Dataset Information

An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment.

ABSTRACT: Physical capability (PC) is conventionally evaluated through performance-based clinical assessments. We aimed to transform a battery of sensor-based functional tests into a clinically applicable assessment tool. We used Exploratory Factor Analysis (EFA) to uncover the underlying latent structure within sensor-based measures obtained in a population-based study. Three hundred four community-dwelling older adults (163 females, 80.9 ± 6.4 years), underwent three functional tests (Quiet Stand, QS, 7-meter Walk, 7MW and Chair Stand, CST) wearing a smartphone at the lower back. Instrumented tests provided 73 sensor-based measures, out of which EFA identified a fifteen-factor model. A priori knowledge and the associations with health-related measures supported the functional interpretation and construct validity analysis of the factors, and provided the basis for developing a conceptual model of PC. For example, the "Walking Impairment" domain obtained from the 7MW test was significantly associated with measures of leg muscle power, gait speed, and overall lower extremity function. To the best of our knowledge, this is the first time that a battery of functional tests, instrumented through a smartphone, is used for outlining a sensor-based conceptual model, which could be suitable for assessing PC in older adults and tracking its changes over time.

SUBMITTER: Coni A

PROVIDER: S-EPMC6567373 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment.

Coni Alice A Mellone Sabato S Colpo Marco M Guralnik Jack M JM Patel Kushang V KV Bandinelli Stefania S Chiari Lorenzo L

Sensors (Basel, Switzerland) 20190514 10

Physical capability (PC) is conventionally evaluated through performance-based clinical assessments. We aimed to transform a battery of sensor-based functional tests into a clinically applicable assessment tool. We used Exploratory Factor Analysis (EFA) to uncover the underlying latent structure within sensor-based measures obtained in a population-based study. Three hundred four community-dwelling older adults (163 females, 80.9 ± 6.4 years), underwent three functional tests (Quiet Stand, QS, 7 ...[more]

PMID: 31091794

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Project description:ObjectiveTo do a quantitative systematic review, including published and unpublished data, examining the associations between individual objective measures of physical capability (grip strength, walking speed, chair rising, and standing balance times) and mortality in community dwelling populations.DesignSystematic review and meta-analysis.Data sourcesRelevant studies published by May 2009 identified through literature searches using Embase (from 1980) and Medline (from 1950) and manual searching of reference lists; unpublished results were obtained from study investigators.Study selectionEligible observational studies were those done in community dwelling people of any age that examined the association of at least one of the specified measures of physical capability (grip strength, walking speed, chair rises, or standing balance) with mortality.Data synthesisEffect estimates obtained were pooled by using random effects meta-analysis models with heterogeneity between studies investigated.ResultsAlthough heterogeneity was detected, consistent evidence was found of associations between all four measures of physical capability and mortality; those people who performed less well in these tests were found to be at higher risk of all cause mortality. For example, the summary hazard ratio for mortality comparing the weakest with the strongest quarter of grip strength (14 studies, 53 476 participants) was 1.67 (95% confidence interval 1.45 to 1.93) after adjustment for age, sex, and body size (I(2)=84.0%, 95% confidence interval 74% to 90%; P from Q statistic <0.001). The summary hazard ratio for mortality comparing the slowest with the fastest quarter of walking speed (five studies, 14 692 participants) was 2.87 (2.22 to 3.72) (I(2)=25.2%, 0% to 70%; P=0.25) after similar adjustments. Whereas studies of the associations of walking speed, chair rising, and standing balance with mortality have only been done in older populations (average age over 70 years), the association of grip strength with mortality was also found in younger populations (five studies had an average age under 60 years).ConclusionsObjective measures of physical capability are predictors of all cause mortality in older community dwelling populations. Such measures may therefore provide useful tools for identifying older people at higher risk of death.

Project description:BackgroundUS military engagements have consistently raised concern over the array of health outcomes experienced by service members postdeployment. Exploratory factor analysis has been used in studies of 1991 Gulf War-related illnesses, and may increase understanding of symptoms and health outcomes associated with current military conflicts in Iraq and Afghanistan. The objective of this study was to use exploratory factor analysis to describe the correlations among numerous physical and psychological symptoms in terms of a smaller number of unobserved variables or factors.MethodsThe Millennium Cohort Study collects extensive self-reported health data from a large, population-based military cohort, providing a unique opportunity to investigate the interrelationships of numerous physical and psychological symptoms among US military personnel. This study used data from the Millennium Cohort Study, a large, population-based military cohort. Exploratory factor analysis was used to examine the covariance structure of symptoms reported by approximately 50,000 cohort members during 2004-2006. Analyses incorporated 89 symptoms, including responses to several validated instruments embedded in the questionnaire. Techniques accommodated the categorical and sometimes incomplete nature of the survey data.ResultsA 14-factor model accounted for 60 percent of the total variance in symptoms data and included factors related to several physical, psychological, and behavioral constructs. A notable finding was that many factors appeared to load in accordance with symptom co-location within the survey instrument, highlighting the difficulty in disassociating the effects of question content, location, and response format on factor structure.ConclusionsThis study demonstrates the potential strengths and weaknesses of exploratory factor analysis to heighten understanding of the complex associations among symptoms. Further research is needed to investigate the relationship between factor analytic results and survey structure, as well as to assess the relationship between factor scores and key exposure variables.

Project description:BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone-based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence-related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups.ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy.MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm.ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001).ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future.

Dataset Information

An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment.

Publications

An Exploratory Factor Analysis of Sensor-Based Physical Capability Assessment.

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