Community Health and Epidemiology

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Perspective on strategies for matching across age and sex in physiology research: “recreationally active” is not good enough
(Frontiers Media SA, 2025-01-06) Schweitzer, Allyson M.; Fuller, Daniel; Fliss, Matthew D.; Mitchell, Cameron J.
Cardiorespiratory fitness (CRF) and muscular fitness are powerful confounders in age and sex-related comparisons. This paper provides a perspective on the benefits and limitations of matching participants by physical activity behaviour, objectively measured fitness and normative fitness percentiles. Data presented herein are a subset of a larger study, and highlight that matching by physical activity, does not necessarily match on other metrics like physical fitness, especially when age-related comparisons are being made. Our data showed that young and older adults matched by physical activity behaviours showed the expected higher CRF and muscular fitness in male and younger participants, but older adults had higher CRF percentiles. This suggests that matching by physical activity behaviour may select older adults with relatively higher CRF. Researchers must choose their matching method carefully to ensure the appropriate aspects of fitness have been matched between groups. For clarity, they should also report when certain aspects of fitness have not been accounted for and give an explanation as to why.
Urban vegetation and well-being: A cross-sectional study in Montreal, Canada
(Wiley, 2025-01-13) Sousa-Silva, Rita; Kestens, Yan; Stephens, Zoé Poirier; Thierry, Benoit; Schoenig, Daniel; Fuller, Daniel; Winters, Meghan; Smargiassi, Audrey
1. As urbanisation continues to accelerate, urban green spaces are increasingly recognised as key elements for enhancing people's health and well-being. However, most research has used vegetation metrics that may not capture the specific associations between different types of vegetation and different mental health outcomes. 2. In this study, we investigate the cross-sectional associations between residential vegetation exposure and individual well-being in Montreal, Canada, using different vegetation and well-being measures: The proportion of grass cover, tree cover, and average NDVI value within buffers of various radii (100–1000 m) were linked to each participant's residence (n = 1072, aged 18 years or older), while well-being was assessed using subjective happiness, emotional well-being, and personal well-being scales. The associations were analysed using generalised additive regression models. 3. Our findings show that more vegetation was linked to enhanced well-being, although the effect sizes were relatively small. Irrespective of the buffer distance, the positive associations for grass and NDVI were more pronounced than those for trees, though these associations varied across the different well-being outcome measures. We also observed that increasing tree coverage has a stronger positive effect on the well-being of individuals who are dissatisfied with the current number of street trees. 4. Synthesis and applications. Everyday exposure to nearby nature is associated with better self-reported mental health, suggesting urban greening policies should focus on including more vegetation within built spaces, from individual street trees to small and large parks. Our study also highlights the importance of distinguishing between different types of vegetation (e.g. grass vs. trees) when studying the effects of vegetation on well-being or other health-related outcomes. Likewise, using different measures of well-being may provide a more nuanced and comprehensive understanding of how vegetation impacts people's well-being.
The promises and perils of a free rural inter-city transportation scheme: A mixed-methods study from Northern Saskatchewan
(Springer Nature, 2025-02-05) Alhassan, Jacob Albin Korem; Fuller, Daniel; Woytowich, Ron
Objective Transportation is a critical health determinant, yet the last decade has witnessed rapid disinvestment across Canada (particularly in rural contexts) with negative health consequences. We sought to explore and describe the benefits and challenges faced in operating the first community-driven free-transportation scheme in Saskatchewan that emerged in response to widespread unavailability of public transportation due to budget cuts (austerity). Methods We conducted a mixed-methods community-based participatory research study involving 22 interviews with bus riders and service administrators. We also performed descriptive statistics and chi-squared analyses on bus rider data (data on 1185 trips routinely collected between July 2023 and December 2023) to explore sociodemographic characteristics and trip purposes of bus riders. Results All trips were completed by 616 community members using the free bus service between July 2023 and December 2023. Community members took an average of 5 trips (median = 2.0) with a maximum of 22 trips being taken by one community member (1.9% of all trips). Most trips were by women (53%), and older adults mostly used the free bus for medical purposes (22% of riders were older adults and 34% of these used the bus for medical reasons). Qualitatively, the bus service has increased access to care and promotes social participation and autonomy, especially for older adults. The service however faces some challenges, including funding disruptions and difficulty recruiting and retaining drivers. Conclusion Free inter-community transportation (i.e. transportation across cities and municipalities) promotes health equity and access. In contexts without access to public transportation, governments could support community-driven initiatives through increased funding.
