Skip to main content
SpringerLink
Log in

Force control deficits in chronic stroke: grip formation and release phases

  • Research Article
  • Published:
Experimental Brain Research Aims and scope Submit manuscript

Abstract

The aim of the study was to develop a novel approach for quantifying stair-stepping in a trajectory tracking task with the goal of understanding how age and stroke-related differences in motor control contribute to force control deficits. Nine stroke participants, nine age-matched controls, and nine young healthy adults performed an isometric gripping task while squeezing, holding, and releasing a cylindrical device. The visual tracking task involved three different rates of force production (5, 10, and 20% maximal force/s). Four outcome measures determined force control deficits: (a) root mean square error, (b) standard deviation, (c) step number, and (d) mean pause duration. Our findings indicate that step number, and especially mean pause duration, differentiated force control deficits in the three groups more effectively than the traditional root mean square error. Moreover, stroke participants showed the largest force control deficits during the grip release phase compared to age-matched and young healthy controls. Importantly, step number and mean pause duration quantified stair-stepping while measuring different constructs than root mean square error. Distinct step and duration interruptions in force modulation by persons post-stroke during the grip release phase provide new information with implications for motor recovery during rehabilitation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Either MLP-50; range 50 lbs or MLP-200; range 200 lbs Transducer Techniques, 4.16 × 1.27 × 1.90 cm, 0.1% sensitivity.

  2. 15LT Grass Technologies Physio-data Amplifier System (Astro-Med Inc., Warwick, RI).

  3. A/D; NI cDAQ-9172 + NI-9215, National Instruments, Austin, TX.

  4. 8.1; National Instruments, Austin, TX.

  5. Version 2007, Microsoft Corporation, Redmond, WA.

  6. Version 7.0, SAS Campus Drive, Cary, NC.

Abbreviations

MVC:

Maximal voluntary contraction

RMSE:

Root mean square error

SD:

Standard deviation of force trace

SEM:

Standard error of measurement

SRD:

Smallest real difference

References

  • Aymard C, Katz R, Lafitte C, Lo E, Penicaud A, Pradat-Diehl P, Raoul S (2000) Presynaptic inhibition and homosynaptic depression: a comparison between lower and upper limbs in normal human subjects and patients with hemiplegia. Brain 123:1688–1702

    Article  PubMed  Google Scholar 

  • Barry BK, Warman GE, Carson RG (2005) Age-related differences in rapid muscle activation after rate of force development training of the elbow flexors. Exp Brain Res 162:122–132

    Article  PubMed  Google Scholar 

  • Beckerman H, Roebroeck ME, Lankhorst GJ, Becher JG, Bezemer PD, Verbeek AL (2001) Smallest real difference, a link between reproducibility and responsiveness. Qual Life Res 10:571–578

    Article  PubMed  CAS  Google Scholar 

  • Bigland-Ritchie B, Johansson R, Lippold OC, Smith S, Woods JJ (1983) Changes in motoneurone firing rates during sustained maximal voluntary contractions. J Physiol 340:335–346

    PubMed  CAS  Google Scholar 

  • Blennerhassett JM, Carey LM, Matyas TA (2006) Grip force regulation during pinch grip lifts under somatosensory guidance: comparison between people with stroke and healthy controls. Arch Phys Med Rehabil 87:418–429

    Article  PubMed  Google Scholar 

  • Bohannon RW (2007) Muscle strength and muscle training after stroke. J Rehabil Med 39:14–20

    Article  PubMed  Google Scholar 

  • Bohannon RW, Smith MB (1987) Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther 67:206–207

    PubMed  CAS  Google Scholar 

  • Brunnstrom S (1970) Movement therapy in hemiplegia: a neurophysiological approach. Harper & Row, New York

    Google Scholar 

  • Budingen HJ, Freund HJ (1976) The relationship between the rate of rise of isometric tension and motor unit recruitment in a human forearm muscle. Pflugers Arch 362:61–67

    Article  PubMed  CAS  Google Scholar 

  • Canning CG, Ada L, O’Dwyer NJ (2000) Abnormal muscle activation characteristics associated with loss of dexterity after stroke. J Neurol Sci 176:45–56

    Article  PubMed  CAS  Google Scholar 

  • Chae J, Yang G, Park BK, Labatia I (2002) Muscle weakness and cocontraction in upper limb hemiparesis: relationship to motor impairment and physical disability. Neurorehabil Neural Repair 16:241–248

    PubMed  Google Scholar 

  • Christou EA, Tracy BL (2006) Aging and variability in motor output. In: Davids K, Bennett S, Newell KM (eds) Movement system variability. Human Kinetics, Champaign, pp 199–215

  • Christou EA, Poston B, Enoka JA, Enoka RM (2007) Different neural adjustments improve endpoint accuracy with practice in young and old adults. J Neurophysiol 97:3340–3350

    Article  PubMed  Google Scholar 

  • Coombes SA, Gamble KM, Cauraugh JH, Janelle CM (2008) Emotional states alter force control during a feedback occluded motor task. Emotion 8:104–113

    Article  PubMed  Google Scholar 

  • Corcos DM, Chen CM, Quinn NP, McAuley J, Rothwell JC (1996) Strength in Parkinson’s disease: relationship to rate of force generation and clinical status. Ann Neurol 39:79–88

    Article  PubMed  CAS  Google Scholar 

  • Cruz EG, Waldinger HC, Kamper DG (2005) Kinetic and kinematic workspaces of the index finger following stroke. Brain 128:1112–1121

    Article  PubMed  CAS  Google Scholar 

  • Darling WG, Cooke JD, Brown SH (1989) Control of simple arm movements in elderly humans. Neurobiol Aging 10:149–157

    Article  PubMed  CAS  Google Scholar 

  • De Luca CJ, Le Fever RS, McCue MP, Xenakis AP (1982) Behaviour of human motor units in different muscles during linearly varying contractions. J Physiol 329:113–128

    PubMed  Google Scholar 

  • Duncan PW, Bode RK, Min Lai S, Perera S (2003) Rasch analysis of a new stroke-specific outcome scale: the Stroke Impact Scale. Arch Phys Med Rehabil 84:950–963

    Article  PubMed  Google Scholar 

  • Enoka RM (1997) Neural strategies in the control of muscle force. Muscle Nerve Suppl 5:66–69

    Article  Google Scholar 

  • Enoka RM, Christou EA, Hunter SK, Kornatz WK, Semmler JG, Taylor AM, Tracy BL (2003) Mechanisms that contribute to differences in motor performance between young and old adults. J Electromyogr Kinesiol 13:1–12

    Article  PubMed  Google Scholar 

  • Fearnhead L, Eales CJ, Fritz VU (1999) Arm function after stroke—can we make a difference? S Afr J Physiother 55:4–7

    Google Scholar 

  • Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”, A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198

    Article  PubMed  CAS  Google Scholar 

  • Ford AB, Katz S (1966) Prognosis after strokes. I. A critical review. Medicine (Baltimore) 45:223–246

    CAS  Google Scholar 

  • Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S (1975) The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med 7:13–31

    PubMed  CAS  Google Scholar 

  • Galganski ME, Fuglevand AJ, Enoka RM (1993) Reduced control of motor output in a human hand muscle of elderly subjects during submaximal contractions. J Neurophysiol 69:2108–2115

    PubMed  CAS  Google Scholar 

  • Hammond MC, Fitts SS, Kraft GH, Nutter PB, Trotter MJ, Robinson LM (1988) Co-contraction in the hemiparetic forearm: quantitative EMG evaluation. Arch Phys Med Rehabil 69:348–351

    PubMed  CAS  Google Scholar 

  • Hermsdorfer J, Mai N (1996) Disturbed grip-force control following cerebral lesions. J Hand Ther 9:33–40

    PubMed  CAS  Google Scholar 

  • Hermsdorfer J, Hagl E, Nowak DA, Marquardt C (2003) Grip force control during object manipulation in cerebral stroke. Clin Neurophysiol 114:915–929

    Article  PubMed  CAS  Google Scholar 

  • Hook P, Sriramoju V, Larsson L (2001) Effects of aging on actin sliding speed on myosin from single skeletal muscle cells of mice, rats, and humans. Am J Physiol Cell Physiol 280:782–788

    Google Scholar 

  • Hopkins WG (2000) Measures of reliability in sports medicine and science. Sports Med 30:1–15

    Article  PubMed  CAS  Google Scholar 

  • Johansson RS, Westling G (1984) Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp Brain Res 56:550–564

    Article  PubMed  CAS  Google Scholar 

  • Kamen G (1983) The acquisition of maximal isometric plantar flexor strength: a force-time curve analysis. J Mot Behav 15:63–73

    PubMed  CAS  Google Scholar 

  • Kamen G, De Luca CJ (1989) Unusual motor unit firing behavior in older adults. Brain Res 482:136–140

    Article  PubMed  CAS  Google Scholar 

  • Kamper DG, Rymer WZ (2001) Impairment of voluntary control of finger motion following stroke: role of inappropriate muscle coactivation. Muscle Nerve 24:673–681

    Article  PubMed  CAS  Google Scholar 

  • Krakauer JW (2006) Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol 19:84–90

    Article  PubMed  Google Scholar 

  • Kriz G, Hermsdorfer J, Marquardt C, Mai N (1995) Feedback-based training of grip force control in patients with brain damage. Arch Phys Med Rehabil 76:653–659

    Article  PubMed  CAS  Google Scholar 

  • Kurillo G, Bajd T, Tercelj M (2004a) The effect of age on the grip force control in lateral grip. Conf Proc IEEE Eng Med Biol Soc 6:4657–4660

    PubMed  CAS  Google Scholar 

  • Kurillo G, Zupan A, Bajd T (2004b) Force tracking system for the assessment of grip force control in patients with neuromuscular diseases. Clin Biomech 19:1014–1021

    Article  Google Scholar 

  • Kurillo G, Gregoric M, Goljar N, Bajd T (2005) Grip force tracking system for assessment and rehabilitation of hand function. Technol Health Care 13:137–149

    PubMed  Google Scholar 

  • Laidlaw DH, Bilodeau M, Enoka RM (2000) Steadiness is reduced and motor unit discharge is more variable in old adults. Muscle Nerve 23:600–612

    Article  PubMed  CAS  Google Scholar 

  • Lang CE, Wagner JM, Bastian AJ, Hu Q, Edwards DF, Sahrmann SA, Dromerick AW (2005) Deficits in grasp versus reach during acute hemiparesis. Exp Brain Res 166:126–136

    Article  PubMed  Google Scholar 

  • Larsson L, Li X, Frontera WR (1997) Effects of aging on shortening velocity and myosin isoform composition in single human skeletal muscle cells. Am J Physiol 272:638–649

    Google Scholar 

  • Levin MF (1996) Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. Brain 119:281–293

    Article  PubMed  Google Scholar 

  • Lewis GN, McNair PJ (2009) Muscle inhibition following tendon stimulation is reduced in chronic stroke. Clin Neurophysiol 120:1732–1740

    Article  PubMed  Google Scholar 

  • Lindberg P, Ody C, Feydy A, Maier MA (2009) Precision in isometric precision grip force is reduced in middle-aged adults. Exp Brain Res 193:213–224

    Article  PubMed  Google Scholar 

  • Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J (2010) Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation 121:e46–e215

    Article  PubMed  Google Scholar 

  • Lodha N, Naik SK, Coombes SA, Cauraugh JH (2010) Force control and degree of motor impairments in chronic stroke. Clin Neurophysiol 121:1952–1961

    Article  PubMed  Google Scholar 

  • Milner-Brown HS, Stein RB, Yemm R (1973) Changes in firing rate of human motor units during linearly changing voluntary contractions. J Physiol 230:371–390

    PubMed  CAS  Google Scholar 

  • Morgan M, Phillips JG, Bradshaw JL, Mattingley JB, Iansek R, Bradshaw JA (1994) Age-related motor slowness: simply strategic? J Gerontol 49:133–139

    Google Scholar 

  • Muntner P, Garrett E, Klag MJ, Coresh J (2002) Trends in stroke prevalence between 1973 and 1991 in the US population 25 to 74 years of age. Stroke 33:1209–1213

    Article  PubMed  Google Scholar 

  • Nakashima K, Rothwell JC, Day BL, Thompson PD, Shannon K, Marsden CD (1989) Reciprocal inhibition between forearm muscles in patients with writer’s cramp and other occupational cramps, symptomatic hemidystonia and hemiparesis due to stroke. Brain 112:681–697

    Article  PubMed  Google Scholar 

  • Newell KM, Corcos DM (1993) Variability and Motor Control. Human Kinetics, Champaign

    Google Scholar 

  • Nowak DA, Hermsdorfer J, Topka H (2003) Deficits of predictive grip force control during object manipulation in acute stroke. J Neurol 250:850–860

