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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

Finger tap errors as a function of target tap combination. Each line shows the occurrence of error taps during multi finger tapping. Error occurrence is given for each finger in % (mean ± SD) of target taps in the relevant condition for patients (left) and in control subjects (right). Example: in 10 % of all one-finger target taps with the index finger (target digit 2), patients also tapped erroneously with the little finger (digit 5). The first four lines describe each one-finger target tap condition, the following six lines every two-finger target tap combination. “Xs” indicate coincidence of target finger(s) and correct tap finger(s). Color scale indicates the level of error: white = no error (0 %), red > 60 % errors
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Fig7: Finger tap errors as a function of target tap combination. Each line shows the occurrence of error taps during multi finger tapping. Error occurrence is given for each finger in % (mean ± SD) of target taps in the relevant condition for patients (left) and in control subjects (right). Example: in 10 % of all one-finger target taps with the index finger (target digit 2), patients also tapped erroneously with the little finger (digit 5). The first four lines describe each one-finger target tap condition, the following six lines every two-finger target tap combination. “Xs” indicate coincidence of target finger(s) and correct tap finger(s). Color scale indicates the level of error: white = no error (0 %), red > 60 % errors

Mentions: The distribution of unwanted extra-finger-taps across fingers is shown in Fig. 7 for both one- and two-finger combinations. Each line in the Table shows the occurrence of unwanted extra-finger-taps as a function of finger combination. For every target combination, patients produced more error in other fingers than control subjects. In the least successful one-finger combination (the ring finger target tap) patients erroneously activated also the middle finger in more than sixty percent of the trials, while this was the case in less than ten percent in controls (Fig. 7). Note that the index and little finger also made errors in this condition, but less frequently (in about 35 %) than the middle finger. This same error pattern across fingers (i.e. middle finger error > index or little finger error) was also present in control subjects, but in an attenuated form. More generally, the pattern of unwanted extra-finger-taps formed a ‘neighborhood’ gradient, such that digits anatomically far from the target (lead) digit produced less error taps than those closer to (or immediate neighbors of) the target digit. This also held for the ‘2–3’ and ‘4–5’ two-finger combinations. Two-finger combination taps of non-adjacent digits (‘2–4’, ‘2–5’, ‘3–5’), showed, in absence of a distance gradient, a balanced error distribution. Similar but attenuated ‘across’ finger error patterns were also observed for the control subjects.Fig. 7


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)

Finger tap errors as a function of target tap combination. Each line shows the occurrence of error taps during multi finger tapping. Error occurrence is given for each finger in % (mean ± SD) of target taps in the relevant condition for patients (left) and in control subjects (right). Example: in 10 % of all one-finger target taps with the index finger (target digit 2), patients also tapped erroneously with the little finger (digit 5). The first four lines describe each one-finger target tap condition, the following six lines every two-finger target tap combination. “Xs” indicate coincidence of target finger(s) and correct tap finger(s). Color scale indicates the level of error: white = no error (0 %), red > 60 % errors
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Finger tap errors as a function of target tap combination. Each line shows the occurrence of error taps during multi finger tapping. Error occurrence is given for each finger in % (mean ± SD) of target taps in the relevant condition for patients (left) and in control subjects (right). Example: in 10 % of all one-finger target taps with the index finger (target digit 2), patients also tapped erroneously with the little finger (digit 5). The first four lines describe each one-finger target tap condition, the following six lines every two-finger target tap combination. “Xs” indicate coincidence of target finger(s) and correct tap finger(s). Color scale indicates the level of error: white = no error (0 %), red > 60 % errors
Mentions: The distribution of unwanted extra-finger-taps across fingers is shown in Fig. 7 for both one- and two-finger combinations. Each line in the Table shows the occurrence of unwanted extra-finger-taps as a function of finger combination. For every target combination, patients produced more error in other fingers than control subjects. In the least successful one-finger combination (the ring finger target tap) patients erroneously activated also the middle finger in more than sixty percent of the trials, while this was the case in less than ten percent in controls (Fig. 7). Note that the index and little finger also made errors in this condition, but less frequently (in about 35 %) than the middle finger. This same error pattern across fingers (i.e. middle finger error > index or little finger error) was also present in control subjects, but in an attenuated form. More generally, the pattern of unwanted extra-finger-taps formed a ‘neighborhood’ gradient, such that digits anatomically far from the target (lead) digit produced less error taps than those closer to (or immediate neighbors of) the target digit. This also held for the ‘2–3’ and ‘4–5’ two-finger combinations. Two-finger combination taps of non-adjacent digits (‘2–4’, ‘2–5’, ‘3–5’), showed, in absence of a distance gradient, a balanced error distribution. Similar but attenuated ‘across’ finger error patterns were also observed for the control subjects.Fig. 7

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