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      Age and environment-related differences in gait in healthy adults using wearables

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          Abstract

          Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger ( n = 33, 18–40 years) and older ( n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.

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          Most cited references45

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          Bootstrap Methods: Another Look at the Jackknife

          B Efron (1979)
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            Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects☆

            Objectives This study aims to (1) elucidate whether the Hawthorne effect exists, (2) explore under what conditions, and (3) estimate the size of any such effect. Study Design and Setting This systematic review summarizes and evaluates the strength of available evidence on the Hawthorne effect. An inclusive definition of any form of research artifact on behavior using this label, and without cointerventions, was adopted. Results Nineteen purposively designed studies were included, providing quantitative data on the size of the effect in eight randomized controlled trials, five quasiexperimental studies, and six observational evaluations of reporting on one's behavior by answering questions or being directly observed and being aware of being studied. Although all but one study was undertaken within health sciences, study methods, contexts, and findings were highly heterogeneous. Most studies reported some evidence of an effect, although significant biases are judged likely because of the complexity of the evaluation object. Conclusion Consequences of research participation for behaviors being investigated do exist, although little can be securely known about the conditions under which they operate, their mechanisms of effects, or their magnitudes. New concepts are needed to guide empirical studies.
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              Walking speed: the functional vital sign.

              Walking speed (WS) is a valid, reliable, and sensitive measure appropriate for assessing and monitoring functional status and overall health in a wide range of populations. These capabilities have led to its designation as the "sixth vital sign". By synthesizing the available evidence on WS, this scholarly review article provides clinicians with a reference tool regarding this robust measure. Recommendations on testing procedures for assessing WS, including optimal distance, inclusion of acceleration and deceleration phases, instructions, and instrumentation are given. After assessing an individual's WS, clinicians need to know what this value represents. Therefore, WS cut-off values and the corresponding predicted outcomes, as well as minimal detectable change values for specific populations and settings are provided.
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                Author and article information

                Contributors
                FikretIsik.Karahanoglu@Pfizer.com
                Journal
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                30 September 2020
                30 September 2020
                2020
                : 3
                : 127
                Affiliations
                GRID grid.410513.2, ISNI 0000 0000 8800 7493, Early Clinical Development, , Pfizer, Inc., ; Cambridge, 02139 MA USA
                Author information
                http://orcid.org/0000-0001-9954-7003
                http://orcid.org/0000-0002-0746-2916
                http://orcid.org/0000-0001-6137-4282
                http://orcid.org/0000-0001-9822-9935
                http://orcid.org/0000-0002-9162-8367
                Article
                334
                10.1038/s41746-020-00334-y
                7528045
                33083562
                12b4b933-c23e-4596-a801-eee09f2dfa1c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 May 2020
                : 31 August 2020
                Funding
                Funded by: This study was sponsored by Pfizer.
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                predictive markers,quality of life,biomedical engineering

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