Hand grip strength measurement is a recognized part of hand function assessment. The standard measurement using the Jamar dynamometer and comparing these results to the recommended normal values suggested by the manufacturers of the Jamar was questioned as these values were based on Western population. A study comparing a novel method of predicting grip strength using our software was conducted on 25 normal subjects using the LIDO kinetic workset (Group A and B ). These results were then compared against our predictive software (Group A) and the expected values supplied together with the Jamar Dynamometer (Group B). In another group, 22 normal subjects were tested using the Jamar (Group C and D) and then matched against the predicted values using their recommended chart (Group C). The last group (Group D) was tested using the Jamar but the values attained were compared to the results from our software. In group A, the predictability of our predictive method was 100% (both R & L) as compared to (R = 64%, L = 68%) in group B, (R = 27.3%, L = 59.1%) in group C and (R = 81.8%, L = 86.4%) in group D. The differences between the predictability of both methods were statistically significant. The data collected using both the Jamar and the LIDO kinetic workset correlated well to the data from our software but not to the values suggested by the manufacturers of Jamar. We conclude that our method of predicting hand grip values are superior to that suggested by the manufacturers of dynamometers. The standard reference for hand grip strength provided by the manufacturers is less accurate in predicting the grip strength of our local population.
An accurate measurement of intrinsic hand muscle strength (IHMS) is required by clinicians for effective clinical decision-making, diagnosis of certain diseases, and evaluation of the outcome of treatment. In practice, the clinicians use Intrins-o-meter and Rotterdam Intrinsic Hand Myometer for IHMS measurement. These are quite bulky, expensive, and possess poor interobserver reliability (37-52%) and sensitivity. The purpose of this study was to develop an alternative lightweight, accurate, cost-effective force measurement device with a simple electronic circuit and test its suitability for IHMS measurement. The device was constructed with ketjenblack/deproteinized natural rubber sensor, 1-MΩ potential divider, and Arduino Uno through the custom-written software. Then, the device was calibrated and tested for accuracy and repeatability within the force range of finger muscles (100 N). The 95% limit of agreement in accuracy from -1.95 N to 2.06 N for 10 to 100 N applied load and repeatability coefficient of ±1.91 N or 6.2% was achieved. Furthermore, the expenditure for the device construction was around US$ 53. For a practical demonstration, the device was tested among 16 participants for isometric strength measurement of the ulnar abductor and dorsal interossei. The results revealed that the performance of the device was suitable for IHMS measurement.
The longitudinal relationship between muscle strength, dietary intake and physical activity among adolescents is not well understood. We investigated the trend and longitudinal effects of dietary intakes and physical activity scores on muscle strength in adolescents. This prospective cohort study consisted of 436 adolescents (134 males; 302 females) aged 13 years at baseline (2012) who were followed up at the ages of 15 (2014) and 17 (2016) years, respectively. We measured muscle strength using a calibrated hand dynamometer, estimated dietary intake with a 7-d dietary history and physical activity scores with a validated physical activity questionnaire for older children. A generalised estimating equation was used to examine the effect of dietary intakes and physical activity on muscle strength changes. The analysis was performed separately by sex. The muscle strength for males and females had increased within the 5-year period. The dietary intakes (energy and macronutrients) also increased initially but plateaued after the age of 15 years for both sexes. Females recorded a significant declining trend in physical activity scores compared with males as they grew older. A significant positive longitudinal relationship was found between protein (β = 0·035; P = 0·016), carbohydrate intake (β = 0·002; P = 0·013) and muscle strength among males. However, no longitudinal relationship was found between dietary intake, physical activity and muscle strength among females. Higher protein and carbohydrate intake among males was associated with higher muscle strength but was not observed in females. Nutrition and physical activity focusing on strength building are required in early adolescence and need to be tailored to males and females accordingly.
Research shows that cardiorespiratory (CRF) and muscular fitness in childhood are associated with a healthier cardiovascular profile in adulthood. Identifying factors associated with measures of fitness in childhood could allow for strategies to optimize cardiovascular health throughout the lifecourse. The aim of this study was to examine the association between dietary patterns and both CRF and muscular fitness in 9⁻11-year-olds. In this study of 398 children, CRF and muscular fitness were assessed using a 20-m shuttle run test and digital hand dynamometer, respectively. Dietary patterns were derived using principal component analysis. Mixed effects linear regression models were used to assess associations between dietary patterns and CRF and muscular fitness. Most children had healthy CRF (99%, FITNESSGRAM) and mean ± SD muscular fitness was 15.2 ± 3.3 kg. Two dietary patterns were identified; “Snacks” and “Fruit and Vegetables”. There were no significant associations between either of the dietary patterns and CRF. Statistically significant but not clinically meaningful associations were seen between dietary patterns and muscular fitness. In an almost exclusively fit cohort, food choice is not meaningfully related to measures of fitness. Further research to investigate diet-fitness relationships in children with lower fitness levels can identify key populations for potential investments in health-promoting behaviors.