Synaptotagmin 9 Modulates Spontaneous Neurotransmitter Release in Striatal Neurons by Regulating Substance P Secretion.

Synaptotagmin 9 (SYT9) is a tandem C2 domain Ca2+ sensor for exocytosis in neuroendocrine cells; its function in neurons remains unclear. Here, we show that, in mixed-sex cultures, SYT9 does not trigger rapid synaptic vesicle exocytosis in mouse cortical, hippocampal, or striatal neurons, unless it is massively overexpressed. In striatal neurons, loss of SYT9 reduced the frequency of spontaneous neurotransmitter release events (minis). We delved into the underlying mechanism and discovered that SYT9 was localized to dense-core vesicles that contain substance P (SP). Loss of SYT9 impaired SP release, causing the observed decrease in mini frequency. This model is further supported by loss of function mutants. Namely, Ca2+ binding to the C2A domain of SYT9 triggered membrane fusion in vitro, and mutations that disrupted this activity abolished the ability of SYT9 to regulate both SP release and mini frequency. We conclude that SYT9 indirectly regulates synaptic transmission in striatal neurons by controlling SP release.SIGNIFICANCE STATEMENT Synaptotagmin 9 (SYT9) has been described as a Ca2+ sensor for dense-core vesicle (DCV) exocytosis in neuroendocrine cells, but its role in neurons remains unclear, despite widespread expression in the brain. This article examines the role of SYT9 in synaptic transmission across cultured cortical, hippocampal, and striatal neuronal preparations. We found that SYT9 regulates spontaneous neurotransmitter release in striatal neurons by serving as a Ca2+ sensor for the release of the neuromodulator substance P from DCVs. This demonstrates a novel role for SYT9 in neurons and uncovers a new field of study into neuromodulation by SYT9, a protein that is widely expressed in the brain.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Impacts of observation frequency on proximity contact data and modeled transmission dynamics.

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

BACKGROUND: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. OBJECTIVE: We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. METHODS: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. RESULTS: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). CONCLUSIONS: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Positron-emitting radiotracers spatially resolve unexpected biogeochemical relationships linked with methane oxidation in Arctic soils.

Arctic soils are marked by cryoturbic features, which impact soil-atmosphere methane (CH4 ) dynamics vital to global climate regulation. Cryoturbic diapirism alters C/N chemistry within frost boils by introducing soluble organic carbon and nutrients, potentially influencing microbial CH4 oxidation. CH4 oxidation in soils, however, requires a spatio-temporal convergence of ecological factors to occur. Spatial delineation of microbial activity with respect to these key microbial and biogeochemical factors at relevant scales is experimentally challenging in inherently complex and heterogeneous natural soil matrices. This work aims to overcome this barrier by spatially linking microbial CH4 oxidation with C/N chemistry and metagenomic characteristics. This is achieved by using positron-emitting radiotracers to visualize millimeter-scale active CH4 uptake areas in Arctic soils with and without diapirism. X-ray absorption spectroscopic speciation of active and inactive areas shows CH4 uptake spatially associates with greater proportions of inorganic N in diapiric frost boils. Metagenomic analyses reveal Ralstonia pickettii associates with CH4 uptake across soils along with pertinent CH4 and inorganic N metabolism associated genes. This study highlights the critical relationship between CH4 and N cycles in Arctic soils, with potential implications for better understanding future climate. Furthermore, our experimental framework presents a novel, widely applicable strategy for unraveling ecological relationships underlying greenhouse gas dynamics under global change.

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.

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.

INTERACT: A comprehensive approach to assess urban form interventions through natural experiments.

