Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review

Abstract

Background Physical 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. Methods This 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. Discussion This 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 registration This protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb.

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

“…In general, accelerometers can be worn on various locations of the body (e.g., hip, wrist, thigh, ankle, chest). Among them, the hip is the most common placement site, particularly for large epidemiological and intervention studies [6,18]. Yet, in recent years, there has been a shift from placement at the hip to placement at the wrist.…”

“…Yet, in recent years, there has been a shift from placement at the hip to placement at the wrist. For example, the National Health and Nutrition Examination Survey (NHANES) previously employed hip-worn uniaxial accelerometers to measure PA (2003-2004 and 2005-2006) but this has recently changed to wrist-worn triaxial accelerometers during recent population-level PA surveillance (2011-2014) among individuals aged ≥6 years [6]. Some advantages of the wrist-worn placement include improved user compliance (e.g., increased wear compliance, longer wear times), being perceived as less intrusive, with an improved capability of assessing sleep behaviors, and with the capability of capturing more upper extremity movements which cannot be captured with hip-placed devices [19][20][21][22].…”

“…This calls for the need to process the raw data to generate meaningful outcomes for use in scientific communities. Currently, there are several raw accelerometer data processing methods to objectively analyze physical behaviors [3,6]. Yet, there is little consensus regarding the most appropriate methods to generate meaningful and interpretable activity metrics (e.g., cut point-free accelerometer metrics) that can be used to facilitate comparison and data harmonization across studies [7].…”

Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2

“…While using conventional geriatric assessment tools or questionnaires provides information on the purpose of the activity, wearable devices can quantify the motion performed. Both provide complementary information to the researchers, which are not interchangeable [ 28 , 29 ]. Likewise, as supported by Yang et al [ 30 ], studies in fall risk assessment should recommend using wearable technologies to supplement nursing home assessment tools.…”

Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review

“…Therefore, the main purpose of this scoping review is to map the wearable‐specific indicators that can be computed from tri‐axial accelerometers and used to provide an all‐encompassing assessment of the PA behavior of an individual in order to provide a systematic summary of available measures. 16 In this review, we focus on WIPAB derived from tri‐axial accelerometer data only (raw and summary‐level data) due to their widespread use in research. The secondary aims consist in identifying the main strengths and limitations of these WIPAB and to identify those that have been used to study the association between PA and certain health conditions.…”