Dataset Information

Assessing physical abilities of sarcopenia patients using gait analysis and smart insole for development of digital biomarker.

ABSTRACT: The aim of this study is to compare variable importance across multiple measurement tools, and to use smart insole and artificial intelligence (AI) gait analysis to create variables that can evaluate the physical abilities of sarcopenia patients. By analyzing and comparing sarcopenia patients with non sarcopenia patients, this study aims to develop predictive and classification models for sarcopenia and discover digital biomarkers. The researchers used smart insole equipment to collect plantar pressure data from 83 patients, and a smart phone to collect video data for pose estimation. A Mann-Whitney U was conducted to compare the sarcopenia group of 23 patients and the control group of 60 patients. Smart insole and pose estimation were used to compare the physical abilities of sarcopenia patients with a control group. Analysis of joint point variables showed significant differences in 12 out of 15 variables, but not in knee mean, ankle range, and hip range. These findings suggest that digital biomarkers can be used to differentiate sarcopenia patients from the normal population with improved accuracy. This study compared musculoskeletal disorder patients to sarcopenia patients using smart insole and pose estimation. Multiple measurement methods are important for accurate sarcopenia diagnosis and digital technology has potential for improving diagnosis and treatment.

SUBMITTER: Kim S

PROVIDER: S-EPMC10313812 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Assessing physical abilities of sarcopenia patients using gait analysis and smart insole for development of digital biomarker.

Kim Shinjune S Park Seongjin S Lee Sangyeob S Seo Sung Hyo SH Kim Hyeon Su HS Cha Yonghan Y Kim Jung-Taek JT Kim Jin-Woo JW Ha Yong-Chan YC Yoo Jun-Il JI

Scientific reports 20230630 1

The aim of this study is to compare variable importance across multiple measurement tools, and to use smart insole and artificial intelligence (AI) gait analysis to create variables that can evaluate the physical abilities of sarcopenia patients. By analyzing and comparing sarcopenia patients with non sarcopenia patients, this study aims to develop predictive and classification models for sarcopenia and discover digital biomarkers. The researchers used smart insole equipment to collect plantar p ...[more]

PMID: 37391464

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Project description:Gait analysis is a systematic study of human locomotion, which can be utilized in variousapplications, such as rehabilitation, clinical diagnostics and sports activities. The various limitationssuch as cost, non-portability, long setup time, post-processing time etc., of the current gait analysistechniques have made them unfeasible for individual use. This led to an increase in research interestin developing smart insoles where wearable sensors can be employed to detect vertical groundreaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortablefor gait analysis, and can monitor plantar pressure frequently through embedded sensors thatconvert the applied pressure to an electrical signal that can be displayed and analyzed further.Several research teams are still working to improve the insoles' features such as size, sensitivity ofinsoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait bydetecting various complications providing recommendation to enhance walking performance. Eventhough systematic sensor calibration approaches have been followed by different teams to calibrateinsoles' sensor, expensive calibration devices were used for calibration such as universal testingmachines or infrared motion capture cameras equipped in motion analysis labs. This paper providesa systematic design and characterization procedure for three different pressure sensors: forcesensitiveresistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that canbe used for detecting vGRF using a smart insole. A simple calibration method based on a load cellis presented as an alternative to the expensive calibration techniques. In addition, to evaluate theperformance of the different sensors as a component for the smart insole, the acquired vGRF fromdifferent insoles were used to compare them. The results showed that the FSR is the most effectivesensor among the three sensors for smart insole applications, whereas the piezoelectric sensors canbe utilized in detecting the start and end of the gait cycle. This study will be useful for any researchgroup in replicating the design of a customized smart insole for gait analysis.

Project description:BackgroundAge-induced sarcopenia negatively affects walking stability and increases the risk of falls, which is the leading cause of accidental death in the elderly.ObjectiveThis study aimed to analyze and contrast body composition and gait characteristics in those with sarcopenia in relation to healthy controls to shed some light on the prevention of falls in elderly patients with sarcopenia.Materials and methodsIn this study, 68 community dwellers were scanned by the Hologic QDR-4500A Dual-energy X-ray absorptiometry (DXA). The appendicular lean mass index (ALMI) results were used to distinguish the normal participants from those with sarcopenia: 24 in the sarcopenia group, and 44 into the normal group. The participants were asked to undergo gait analysis on a plantar pressure measurement system. Statistical analysis was conducted to contrast both groups' gait and butterfly parameters from their gait test, and then a gait forward dynamics method was performed to quantify the analysis for both groups.ResultsThe ALMI of the female was not related to their age (r = 0.06) while that of the male was weakly related (r = 0.17). Body mass index (BMI) from both groups was normal, although with a statistically greater BMI from the normal group compared with sarcopenia (p < 0.001). Greater values and significant differences were found in step length and stride length from the normal elderly group (p < 0.01), and so was the length of the gait line and single support line (p < 0.05). Gait forward dynamics analysis results showed no motor neural or musculoskeletal disorders in their gait performance from the sarcopenia group.ConclusionFor the elderly, age did not largely affect the ALMI, BMI, or T-score, but BMI and ALMI were strongly correlated. In this study, significant differences were found in certain gait parameters between the elderly with sarcopenia and the normal elderly, which were related to absolute muscle strength, suggesting that sarcopenia was a disease mainly caused by decreased muscle mass. In addition, when abnormities were identified in step length, stride length, length of gait line, or length of single support line, it is proposed to take a DXA scan to confirm whether the elderly suffer from sarcopenia.

