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A novel method for the quantification of key components of manual dexterity after stroke.

Térémetz M, Colle F, Hamdoun S, Maier MA, Lindberg PG - J Neuroeng Rehabil (2015)

Bottom Line: Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls.Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients.

View Article: PubMed Central - PubMed

Affiliation: FR3636 CNRS, Université Paris Descartes, Sorbonne Paris Cité, 75006, Paris, France. mteremetz@gmail.com.

ABSTRACT

Background: A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

Methods: 10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

Results: Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

Conclusions: Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

No MeSH data available.


Related in: MedlinePlus

Correlations with clinical scores. a-b FFM single finger tapping (N = 10): a Correlation between 1-3Hz slope and the ARAT scores. b Correlation between 1-3Hz slope and the Moberg pick-up scores. c-d FFM multi-finger tapping (N = 9). c Correlation between success rate and the ARAT scores. d Correlation between success rate and the Moberg pick-up scores
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Fig9: Correlations with clinical scores. a-b FFM single finger tapping (N = 10): a Correlation between 1-3Hz slope and the ARAT scores. b Correlation between 1-3Hz slope and the Moberg pick-up scores. c-d FFM multi-finger tapping (N = 9). c Correlation between success rate and the ARAT scores. d Correlation between success rate and the Moberg pick-up scores

Mentions: We tested for correlations between the obtained performance measures in the FFM tasks and the ARAT or the Moberg pick-up test scores. Single finger tapping 1-3Hz slope appeared to be correlated with the ARAT score (Fig. 9a, R = 0.88; P = 0.0003) and with %Pick Up scores (Fig. 9b, R = 0.77; P = 0.004). The higher the slope during the single finger tapping task, the better were their ARAT or Pick Up scores. Multi-finger tapping success rate also appeared to be correlated with the ARAT score (Fig. 9c, R = 0.73; P = 0.03) and with %Pick Up (Fig. 9d, R = 0.77; P = 0.02). Again, a higher success rate in the multi-finger tapping task was found in patients with higher ARAT or %Pick Up scores. For the Finger force tracking task we did not find any correlations between performance variables and clinical measures. We also tested the inter-relations between the 6 measures used for the description of the dexterity profiles and we found four significant correlations among the 15 comparisons (Table 3). The strongest correlation was between 1-3Hz slope and the unwanted extra-finger-taps (1F) (R2 = 0.55).Fig. 9


A novel method for the quantification of key components of manual dexterity after stroke.

Térémetz M, Colle F, Hamdoun S, Maier MA, Lindberg PG - J Neuroeng Rehabil (2015)

Correlations with clinical scores. a-b FFM single finger tapping (N = 10): a Correlation between 1-3Hz slope and the ARAT scores. b Correlation between 1-3Hz slope and the Moberg pick-up scores. c-d FFM multi-finger tapping (N = 9). c Correlation between success rate and the ARAT scores. d Correlation between success rate and the Moberg pick-up scores
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522286&req=5

Fig9: Correlations with clinical scores. a-b FFM single finger tapping (N = 10): a Correlation between 1-3Hz slope and the ARAT scores. b Correlation between 1-3Hz slope and the Moberg pick-up scores. c-d FFM multi-finger tapping (N = 9). c Correlation between success rate and the ARAT scores. d Correlation between success rate and the Moberg pick-up scores
Mentions: We tested for correlations between the obtained performance measures in the FFM tasks and the ARAT or the Moberg pick-up test scores. Single finger tapping 1-3Hz slope appeared to be correlated with the ARAT score (Fig. 9a, R = 0.88; P = 0.0003) and with %Pick Up scores (Fig. 9b, R = 0.77; P = 0.004). The higher the slope during the single finger tapping task, the better were their ARAT or Pick Up scores. Multi-finger tapping success rate also appeared to be correlated with the ARAT score (Fig. 9c, R = 0.73; P = 0.03) and with %Pick Up (Fig. 9d, R = 0.77; P = 0.02). Again, a higher success rate in the multi-finger tapping task was found in patients with higher ARAT or %Pick Up scores. For the Finger force tracking task we did not find any correlations between performance variables and clinical measures. We also tested the inter-relations between the 6 measures used for the description of the dexterity profiles and we found four significant correlations among the 15 comparisons (Table 3). The strongest correlation was between 1-3Hz slope and the unwanted extra-finger-taps (1F) (R2 = 0.55).Fig. 9

Bottom Line: Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls.Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients.

View Article: PubMed Central - PubMed

Affiliation: FR3636 CNRS, Université Paris Descartes, Sorbonne Paris Cité, 75006, Paris, France. mteremetz@gmail.com.

ABSTRACT

Background: A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

Methods: 10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

Results: Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

Conclusions: Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

No MeSH data available.


Related in: MedlinePlus