Developing a Physical Activity Ontology to Support the Interoperability of Physical Activity Data (Preprint)

BACKGROUND Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical and microbiological data will lead to new insights crucial for improving human health, it has been hampered partly because of the large variations in the way the data are collected and presented. OBJECTIVE The aim of this study was to develop a Physical Activity Ontology (PACO) to support structuring and standardizing heterogeneous descriptions of physical activities. METHODS We prepared a corpus of 1140 unique sentences collected from various physical activity questionnaires and scales as well as existing standardized terminologies and ontologies. We extracted concepts relevant to physical activity from the corpus using a natural language processing toolkit called Multipurpose Text Processing Tool. The target concepts were formalized into an ontology using Protégé (version 4). Evaluation of PACO was performed to ensure logical and structural consistency as well as adherence to the best practice principles of building an ontology. A use case application of PACO was demonstrated by structuring and standardizing 36 exercise habit statements and then automatically classifying them to a defined class of either sufficiently active or insufficiently active using FaCT++, an ontology reasoner available in Protégé. RESULTS PACO was constructed using 268 unique concepts extracted from the questionnaires and assessment scales. PACO contains 225 classes including 9 defined classes, 20 object properties, 1 data property, and 23 instances (excluding 36 exercise statements). The maximum depth of classes is 4, and the maximum number of siblings is 38. The evaluations with ontology auditing tools confirmed that PACO is structurally and logically consistent and satisfies the majority of the best practice rules of ontology authoring. We showed in a small sample of 36 exercise habit statements that we could formally represent them using PACO concepts and object properties. The formal representation was used to infer a patient activity status category of sufficiently active or insufficiently active using the FaCT++ reasoner. CONCLUSIONS As a first step toward standardizing and structuring heterogeneous descriptions of physical activities for integrative data analyses, PACO was constructed based on the concepts collected from physical activity questionnaires and assessment scales. PACO was evaluated to be structurally consistent and compliant to ontology authoring principles. PACO was also demonstrated to be potentially useful in standardizing heterogeneous physical activity descriptions and classifying them into clinically meaningful categories that reflect adequacy of exercise.

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Faculty Opinions recommendation of Large-scale physical activity data reveal worldwide activity inequality.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727795643.793534116 ◽

2017 ◽

Author(s):

Mark A Febbraio

Keyword(s):

Physical Activity ◽

Large Scale ◽

Activity Data ◽

Physical Activity Data

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A roadmap: Social distancing physical activity data collection to protect those with intellectual disabilities

Journal of Intellectual Disabilities ◽

10.1177/17446295211009655 ◽

2021 ◽

pp. 174462952110096

Author(s):

Whitley J Stone ◽

Kayla M Baker

Keyword(s):

Physical Activity ◽

Data Collection ◽

Intellectual Disabilities ◽

Research Methods ◽

Virus Transmission ◽

Future Research ◽

Activity Data ◽

Physical Activity Data ◽

Social Distancing ◽

Novel Coronavirus

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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A Case Study of International Students’ Social Adjustment, Friendship Development, and Physical Activity

Journal of International Students ◽

10.32674/jis.v8i1.171 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 3

Author(s):

Shuang Li ◽

Sam Zizzi

Keyword(s):

Physical Activity ◽

International Students ◽

Social Adjustment ◽

Physical Activities ◽

Cultural Barriers ◽

Social Aspect ◽

Activity Data ◽

Current Case ◽

Friendship Development

Previous literature has focused on international student’s social transition and monocultural and bicultural ties. Little research has explored international students’ multicultural friendship development and the role that physical activity plays in their social interaction. The current case study explored a group of international students’ friendship development and the social aspect of physical activity. Data from individual interviews (pre and post), and 5 months of participant observations was used to describe two female international students’ experiences. Results suggested international students faced both language and cultural barriers in their social interactions with American students. Engagement in physical activities served as the context where observations of peer-to-peer social behaviors happened. Researchers argued that the multicultural friendship developed and strengthened by engaging in these physical activities. This paper also discussed implications and limitations of the study as well as future recommendations.

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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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