Wearable Technology for Detecting Significant Moments in Individuals with Dementia.
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ABSTRACT:
SUBMITTER: Lai Kwan C
PROVIDER: S-EPMC6778872 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Wearable Technology for Detecting Significant Moments in Individuals with Dementia.
Lai Kwan Chelsey C Mahdid Yacine Y Motta Ochoa Rossio R Lee Keven K Park Melissa M Blain-Moraes Stefanie S
BioMed research international 20190925
The detection of significant moments can support the care of individuals with dementia by making visible what is most meaningful to them and maintaining a sense of interpersonal connection. We present a novel intelligent assistive technology (IAT) for the detection of significant moments based on patterns of physiological signal changes in individuals with dementia and their caregivers. The parameters of the IAT are tailored to each individual's idiosyncratic physiological response patterns thro ...[more]
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Project description:Background: The objective of this analysis was to systematically review studies employing wearable technology in patients with dementia by quantifying differences in digitally captured physiological endpoints. Methods: This systematic review and meta-analysis was based on web searches of Cochrane Database, PsycInfo, Pubmed, Embase, and IEEE between October 25-31st, 2017. Observational studies providing physiological data measured by wearable technology on participants with dementia with a mean age ≥50. Data were extracted according to PRISMA guidelines and methodological quality assessed independently using Downs and Black criteria. Standardized mean differences between cases and controls were estimated using random-effects models. Results: Forty-eight studies from 18,456 screened abstracts (Dementia: n = 2,516, Control: n = 1,224) met inclusion criteria for the systematic review. Nineteen of these studies were included in one or multiple meta-analyses (Dementia: n = 617, Control: n = 406). Participants with dementia demonstrated lower levels of daily activity (standardized mean difference (SMD), -1.60; 95% CI, -2.66 to -0.55), decreased sleep efficiency (SMD, -0.52; 95% CI, -0.89 to -0.16), and greater intradaily circadian variability (SMD, 0.46; 95% CI, 0.27 to 0.65) than controls, among other measures. Statistical between-study heterogeneity was observed, possibly due to variation in testing duration, device type or patient setting. Conclusions and Relevance: Digitally captured data using wearable devices revealed that adults with dementia were less active, demonstrated increased fragmentation of their sleep-wake cycle and a loss of typical diurnal variation in circadian rhythm as compared to controls.
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Project description:Gun violence continues to be a staggering and seemingly intractable issue in many communities. The prevalence of gun violence among the sub-population of individuals under court-ordered community supervision provides an opportunity for intervention using remote monitoring technology. Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. This paper presents the first results demonstrating the feasibility of using wearable inertial sensors to recognize wrist movements and other signals corresponding to firearm usage. Data were collected from accelerometers worn on the wrists of subjects shooting a number of different firearms, conducting routine daily activities, and participating in activities and tasks that could be potentially confused with firearm discharges. A training sample was used to construct a combined detector and classifier for individual gunshots, which achieved a classification accuracy of 99.4 percent when tested against a hold-out sample of observations. These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges.
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Project description:BackgroundTo experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct.ObjectiveThis study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension.MethodsData were collected through a vignette-based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI).ResultsConvergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings (>0.70), average variance extracted values (>0.50), and minimum item to total correlations (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention).ConclusionsThe wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.
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Project description:We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labeled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.
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