Change in hand dexterity and habitual gait speed reflects cognitive decline over time in healthy older adults: a longitudinal study

[Purpose] There is a relationship between physical and cognitive functions; therefore, impairment of physical function would mean cognitive decline. This study aimed to investigate the association between change in physical and cognitive functions. [Subjects and Methods] Participants were 169 healthy community-dwelling older adults who attend the survey after three years from baseline (mean age, 72.4 ± 4.8 years). Grip strength, one-leg standing balance, five-times-sit-to-stand test, timed up and go, 5-m habitual walk, and a peg-moving task were used to evaluate physical performance. Five cognitive function tests were used to assess attention, memory, visuospatial function, verbal fluency, and reasoning. Cognitive function was defined as the cumulative score of these tests. [Results] At baseline, five-times-sit-to-stand test, timed up and go, and hand dexterity were independently associated with cognitive function. In longitudinal analyses, changes in habitual walking speed and hand dexterity were significantly associated with change in cognitive function. [Conclusion] Deterioration of specific physical function, such as hand dexterity and walking ability, may be associated with progression of cognitive decline. Decreasing extent of daily functions, such as hand dexterity and walking ability, can be useful indices to grasp changes in cognitive function

Precisión de las estadísticas subjetivas de indicadores clave del rendimiento en tenis

The compilation of stats by performance analysis is common in matches with top professional tennis players. However, outside the top level such objectively evaluated stats and feedback for players are rare. With this in mind, an original method was developed that asks players to subjectively evaluate the match stats. This study aimed to investigate the accuracy of subjective stats in tennis. The participants were 30 male collegiate athletes, including some who had participated in national-level competitions. The participants played a 6-game, 1-set practice match, and immediately after the match subjectively evaluated the stats of key performance indicators such as percentages, number of shots, and rally patterns. Objective stats were aggregated using video clips recorded by a digital camera or smartphone. Based on Bland-Altman plots show that subjectively evaluating their own performance indicators helped to confirm the objective stats. Although some variables showed fixed or proportional biases, the mean differences were not significant (percentage of first serve in: 1.733% points; double faults: 0.400 times; net plays: -0.767 times; unforced errors: -2.133 times). These findings support the implementation of a subjective evaluation of key performance indicators in tennis players who might have difficulty incorporating objective evaluations.La recopilación de estadísticas mediante el análisis del rendimiento es común en partidos con jugadores profesionales de élite de tenis. Sin embargo, este tipo de estadísticas y retroalimentación evaluadas objetivamente son poco frecuentes en los niveles de rendimiento inferiores. Teniendo esto en cuenta, se desarrolló un método original que pide a los jugadores que evalúen subjetivamente las estadísticas de juego. El objetivo de este estudio era investigar la precisión de las estadísticas subjetivas en tenis. Los participantes fueron 30 atletas hombres universitarios; algunos de ellos habían participado en competencias nacionales. Los participantes jugaron un partido de práctica a 6 juegos y 1 set, e inmediatamente después evaluaron subjetivamente las estadísticas de indicadores clave del rendimiento tales como porcentajes, número de golpes y patrones de intercambio de golpes. Se añadieron estadísticas objetivas a través de videos grabados con una cámara digital o un teléfono inteligente. Los gráficos de Bland-Altman sugieren que evaluar subjetivamente sus indicadores de rendimiento les ayudó a confirmar las estadísticas objetivas. Aunque algunas variables mostraron sesgos fijos o proporcionales, las diferencias medias no fueron significativas (porcentaje de primeros saques: 1,733 % puntos; dobles faltas: 0,400 veces; jugadas de red: -0,767 veces; errores no forzados: -2,133 veces). Estos hallazgos apoyan la implementación de una evaluación subjetiva de los indicadores clave del rendimiento en jugadores de tenis con dificultades para aplicar evaluaciones objetivas

Detección de similitudes y diferencias dentro de un mismo movimiento de golpeo mediante un análisis del rendimiento basado en inteligencia artificial: ejemplo del servicio en tenis