Unpacking the Mood States of Children and Youth in Saskatchewan, Canada, in the Context of the COVID-19 Pandemic: Insights from the “See Us, Hear Us 2.0” Study
(Children, 2025-01-10) Shakurun, Nahin; Hinz, Tamara; Adeyinka, Daniel Adedayo; Muhajarine, Nazeem
Background/Objectives: The COVID-19 pandemic created a growing need for insights into the mental health of children and youth and their use of coping mechanisms during this period. We assessed mood symptoms and related factors among children and youth in Saskatchewan. We examined if coping abilities mediated the relationship between risk factors and mood states. Methods: “See Us, Hear Us 2.0”, a cross-sectional study of 563 child–parent dyads, provided the data. The dependent variable, current mood state, was measured by the CoRonavIruS health Impact Survey (CRISIS) scale. Independent variables included sociodemographics, behaviors, household conditions, and coping ability. Multiple linear regression and mediation analyses were conducted, ensuring sample representativeness with sampling weights and addressing missing data through multiple imputations. Results: The participants reported mood symptoms (“moderate” to “extreme”) ranging from 23% to 38% on the CRISIS scale. Factors such as older children, hybrid learning, disrupted activities, and increased screen time worsened moods. The ethnic minority groups (BIPOC) living in mid-sized cities/towns experienced more negative moods compared to Whites residing in cities. Coping ability mediated the relationship between extracurricular activities and mood states. Conclusions: Our results underscore the importance of tailored interventions, recognizing the diverse needs of specific age groups, gender identities, and ethnicities and addressing the adverse effects of the pandemic- related disruptions on the mental health and well-being of school children in Saskatchewan. Our study also suggests prioritizing the diverse needs of children and youth during the planning and implementation of mental health services in the province.
ALF–Score—A novel approach to build a predictive network–based walkability scoring system
(PLoS One, 2022) S. Alfosool, Ali M.; Chen, Yuanzhu; Fuller, Daniel
Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, using the road structure as nodes is not widely discussed in existing methods. Most walkability measures only provide area–based scores with low spatial resolution, have a one–size–fits–all approach, and do not consider individuals opinion. Active Living Feature Score (ALF–Score) is a network–based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high–confidence ground–truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and points of interest features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the road.
Predicting lying, sitting and walking at different intensities using smartphone accelerometers at three different wear locations: hands, pant pockets, backpack
(BMJ Open Sport & Exercise Medicine, 2022-04) Khataeipour, Seyed Javad; Anaraki, Javad Rahimipour; Bozorgi, Arastoo; Rayner, Machal; Basset, Fabien A; Fuller, Daniel
Objective This study uses machine learning (ML) to develop methods for estimating activity type/intensity using smartphones, to evaluate the accuracy of these models for classifying activity, and to evaluate differences in accuracy between three different wear locations. Method Forty-eight participants were recruited to complete a series of activities while carrying Samsung phones in three different locations: backpack, right hand and right pocket. They were asked to sit, lie down, walk and run three Metabolic Equivalent Task (METs), five METs and at seven METs. Raw accelerometer data were collected. We used the R, activity counts package, to calculate activity counts and generated new features based on the raw accelerometer data. We evaluated and compared several ML algorithms; Random Forest (RF), Support Vector Machine, Naïve Bayes, Decision Tree, Linear Discriminant Analysis and k-Nearest Neighbours using the caret package (V.6.0–86). Using the combination of the raw accelerometer data and the computed features leads to high model accuracy. Results Using raw accelerometer data, RF models achieved an accuracy of 92.90% for the right pocket location, 89% for the right hand location and 90.8% for the backpack location. Using activity counts, RF models achieved an accuracy of 51.4% for the right pocket location, 48.5% for the right hand location and 52.1% for the backpack location. Conclusion Our results suggest that using smartphones to measure physical activity is accurate for estimating activity type/intensity and ML methods, such as RF with feature engineering techniques can accurately classify physical activity intensity levels in laboratory settings.
