentric hamstring force and countermovement jumps were assessed via proprietary systems once per week. Heart rate recovery was assessed via a standard submaximal run test on a grass-covered field with players wearing a heart rate monitor. The heart rate recovery was calculated by subtracting average heart rate during final 10 seconds of rest from average heart rate during final 30 seconds of exercise. Typical test error was reported as coefficient of variation percentage (CV%) and intraclass coefficients. Sensitivity was calculated by dividing weekly CV% by test CV% to produce a signal to noise ratio. Results: All measures displayed acceptable sensitivity. Signal to noise ratio ranged from 1.3 to 11.1. Intraclass coefficients ranged from .30 to .97 for all measures. Conclusions: The heart rate recovery test, countermovement jump test, eccentric hamstring force test, and perceptual wellness all possess acceptable measurement sensitivity. Signal to noise ratio analysis is a novel method of assessing measurement characteristics of monitoring tools. These data can be used by coaches and scientists to identify meaningful changes in common measures of fitness and fatigue in professional Australian football.

Concerns about sleep problems and falls in older adults are significant. This article reviews the association between sleep problems and falls in community-dwelling adults aged 65 years or older. Multiple databases were searched from inception until 2015 using sleep, sleep disorders, and falls as keywords, limiting to studies published in English in peer-reviewed journals. After screening and assessing for eligibility, 18 articles were selected based on the inclusion and exclusion criteria. Findings of an association between sleep problems and risk of falls are conflicting, but some specific sleep problems such as extremely short sleep duration, daytime sleepiness and naps appear to be significantly related to falls in older adults. Methodological limitations including variability in covariates included in the analyses and measurement of the exposure and outcome variables were identified. The results of this review identified the need to have comparable definitions, validated tools, and rigorous design of future studies.

Abstract Sleep is a complex process, sensitive to aging, with theoretical and evidentiary basis for influence on multiple health outcomes. Recent scholarship has argued for a ‘multi-dimensional’ approach to sleep health, that is, a recognition that healthy sleep consists of more than its quantity (duration) and is more than the absence of sleep disorders. This new conception of sleep health acknowledges sleep’s complexity yet presents challenges for methodological treatment. How do we operationalize/analyze multiple dimensions of sleep, some of which are correlated due to physiological reasons, common measurement tools, or sensitivity to common stressors? Is it sensible to talk about ‘sleep health’ as a single, composite entity with multiple components, akin to a dietary pattern rather than a collection of individual nutrients? Exemplar data from a racial-ethnic disparities project in aging adults suggest the utility of a composite approach, and the value of considering inter-correlations among sleep metrics.

This chapter is a primer on sleep epidemiology—the methods of assessment on how sleep is measured (e.g., self-report [such as the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale] vs. with use of objective tools such as actigraphy); validity of sleep measurements; the different dimensions of sleep health and disorders that are of interest (e.g., sleep duration, sleep quality, sleep fragmentation, insomnia, obstructive sleep apnea, social jetlag, snoring, narcolepsy, etc.); general sleep biology and physiology; and why sleep matters (i.e., the epidemiologic consequences of poor sleep health, e.g., connection to other health behaviors and health outcomes such as drug use; sexual risk behaviors; depression; dietary behaviors such as sugar-sweetened beverage consumption; cardiometabolic diseases like obesity, diabetes, and hypertension; and cancer outcomes such as breast cancer).

Background: Deep brain stimulation (DBS) is a promising adjuvant therapy for Parkinson’s disease (PD) in intermediate and advanced states, improving motor symptoms and life quality. Objectives: To investigate the efficacy of DBS in PD treatment. Design and Setting: Systematic review performed on medical school in Brazil. Methods: A literature review in the MedLine database was conducted using the following descriptors: “Deep Brain Stimulation”, “Parkinson’s Disease” and its variations according to MeSH. Only randomized controlled trials conducted in humans and published in English in the last 10 years were included. The PRISMA statement was used to improve this systematic review’s findings. Results: Three clinical trials that evaluated the effects of DBS in PD’s management were analysed. The main result measurement tools used in the studies were the PDQ-39-SI and UPDRS-III scores. Hacked ML et al. (2018) concluded that drug therapy + DBS was significantly superior to isolated drug therapy (p <0.0002). Schuepbach WMM et al. (2019) found that the experimental group presented significantly better outcomes in comparison to the control group (p <0,05). Birchall EL et al. (2016) suggested that the DBS device implant in the subthalamic nucleus caused significant improvement in depressive symptoms, motor symptoms, quality of sleep (p <0.0001) and quality of life (p=0.0005) in comparison to the control group. Conclusions: DBS has shown to be an efficient therapy for PD, once it promoted better outcomes regarding motor symptoms, depression, quality of life and quality of sleep. However, it is necessary to be aware of eventual side effects in the post-operative period.

Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.