This research was supported in part by grants from the Advanced Research Initiative for Human High Performance (ARIHHP), University of Tsukuba [grant number 2022(I)6], and by a JSPS KAKENHI Grant [grant number 22K17747].Artificial intelligence (AI) -based performance analysis has the potential to support feedback in coaching; however, a useful method has not yet been proposed. This study aims to develop an AI-based performance analysis to support tennis coaching. Specifically, we investigate the accuracy of detecting similarities and differences within the same shot movement. The participants were two tennis players with more than ten years of tennis experience. This study targeted service in tennis and videos of the 1st and 2nd service from both sides were recorded using a smartphone located on the fence behind the participant. The analysis code was executed in Python, and the main part involved the use of BlazePose, which estimates the X-, Y-, and Z-coordinates of a human pose. Video clips of 2 s were cut, with a 1 s overlap between each clip, and one of the clips was manually chosen as the standard clip. The clips were compared with the comparison clips, and the difference scores for the total and each body part were automatically calculated. As a result, a certain accuracy (≥ 70%) was confirmed for detecting overlapping phases between clips. Moreover, manually evaluated body parts that showed different movements by a certified coach corresponded to the top three different parts in the AI-based analysis for 8 of the 12 conditions. Performance analysis provides feedback in tennis coaching.El análisis del rendimiento basado en inteligencia artificial (IA) tiene el potencial de apoyar la retroalimentación en el entrenamiento. Sin embargo, aún no se ha propuesto un método útil. El objetivo de este estudio es desarrollar un análisis del rendimiento basado en IA para apoyar el entrenamiento de tenis. En concreto, se investiga la precisión en la detección de similitudes y diferencias dentro de un mismo movimiento de golpeo. Los participantes fueron dos tenistas con más de diez años de experiencia en tenis a nivel regional. Este estudio se centró en el servicio en tenis y se grabaron videos de los dos primeros servicios desde ambos lados de la cancha (número de servicios: 40 intentos) con un teléfono inteligente situado en la valla detrás del participante. El código de análisis se ejecutó en Python, y la parte principal involucró el uso de BlazePose, que estima las coordenadas X, Y y Z de una posición humana. Se cortaron videos de 2 s, con un solapamiento de 1 s entre cada video, y se eligió manualmente uno de ellos como el video estándar. Los videos se compararon con los de comparación y se calcularon automáticamente las puntuaciones de diferencia para el total y para cada parte del cuerpo. Se realizó un análisis basado en IA que consideraba 12 condiciones y combinaba los dos primeros servicios desde ambos lados y de los diferentes jugadores. Como resultado, se confirmó cierta precisión (≥ 70%) en la detección de fases solapadas entre videos. Además, las partes del cuerpo evaluadas manualmente que mostraban movimientos diferentes por un entrenador certificado correspondían con las tres primeras partes diferentes del análisis basado en IA para 8 de las 12 condiciones. El análisis de rendimiento basado en IA propuesto puede extraer eficazmente fases similares o solapadas y sugerir partes del cuerpo que muestran movimientos diferentes.University of Tsukuba [2022(I)6]JSPS KAKENHI Grant [22K17747

Detección de similitudes y diferencias dentro de un mismo movimiento de golpeo mediante un análisis del rendimiento basado en inteligencia artificial: ejemplo del servicio en tenis