Study protocol for CELLAR (COVID-related Eating Limitations and Latent dietary effects in the Atlantic Region): population-based observational study to monitor dietary intakes and purchasing during COVID-19 in four Atlantic Canadian provinces
(BMJ Open, 2022) Mah, Catherine L; Foster, Karen; Jago, Emily; Hajizadeh, Mohammad; Luongo, Gabriella; Taylor, Nathan; Fuller, Daniel; Yi, Yanqing; Esan, Olukorede T; Lukic, Ryan; Clarke, Maria; Wranik, Wiesława Dominika; Brimblecombe, Julie Kay; Peeters, Anna
Introduction Poor diet is a leading preventable risk for the global burden of non-communicable disease. Robust measurement is needed to determine the effect of COVID-19 on dietary intakes and consumer purchasing, given the widespread changes to consumer food environments and economic precarity. The research objectives are as follows: (1) describe dietary intakes of foods, beverages and nutrients of concern during the COVID-19 pandemic; (2) quantify change in diet during COVID-19 as compared with prepandemic, previously captured in the provincial samples of the population-representative 2015 Canadian Community Health Survey-Nutrition and (3) examine how household purchasing practices predict dietary intakes during COVID-19. Methods and analyses Observational study of diet, using a population-based stratified probability sampling strategy allocated via dual-frame (landline and cellphone) calls to random-digit dialled numbers, followed by age-sex group quotas. The base population comprises the four provinces of the Atlantic region of Canada, jurisdictions with an excess burden of pre-existing dietary risk, compared with the rest of Canada. Our aim is n=1000 to obtain reliable estimates at a regional level to describe intakes and compare with prepandemic baseline. Data collection entails 12 weeks participation: (1) enrolment with sociodemographics (key dietary risk predictors such as age, sex, gender, pre-COVID-19 income, employment, household composition, receipt of economic relief, rural residence); (2) two 24hour diet recalls using the online ASA-24 Canada 2018 tool; and (3) online uploads of household food purchase receipts over the 12 weeks enrolled. Participation incentives will be offered.
Why machine learning (ML) has failed physical activity research and how we can improve
(BMJ Open Sport & Exercise Medicine, 2022) Fuller, Daniel; Ferber, Reed; Stanley, Kevin
Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods’ broad training and application in the field.
Using combined Global Position System and accelerometer data points to examine how built environments and gentrification are associated with physical activity in four Canadian cities
(International Journal of Behavioral Nutrition and Physical Activity, 2022) Firth, Caislin; Kestens, Yan; Winters, Meghan; Stanley, Kevin; Bell, Scott; Thierry, Benoit; Phillips, Kole; Poirier-Stephens, Zoe; Fuller, Daniel
Background Built and social environments are associated with physical activity. Global Positioning Systems (GPS) and accelerometer data can capture how people move through their environments and provide promising tools to better understand associations between environmental characteristics and physical activity. The purpose of this study is to examine the associations between GPS-derived exposure to built environment and gentrification characteristics and accelerometer-measured physical activity in a sample of adults across four cities. Methods We used wave 1 data from the Interventions, Research, and Action in Cities Team, a cohort of adults living in the Canadian cities of Victoria, Vancouver, Saskatoon, and Montreal. A subsample of participants wore a SenseDoc device for 10 days during May 2017–January 2019 to record GPS and accelerometry data. Two physical activity outcomes were derived from SenseDoc data: time spent in light, moderate, and vigorous physical activity; and time spent in moderate or vigorous physical activity. Using corresponding GPS coordinates, we summarized physical activity outcomes by dissemination area—a Canadian census geography that represents areas where 400 to 700 people live- and joined to built (active living space, proximity to amenities, and urban compactness) and gentrification measures. We examined the associations between environmental measures and physical activity outcomes using multi-level negative binomial regression models that were stratified by city and adjusted for covariates (weekday/weekend), home dissemination area, precipitation, temperature) and participant-level characteristics obtained from a survey (age, gender, income, race). Results We found that adults spent more time being physically active near their homes, and in environments that were more walkable and near parks and less time in urban compact areas, regardless of where participants lived. Our analysis also highlighted how proximity to different amenities was linked to physical activity across different cities. Conclusions Our study provides insights into how built environment and gentrification characteristics are associated with the amount of time adults spend being physically active in four Canadian cities. These findings enhance our understanding of the influence that environments have on physical activity over time and space, and can support policies to increase physical activity.
