Mapping features and patterns of accelerometry data on human movement in different age groups and associated health problems: A cross-sectional study.

PURPOSE: Human movement is considered one of the important factors for maintaining an independent life. Individuals in different age groups have different characteristics of locomotion patterns and some health conditions can affect or be affected by mobility changes. Few studies clarify or present data about the influence of different ages and biopsychosocial factors on accelerometry features. The aim of this study was to identify characteristics and variables in the frequency signals for different age groups and their relationship with associated health conditions in raw accelerometry data obtained from the use of a triaxial accelerometer during 7 days of activities of daily living. METHOD: A cross-sectional study was conducted based on the database of the first evaluations of the Epidemiological Study of Movement (EPIMOV) cohort. Frequency, signal amplitude, and entropy accelerometry features of EPIMOV participants who used a triaxial accelerometer for 7 days were extracted. Sociodemographic, clinical, anthropometric and physical activity assessments were also performed. Two-way ANOVA was performed to compare accelerometry features within different age groups. A series of stepwise multiple regressions were performed on accelerometry variables to analyze their relationships with demographic, anthropometric and cardiovascular risk variables. RESULTS: The sample consisted mostly of female, white, and high school graduates. The most prevalent cardiovascular risk factors were sedentary behavior and obesity. When analyzing the accelerometry variables, it was possible to observe that the entropy feature, and the counts, decrease in the group of older adults, while the feature of harmonic components of gait (frequency × amplitude) increases in the group of older adults. Regarding the amplitude feature, there were no significant differences between the groups. Through stepwise multiple linear regression, it was possible to observe that demographic, anthropometric and cardiovascular risk factors are associated with most accelerometry variables. CONCLUSION: The results confirm that human movement can be influenced by different ages, sex, demographic, anthropometric and cardiovascular risk factors. Future studies and clinical analyzes can use the methods proposed in this research to adjust movement patterns for sex and different age groups, thus obtaining new interpretations about human movement.

Foreseeing future falls with accelerometer features in active community-dwelling older persons with no recent history of falls.

BACKGROUND: Acceleration sensors are a viable option for monitoring gait patterns and its application on monitoring falls and risk of falling. However the literature still lacks prospective studies to investigate such risk before the occurrence of falls. OBJECTIVE: To investigate features extracted from accelerometer signals with the purpose of predicting future falls in individuals with no recent history of falls. METHODS: In this study we investigate the risk of fall in active and healthy community-dwelling living older persons with no recent history of falls, using a single accelerometer and variants of the Timed Up and Go (TUG) test. A prospective study was conducted with 74 healthy non-fallers older persons. After collecting acceleration data from the participants at the baseline, the occurrence of falls (outcome) was monitored quarterly during one year. A set of frequency features were extracted from the signal and their ability to predict falls was evaluated. RESULTS: The best individual feature result shows an accuracy of 0.75, sensitivity of 0.71 and specificity of 0.76. A fusion of the three best features increases the sensitivity to 0.86. On the other hand, the cut-off points of the TUG seconds, often used to assess fall risk, did not demonstrate adequate sensitivity. CONCLUSION: The results confirms previous evidence that accelerometer features can better estimate fall risk, and support potential applications that try to infer falls risk in less restricted scenarios, even in a sample stratified by age and gender composed of active and healthy community-dwelling living older persons.

Tailoring digital apps to support active ageing in a low income community.

Despite physical activity being one of the determinants of healthy aging, older people tend to become less active over the years. Maintaining physical activity levels during the life course is a motivational challenge. Digital tools have been used to change this pattern, such as smartphone applications to support physical activity; but there is a lack of in-depth research on the diversity of user's experiences, especially considering older users or non-users of information and communication technologies. OBJECTIVE: Our goal was to identify requirements for designing a mobile app to encourage physical activity in a low-income community population of older people in Brazil (i.e. over 40 years old). METHOD: We conducted a qualitative focus group study, involving by co-design of a physical activity application (Pacer)®. Seventeen volunteers were divided into 2 focus groups of physical active and insufficiently active, and 2 further 4 subgroups in each characterised by digital engagement. The following procedures were performed: (i) baseline assessments; (ii) a focus group with physically active older people and a focus group with insufficiently active older people (iii) design activities with both groups to re-design Pacer. RESULTS: Developing physical activity apps for older people should consider the following features: free application, simple interface, motivational messages using audio and visual information, sharing information among users, multimedia input and sharing and user customisation. In particular, we recommend that exercise apps in low-income communities be tailored to our four categories of users differing in baseline physical activity and digital engagement, to match the social and behavioural preferences we discovered.

Fall detection and fall risk assessment in older person using wearable sensors: A systematic review.

BACKGROUND: wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. OBJECTIVE: To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies. METHODS: A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application. RESULTS: We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application. CONCLUSION: This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.

Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go.

Devices and sensors for identification of fallers can be used to implement actions to prevent falls and to allow the elderly to live an independent life while reducing the long-term care costs. In this study we aimed to investigate the accuracy of Timed Up and Go test, for fallers' identification, using fusion of features extracted from accelerometer data. Single and dual tasks TUG (manual and cognitive) were performed by a final sample (94% power) of 36 community dwelling healthy older persons (18 fallers paired with 18 non-fallers) while they wear a single triaxial accelerometer at waist with sampling rate of 200Hz. The segmentation of the TUG different trials and its comparative analysis allows to better discriminate fallers from non-fallers, while conventional functional tests fail to do so. In addition, we show that the fusion of features improve the discrimination power, achieving AUC of 0.84 (Sensitivity = Specificity = 0.83, 95% CI 0.62-0.91), and demonstrating the clinical relevance of the study. We concluded that features extracted from segmented TUG trials acquired with dual tasks has potential to improve performance when identifying fallers via accelerometer sensors, which can improve TUG accuracy for clinical and epidemiological applications.