Project description:PurposeThe accumulation of advanced glycation end products (AGEs) is associated with various diseases and age-related impairments, including loss of muscle mass and function. We investigated the association between plasma pentosidine, which is one of the AGEs, and sarcopenia, low gait speed, and mortality in patients with cirrhosis.MethodsThis retrospective study divided 128 patients with cirrhosis into three groups by 25th and 75th quartiles of baseline plasma pentosidine levels: low (L)-, intermediate (I)-, and high (H)-pentosidine (Pen) groups. Sarcopenia was diagnosed following the Japan Society of Hepatology criteria. Low gait speed was defined as <0.8 m/s. The cumulative survival rates were compared between the three groups. Cox proportional hazards regression analysis was performed to identify independent factors associated with mortality.ResultsOf the 128 patients, 40 (31.3%) and 34 (26.6%) had sarcopenia and low gait speed, respectively. The prevalence of sarcopenia and low gait speed significantly increased stepwise with increasing plasma pentosidine levels, with the highest in the H-Pen group (59.4% [19/32] and 56.3% [18/32], respectively) and lowest in the L-Pen group (18.8% [6/32] and 6.3% [2/32], respectively). Multivariate analysis identified plasma pentosidine levels as a significant and independent factor associated with sarcopenia (odds ratio [OR], 1.07; p = 0.036) and low gait speed (OR, 1.06; p = 0.036), with the cutoff levels of 0.0792 μg/mL (sensitivity/specificity, 0.600/0.773) and 0.0745 μg/mL (sensitivity/specificity, 0.735/0.691), respectively. The cumulative survival rates were significantly lower in the H-Pen group than in the L-Pen (hazard ratio [HR], 11.7; p = 0.001) and I-Pen (HR, 4.03; p < 0.001) groups. Plasma pentosidine levels were identified as a significant and independent prognostic factor (HR, 1.07; p < 0.001).ConclusionPlasma pentosidine levels are associated with sarcopenia, low gait speed, and mortality and may serve as a useful surrogate biomarker for these clinical events in patients with cirrhosis.

Project description:BACKGROUND:Within the United Kingdom's National Health System (NHS), patients suffering from obesity may be provided with bariatric surgery. After receiving surgery many of these patients require further support to continue to lose more weight or to maintain a healthy weight. Remotely monitoring such patients' physical activity and other health-related variables could provide healthworkers with a more 'ecologically valid' picture of these patients' behaviours to then provide more personalised support. The current study assesses the feasibility of two smartphone apps to do so. In addition, the study looks at the barriers and facilitators patients experience to using these apps effectively. METHODS:Participants with a BMI > 35 kg/m2 being considered for and who had previously undergone bariatric surgery were recruited. Participants were asked to install two mobile phone apps. The 'Moves' app automatically tracked participants' physical activity and the 'WLCompanion' app prompted participants to set goals and input other health-related information. Then, to learn about participants' facilitators and barriers to using the apps, some participants were asked to complete a survey informed by the Theoretical Domains Framework. The data were analysed using regressions and descriptive statistics. RESULTS:Of the 494 participants originally enrolled, 274 participants data were included in the analyses about their activity pre- and/or post-bariatric surgery (ages 18-65, M = 44.02, SD ± 11.29). Further analyses were performed on those 36 participants whose activity was tracked both pre- and post-surgery. Participants' activity levels pre- and post-surgery did not differ. In addition, 54 participants' survey responses suggested that the main facilitator to their continued use of the Moves app was its automatic nature, and the main barrier was its battery drain. CONCLUSIONS:The current study tracked physical activity in patients considered for and who had previously undergone bariatric surgery. The results should be interpreted with caution because of the small number of participants whose data meet the inclusion criteria and the barriers participants encountered to using the apps. Future studies should take note of the barriers to develop more user-friendly apps. TRIAL REGISTRATION:ClinicalTrials.gov- NCT01365416 on the 3rd of June 2011.

Dataset Information

Assessing physical abilities of sarcopenia patients using gait analysis and smart insole for development of digital biomarker.

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Assessing physical abilities of sarcopenia patients using gait analysis and smart insole for development of digital biomarker.

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