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BackgroundWearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way.ObjectiveWe conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.MethodsIn adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria.ResultsThe search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy.ConclusionsSleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Previous studies identified various mechanisms of light scattering reduction in tissue induced by chemical agents. Our results suggest that dehydration is an important mechanism of optical clearing in collagenous and cellular tissue. Photographic and optical coherence tomography images indicate that air-immersed skin and tendon specimens become similarly transparent to glycerol-immersed specimens. Transmission electron microscopy images reveal that dehydration causes individual scattering particles such as collagen fibrils and organelles to become more densely packed, but does not significantly alter size. A heuristic particle-interaction model predicts that the scattering particle volume fraction increase can contribute substantially to optical clearing in collagenous and cellular tissue.

This paper presents the results of a study comparing student learning in an inquiry-based and a traditional course in biotransport. Collaborating learning scientists and biomedical engineers designed and implemented an inquiry-based method of instruction that followed learning principles presented in the National Research Council report "How People Learn" (HPL). In this study, the intervention group was taught a core biomedical engineering course in biotransport following the HPL method. The control group was taught by traditional didactic lecture methods. A primary objective of the study was to identify instructional methods that facilitate the early development of adaptive expertise (AE). AE requires a combination of two types of engineering skills: subject knowledge and the ability to think innovatively in new contexts. Therefore, student learning in biotransport was measured in two dimensions: A pre and posttest measured knowledge acquisition in the domain and development of innovative problem-solving abilities. HPL and traditional students' test scores were compared. Results show that HPL and traditional students made equivalent knowledge gains, but that HPL students demonstrated significantly greater improvement in innovative thinking abilities. We discuss these results in terms of their implications for improving undergraduate engineering education.

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The purpose of this study was to determine the kinetics of HSP70 expression in response to mild thermal stress. The rationale is to produce a basis for design of optimal heating methods to induce HSP70 expression for preconditioning in cardiac surgery. Bovine aortic endothelial cells were heated at 42 degrees C for 0.5 to 5 hours followed by 37 degrees C recovery for 1 to 48 hours. Quantitative analysis of western blot results showed HSP70 expression kinetics is a coupled function of heating temperature and time and of post-heating duration. Bimodal HSP70 expression kinetics were identified which may be an important cause of the "second window of protection" observed by other researchers.

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The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland–Altman plot as a measure of agreement, and we provide formulae to calculate them along with illustrative examples for different types of study designs, specifically single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses.