GPS-Derived Daily Mobility and Daily Well-Being in Community-Dwelling Older Adults.

INTRODUCTION: Mobility as a multidimensional concept has rarely been examined as a day-to-day varying phenomenon in its within-person association with older adults' daily well-being. This study examined associations between daily mobility and daily well-being in community-dwelling older adults with a set of GPS-derived mobility indicators that were representative of older adults' daily mobility. METHODS: Participants wore a custom-built mobile GPS sensor ("uTrail") and completed smartphone-based experience sampling questionnaires on momentary affective states (7 times per day) and daily life satisfaction (in the evening). Analyses included data across 947 days from 109 Swiss older adults aged 65-89 years. RESULTS: Multilevel modeling showed that, within persons, a day with a larger life space area, more time spent in passive transport modes, and a higher number of different locations was associated with higher daily life satisfaction but not daily positive or negative affect. Follow-up analysis showed that the daily maximum distance from home was positively associated with daily life satisfaction, providing a first indication that exposure to non-habitual environments might be a possible underlying mechanism to explain the effects of mobility. CONCLUSIONS: Traveling a long distance away from home and visiting diverse locations may be a way to improve life satisfaction. Results are discussed in the context of research on healthy aging.

Eigenbehaviour as an Indicator of Cognitive Abilities.

With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively-it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

Psychometric properties of the MOBITEC-GP mobile application for real-life mobility assessment in older adults.

Aim of this study was to test the reliability and validity of the life-space measures and walking speed delivered by the MOBITEC-GP app. Participants underwent several supervised walking speed assessments as well as a 1-week life-space assessment during two assessment sessions 9 days apart. Fifty-seven older adults (47.4% male, mean age= 75.3 (±5.9) years) were included in the study. The MOBITEC-GP app showed moderate to excellent test-retest reliability (ICCs between 0.584 and 0.920) and validity (ICCs between 0.468 and 0.950) of walking speed measurements of 50 meters and above and of most 1-week life-space parameters, including life-space area, time spent out-of-home, and action range. The MOBITEC-GP app for Android is a reliable and valid tool for the assessment of real-life walking speed (at distances of 50 metres and above) and life-space parameters of older adults. Future studies should look into technical issues more systematically in order to avoid invalid measurements.

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection.

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.

Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research.

BACKGROUND: GPS tracking is increasingly used in health and aging research to objectively and unobtrusively assess individuals' daily-life mobility. However, mobility is a complex concept and its thorough description based on GPS-derived mobility indicators remains challenging. METHODS: With the aim of reflecting the breadth of aspects incorporated in daily mobility, we propose a conceptual framework to classify GPS-derived mobility indicators based on their characteristic and analytical properties for application in health and aging research. In order to demonstrate how the classification framework can be applied, existing mobility indicators as used in existing studies are classified according to the proposed framework. Then, we propose and compute a set of selected mobility indicators based on real-life GPS data of 95 older adults that reflects diverse aspects of individuals' daily mobility. To explore latent dimensions that underlie the mobility indicators, we conduct a factor analysis. RESULTS: The proposed framework enables a conceptual classification of mobility indicators based on the characteristic and analytical aspects they reflect. Characteristic aspects inform about the content of the mobility indicator and comprise categories related to space, time, movement scope, and attribute. Analytical aspects inform how a mobility indicator is aggregated with respect to temporal scale and statistical property. The proposed categories complement existing studies that often underrepresent mobility indicators involving timing, temporal distributions, and stop-move segmentations of movements. The factor analysis uncovers the following six dimensions required to obtain a comprehensive view of an older adult's daily mobility: extent of life space, quantity of out-of-home activities, time spent in active transport modes, stability of life space, elongation of life space, and timing of mobility. CONCLUSION: This research advocates incorporating GPS-based mobility indicators that reflect the multi-dimensional nature of individuals' daily mobility in future health- and aging-related research. This will foster a better understanding of what aspects of mobility are key to healthy aging.

MOBIlity assessment with modern TEChnology in older patients' real-life by the General Practitioner: the MOBITEC-GP study protocol.

BACKGROUND: Mobility limitations in older adults are associated with poor clinical outcomes including higher mortality and disability rates. A decline in mobility (including physical function and life-space) is detectable and should be discovered as early as possible, as it can still be stabilized or even reversed in early stages by targeted interventions. General practitioners (GPs) would be in the ideal position to monitor the mobility of their older patients. However, easy-to-use and valid instruments for GPs to conduct mobility assessment in the real-life practice setting are missing. Modern technologies such as the global positioning system (GPS) and inertial measurement units (IMUs) - nowadays embedded in every smartphone - could facilitate monitoring of different aspects of mobility in the GP's practice. METHODS: This project's aim is to provide GPs with a novel smartphone application that allows them to quantify their older patients' mobility. The project consists of three parts: development of the GPS- and IMU-based application, evaluation of its validity and reliability (Study 1), and evaluation of its applicability and acceptance (Study 2). In Study 1, participants (target N = 72, aged 65+, ≥2 chronic diseases) will perform a battery of walking tests (varying distances; varying levels of standardization). Besides videotaping and timing (gold standard), a high-end GPS device, a medium-accuracy GPS/IMU logger and three different smartphone models will be used to determine mobility parameters such as gait speed. Furthermore, participants will wear the medium-accuracy GPS/IMU logger and a smartphone for a week to determine their life-space mobility. Participants will be re-assessed after 1 week. In Study 2, participants (target N = 60, aged 65+, ≥2 chronic diseases) will be instructed on how to use the application by themselves. Participants will perform mobility assessments independently at their own homes. Aggregated test results will also be presented to GPs. Acceptance of the application will be assessed among patients and GPs. The application will then be finalized and publicly released. DISCUSSION: If successful, the MOBITEC-GP application will offer health care providers the opportunity to follow their patients' mobility over time and to recognize impending needs (e.g. for targeted exercise) within pre-clinical stages of decline.

Assessing Older Adults' Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators.

Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators.

Environmental factors drive language density more in food-producing than in hunter-gatherer populations.

Linguistic diversity is a key aspect of human population diversity and shapes much of our social and cognitive lives. To a considerable extent, the distribution of this diversity is driven by environmental factors such as climate or coast access. An unresolved question is whether the relevant factors have remained constant over time. Here, we address this question at a global scale. We approximate the difference between pre- versus post-Neolithic populations by the difference between modern hunter-gatherer versus food-producing populations. Using a novel geostatistical approach of estimating language and language family densities, we show that environmental-chiefly climate factors-have driven the language density of food-producing populations considerably more strongly than the language density of hunter-gatherer populations. Current evidence suggests that the population dynamics of modern hunter-gatherers is very similar to that of what can be reconstructed from the Palaeolithic record. Based on this, we cautiously infer that the impact of environmental factors on language densities underwent a substantial change with the transition to agriculture. After this transition, the environmental impact on language diversity in food-producing populations has remained relatively stable since it can also be detected-albeit in slightly weaker form-in models that capture the reduced linguistic diversity during large-scale language spreads in the Mid-Holocene.

Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI.

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks.

Association between lower extremity physical function and physical activity after ischemic stroke: Longitudinal findings from the MOBITEC-Stroke project.

Background: Stroke often results in physical impairments. Physical activity is crucial for rehabilitation, enhancing mobility, strength, and overall health. This study examines the association between Timed Up-and-Go (TUG) test performance and changes in physical activity to improve lower extremity physical function. Methods: The MOBITEC-Stroke Cohort Study ("Recovery of mobility function and life-space mobility after ischemic stroke") included patients with a first incidence of stroke. Data assessed 3 and 12 months after stroke were used for analysis. Linear regression model adjusted for age, sex, instrumental activities of daily living, Falls Efficacy Scale-International, modified Ranking Scale, and National Institutes of Health Stroke Scale-score was used to examine the relationship between lower extremity physical function (i.e., TUG) and change in physical activity (i.e., minutes of physical activity measured with a wrist-worn accelerometer over 1 week). Results: Longitudinal data of 49 patients (65% male, mean age 71.2 (SD: 10.4) years) were analyzed. Mean daily physical activity was 291.6 (SD: 96.2) min at 3 months and 298.9 (SD: 94.4) min at 12 months, with a change from 3 to 12 months of 7.3 min (95% CI: -9.4 to 24.0; p = 0.394) post-stroke. We observed significant relationships between the baseline TUG performance and the change in total physical activity over 9 months (p = 0.011) and between the change of TUG performance over time and the change in total physical activity (p = 0.022). Conclusion: Our findings indicate that better initial lower extremity physical function and higher improvements in function over time are associated with a greater increase in physical activity levels after stroke. This suggests that interventions aimed at maintaining and improving lower extremity physical function may positively affect physical activity levels.

Timed up-and-go performance is associated with objectively measured life space in patients 3 months after ischemic stroke: a cross-sectional observational study.

BACKGROUND: Stroke is a common cause of mobility limitation, including a reduction in life space. Life space is defined as the spatial extent in which a person moves within a specified period of time. We aimed to analyze patients' objective and self-reported life space and clinical stroke characteristics. METHODS: MOBITEC-Stroke is a prospective observational cohort study addressing poststroke mobility. This cross-sectional analysis refers to 3-month data. Life space was assessed by a portable tracking device (7 consecutive days) and by self-report (Life-Space Assessment; LSA). We analysed the timed up-and-go (TUG) test, stroke severity (National Institutes of Health Stroke Scale; NIHSS), and the level of functional outcome (modified Rankin Scale; mRS) in relation to participants' objective (distance- and area-related life-space parameters) and self-reported (LSA) life space by multivariable linear regression analyses, adjusted for age, sex, and residential area. RESULTS: We included 41 patients, mean age 70.7 (SD11.0) years, 29.3% female, NIHSS score 1.76 (SD1.68). We found a positive relationship between TUG performance and maximum distance from home (p = 0.006), convex hull area (i.e. area enclosing all Global Navigation Satellite System [GNSS] fixes, represented as a polygon linking the outermost points; p = 0.009), perimeter of the convex hull area (i.e. total length of the boundary of the convex hull area; p = 0.008), as well as the standard ellipse area (i.e. the two-dimensional ellipse containing approximately 63% of GNSS points; p = 0.023), in multivariable regression analyses. CONCLUSION: The TUG, an easily applicable bedside test, seems to be a useful indicator for patients' life space 3 months poststroke and may be a clinically useful measure to document the motor rehabilitative process.

Self-reported life-space mobility in the first year after ischemic stroke: longitudinal findings from the MOBITEC-Stroke project.

BACKGROUND: Life-space mobility is defined as the size of the area in which a person moves about within a specified period of time. Our study aimed to characterize life-space mobility, identify factors associated with its course, and detect typical trajectories in the first year after ischemic stroke. METHODS: MOBITEC-Stroke (ISRCTN85999967; 13/08/2020) was a cohort study with assessments performed 3, 6, 9 and 12 months after stroke onset. We applied linear mixed effects models (LMMs) with life-space mobility (Life-Space Assessment; LSA) as outcome and time point, sex, age, pre-stroke mobility limitation, stroke severity (National Institutes of Health Stroke Scale; NIHSS), modified Rankin Scale, comorbidities, neighborhood characteristics, availability of a car, Falls Efficacy Scale-International (FES-I), and lower extremity physical function (log-transformed timed up-and-go; TUG) as independent variables. We elucidated typical trajectories of LSA by latent class growth analysis (LCGA) and performed univariate tests for differences between classes. RESULTS: In 59 participants (mean age 71.6, SD 10.0 years; 33.9% women), mean LSA at 3 months was 69.3 (SD 27.3). LMMs revealed evidence (p ≤ 0.05) that pre-stroke mobility limitation, NIHSS, comorbidities, and FES-I were independently associated with the course of LSA; there was no evidence for a significant effect of time point. LCGA revealed three classes: "low stable", "average stable", and "high increasing". Classes differed with regard to LSA starting value, pre-stroke mobility limitation, FES-I, and log-transformed TUG time. CONCLUSION: Routinely assessing LSA starting value, pre-stroke mobility limitation, and FES-I may help clinicians identify patients at increased risk of failure to improve LSA.

Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS).

Prominent theories of aging emphasize the importance of resource allocation processes as a means to maintain functional ability, well-being and quality of life. Little is known about which activities and what activity patterns actually characterize the daily lives of healthy older adults in key domains of functioning, including the spatial, physical, social, and cognitive domains. This study aims to gain a comprehensive understanding of daily activities of community-dwelling older adults over an extended period of time and across a diverse range of activity domains, and to examine associations between daily activities, health and well-being at the within- and between-person levels. It also aims to examine contextual correlates of the relations between daily activities, health, and well-being. At its core, this ambulatory assessment (AA) study with a sample of 150 community-dwelling older adults aged 65 to 91 years measured spatial, physical, social, and cognitive activities across 30 days using a custom-built mobile sensor ("uTrail"), including GPS, accelerometer, and audio recording. In addition, during the first 15 days, self-reports of daily activities, psychological correlates, contexts, and cognitive performance in an ambulatory working memory task were assessed 7 times per day using smartphones. Surrounding the ambulatory assessment period, participants completed an initial baseline assessment including a telephone survey, web-based questionnaires, and a laboratory-based cognitive and physical testing session. They also participated in an intermediate laboratory session in the laboratory at half-time of the 30-day ambulatory assessment period, and finally returned to the laboratory for a posttest assessment. In sum, this is the first study which combines multi-domain activity sensing and self-report ambulatory assessment methods to observe daily life activities as indicators of functional ability in healthy older adults unfolding over an extended period (i.e., 1 month). It offers a unique opportunity to describe and understand the diverse individual real-life functional ability profiles characterizing later life.

Assessing the Transferability of Physical Activity Type Detection Models: Influence of Age Group Is Underappreciated.

Increasing the amount of physical activity (PA) in older adults that have shifted to a sedentary lifestyle is a determining factor in decreasing health and social costs. It is, therefore, imperative to develop objective methods that accurately detect daily PA types and provide detailed PA guidance for healthy aging. Most of the existing techniques have been applied in the younger generation or validated in the laboratory. To what extent, these methods are transferable to real-life and older adults are a question that this paper aims to answer. Sixty-three participants, including 33 younger and 30 older healthy adults, participated in our study. Each participant wore five devices mounted on the left and right hips, right knee, chest, and left pocket and collected accelerometer and GPS data in both semi-structured and real-life environments. Using this dataset, we developed machine-learning models to detect PA types walking, non-level walking, jogging/running, sitting, standing, and lying. Besides, we examined the accuracy of the models within-and between-age groups applying different scenarios and validation approaches. The within-age models showed convincing classification results. The findings indicate that due to age-related behavioral differences, there are more confusion errors between walking, non-level walking, and running in older adults' results. Using semi-structured training data, the younger adults' models outperformed older adults' models. However, using real-life training data alone or in combination with semi-structured data generated better results for older adults who had high real-life data quality. Assessing the transferability of the models to older adults showed that the models trained with younger adults' data were only weakly transferable. However, training the models with a combined dataset of both age groups led to reliable transferability of results to the data of the older subgroup. We show that age-related behavioral differences can alter the PA classification performance. We demonstrate that PA type detection models that rely on combined datasets of young and older adults are strongly transferable to real-life and older adults' data. Our results yield significant time and cost savings for future PA studies by reducing the overall volume of training data required.

Can Bayesian phylogeography reconstruct migrations and expansions in linguistic evolution?

Bayesian phylogeography has been used in historical linguistics to reconstruct homelands and expansions of language families, but the reliability of these reconstructions has remained unclear. We contribute to this discussion with a simulation study where we distinguish two types of spatial processes: migration, where populations or languages leave one place for another, and expansion, where populations or languages gradually expand their territory. We simulate migration and expansion in two scenarios with varying degrees of spatial directional trends and evaluate the performance of state-of-the-art phylogeographic methods. Our results show that these methods fail to reconstruct migrations, but work surprisingly well on expansions, even under severe directional trends. We demonstrate that migrations and expansions have typical phylogenetic and spatial patterns, which in the one case inhibit and in the other facilitate phylogeographic reconstruction. Furthermore, we propose descriptive statistics to identify whether a real sample of languages, their relationship and spatial distribution, better fits a migration or an expansion scenario. Bringing together the results of the simulation study and theoretical arguments, we make recommendations for assessing the adequacy of phylogeographic models to reconstruct the spatial evolution of languages.

Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact.

When speakers of different languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographical areas of past human interaction and migration. However, other factors may contribute to similarities between languages. Inheritance from a shared ancestral language and universal preference for a linguistic property may both overshadow contact signals. How can we find geographical contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes, an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities are better explained by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographical prior ensures their spatial coherence. We test sBayes on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by findings from previous studies. While we focus on detecting language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.

Map-based assessment of older adults' life space: validity and reliability.

BACKGROUND: Map-based tools have recently found their way into health-related research. They can potentially be used to quantify older adults' life-space. This study aimed to evaluate the validity (vs. GPS) and the test-retest reliability of a map-based life-space assessment (MBA). METHODS: Life-space of one full week was assessed by GPS and by MBA. MBA was repeated after approximately 3 weeks. Distance-related (mean and maximum distance from home) and area-related (convex hull, standard deviational ellipse) life-space indicators were calculated. Intraclass correlations (MBA vs. GPS and test-retest) were calculated in addition to Bland-Altman analyses (MBA vs. GPS). RESULTS: Fifty-eight older adults (mean age 74, standard deviation 5.5 years; 39.7% women) participated in the study. Bland-Altman analyses showed the highest agreement between methods for the maximum distance from home. Intraclass correlation coefficients ranged between 0.19 (95% confidence interval 0 to 0.47) for convex hull and 0.72 (95% confidence interval 0.52 to 0.84) for maximum distance from home. Intraclass correlation coefficients for test-retest reliability ranged between 0.04 (95% confidence interval 0 to 0.30) for convex hull and 0.43 (95% confidence interval 0.19 to 0.62) for mean distance from home. CONCLUSIONS: While acceptable validity and reliability were found for the distance-related life-space parameters, MBA cannot be recommended for the assessment of area-related life-space parameters.

Self-reported versus GPS-derived indicators of daily mobility in a sample of healthy older adults.

In light of novel opportunities to use sensor data to observe individuals' day-to-day mobility in the context of healthy aging research, it is important to understand how meaningful mobility indicators can be extracted from such data and to which degree these sensor-derived indicators are comparable to corresponding self-reports. We used sensor (GPS and accelerometer) and self-reported data from 27 healthy older adults (≥67 years) who participated in the MOASIS project over a 30-day period. Based on sensor data we computed three commonly used daily mobility indicators: life space (LS), travel duration using passive (i.e., motorized) modes of transportation (pMOT) and travel duration using active (i.e., non-motorized) modes of transportation (aMOT). We assessed the degree to which these sensor-derived indicators compare to corresponding self-reports at a within-person level, computing intraindividual correlations (iCorrs), subsequently assessing whether iCorrs can be associated with participants' socio-demographic characteristics on a between-person level. Moderate to large positive mean iCorrs between the respective self-reported and sensor-derived indicators were found (r = 0.75 for LS, 0.51 for pMOT and 0.36 for aMOT). In comparison to sensor-derived indicators, self-reported LS slightly underestimates, while self-reported aMOT as well as pMOT considerably overestimate the amount of daily mobility. Participants with access to a car have higher probabilities of agreement in the pMOT indicator. Sensor-based assessments are promising as they are "objective", involve less participant burden and observations can be extended over long periods. The findings of this paper help researchers on mobility and aging to estimate the magnitude and direction of potential differences in the assessed variable due to the assessment methods.

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review.

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

Predictors of real-life mobility in community-dwelling older adults: an exploration based on a comprehensive framework for analyzing mobility.

BACKGROUND: Reduced mobility is associated with a plethora of adverse outcomes. To support older adults in maintaining their independence, it first is important to have deeper knowledge of factors that impact on their mobility. Based on a framework that encompasses demographical, environmental, physical, cognitive, psychological and social domains, this study explores predictors of different aspects of real-life mobility in community-dwelling older adults. METHODS: Data were obtained in two study waves with a total sample of n = 154. Real-life mobility (physical activity-based mobility and life-space mobility) was assessed over one week using smartphones. Active and gait time and number of steps were calculated from inertial sensor data, and life-space area, total distance, and action range were calculated from GPS data. Demographic measures included age, gender and education. Physical functioning was assessed based on measures of cardiovascular fitness, leg and handgrip strength, balance and gait function; cognitive functioning was assessed based on measures of attention and executive function. Psychological and social assessments included measures of self-efficacy, depression, rigidity, arousal, and loneliness, sociableness, perceived help availability, perceived ageism and social networks. Maximum temperature was used to assess weather conditions on monitoring days. RESULTS: Multiple regression analyses indicated just physical and psychological measures accounted for significant but rather low proportions of variance (5-30%) in real-life mobility. Strength measures were retained in most of the regression models. Cognitive and social measures did not remain as significant predictors in any of the models. CONCLUSIONS: In older adults without mobility limitations, real-life mobility was associated primarily with measures of physical functioning. Psychological functioning also seemed to play a role for real-life mobility, though the associations were more pronounced for physical activity-based mobility than life-space mobility. Further factors should be assessed in order to achieve more conclusive results about predictors of real-life mobility in community-dwelling older adults.