Dataset Information

Statistical methods to model and evaluate physical activity programs, using step counts: A systematic review.

ABSTRACT:

Background

Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world's adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.

Methods

Adhering to PRISMA guidelines, this review systematically searched for relevant journal articles which were published between January 2000 and August 2017 in any of three databases (PubMed, PsycINFO and Web of Science). Only the journal articles which used a statistical model in analysing step counts for a healthy sample of participants, enrolled in an intervention involving physical exercise or a physical activity program, were included in this study. In these programs the activities considered were natural elements of everyday life rather than special activity interventions.

Results

This systematic review was able to identify 78 unique articles describing statistical models for analysing step counts obtained through physical activity programs. General linear models and generalized linear models were the most popular methods used followed by multilevel models, while structural equation modeling was only used for measuring the personal and psychological factors related to step counts. Surprisingly no use was made of time series analysis for analysing step count data. The review also suggested several strategies for the personalisation of physical activity programs.

Conclusions

Overall, it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial, demographic, weather and climatic factors. Statistical models can provide a better understanding of the impact of these factors, allowing for the provision of more personalised physical activity programs, which are expected to produce better immediate and long-term outcomes for participants. It is hoped that this review will identify the statistical methods which are most suitable for this purpose.

SUBMITTER: Silva SSM

PROVIDER: S-EPMC6214537 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Statistical methods to model and evaluate physical activity programs, using step counts: A systematic review.

Silva S S M SSM Jayawardana Madawa W MW Meyer Denny D

PloS one 20181102 11

<h4>Background</h4>Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world's adolescents are insufficiently physically active. In response to this prob ...[more]

PMID: 30388164

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Project description:PURPOSE:A systematic primary literature review was conducted to evaluate the relationship of physical activity-as measured by daily step counts-with all-cause mortality, cardiovascular disease mortality, incident cardiovascular disease, and type 2 diabetes mellitus; to evaluate the shape of dose-response relationships; and to interpret findings in the context of development of the Physical Activity Guidelines for Americans, Second Edition. METHODS:A primary literature search encompassing 2011 to March 2018 for existing literature reporting on these relationships was conducted. RESULTS:Eleven pertinent articles were identified. Seven longitudinal studies examined the relationship between daily step counts and mortality, disease incidence, or risk. Two studies examined objectively measured steps per day and all-cause mortality; one was restricted to a relatively small elderly population. One study examined cardiovascular events, defined as cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke. The other four longitudinal studies addressed incident type 2 diabetes. All longitudinal studies reported an inverse relationship between steps per day and outcome risk. In one study, 531 cardiovascular events occurred during more than 45,000 person-years of follow-up. Before intervention, each increment of 2000 steps per day up to 10,000 steps was associated with a 10% lower cardiovascular event rate. Also, for every increase of 2000 steps per day over baseline, there was an 8% yearly reduction in cardiovascular event rate in individuals with impaired glucose tolerance. CONCLUSIONS:Daily step count is a readily accessible means by which to monitor and set physical activity goals. Recent evidence supports previously limited evidence of an inverse dose-response relationship of daily steps with important health outcomes, including all-cause mortality, cardiovascular events, and type 2 diabetes. However, more independent studies will be required before these observations can be translated into public health guidelines.

Project description:Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks.

Project description:ObjectivesThis paper describes the development of a physical activity questionnaire (PAQ) designed for Chinese adolescents and their mothers in urban and rural settings, and reports on results of the PAQ, pedometry, and hand grip dynamometry from the Chinese Children and Families Cohort Study pilot investigation (CFCS).MethodsAs part of a pilot investigation to evaluate the feasibility to follow-up and obtain detailed nutrition, dietary, physical activity, and ultraviolet radiation (UVR) data from CFCS participants, data were collected in 2013 for 93 adolescent/mother pairs from a rural (n = 41) and an urban site (n = 52) in two provinces. Respondents were asked to wear a pedometer for seven days (Omron HJ-151), use a Takei Digital Grip Strength Dynamometer on (each hand; three trials; two separate days), and complete a 39 item, eight domain PAQ covering the past year. Self-reported physical activity (PA) was linked to metabolic equivalent of task (MET) scores in kcal/kg/hr and used to calculate METs for different domains of PA and intensity categories.ResultsCompliance was high (95%) in this measurement protocol administered by health staff during a series of data collection efforts at home and local clinics or health centers. Step counts were highly variable, averaging between 5000 and 10000 per day with somewhat higher step counts in rural adolescent boys. Maximum grip strength (Kgs) was greater in children (Mean = 36.5, SE = 0.8) than mothers (Mean = 28.8, SE = 0.8) and similar in the urban (Mean = 29.6, SE = 0.6) compared to the rural (Mean = 29.6, SE = 0.5) communities overall. Grip strength, step counts, and measures of time spent in different activities or activity intensities were uncorrelated.ConclusionDevice and question-based measurement of PA and strength were readily accepted in these Chinese urban and rural populations. The PAQ on physical activity in the past year produced some plausible population averages, but individual responses suggested recall challenges. If data about specific activities are required, future studies should explore use of standardized survey questions concerning such fewer specific activities or instruments examining shorter time periods such as one, three, or seven day recalls.

Project description:BackgroundRegular physical activity (PA) is crucial for well-being; however, healthy habits are difficult to create and maintain. Interventions delivered via conversational agents (eg, chatbots or virtual agents) are a novel and potentially accessible way to promote PA. Thus, it is important to understand the evolving landscape of research that uses conversational agents.ObjectiveThis mixed methods systematic review aims to summarize the usability and effectiveness of conversational agents in promoting PA, describe common theories and intervention components used, and identify areas for further development.MethodsWe conducted a mixed methods systematic review. We searched seven electronic databases (PsycINFO, PubMed, Embase, CINAHL, ACM Digital Library, Scopus, and Web of Science) for quantitative, qualitative, and mixed methods studies that conveyed primary research on automated conversational agents designed to increase PA. The studies were independently screened, and their methodological quality was assessed using the Mixed Methods Appraisal Tool by 2 reviewers. Data on intervention impact and effectiveness, treatment characteristics, and challenges were extracted and analyzed using parallel-results convergent synthesis and narrative summary.ResultsIn total, 255 studies were identified, 7.8% (20) of which met our inclusion criteria. The methodological quality of the studies was varied. Overall, conversational agents had moderate usability and feasibility. Those that were evaluated through randomized controlled trials were found to be effective in promoting PA. Common challenges facing interventions were repetitive program content, high attrition, technical issues, and safety and privacy concerns.ConclusionsConversational agents hold promise for PA interventions. However, there is a lack of rigorous research on long-term intervention effectiveness and patient safety. Future interventions should be based on evidence-informed theories and treatment approaches and should address users' desires for program variety, natural language processing, delivery via mobile devices, and safety and privacy concerns.

Dataset Information

Statistical methods to model and evaluate physical activity programs, using step counts: A systematic review.

Background

Methods

Results

Conclusions

Publications

Statistical methods to model and evaluate physical activity programs, using step counts: A systematic review.

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