Resources
Wearable Device Guides
Reports
Device-based PA measurement is important for calculating consumer physical activity data, as well as analyzing the relationship between physical activity and various matters. Accelerometers are an essential tool for device-based physical activity measurement. With accelerometer-based devices, daily physical activity can often be classified into activity intensities or categories, depending on the algorithm associated with a device. Using this predicted information, a device can then estimate an individual’s energy expenditure, among other things.
When classifying an activity by intensity, an algorithm often uses either an activity counts method, which relies on cut-points to predict an intensity category, or automatic classification, in which a specific activity is predicted. Though both methods are beneficial in various situations, the automatic classification method is the more widely accepted method due to its accurate estimates of specific data. Research-based accelerometer devices are where these two methods are commonly found. For research, collected data must be consistent and therefore, automatically classifying physical activity into intensity or activity categorize can help minimalize human error.
Accelerometers are an essential device for estimating PA data from wearable devices. Using an accelerometer-based device, PA types and intensities can be predicted. Depending on the algorithm that a device uses, specific forms of PA can be directly predicted from accelerometer data, allowing for a device to accurately measure various PA data, including energy expenditure. In device-based PA measurement, different devices categorize PA in different ways, including activity counts and automatic classification. When considering an accelerometer algorithm, a variety of factors affect the accuracy and type of data output. These factors include these classification methods, as well as the frequency at which data is collected, the noise filters incorporated, the model used for the algorithm and whether the algorithm is proprietary or open-source.
Developing Apps For Population Health Research
By: Shal K, Fuller D
This report aims to give a general overview of developing mHealth research applications and decisions to be made depending on the goals the research. This area of research is largely influenced by the lack of physical activity in Canada and the use of eHealth technologies to improve the Health of the Canadian population. This discourse on the idea where population health research and programming meet is divided into four main sections. The first section goes over the pros and cons of using mHealth technologies for research. It provides an understanding of the positive and negative effects of using smartphones in a study for data collection.
The second section covers the ethical considerations researchers should be aware of in general research and research with the use of mHealth technologies. It includes a discussion of the policies governing research in Canada and participants’ safety when using mHealth applications. Thirdly, there is a section discussing potential challenges that researchers may face when implementing mHealth research. This section covers the obstacles that may occur both with technology and the research team, which can sometimes hinder research progress if not appropriately planned.
The fourth section covers the technical aspects of app development and is divided into four subsections. The first subsection delves into selecting an operating system to host an application and developing content for mobile applications for the particular operating system selected. The second subsection entails understanding APIs, how they work, and why they are necessary to develop applications that collect health data through wearable devices. The final subsection discusses challenges that developers may face when integrating different companies’ APIs into their application. All ideas communicated fall under the report’s scope and are influenced by the goals of my work term placement organization.
BikeMaps.org in St. John’s, Newfoundland and Labrador – Report to Stakeholders
By: Slaney J, Chafe B, Fuller D
This stakeholder report describes the promotion and implementation of BikeMaps.org in St. John’s, NL. BikeMaps is a crowdsourced geo-mapping tool for people to report bicycling near misses, collisions, hazards, and thefts. There is a lack of police-reported cycling-related incident data.[1] BikeMaps provides a platform for the collection of this missing data and improve cycling safety around the world. This report will focus on the impact BikeMaps.org has in St. John’s, NL.
Health and Economic Benefits of Walking in St. John’s, Newfoundland and Labrador
By: Holloway A, Chafe B, Fuller D
Using HEAT, our assessment identified the potential health and economic benefits of increased walking in St. John’s, NL. City-specific data inputs on population demographics, physical activity, and air pollution exposure were used to calculate estimates of premature deaths prevented, carbon emissions reduced, and health-economic impacts. Our objective was to estimate the benefit-cost ratio of potential walking infrastructure investments and provide recommendations for city council to promote and support a walkable St. John’s.
Our main analysis evaluated the health-economic impact of doubling the mode share of walking in St. John’s from 4.6% to 9.2% – an increase of 3 minutes per person per day of walking. We estimated the economic benefit to be $117,656,000 and the health benefit to be 18 premature deaths prevented over a 10-year period. If $3 million were invested annually, the benefit-cost ratio would be 4 – meaning that the benefit would be 4 times greater than the cost. These results are largely driven by physical activity benefits and premature deaths prevented, rather than carbon emission reductions. A variety of sensitivity analyses highlighted which data inputs had the most significant effect on assessed outcomes. These were the time horizon, the value of statistical life, and the walking mode share target.
The Bike Network Design Jam was held on September 22, 2018 at the Memorial University Signal Hill Campus in conjunction with the Happy City Neighbourhood Summit. Fourteen people participated in the Design Jam. There were 3 objectives for the day
- Design a fully connected bike network for the Avalon
- Define 3 priority projects for the City of St. John’s new cycling plan
- Define pain points for cycling in the City of St. John’s
Overall, participants were optimistic that their plan would increase cycling in St. John’s and promote more sustainable transportation by encouraging people to combine cycling with transit. The designs show what a true connected cycling network could look like in St. John’s and on the Avalon.
Health is an important value for Canadians. Chronic disease, physical inactivity and obesity are high (Public Health Agency of Canada, 2016). Advances in technology, including mobile apps, have provided researchers with new ways to collect data. The purpose of this paper is to understand how researchers have developed apps for population health research. The first objective of this paper is to understand the development process for population health research apps. The second objective is to provide recommendations for researchers who are planning to develop population health research apps.