BACKGROUND: Urban form interventions can result in positive and negative impacts on physical activity, social participation, and well-being, and inequities in these outcomes. Natural experiment studies can advance our understanding of causal effects and processes related to urban form interventions. The INTErventions, Research, and Action in Cities Team (INTERACT) is a pan-Canadian collaboration of interdisciplinary scientists, urban planners, and public health decision makers advancing research on the design of healthy and sustainable cities for all. Our objectives are to use natural experiment studies to deliver timely evidence about how urban form interventions influence health, and to develop methods and tools to facilitate such studies going forward. METHODS: INTERACT will evaluate natural experiments in four Canadian cities: the Arbutus Greenway in Vancouver, British Columbia; the All Ages and Abilities Cycling Network in Victoria, BC; a new Bus Rapid Transit system in Saskatoon, Saskatchewan; and components of the Sustainable Development Plan 2016-2020 in Montreal, Quebec, a plan that includes urban form changes initiated by the city and approximately 230 partnering organizations. We will recruit a cohort of between 300 and 3000 adult participants, age 18 or older, in each city and collect data at three time points. Participants will complete health and activity space surveys and provide sensor-based location and physical activity data. We will conduct qualitative interviews with a subsample of participants in each city. Our analysis methods will combine machine learning methods for detecting transportation mode use and physical activity, use temporal Geographic Information Systems to quantify changes to urban intervention exposure, and apply analytic methods for natural experiment studies including interrupted time series analysis. DISCUSSION: INTERACT aims to advance the evidence base on population health intervention research and address challenges related to big data, knowledge mobilization and engagement, ethics, and causality. We will collect ~ 100 TB of sensor data from participants over 5 years. We will address these challenges using interdisciplinary partnerships, training of highly qualified personnel, and modern methodologies for using sensor-based data.

External Ion Access in the Na/K Pump: Kinetics of Na+, K+, and Quaternary Amine Interaction.

Na/K pumps build essential ion gradients across the plasmalemma of animal cells by coupling the extrusion of three Na+, with the import of two K+ and the hydrolysis of one ATP molecule. The mechanisms of selectivity and competition between Na+, K+, and inhibitory amines remain unclear. We measured the effects of external tetrapropylammonium (TPA+) and ethylenediamine (EDA2+) on three different Na/K pump transport modes in voltage-clamped Xenopus oocytes: 1) outward pump current (IP), 2) passive inward H+ current at negative voltages without Na+ or K+ (IH), and 3) transient charge movement reporting the voltage-dependent extracellular binding/release of Na+ (QNa). Both amines competed with K+ to inhibit IP. TPA+ inhibited IH without competing with H+, whereas EDA2+ did not alter IH at pH 7.6. TPA+ competed with Na+ in QNa measurements, reducing Na+-apparent affinity, evidenced by a ∼-75 mV shift in the charge-voltage curve (at 20 mM TPA+) without reduction of the total charge moved (Qtot). In contrast, EDA2+ and K+ did not compete with Na+ to inhibit QNa; both reduced Qtot without decreasing Na+-apparent affinity. EDA2+ (15 mM) right-shifted the charge-voltage curve by ∼+50 mV. Simultaneous occlusion of EDA2+ and Na+ by an E2P conformation unable to reach E1P was demonstrated by voltage-clamp fluorometry. Trypsinolysis experiments showed that EDA2+-bound pumps are much more proteolysis-resistant than Na+-, K+-, or TPA+-bound pumps, therefore uncovering unique EDA2+-bound conformations. K+ effects on QNa and IH were also evaluated in pumps inhibited with beryllium fluoride, a phosphate mimic. K+ reduced Qtot without shifting the charge-voltage curve, indicating noncompetitive effects, and partially inhibited IH to the same extent as TPA+ in non-beryllium-fluorinated pumps. These results demonstrate that K+ interacts with beryllium-fluorinated pumps inducing conformational changes that alter QNa and IH, suggesting that there are two external access pathways for proton transport by IH.

Intracellular Requirements for Passive Proton Transport through the Na+,K+-ATPase.

