Dataset Information

Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study.

ABSTRACT: OBJECTIVES:The purpose of this study was to develop a replicable methodology of mobile ecological momentary assessments (EMAs) to capture prospective physical activity (PA) within free-living social and physical contexts by leveraging citizen-owned smartphones running on both Android and iOS systems. DESIGN:Data were obtained from the cross-sectional pilots of the SMART Platform, a citizen science and mobile health initiative. SETTING:The cities of Regina and Saskatoon, Canada. PARTICIPANTS:538 citizen scientists (?18 years) provided PA data during eight consecutive days using a custom-built smartphone application, and after applying a rigid inclusion criteria, 89 were included in the final analysis. OUTCOME MEASURES:EMAs enabled reporting of light, moderate, and vigorous PA, as well as physical and social contexts of PA. Retrospective PA was reported using International Physical Activity Questionnaire (IPAQ). For both measures, PA intensities were categorised into mean minutes of light and moderate-to-vigorous PA per day. Wilcoxon signed ranks tests and Spearman correlation procedures were conducted to compare PA intensities reported via EMAs and IPAQ. RESULTS:Using EMAs, citizen scientists reported 140.91, 87.16 and 70.38 mean min/day of overall, light and moderate-to-vigorous PA, respectively, whereas using IPAQ they reported 194.39, 116.99 and 98.42 mean min/day of overall, light and moderate-to-vigorous PA, respectively. Overall (?=0.414, p<0.001), light (?=0.261, p=0.012) and moderate-to-vigorous PA (?=0.316, p=0.009) were fairly correlated between EMA and IPAQ. In comparison with EMAs, using IPAQ, citizen scientists reported significantly greater overall PA in active transportation (p=0.002) and recreation, sport and leisure-time domains (p=0.003). CONCLUSIONS:This digital epidemiological and citizen science methodology adapted mobile EMAs to capture not only prospective PA, but also important physical and social contexts within which individuals accumulate PA. Ubiquitous tools can be leveraged via citizen science to capture accurate active living patterns of large populations in free-living conditions through innovative EMAs.

SUBMITTER: Katapally TR

PROVIDER: S-EPMC7322321 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study.

Katapally Tarun Reddy TR Chu Luan Manh LM

BMJ open 20200628 6

<h4>Objectives</h4>The purpose of this study was to develop a replicable methodology of mobile ecological momentary assessments (EMAs) to capture prospective physical activity (PA) within free-living social and physical contexts by leveraging citizen-owned smartphones running on both Android and iOS systems.<h4>Design</h4>Data were obtained from the cross-sectional pilots of the SMART Platform, a citizen science and mobile health initiative.<h4>Setting</h4>The cities of Regina and Saskatoon, Can ...[more]

PMID: 32595163

Similar Datasets

Project description:Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation-and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with limitations in terms of subject pool or decisions' context, which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate data collection protocols to maintain the same data quality that one can obtain in the laboratories. In this article we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigor, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments, the satisfaction level of participants, or the technical parameters that demonstrate the robustness of the platform and the quality of the data collected.

Project description:Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Project description:ObjectivesAdaptive platforms allow for the evaluation of multiple interventions at a lower cost and have been growing in popularity, especially during the COVID-19 pandemic. The objective of this review is to summarize published platform trials, examine specific methodological design features among these studies, and hopefully aid readers in the evaluation and interpretation of platform trial results.MethodsWe performed a systematic review of EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and clinicaltrials.gov from January 2015 to January 2022 for protocols or results of platform trials. Pairs of reviewers, working independently and in duplicate, collected data on trial characteristics of trial registrations, protocols, and publications of platform trials. We reported our results using total numbers and percentages, as well as medians with interquartile range (IQR) when appropriate.ResultsWe identified 15,277 unique search records and screened 14,403 titles and abstracts after duplicates were removed. We identified 98 unique randomized platform trials. Sixteen platform trials were sourced from a systematic review completed in 2019, which included platform trials reported prior to 2015. Most platform trials (n = 67, 68.3%) were registered between 2020 and 2022, coinciding with the COVID-19 pandemic. The included platform trials primarily recruited or plan to recruit patients from North America or Europe, with most subjects being recruited from the United States (n = 39, 39.7%) and the United Kingdom (n = 31, 31.6%). Bayesian methods were used in 28.6% (n = 28) of platform RCTs and frequentist methods in 66.3% (n = 65) of trials, including 1 (1%) that used methods from both paradigms. Out of the twenty-five trials with peer-reviewed publication of results, seven trials used Bayesian methods (28%), and of those, two (8%) used a predefined sample size calculation while the remainder used pre-specified probabilities of futility, harm, or benefit calculated at (pre-specified) intervals to inform decisions about stopping interventions or the entire trial. Seventeen (68%) peer-reviewed publications used frequentist methods. Out of the seven published Bayesian trials, seven (100%) reported thresholds for benefit. The threshold for benefit ranged from 80% to >99%.ConclusionWe identified and summarized key components of platform trials, including the basics of the methodological and statistical considerations. Ultimately, improving standardization and reporting in platform trials require an understanding of the current landscape. We provide the most updated and rigorous review of platform trials to date.

Dataset Information

Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study.

Publications

Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets