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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

Sequential finger tapping. Group comparison between control subjects (square) and stroke patients (circle). a Mean success rate across all trials (learning and recall, sequence A, B and C) of the sequential finger tapping task. A success rate of 1 indicates perfect performance. b Mean number of correct taps (max = 5) for the first half (‘1’) and the second half (‘2’) of the learning phase of for each sequence (A, B and C). Note: patients and controls had similar numbers of correct taps at the first half of sequence A, controls subsequently increased their performance significantly (+++). In controls, learning during sequence A improved initial performance in subsequent sequences B and C: they had significantly more correct taps at the first halves of the sequences B and C (B: P = 0.04; C: P = 0.03) compared to patients. Significant differences between and within groups are indicated
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Fig4: Sequential finger tapping. Group comparison between control subjects (square) and stroke patients (circle). a Mean success rate across all trials (learning and recall, sequence A, B and C) of the sequential finger tapping task. A success rate of 1 indicates perfect performance. b Mean number of correct taps (max = 5) for the first half (‘1’) and the second half (‘2’) of the learning phase of for each sequence (A, B and C). Note: patients and controls had similar numbers of correct taps at the first half of sequence A, controls subsequently increased their performance significantly (+++). In controls, learning during sequence A improved initial performance in subsequent sequences B and C: they had significantly more correct taps at the first halves of the sequences B and C (B: P = 0.04; C: P = 0.03) compared to patients. Significant differences between and within groups are indicated

Mentions: The sequential finger tapping task turned out to be difficult for some patients. Control subjects achieved an average success rate of 0.66 ± 0.2, measured across all trials of the two conditions (learning and recall phases) and across the three different sequences (A, B, C). The four patients that accomplished this task reached a significantly lower success rate of 0.23 ± 0.28 (Fig. 4a, GROUP effect: F = 8.21; P = 0.017). Both groups showed similar performance in the first half of sequence A (Fig. 4b). During the learning phase (i.e. the cued condition), controls improved their performance by passing from a mean number of 2.7 (/5) correct taps to 4.2 (/5) between the first half and the second half of the learning phase for sequence A (P = 4 × 10−6; Fig. 4b). Controls showed maintained performance without obvious learning for the subsequent sequences B and C. In the patients significant improvement of performance between the first and the second half of the learning phase was only seen during the last sequence (sequence C): they passed from 2.5 (/5) correct taps to 3.4 (/5) (P = 0.02; Fig. 4b). In patients, no improvement was apparent during the first two sequences A and B. No significant group differences were found in the second halves of each sequence (Fig. 4b) nor in the recall phases.Fig. 4


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)

Sequential finger tapping. Group comparison between control subjects (square) and stroke patients (circle). a Mean success rate across all trials (learning and recall, sequence A, B and C) of the sequential finger tapping task. A success rate of 1 indicates perfect performance. b Mean number of correct taps (max = 5) for the first half (‘1’) and the second half (‘2’) of the learning phase of for each sequence (A, B and C). Note: patients and controls had similar numbers of correct taps at the first half of sequence A, controls subsequently increased their performance significantly (+++). In controls, learning during sequence A improved initial performance in subsequent sequences B and C: they had significantly more correct taps at the first halves of the sequences B and C (B: P = 0.04; C: P = 0.03) compared to patients. Significant differences between and within groups are indicated
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Sequential finger tapping. Group comparison between control subjects (square) and stroke patients (circle). a Mean success rate across all trials (learning and recall, sequence A, B and C) of the sequential finger tapping task. A success rate of 1 indicates perfect performance. b Mean number of correct taps (max = 5) for the first half (‘1’) and the second half (‘2’) of the learning phase of for each sequence (A, B and C). Note: patients and controls had similar numbers of correct taps at the first half of sequence A, controls subsequently increased their performance significantly (+++). In controls, learning during sequence A improved initial performance in subsequent sequences B and C: they had significantly more correct taps at the first halves of the sequences B and C (B: P = 0.04; C: P = 0.03) compared to patients. Significant differences between and within groups are indicated
Mentions: The sequential finger tapping task turned out to be difficult for some patients. Control subjects achieved an average success rate of 0.66 ± 0.2, measured across all trials of the two conditions (learning and recall phases) and across the three different sequences (A, B, C). The four patients that accomplished this task reached a significantly lower success rate of 0.23 ± 0.28 (Fig. 4a, GROUP effect: F = 8.21; P = 0.017). Both groups showed similar performance in the first half of sequence A (Fig. 4b). During the learning phase (i.e. the cued condition), controls improved their performance by passing from a mean number of 2.7 (/5) correct taps to 4.2 (/5) between the first half and the second half of the learning phase for sequence A (P = 4 × 10−6; Fig. 4b). Controls showed maintained performance without obvious learning for the subsequent sequences B and C. In the patients significant improvement of performance between the first and the second half of the learning phase was only seen during the last sequence (sequence C): they passed from 2.5 (/5) correct taps to 3.4 (/5) (P = 0.02; Fig. 4b). In patients, no improvement was apparent during the first two sequences A and B. No significant group differences were found in the second halves of each sequence (Fig. 4b) nor in the recall phases.Fig. 4

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