Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review.
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SUBMITTER: Backes A
PROVIDER: S-EPMC9298329 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPhysical activity (PA) is a complex multidimensional human behaviour. Currently, there is no standardised approach for measuring PA using wearable accelerometers in health research. The total volume of PA is an important variable because it includes the frequency, intensity and duration of activity bouts, but it reduces them down to a single summary variable. Therefore, analytical approaches using accelerometer raw time series data taking into account the way PA are accumulated over time may provide more clinically relevant features of physical behaviour. Advances on these fields are highly needed in the context of the rapid development of digital health studies using connected trackers and smartwatches. The objective of this review will be to map advanced analytical approaches and their multidimensional summary variables used to provide a comprehensive picture of PA behaviour.MethodsThis scoping review will be guided by the Arksey and O'Malley methodological framework. A search for relevant publications will be undertaken in MEDLINE (PubMed), Embase and Web of Science databases. The selection of articles will be limited to studies published in English from January 2010 onwards. Studies including analytical methods that go beyond total PA volume, average daily acceleration and the conventional cut-point approaches, involving tri-axial accelerometer data will be included. Two reviewers will independently screen all citations, full-text articles and extract data. The data will be collated, stored and charted to provide a descriptive summary of the analytical methods and outputs, their strengths and limitations and their association with different health outcomes.DiscussionThis protocol describes a systematic method to identify, map and synthesise advanced analytical approaches and their multidimensional summary variables used to investigate PA behaviour and identify potentially clinically relevant features. The results of this review will be useful to guide future research related to analysing PA patterns, investigate their association with health conditions and suggest appropriate recommendations for changes in PA behaviour. The results may be of interest to sports scientists, clinical researchers, epidemiologists and smartphone application developers in the field of PA assessment.Scoping review registrationThis protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb .
| S-EPMC7648952 | biostudies-literature
Project description:BackgroundActivity-Based Funding (ABF) has been implemented across many countries as a means to incentivise efficient hospital care delivery and resource use. Previous reviews have assessed the impact of ABF implementation on a range of outcomes across health systems. However, no comprehensive review of the methods used to generate this evidence has been undertaken. The aim of this review is to identify and assess the analytical methods employed in research on ABF hospital performance outcomes.MethodsWe conducted a scoping review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. Five academic databases and reference lists of included studies were used to identify studies assessing the impact of ABF on hospital performance outcomes. Peer-reviewed quantitative studies published between 2000 and 2019 considering ABF implementation outside the U.S. were included. Qualitative studies, policy discussions and commentaries were excluded. Abstracts and full text studies were double screened to ensure consistency. All analytical approaches and their relative strengths and weaknesses were charted and summarised.ResultsWe identified 19 studies that assessed hospital performance outcomes from introduction of ABF in England, Korea, Norway, Portugal, Israel, the Netherlands, Canada, Italy, Japan, Belgium, China, and Austria. Quasi-experimental methods were used across most reviewed studies. The most commonly used assessment methods were different forms of interrupted time series analyses. Few studies used difference-in-differences or similar methods to compare outcome changes over time relative to comparator groups. The main hospital performance outcome measures examined were case numbers, length of stay, mortality and readmission.ConclusionsNon-experimental study designs continue to be the most widely used method in the assessment of ABF impacts. Quasi-experimental approaches examining the impact of ABF implementation on outcomes relative to comparator groups not subject to the reform should be applied where possible to facilitate identification of effects. These approaches provide a more robust evidence-base for informing future financing reform and policy.
| S-EPMC8132407 | biostudies-literature
Project description:BackgroundAccurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children.MethodsWe searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN).ResultsSixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes.ConclusionsUp to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality.Prospero registration numberCRD42020184751.
