Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Objective: This study investigated how educational levels modify the relationship between the standard deviation of NN intervals (SDNN) of heart rate variability and the development of post-traumatic stress disorder (PTSD). Methods: Participants with physical injuries were enrolled from a trauma center and monitored over two years. Initial assessments included SDNN and educational attainment, along with socio-demographic and clinical variables. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5.Logistic regression analyses were conducted. Results: Of the 538 participants, 58 (10.8%) developed PTSD during the follow-up period. A significant interaction effect was observed: lower SDNN was significantly linked to PTSD in individuals with higher education, but not in those with lower education. Conclusion The study identified education-dependent associations between SDNN and PTSD development, emphasizing the importance of tailored PTSD prevention strategies that consider both SDNN and educational levels.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Objective: This study investigated how educational levels modify the relationship between the standard deviation of NN intervals (SDNN) of heart rate variability and the development of post-traumatic stress disorder (PTSD). Methods: Participants with physical injuries were enrolled from a trauma center and monitored over two years. Initial assessments included SDNN and educational attainment, along with socio-demographic and clinical variables. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5.Logistic regression analyses were conducted. Results: Of the 538 participants, 58 (10.8%) developed PTSD during the follow-up period. A significant interaction effect was observed: lower SDNN was significantly linked to PTSD in individuals with higher education, but not in those with lower education. Conclusion The study identified education-dependent associations between SDNN and PTSD development, emphasizing the importance of tailored PTSD prevention strategies that consider both SDNN and educational levels.

Objective: This study investigated how educational levels modify the relationship between the standard deviation of NN intervals (SDNN) of heart rate variability and the development of post-traumatic stress disorder (PTSD). Methods: Participants with physical injuries were enrolled from a trauma center and monitored over two years. Initial assessments included SDNN and educational attainment, along with socio-demographic and clinical variables. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5.Logistic regression analyses were conducted. Results: Of the 538 participants, 58 (10.8%) developed PTSD during the follow-up period. A significant interaction effect was observed: lower SDNN was significantly linked to PTSD in individuals with higher education, but not in those with lower education. Conclusion The study identified education-dependent associations between SDNN and PTSD development, emphasizing the importance of tailored PTSD prevention strategies that consider both SDNN and educational levels.

Objective: This study investigated how educational levels modify the relationship between the standard deviation of NN intervals (SDNN) of heart rate variability and the development of post-traumatic stress disorder (PTSD). Methods: Participants with physical injuries were enrolled from a trauma center and monitored over two years. Initial assessments included SDNN and educational attainment, along with socio-demographic and clinical variables. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5.Logistic regression analyses were conducted. Results: Of the 538 participants, 58 (10.8%) developed PTSD during the follow-up period. A significant interaction effect was observed: lower SDNN was significantly linked to PTSD in individuals with higher education, but not in those with lower education. Conclusion The study identified education-dependent associations between SDNN and PTSD development, emphasizing the importance of tailored PTSD prevention strategies that consider both SDNN and educational levels.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Objective: Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. Materials and Methods: We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017–May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. Results: The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS–HHUS agreement was 95.9% (236/246; Cohen’s kappa = 0.883). Conclusion Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.