The Na+,K+-ATPase (NKA or Na/K pump) hydrolyzes one ATP to exchange three intracellular Na+ (Na+i) for two extracellular K+ (K+o) across the plasma membrane by cycling through a set of reversible transitions between phosphorylated and dephosphorylated conformations, alternately opening ion-binding sites externally (E2) or internally (E1). With subsaturating [Na+]o and [K+]o, the phosphorylated E2P conformation passively imports protons generating an inward current (IH), which may be exacerbated in NKA-subunit mutations associated with human disease. To elucidate the mechanisms of IH, we studied the effects of intracellular ligands (transported ions, nucleotides, and beryllium fluoride) on IH and, for comparison, on transient currents measured at normal Na+o (QNa). Utilizing inside-out patches from Xenopus oocytes heterologously expressing NKA, we observed that 1) in the presence of Na+i, IH and QNa were both activated by ATP, but not ADP; 2) the [Na+]i dependence of IH in saturating ATP showed K0.5,Na = 1.8 ± 0.2 mM and the [ATP] dependence at saturating [Na+]i yielded K0.5,ATP = 48 ± 11 µM (in comparison, Na+i-dependent QNa yields K0.5,Na = 0.8 ± 0.2 mM and K0.5,ATP = 0.43 ± 0.03 µM; 3) ATP activated IH in the presence of K+i (∼15% of the IH observed in Na+i) only when Mg2+i was also present; and 4) beryllium fluoride induced maximal IH even in the absence of nucleotide. These data indicate that IH occurs when NKA is in an externally open E2P state with nucleotide bound, a conformation that can be reached through forward Na/K pump phosphorylation of E1, with Na+i and ATP, or by backward binding of K+i to E1, which drives the pump to the occluded E2(2K), where free Pi (at the micromolar levels found in millimolar ATP solutions) promotes external release of occluded K+ by backdoor NKA phosphorylation. Maximal IH through beryllium-fluorinated NKA indicates that this complex mimics ATP-bound E2P states.

Longitudinal active living research to address physical inactivity and sedentary behaviour in children in transition from preadolescence to adolescence.

BACKGROUND: Children can be highly active and highly sedentary on the same day! For instance, a child can spend a couple of hours playing sports, and then spend the rest of the day in front of a screen. A focus on examining both physical activity and sedentary behaviour throughout the day and in all seasons in a year is necessary to generate comprehensive evidence to curb childhood obesity. To achieve this, we need to understand where within a city are children active or sedentary in all seasons. This active living study based in Saskatoon, Canada, aims to understand the role played by modifiable urban built environments in mitigating, or exacerbating, seasonal effects on children's physical activity and sedentary behaviour in a population of children in transition from preadolescence to adolescence. METHODS/DESIGN: Designed as an observational, longitudinal investigation this study will recruit 800 Canadian children 10-14 years of age. Data will be obtained from children representing all socioeconomic categories within all types of neighbourhoods built in a range of urban designs. Built environment characteristics will be measured using previously validated neighbourhood audit and observational tools. Neighbourhood level socioeconomic variables customized to Saskatoon neighbourhoods from 2011 Statistics Canada's National Household Survey will be used to control for neighbourhood social environment. The validated Smart Cities Healthy Kids questionnaire will be administered to capture children's behaviour and perception of a range of factors that influence their activity, household (including family socioeconomic factors), parental, peer and neighbourhood influence on independent mobility. The outcome measures, different intensities of physical activity and sedentary behaviour, will be collected using global positioning system equipped accelerometers in all four seasons. Each accelerometry cycle will be matched with weather data obtained from Environment Canada. Extensive weather data will be accessed and classified into one of six distinct air mass categories for each day of accelerometry. Computational and spatial analytical techniques will be utilized to understand the multi-level influence of environmental exposures on physical activity and sedentary behaviour in all seasons. DISCUSSION: This approach will help us understand the influence of urban environment on children's activity, thus paving the way to modify urban spaces to increase physical activity and decrease sedentary behaviour in children in all four seasons. Lack of physical activity and rising sedentariness is associated with rising childhood obesity, and childhood obesity in turn is linked to many chronic conditions over the life course. Understanding the interaction of children with urban spaces will reveal new knowledge, and when translated to actions will provide a strong basis for informing future urban planning policy.