El análisis del rendimiento basado en inteligencia artificial (IA) tiene el potencial de apoyar la retroalimentación en el entrenamiento. Sin embargo, aún no se ha propuesto un método útil. El objetivo de este estudio es desarrollar un análisis del rendimiento basado en IA para apoyar el entrenamiento de tenis. En concreto, se investiga la precisión en la detección de similitudes y diferencias dentro de un mismo movimiento de golpeo. Los participantes fueron dos tenistas con más de diez años de experiencia en tenis a nivel regional. Este estudio se centró en el servicio en tenis y se grabaron videos de los dos primeros servicios desde ambos lados de la cancha (número de servicios: 40 intentos) con un teléfono inteligente situado en la valla detrás del participante. El código de análisis se ejecutó en Python, y la parte principal involucró el uso de BlazePose, que estima las coordenadas X, Y y Z de una posición humana. Se cortaron videos de 2 s, con un solapamiento de 1 s entre cada video, y se eligió manualmente uno de ellos como el video estándar. Los videos se compararon con los de comparación y se calcularon automáticamente las puntuaciones de diferencia para el total y para cada parte del cuerpo. Se realizó un análisis basado en IA que consideraba 12 condiciones y combinaba los dos primeros servicios desde ambos lados y de los diferentes jugadores. Como resultado, se confirmó cierta precisión (≥ 70%) en la detección de fases solapadas entre videos. Además, las partes del cuerpo evaluadas manualmente que mostraban movimientos diferentes por un entrenador certificado correspondían con las tres primeras partes diferentes del análisis basado en IA para 8 de las 12 condiciones. El análisis de rendimiento basado en IA propuesto puede extraer eficazmente fases similares o solapadas y sugerir partes del cuerpo que muestran movimientos diferentes.Artificial intelligence (AI) -based performance analysis has the potential to support feedback in coaching; however, a useful method has not yet been proposed. This study aims to develop an AI-based performance analysis to support tennis coaching. Specifically, we investigate the accuracy of detecting similarities and differences within the same shot movement. The participants were two tennis players with more than ten years of tennis experience at the regional level. This study targeted service in tennis and videos of the 1st and 2nd service from both sides (number of services: 40 attempts) were recorded using a smartphone located on the fence behind the participant. The analysis code was executed in Python, and the main part involved the use of BlazePose, which estimates the X-, Y-, and Z-coordinates of a human pose. Video clips of 2 s were cut, with a 1 s overlap between each clip, and one of the clips was manually chosen as the standard clip. The clips were compared with the comparison clips, and the difference scores for the total and each body part were automatically calculated. An AI-based analysis was conducted considering 12 conditions combining the 1st and 2nd services from both sides and different players. As a result, a certain accuracy (≥ 70%) was confirmed for detecting overlapping phases between clips. Moreover, manually evaluated body parts that showed different movements by a certified coach corresponded to the top three different parts in the AI-based analysis for 8 of the 12 conditions. The proposed AI-based performance analysis can effectively extract similar or overlapping phases and suggest body parts exhibiting different movements

Association between difficulty initiating sleep in older adults and the combination of leisure-time physical activity and consumption of milk and milk products: a cross-sectional study

Background: Research has shown that engaging in leisure-time physical activity (LTPA) and consuming dairy foods can lead to better sleep. Combining these two non-invasive prescriptions may be more effective for helping people fall asleep. This study investigates whether participating in LTPA in conjunction with consuming milk and milk products has a beneficial association with difficulty initiating sleep (DIS) among older adults. Methods: The present study looked at 421 community-dwelling older people aged 65 years and older living in Ibaraki prefecture, Japan (mean age 74.9 ± 5.5 years, male 43.7%). We measured LTPA and sleep latency with the Physical Activity Scale for the Elderly and the Pittsburgh Sleep Quality Index, respectively. Participants who needed 30 minutes or more to fall asleep were defined as having DIS. We assessed dairy consumption as participants’ habitual intake of milk, yogurt and cheese. Results: After adjusting for covariates, participants who engaged in sufficient levels of LTPA as well as consumed milk (OR = 0.27, 95% CI = 0.10-0.73) or cheese (OR = 0.34, 95% CI = 0.14-0.85) were less likely to complain of DIS compared with people who neither engaged in LTPA nor ingested milk or cheese. Conclusions: Our findings suggest that the combination of engaging in LTPA and consuming milk or cheese is necessary as a prescription to improve falling asleep for older adults suffering from DIS. Additionally, engaging in LTPA along with dairy consumption may effectively improve a problem with falling asleep.peerReviewe

Relationships between Participation in Volunteer-Managed Exercises, Distance to Exercise Facilities, and Interpersonal Social Networks in Older Adults: A Cross-Sectional Study in Japan

This study aimed to examine the factors related to participation in volunteer-managed preventive care exercises by focusing on the distance to exercise facilities and interpersonal social networks. A postal mail survey was conducted in 2013 in Kasama City in a rural region of Japan. Older adults (aged ≥ 65 years) who were living independently (n = 16,870) were targeted. Potential participants who were aware of silver-rehabili taisou exercise (SRTE) and/or square-stepping exercise (SSE) were included in the analysis (n = 4005). A multiple logistic regression analysis revealed that social and environmental factors were associated with participation in SRTE and SSE. After adjusting for confounding variables, exercise participation was negatively associated with an extensive distance from an exercise facility in both sexes for SRTE and SSE. Among women, participation in SRTE was negatively associated with weak interpersonal social networks (odds ratio (OR) = 0.57), and participation in SRTE and SSE was negatively associated with being a car passenger (SRTE, OR = 0.76; SSE, OR = 0.60). However, there were no significant interactions between sex and social and environmental factors. Our findings suggest the importance of considering location and transportation to promote participation in preventive care exercise