INVESTIGATING THE ADHERENCE TO THE 5-MINUTE RESTING INSTRUCTION BEFORE SELF-MEASURING HOME BLOOD PRESSURE USING ACCELEROMETER-BASED PHYSICAL ACTIVITY DATA

The novel coronavirus may impact exercise habits of those with intellectual disabilities. Due to the mandated discontinuation of face-to-face research, investigators must adapt projects to protect all involved while collecting objective physical activity metrics. This brief report outlines a modification process of research methods to adhere to social distancing mandates present during COVID-19. Actions taken included electronic consent and assent forms, an electronic survey, and mailing an accelerometer with included instructions. The amended research methods were implemented without risk for virus transmission or undue burden on the research team, participant, or caregiver. Recruitment was likely impacted by the coronavirus-mediated quarantine, plausibly resulting in bias. Objective physical activity data collection can be sufficiently modified to protect those with intellectual disabilities and investigators. Future research designs may require greater participant incentives and the creation of in-home participation.

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Association of Activity and Chronic Disease Risk Factors: Utility and Limitations of Objectively Measured Physical Activity Data

Journal of the American Dietetic Association ◽

10.1016/j.jada.2008.03.018 ◽

2008 ◽

Vol 108 (6) ◽

pp. 945-947

Author(s):

Arlene Hankinson

Keyword(s):

Physical Activity ◽

Risk Factors ◽

Chronic Disease ◽

Disease Risk ◽

Activity Data ◽

Physical Activity Data ◽

Chronic Disease Risk ◽

Objectively Measured ◽

Objectively Measured Physical Activity

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Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes

10.1109/iembs.2009.5332466 ◽

2009 ◽

Cited By ~ 16

Author(s):

J.J. Valletta ◽

A.J. Chipperfield ◽

C.D. Byrne

Keyword(s):

Physical Activity ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Gaussian Process ◽

Free Living ◽

Activity Data ◽

Glucose Response ◽

Physical Activity Data ◽

Blood Glucose Response

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Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2020-0005 ◽

2020 ◽

pp. 1-14

Author(s):

Anna M.J. Iveson ◽

Malcolm H. Granat ◽

Brian M. Ellis ◽

Philippa M. Dall

Keyword(s):

Physical Activity ◽

Outcome Measures ◽

Global Positioning System ◽

Gps Data ◽

Positioning System ◽

Activity Data ◽

Data Set ◽

Physical Activity Data ◽

Global Positioning ◽

Event Based

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.

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Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

10.31234/osf.io/a9ju4 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ian R Kleckner ◽

Mallory Feldman ◽

Matthew Goodwin ◽

Karen S. Quigley

Keyword(s):

Physical Activity ◽

Consumer Electronics ◽

Use Cases ◽

Assessment Procedure ◽

Laboratory System ◽

Activity Data ◽

Physiological Measurement ◽

Physical Activity Data ◽

Quantitative Analyses ◽

Consumer Devices

Commercially available consumer electronics (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired Mindware laboratory system. Our evaluations are performed in two studies (N=10, N=11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

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