Retrospective Review of Critically Ill Patients Experiencing Alcohol Withdrawal: Dexmedetomidine Versus Propofol and/or Lorazepam Continuous Infusions.

BACKGROUND: Alcohol withdrawal symptoms can be difficult to manage and may lead to an intensive care unit (ICU) admission. Patients experiencing severe alcohol withdrawal often require high doses of sedatives, which can lead to respiratory depression and the need for endotracheal intubation. Dexmedetomidine, an alpha-2 adrenoreceptor agonist, provides adequate sedation with little effect on respiratory function when compared to other sedatives. OBJECTIVE: To evaluate sedation with a continuous infusion of dexmedetomidine versus propofol and/or lorazepam in critically ill patients experiencing alcohol withdrawal. METHODS: A retrospective chart review was conducted on ICU admissions between March 2002 and April 2009 for alcohol withdrawal patients who necessitated treatment with a continuous infusion of dexmedetomidine, propofol, and/or lorazepam. Primary outcomes included the incidence of mechanical ventilation, length of mechanical ventilation (if applicable), and ICU and hospital length of stay. RESULTS: Fifteen patients were treated with a continuous infusion of dexmedetomidine, and 17 were treated with an infusion of propofol and/or lorazepam. Two patients (13.3%) required intubation and mechanical ventilation in the dexmedetomidine group versus 10 (58.8%) in the propofol and/or lorazepam group (P = .006). Length of stay in the ICU was 53 hours for patients treated with dexmedetomidine versus 114.9 hours in the propofol and/or lorazepam group (P = .016). Hospital length of stay was less for the dexmedetomidine group, 135.8 hours versus 241.1 hours in the propofol and/or lorazepam group (P = .008). CONCLUSIONS: Dexmedetomidine use was associated with a decrease in the incidence of endotracheal intubation when used to sedate patients experiencing alcohol withdrawal. Patients transferred to a lower level of care faster and were discharged from the hospital sooner when treated with dexmedetomidine.

Changes in physical activity among Canadian adults more than 6 months into the COVID-19 pandemic: a secondary analysis of the INTERACT cohort study.

OBJECTIVE: To estimate the effect of (a) the COVID-19 pandemic and (b) COVID-19 restriction stringency on daily minutes of device-measured moderate-to-vigorous physical activity (MVPA). DESIGN: Physical activity data were collected from the INTerventions, Equity, Research and Action in Cities Team (INTERACT) cohorts in Montreal, Saskatoon and Vancouver before (May 2018 to February 2019, 'phase 1') and during the pandemic (October 2020 to February 2021, 'phase 2'). We estimated the effect of the two exposures by comparing daily MVPA measured (a) before vs during the pandemic (phase 1 vs phase 2) and (b) at different levels of COVID-19 restriction stringency during phase 2. Separate mixed effects negative binomial regression models were used to estimate the association between each exposure and daily MVPA, with and without controlling for confounders. Analyses were conducted on person-days with at least 600 min of wear time. Effect modification by gender, age, income, employment status, education, children in the home and city was assessed via stratification. SETTING: Montreal (Quebec), Saskatoon (Saskatchewan) and Vancouver (British Columbia), Canada. MAIN OUTCOME MEASURE: Daily minutes of MVPA, as measured using SenseDoc, a research-grade accelerometer device. RESULTS: Daily minutes of MVPA were 21% lower in phase 2 (October 2020 to February 2021) compared with phase 1 (May 2018 to February 2019), controlling for gender, age, employment status, household income, education, city, weather and wear time (rate ratio=0.79, 95% CI 0.69, 0.92). This did not appear to be driven by changes in the sample or timing of data collection between phases. The results suggested effect modification by employment, household income and education. Restriction stringency was not associated with daily MVPA between October 2020 and February 2021 (adjusted rate ratio=0.99, 95% CI 0.96, 1.03). CONCLUSIONS: Between October 2020 and February 2021, daily minutes of MVPA were significantly lower than 2 years prior, but were not associated with daily COVID-19 restriction stringency.

Wave 1 results of the INTerventions, Research, and Action in Cities Team (INTERACT) cohort study: Examining spatio-temporal measures for urban environments and health.

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.

Leveraging H1N1 infection transmission modeling with proximity sensor microdata.

BACKGROUND: The contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network. The impact of the interaction between contact network and disease dynamics on infection spread has been investigated using both synthetic and empirically gathered micro-contact data, establishing the utility of micro-contact data for epidemiological insight. However, the infection models tied to empirical contact data were highly stylized and were not calibrated or compared against temporally coincident infection rates, or omitted critical non-network based risk factors such as age or vaccination status. METHODS: In this paper we present an agent-based simulation model firmly grounded in disease dynamics, incorporating a detailed characterization of the natural history of infection, and 13 weeks worth of micro-contact and participant health and risk factor information gathered during the 2009 H1N1 flu pandemic. RESULTS: We demonstrate that the micro-contact data-based model yields results consistent with the case counts observed in the study population, derive novel metrics based on the logarithm of the time degree for evaluating individual risk based on contact dynamic properties, and present preliminary findings pertaining to the impact of internal network structures on the spread of disease at an individual level. CONCLUSIONS: Through the analysis of detailed output of Monte Carlo ensembles of agent based simulations we were able to recreate many possible scenarios of infection transmission using an empirically grounded dynamic contact network, providing a validated and grounded simulation framework and methodology. We confirmed recent findings on the importance of contact dynamics, and extended the analysis to new measures of the relative risk of different contact dynamics. Because exponentially more time spent with others correlates to a linear increase in infection probability, we conclude that network dynamics have an important, but not dominant impact on infection transmission for H1N1 transmission in our study population.

Temporal aggregation impacts on epidemiological simulations employing microcontact data.

BACKGROUND: Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes. METHODS: We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks. RESULTS: No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation. CONCLUSIONS: The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

Why machine learning (ML) has failed physical activity research and how we can improve.

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.

A theory-based model of cumulative activity.

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.

The Effect of Season and Neighbourhood-Built Environment on Home Area Sedentary Behaviour in 9-14 Year Old Children.

There is little understanding of how the built environment shapes activity behaviours in children over different seasons. This study sought to establish how seasonal weather patterns, in a given year in a mid-western Canadian city, affect sedentary time (SED) in youth and how the relationship between season and SED are moderated by the built environment in their home neighbourhood. Families with children aged 9-14 years were recruited from the prairie city of Saskatoon, Canada. Location-specific, device-based SED was captured in children during three timeframes over a one-year period using GPS-paired accelerometers. Multilevel models are presented. Children accumulated significantly greater levels of SED in spring but significantly less SED in the fall months in comparison to the winter months. Children living in neighbourhoods with the highest density of destinations accumulated significantly less SED while in their home area in comparison to their counterparts, and this effect was more pronounced in the spring and summer months. On weekends, the rise in sedentariness within the home area was completely diminished in children living in neighbourhoods with the greatest number of destinations and highest activity friendliness. These results suggested that increasing neighbourhood amenities can lead to a reduced sedentariness of youth, though more so in the warmers months of the year.

The future of activity space and health research.

We discuss the future of activity space and health research in the context of a recently published systematic review. Our discussion outlines a number of elements for reflection among the research community. We need to think beyond activity space and reconceptualize exposure in era of high volume, high precision location data. We need to develop standardized methods for understanding global positioning system data. We must adopt replicable scientific computing processes and machine learning models. Finally, we must embrace modern notions of causality in order to contend with the conceptual challenges faced by our research field.

Intrinsic dimensionality of human behavioral activity data.

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.