| S-EPMC9461103 | biostudies-literature
Project description:OBJECTIVES:Interrupted time series (ITS) design involves collecting data across multiple time points before and after the implementation of an intervention to assess the effect of the intervention on an outcome. ITS designs have become increasingly common in recent times with frequent use in assessing impact of evidence implementation interventions. Several statistical methods are currently available for analysing data from ITS designs; however, there is a lack of guidance on which methods are optimal for different data types and on their implications in interpreting results. Our objective is to conduct a scoping review of existing methods for analysing ITS data, to summarise their characteristics and properties, as well as to examine how the results are reported. We also aim to identify gaps and methodological deficiencies. METHODS AND ANALYSIS:We will search electronic databases from inception until August 2016 (eg, MEDLINE and JSTOR). Two reviewers will independently screen titles, abstracts and full-text articles and complete the data abstraction. The anticipated outcome will be a summarised description of all the methods that have been used in analysing ITS data in health research, how those methods were applied, their strengths and limitations and the transparency of interpretation/reporting of the results. We will provide summary tables of the characteristics of the included studies. We will also describe the similarities and differences of the various methods. ETHICS AND DISSEMINATION:Ethical approval is not required for this study since we are just considering the methods used in the analysis and there will not be identifiable patient data. Results will be disseminated through open access peer-reviewed publications.
| S-EPMC5726134 | biostudies-literature
Project description:(1) Background: Physical inactivity is prevalent in rural heart failure (HF) patients. To evaluate the effectiveness of interventions aimed at improving physical activity (PA), we need an accurate, reliable PA assessment tool that is feasible and acceptable to HF patients. The purpose of this study was to examine the feasibility and reliability of using an accelerometer to assess HF patients' PA. (2) Method: A total of 100 HF patients discharged from a rural hospital participated in the study and wore an accelerometer at baseline, 3, and 6 months following discharge. (3) Result: The daily average wear time across all three time points was 15.7 (±3.3) h for weekdays, and 15.8 (±3.7) h for weekends. Approximately 50% of the participants adhered to the device wear protocol at baseline, 3, and 6 months. Factors related to wear time were also examined. Acceptable reliability assessed by intra-class correlation (ICC > 0.879) was found for daily activity calories, activity counts per minutes, and time spent on moderate or greater PA. (4) Conclusion: The present findings suggest that an accelerometer is a feasible and reliable measure of habitual PA in rural HF patients over time.
| S-EPMC6371161 | biostudies-literature
Project description:Physical activity (PA) has been shown to associate with many health benefits but studies with metabolome-wide associations with PA are still lacking. Metabolome studies may deepen the mechanistic understanding of PA on the metabolic pathways related to health outcomes. The aim of the present study was to study the association of accelerometer based sedentary time (SB) and PA with metabolome measures. SB and PA were measured by a hip-worn accelerometer in 314 young adult men (age: mean 28, standard deviation 7 years). Metabolome was analyzed from fasting serum samples consisting of 66 metabolome measures (nuclear magnetic resonance-based metabolomics). The associations were analyzed using a single and compositional approach with regression analysis. The compositional analysis revealed that 4 metabolome variables were significantly (γ: 0.32-0.44, p ≤ 0.002), and 13 variables with a trend towards significance (p < 0.05), associated with SB with varying metabolic pathways. Trends towards significant associations (p < 0.05) were observed with 5 variables with moderate-to-vigorous and 1 variable with light intensity PA with varying metabolic pathways. The present study revealed possible mechanistic pathways relevant for the interaction between especially SB but also PA of moderate-to-vigorous intensity with ketone bodies and amino acid concentration related to exercised-induced energy production and lipid metabolism.
| S-EPMC9414649 | biostudies-literature
Project description:The 2003-2004 and 2005-2006 cycles of the National Health and Nutrition Examination Survey (NHANES) were among the first population-level studies to incorporate objectively measured physical activity and sedentary behavior, allowing for greater understanding of these behaviors. However, there has yet to be a comprehensive examination of these data in cancer survivors, including short- and long-term survivors of all cancer types. Therefore, the purpose of this analysis was to use these data to describe activity behaviors in short- and long-term cancer survivors of various types. A secondary aim was to compare activity patterns of cancer survivors to that of the general population. Cancer survivors (n = 508) and age-matched individuals not diagnosed with cancer (n = 1,016) were identified from a subsample of adults with activity measured by accelerometer. Physical activity and sedentary behavior were summarized across cancer type and demographics; multivariate regression was used to evaluate differences between survivors and those not diagnosed with cancer. On average, cancer survivors were 61.4 (95% CI: 59.6, 63.2) years of age; 57% were female. Physical activity and sedentary behavior patterns varied by cancer diagnosis, demographic variables, and time since diagnosis. Survivors performed 307 min/day of light-intensity physical activity (95% CI: 295, 319), 16 min/day of moderate-vigorous intensity activity (95% CI: 14, 17); only 8% met physical activity recommendations. These individuals also reported 519 (CI: 506, 532) minutes of sedentary time, with 86 (CI: 84, 88) breaks in sedentary behavior per day. Compared to non-cancer survivors, after adjustment for potential confounders, survivors performed less light-intensity activity (P = 0.01), were more sedentary (P = 0.01), and took fewer breaks in sedentary time (P = 0.04), though there were no differences in any other activity variables. These results suggest that cancer survivors are insufficiently active. Relative to adults of similar age not diagnosed with cancer, they engage in more sedentary time with fewer breaks. As such, sedentary behavior and light-intensity activity may be important intervention targets, particularly for those for whom moderate-to-vigorous activity is not well accepted.
| S-EPMC5555687 | biostudies-other
Project description:BackgroundHealthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.MethodThis review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.ResultsThe literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.ConclusionVarious data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.
| S-EPMC10523647 | biostudies-literature
Project description:ObjectivesPopular methods for analyzing accelerometer data often use a single physical activity outcome variable such as average-weekly or total physical activity. These approaches limit the types of research questions that can be answered and fail to utilize the detailed, time-specific information available from accelerometers. This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods.DesignThe motivating example was the Active by Choice Today trial. Simulations were used to test whether the application of time-specific hypotheses about when physical activity intervention treatment effects were expected to occur (e.g., after-school hours) increased power to detect effects compared to traditional methods.MethodsSix simulation conditions were tested: (1) no treatment effects (to test the type 1 error rate), (2) time-specific effects, but no traditionally-tested effects, (3) traditionally-tested effects, but no time-specific effects, and (4) combinations of traditional and time-specific effects in 3 proportions.ResultsResults showed the proposed multilevel approach demonstrated appropriate type 1 error rates and increased power to detect treatment effects during hypothesized times by 31-38 percentage points compared to traditional approaches. This was consistent across varying proportions of traditional versus time-specific effects, and there was no loss of power using the multilevel approach when only traditional effects were present.ConclusionsThe current study showed potential advantages of testing time-specific hypotheses about intervention effects using a multilevel time-specific approach. This approach may show intervention effects when traditional approaches do not. Future research should explore the application of this additional analytic tool for accelerometer physical activity estimates.
| S-EPMC4362866 | biostudies-literature
Project description:ObjectiveThe objective of this study was to pool, harmonise and re-analyse national accelerometer data from adults in four European countries in order to describe population levels of sedentary time and physical inactivity.MethodsFive cross-sectional studies were included from England, Portugal, Norway and Sweden. ActiGraph accelerometer count data were centrally processed using the same algorithms. Multivariable logistic regression analyses were conducted to study the associations of sedentary time and physical inactivity with sex, age, weight status and educational level, in both the pooled sample and the separate study samples.ResultsData from 9509 participants were used. On average, participants were sedentary for 530 min/day, and accumulated 36 min/day of moderate to vigorous intensity physical activity. Twenty-three percent accumulated more than 10 h of sedentary time/day, and 72% did not meet the physical activity recommendations. Nine percent of all participants were classified as high sedentary and low active. Participants from Norway showed the highest levels of sedentary time, while participants from England were the least physically active. Age and weight status were positively associated with sedentary time and not meeting the physical activity recommendations. Men and higher-educated people were more likely to be highly sedentary, while women and lower-educated people were more likely to be inactive.ConclusionsWe found high levels of sedentary time and physical inactivity in four European countries. Older people and obese people were most likely to display these behaviours and thus deserve special attention in interventions and policy planning. In order to monitor these behaviours, accelerometer-based cross-European surveillance is recommended.
| S-EPMC5488150 | biostudies-literature