Recruiting Participants for Population Health Intervention Research: Effectiveness and Costs of Recruitment Methods for a Cohort Study
(Journal of Medical Internet Research, 2021) Wasfi, Rania; Poirier Stephens, Zoe; Sones, Meridith; Laberee, Karen; Pugh, Caitlin; Fuller, Daniel; Winters, Meghan; Kestens, Yan
Background: Public health research studies often rely on population-based participation and draw on various recruitment methods to establish samples. Increasingly, researchers are turning to web-based recruitment tools. However, few studies detail traditional and web-based recruitment efforts in terms of costs and potential biases. Objective: This study aims to report on and evaluate the cost-effectiveness, time effectiveness, and sociodemographic representation of diverse recruitment methods used to enroll participants in 3 cities of the Interventions, Research, and Action in Cities Team (INTERACT) study, a cohort study conducted in Canadian cities. Methods: Over 2017 and 2018 in Vancouver, Saskatoon, and Montreal, the INTERACT study used the following recruitment methods: mailed letters, social media (including sponsored Facebook advertisements), news media, partner communications, snowball recruitment, in-person recruitment, and posters. Participation in the study involved answering web-based questionnaires (at minimum), activating a smartphone app to share sensor data, and wearing a device for mobility and physical activity monitoring. We describe sociodemographic characteristics by the recruitment method and analyze performance indicators, including cost, completion rate, and time effectiveness. Effectiveness included calculating cost per completer (ie, a participant who completed at least one questionnaire), the completion rate of a health questionnaire, and the delay between completion of eligibility and health questionnaires. Cost included producing materials (ie, printing costs), transmitting recruitment messages (ie, mailing list rental, postage, and sponsored Facebook posts charges), and staff time. In Montreal, the largest INTERACT sample, we modeled the number of daily recruits through generalized linear models accounting for the distributed lagged effects of recruitment campaigns. Results: Overall, 1791 participants were recruited from 3 cities and completed at least one questionnaire: 318 in Vancouver, 315 in Saskatoon, and 1158 in Montreal. In all cities, most participants chose to participate fully (questionnaires, apps, and devices). The costs associated with a completed participant varied across recruitment methods and by city. Facebook advertisements generated the most recruits (n=687), at a cost of CAD $15.04 (US $11.57; including staff time) per completer. Mailed letters were the costliest, at CAD $108.30 (US $83.3) per completer but served to reach older participants. All methods resulted in a gender imbalance, with women participating more, specifically with social media. Partner newsletters resulted in the participation of younger adults and were cost-efficient (CAD $5.16 [US $3.97] per completer). A generalized linear model for daily Montreal recruitment identified 2-day lag effects on most recruitment methods, except for the snowball campaign (4 days), letters (15 days), and reminder cards (5 days). Conclusions: This study presents comprehensive data on the costs, effectiveness, and bias of population recruitment in a cohort study in 3 Canadian cities. More comprehensive documentation and reporting of recruitment efforts across studies are needed to improve our capacity to conduct inclusive intervention research.
Wave 1 results of the INTerventions, Research, and Action in Cities Team (INTERACT) cohort study: Examining spatio-temporal measures for urban environments and health
(Health & Place, 2021) Fuller, Daniel; Bell, Scott; Firth, Caislin; Nazeem, Muhajarine; Nelson, Trisalyn; Stanley, Kevin; Sones, Meridith; Smith, Jefferson; Thierry, Benoit; Laberee, Karen; Stephens, Zoe Poirier; Phillips, Kole; Kestens, Yan; Winters, Meghan
Built environment interventions have the potential to improve population health and reduce health inequities. The objective of this paper is to present the first wave of the INTErventions, Research, and Action in Cities Team (INTERACT) cohort studies in Victoria, Vancouver, Saskatoon, and Montreal, Canada. We examine how our cohorts compared to Canadian census data and present summary data for our outcomes of interest (physical activity, well-being, and social connectedness). We also compare location data and activity spaces from survey data, research-grade GPS and accelerometer devices, and a smartphone app, and compile measures of proximity to select built environment interventions.
Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition
(Biosensors, 2022-07) Barua, Arnab; Fuller, Daniel; Musa, Sumayyah; Jiang, Xianta
Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the sensor orientation problem poses challenges in HAR, and the length of windows as inputs for the deep neural networks has mostly been adopted arbitrarily. This paper explores the effect of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM in recognizing six human activities; sitting, lying, walking and running at three different speeds using data from an accelerometer sensor encapsulated into a smartphone. Forty-two participants performed the six mentioned activities by keeping smartphones in their pants pockets with arbitrary orientation. We conducted an inter-participant evaluation using 1D-CNN-LSTM architecture. We found that the average accuracy of the classifier was saturated to 80 ± 8.07% for window lengths greater than 65 using only four selected simple orientation invariant heuristic features. In addition, precision, recall and F1-measure in recognizing stationary activities such as sitting and lying decreased with increment of window length, whereas we encountered an increment in recognizing the non-stationary activities.
ALF-Score++, a novel approach to transfer knowledge and predict network-based walkability scores across cities
(Scientific Reports, 2022-08) Alfosool, Ali M. S.; Chen, Yuanzhu; Fuller, Daniel
Walkability is an important measure with strong ties to our health. However, there are existing gaps in the literature. Our previous work proposed new approaches to address existing limitations. This paper explores new ways of applying transferability using transfer-learning. Road networks, POIs, and road-related characteristics grow/change over time. Moreover, calculating walkability for all locations in all cities is very time-consuming. Transferability enables reuse of already-learned knowledge for continued learning, reduce training time, resource consumption, training labels and improve prediction accuracy. We propose ALF-Score++, that reuses trained models to generate transferable models capable of predicting walkability score for cities not seen in the process. We trained transfer-learned models for St. John’s NL and Montréal QC and used them to predict walkability scores for Kingston ON and Vancouver BC. MAE error of 13.87 units (ranging 0–100) was achieved for transfer-learning using MLP and 4.56 units for direct-training (random forest) on personalized clusters.
A theory-based model of cumulative activity
(Scientific Reports, 2022-09) Phillips, Kole; Stanley, Kevin; Fuller, Daniel
Energy expenditure can be used to examine the health of individuals and the impact of environmental factors on physical activity. One of the more common ways to quantify energy expenditure is to process accelerometer data into some unit of measurement for this expenditure, such as Actigraph activity counts, and bin those measures into physical activity levels. However, accepted thresholds can vary between demographics, and some units of energy measurements do not currently have agreed upon thresholds. We present an approach which computes unique thresholds for each individual, using piecewise exponential functions to model the characteristics of their overall physical activity patterns corresponding to well established sedentary, light, moderate and vigorous activity levels from the literature. Models are fit using existing piecewise fitting techniques and software. Most participants’ activity intensity profile is exceptionally well modeled as piecewise exponential decay. Using this model, we find emergent groupings of participant behavior and categorize individuals into non-vigorous, consistent, moderately active, or extremely active activity intensity profiles. In the supplemental materials, we demonstrate that the parameters of the model correlate with demographics of age, household size, and level of education, inform behavior change under COVID lockdown, and are reasonably robust to signal frequency.
Changes in physical activity after joining a bikeshare program: A cohort of new bikeshare users
(International Journal of Behavioral Nutrition and Physical Activity, 2022-10) Auchincloss, Amy; Michael, Yvonne L.; Niamatullah, Saima; Li, Siyu; Melly, Steven J; Pharis, Meagan L; Fuller, Daniel
Background There are hundreds of bikeshare programs worldwide, yet few health-related evaluations have been conducted. We enrolled a cohort of new bikeshare members in Philadelphia (Pennsylvania, USA) to assess whether within-person moderate and vigorous physical activity (MVPA) increased with higher use of the program and whether effects differed for vulnerable sub-groups. Methods During 2015–2018, 1031 new members completed baseline and one-year follow-up online surveys regarding their personal characteristics and past 7-day MVPA minutes per week (minutes per week with- and without walking). Participants were linked to their bikeshare trips to objectively assess program use. Negative binomial (for continuous outcomes) and multinomial (for categorical outcomes) regression adjusted for person characteristics (socio-demographics, health), weather, biking-infrastructure, and baseline biking. Results Participant median age was 30, 25% were of Black or Latino race/ethnicity, and 30% were socioeconomically disadvantaged. By follow-up, personal bike ownership increased and 75% used bikeshare, although most used it infrequently. Per 10 day change in past year (PY) bikeshare use, non-walking MVPA min/wk increased 3% (roughly 6 min/wk, P < 0.014). More days of bikeshare was also associated with change from inactive to more active (odds ratio for ≥ 15 days in PY vs. no bikeshare use 1.80, CI 1.05–3.09, P < 0.03). Results were consistent across vulnerable sub-groups. In general, impacts on MVPA were similar when exposure was personal bike or bikeshare. Conclusions Bikeshare facilitated increases in cycling, slightly increased non-walking MVPA, and showed potential for activating inactive adults; however, for larger program impact, members will need to use it more frequently.
Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities
(Scientific Reports, 2022-11) Doiron, Dany; Setton, Eleanor M; Brook, Jeffrey R; Kestens, Yan; McCormack, Gavin R; Winters, Meghan; Shooshtari, Mahdi; Azami, Sajjad; Fuller, Daniel
New ‘big data’ streams such as street-level imagery are offering unprecedented possibilities for developing health-relevant data on the urban environment. Urban environmental features derived from street-level imagery have been used to assess pedestrian-friendly neighbourhood design and to predict active commuting, but few such studies have been conducted in Canada. Using 1.15 million Google Street View (GSV) images in seven Canadian cities, we applied image segmentation and object detection computer vision methods to extract data on persons, bicycles, buildings, sidewalks, open sky (without trees or buildings), and vegetation at postal codes. The associations between urban features and walk-to-work rates obtained from the Canadian Census were assessed. We also assessed how GSV-derived urban features perform in predicting walk-to-work rates relative to more widely used walkability measures. Results showed that features derived from street-level images are better able to predict the percent of people walking to work as their primary mode of transportation compared to data derived from traditional walkability metrics. Given the increasing coverage of street-level imagery around the world, there is considerable potential for machine learning and computer vision to help researchers study patterns of active transportation and other health-related behaviours and exposures.
Understanding the Perspectives of Latino Adults Who Smoke on Physical Activity: A Qualitative Study
(International Journal of Environmental Research, 2023-02) Hernández-Torres, Ruthmarie; Alaniz-Cantu, Edgar; Bautista Rojas, Marie Valeria; Lara, Daimarelys; Merritt, Sarah; DeJesus, Elisa; D’Abundo, Michelle Lee; McIntosh, Scott; Ossip, Deborah J; Fuller, Daniel; Rivera, M. Patricia; Cupertino, Paula; Cartujano-Barrera, Francisco
Smoking and sedentary lifestyle frequently co-occur among Latinos. Evidence suggests that moderate to vigorous physical activity (MVPA) may enhance smoking cessation rates. However, this synergistic phenomenon has not been studied among Latinos, the largest minority group in the United States. This qualitative study consisted of semi-structured interviews in English or Spanish with Latino adults who smoke (n = 20) to understand their perspectives on physical activity. Participants were recruited using community-based recruitment strategies. The Health Belief Model was used as a framework for qualitative theoretical analysis. Multiple perceived benefits (e.g., mood management, strategy to quit smoking), susceptibility (e.g., risk of cardiovascular diseases, physical impairment), and barriers (e.g., lack of social support, low financial resources) of being physically active were identified. Moreover, multiple cues to action to do physical activity (e.g., being a role model, spending time with family and friends) were identified. These factors provide concrete operational strategies to address smoking cessation and physical activity among Latinos. Further research is needed on how best to integrate these perspectives into smoking cessation interventions.
The Metaverse, the Built Environment, and Public Health: Opportunities and Uncertainties
(Journal of Medical Internet Research, 2023) Koohsari, Mohammad Javad; McCormack, Gavin R; Nakaya, Tomoki; Yasunaga, Akitomo; Fuller, Daniel; Nagai, Yukari; Oka, Koichiro
There has been a growing interest in the “metaverse,” and discourse about how this platform may contribute to different fields of science is already beginning to emerge. In this paper, we discuss key opportunities and uncertainties about how a metaverse might contribute to advancing knowledge in the interdisciplinary field of the built environment and public health aimed at reducing noncommunicable diseases.
Development of a ParticipACTION App–Based Intervention for Improving Postsecondary Students’ 24-Hour Movement Guideline Behaviors: Protocol for the Application of Intervention Mapping
(JMIR Research Protocols, 2023) Flood, Stephanie M; Thompson, Brooke; Faulkner, Guy; Vanderloo, Leigh M; Blackett, Beth; Dolf, Matt; Latimer-Cheung, Amy E; Duggan, Mary; Di Sebastiano, Katie M; Lane, Kirstin N; Brouwers, Melissa C; McKenna, Julia; Chulak-Bozzer, Tala; Fuller, Daniel; Ruissen, Geralyn R; Sturrock, Shelby L; Tomasone, JenniferR
Background: The Canadian 24-Hour Movement Guidelines for adults provide specific recommendations for levels of physical activity, sedentary behavior, and sleep (ie, the movement behaviors) required for optimal health. Performance of the movement behaviors is associated with improved mental well-being. However, most postsecondary students do not meet the movement behavior recommendations within the Canadian 24-Hour Movement Guidelines and experience increased stress and declining well-being, suggesting the need for an intervention targeting students’ movement behaviors. Objective: We aimed to develop and implement a theory-informed intervention intended to improve the movement behaviors and mental well-being of first-year postsecondary students. Methods: The Intervention Mapping protocol was applied in the development and implementation of the intervention. Intervention Mapping entailed performing a needs assessment, determining the intervention outcomes, selecting theory- and evidence-based change methods and applications, preparing and producing intervention plans and materials, developing the implementation plan, and finally developing an evaluation plan. The Theoretical Domains Framework and the Behavior Change Wheel were also used in conjunction with the Intervention Mapping protocol to ensure a solid theoretical basis for the intervention. This protocol led to the development and implementation of a 6-week, theory-informed ParticipACTION app–based intervention aimed at helping first-year postsecondary students improve their movement behaviors and mental well-being. The developed app content provided students with information on each of the movement behaviors and behavioral strategies (ie, goal setting, action planning, monitoring, and coping planning). The use of Intervention Mapping allowed for the continuous involvement of various multidisciplinary partners and end users, ensuring that the intervention design and implementation was appropriate for the target audience. The feasibility, acceptability, and potential impact of the intervention will be examined in a subsequent proof-of-concept study at 2 Canadian university campuses. Results: Participant recruitment occurred during September 2021, and the intervention was conducted from October to December 2021. The deadline for completion of the postintervention questionnaire by participants was mid-December 2021. The analysis of data examining the feasibility, acceptability, and potential impact of the intervention began in January 2022, with the publication of the proof-of-concept evaluation expected in 2023. Conclusions: Intervention Mapping with the Theoretical Domains Framework and Behavior Change Wheel was a useful approach to combine evidence and theoretical concepts to guide the design and implementation of a ParticipACTION app–based intervention targeting postsecondary students’ movement behaviors and mental well-being. This process may serve as an example for other researchers developing multiple behavior change app–based interventions. Should the forthcoming evaluation demonstrate the intervention’s acceptability, feasibility, and potential impact, the intervention may provide a scalable method of improving postsecondary students’ movement behaviors and mental well-being.
The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada
(Habitat International, 2023-09) Kiani, Behzad; Mamiya, Hiroshi; Thierry, Benoit; Firth, Caislin; Fuller, Daniel; Winters, Meghan; Kestens, Yan
Increases in cycling infrastructure might be linked to gentrification. However, there is little empirical evidence investigating the existence and directionality of this possible relationship. This study examined the temporal sequence involved in the relation between gentrification and increases in the cycling infrastructure in Montreal, Canada. We analyzed changes in cycling infrastructure between 2006, 2011, and 2016, considering cyclist-only paths, multi-use paths, and on-street bike lanes. The Ding measure was used to identify gentrified census tracts (CTs) using census data. We implemented logistic regression models with and without geographically weighted regression specification at the CT level to test three scenarios; whether an increase in cycling infrastructure (2006–2011) was associated with subsequent gentrification (2011–2016); whether gentrification (2006–2011) was associated with subsequent increase in cycling infrastructure (2011–2016); or if these phenomena happened simultaneously (2011–2016). Increase in cycling infrastructure was not linked to subsequent gentrification, nor did these two phenomena happen simultaneously. However, gentrified CTs had a 44% greater chance of a subsequent increase in cycling infrastructure, with varying strengths of associations across the study area. When planning increases in cycling infrastructure, it is crucial to take an equity-based approach that underlying sociodemographic dynamics of urban CTs. To achieve this, cities need to engage in broad upstream community engagement, ensuring the inclusion of a diverse range of voices in the decision-making process.

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