Polynoms (up to the third degree) were the best predictive functions. On the bivariate level, height was best correlated to grip (46.2% explained variance) and pinch strength (37.7% explained variance) in a linear relationship, followed by sex, age, weight, and occupational demand on the hand. Multivariate regression provided predicted values close to the empirical ones explaining 76.6% of the variance for grip strength and 67.7% for pinch strength.

This study aimed to quantify the predictive power of easily assessable demographic and/or anatomical factors such as sex, age, occupational demands on the hand, body height, and body weight on grip and pinch strength. The second aim was to predict grip and pinch strength by a regression model of these factors.

Few studies examined predictors of hand strength itself. Strong predictors are sex, age, body height and mid-forearm circumference [ 20 , 21 ]. Weaker predictors are body weight and hand size measures [ 21 ]. Grip strength has often been taken as surrogate for overall strength but this should be done with caution since correlation of the two strength measures is high in many but also low in few settings [ 22 ]. Grip strength has a substantial north-south gradient from 24.2 kg in Denmark to 14.2 kg in Calabria in men [ 23 , 24 ]. Age-and gender-specific grip and key pinch strengths have recently been evaluated in a large population-based field study in Switzerland [ 25 ]. We used the data from that study to determine the predictive impact of various cofactors.

In addition to its predictive value grip strength and key pinch strength are considered to be "objective" outcome parameters and are used to quantify outcome after orthopaedic interventions [ 18 ]. Many investigators normalize measured values such as percentage of the contralateral, non-affected side. However, when the contralateral side is also compromised by the underlying disease or its treatment, other benchmarks are required. Although numerous publications provide normative data for grip strength and key pinch strength, it would be helpful to predict the expected hand strength values for an individual based on easily measured factors [ 19 ].

The hand represents the most sophisticated and differentiated musculoskeletal tool in the human being, demanding the largest capacity of the nervous system in relation to its size. Full function and adequate strength of the hand are preconditions for dealing with the demands of daily life. Hand strength has been identified as an important factor predicting not only disability in musculoskeletal diseases such as rheumatoid arthritis [ 1 ], but also bone mineral density [ 2 , 3 ], and the likelihood of falls and fractures in osteoporosis [ 4 , 5 ]. It even predicts complications and general morbidity after surgical interventions [ 6 ], general disability and future outcome in older age [ 7 - 9 ], economic consequences of diseases [ 10 ] as well as cause-specific and overall mortality in elderly people [ 5 , 11 - 13 ]. Arteriosclerosis is the most frequent cause of morbidity and mortality and grip strength is one of the strongest predictors of its consequences, e.g. myocardial infarction or stroke and post-event recovery [ 12 , 14 , 15 ]. In contrast, grip strength is almost completely independent from depression and return to work [ 16 , 17 ].

In a second step, all single optimal functions were combined together into a stepwise multivariate multifunctional regression to maximize explained variance. Regression coefficients, level of significance for predictive power, bivariate and partial correlations were determined. Addition and omission of one regression term had to significantly change the r square (=explained variance) of the regression model for stepwise inclusion as being tested by the F-test [ 28 , 29 ]. All possible order of the variables were tested in adding / omitting to find the optimal model.

Grip and pinch strength in kg were predicted as independent variables for the dominant hand. First, bivariate curve adaptations were calculated using linear, quadratic, cubic, inverse, composite (inverse and polynom together), exponential, logarithmic, logistic, and s-shaped function characteristics of the dependent variable to find the optimal curve adaptation for each single cofactor explaining maximal variance of the dependent strength variable. The body mass index, BMI = weight/height 2 was also taken into account by height and weight and by modeling all possibilities of linear, quadratic and inverse terms. In principle, every bivariate characteristic can be approximated by a polynom, y = a 0 +a 1 x+a 2 x 2 +...+a n x n , the higher the degree n the more precise the approximation [ 27 ].

All to the present analysis underlying data are described in detail in the descriptive original report [ 25 ]. In 2006 and 2007, data were obtained for 1023 persons. 4.4% were classified as ambidextrous and excluded from the analysis. The remaining 978 persons provided the data for the grip and