    Article  PubMed  Google Scholar 

  • Orizio C, Baruzzi E, Gaffurini P, Diemont B, Gobbo M (2010) Electromyogram and force fluctuation during different linearly varying isometric motor tasks. J Electromyogr Kinesiol 20:732–741

    Article  PubMed  CAS  Google Scholar 

  • Patten C (2000) Reeducating muscle force control in older persons through strength training. Top Geriatr Rehabil 15:47–59

    Google Scholar 

  • Patten C, Kothari D, Whitney J, Lexell J, Lum PS (2003) Reliability and responsiveness of elbow trajectory tracking in chronic poststroke hemiparesis. J Rehabil Res Dev 40:487–500

    Article  PubMed  Google Scholar 

  • Patten C, Lexell J, Brown HE (2004) Weakness and strength training in persons with poststroke hemiplegia: rationale, method, and efficacy. J Rehabil Res Dev 41:293–312

    Article  PubMed  Google Scholar 

  • Perdan J, Kamnik R, Obreza P, Kurillo G, Bajd T, Munih M (2008) Design and evaluation of a functional electrical stimulation system for hand sensorimotor augmentation. Neuromodulation 11:208–215

    Article  Google Scholar 

  • Robichaud JA, Pfann KD, Vaillancourt DE, Comella CL, Corcos DM (2005) Force control and disease severity in Parkinson’s disease. Mov Disord 20:441–450

    Article  PubMed  Google Scholar 

  • Ryan ED, Beck TW, Herda TJ, Hartman MJ, Stout JR, Housh TJ, Cramer JT (2008) Mechanomyographic amplitude and mean power frequency responses during isometric ramp vs. step muscle actions. J Neurosci Methods 168:293–305

    Article  PubMed  Google Scholar 

  • Seo NJ, Rymer WZ, Kamper DG (2009) Delays in grip initiation and termination in persons with stroke: effects of arm support and active muscle stretch exercise. J Neurophysiol 101:3108–3115

    Article  PubMed  Google Scholar 

  • Sosnoff JJ, Newell KM (2006a) Are age-related increases in force variability due to decrements in strength? Exp Brain Res 174:86–94

    Article  PubMed  Google Scholar 

  • Sosnoff JJ, Newell KM (2006b) The generalization of perceptual-motor intra-individual variability in young and old adults. J Gerontol B Psychol Sci Soc Sci 61:304–310

    Google Scholar 

  • Spiegel KM, Stratton J, Burke JR, Glendinning DS, Enoka RM (1996) The influence of age on the assessment of motor unit activation in a human hand muscle. Exp Physiol 81:805–819

    PubMed  CAS  Google Scholar 

  • Spraker MB, Corcos DM, Vaillancourt DE (2009) Cortical and subcortical mechanisms for precisely controlled force generation and force relaxation. Cereb Cortex 19:2640–2650

    Article  PubMed  Google Scholar 

  • Stoeckmann TM, Sullivan KJ, Scheidt RA (2009) Elastic, viscous, and mass load effects on poststroke muscle recruitment and co-contraction during reaching: a pilot study. Phys Ther 89:665–678

    Article  PubMed  Google Scholar 

  • Takahashi CD, Reinkensmeyer DJ (2003) Hemiparetic stroke impairs anticipatory control of arm movement. Exp Brain Res 149:131–140

    PubMed  Google Scholar 

  • Tubiana R (1981) Architecture and functions of the hand. In: Tubiana R (ed) The hand. W B Saunders, Philadelphia

    Google Scholar 

  • Vaillancourt DE, Newell KM (2003) Aging and the time and frequency structure of force output variability. J Appl Physiol 94:903–912

    PubMed  Google Scholar 

  • Vaillancourt DE, Larsson L, Newell KM (2003) Effects of aging on force variability, single motor unit discharge patterns, and the structure of 10, 20, and 40 Hz EMG activity. Neurobiol Aging 24:25–35

    Article  PubMed  Google Scholar 

  • Vidoni ED, Boyd LA (2009) Preserved motor learning after stroke is related to the degree of proprioceptive deficit. Behav Brain Funct 5:36

    Article  PubMed  Google Scholar 

  • Voelcker-Rehage C, Alberts JL (2005) Age-related changes in grasping force modulation. Exp Brain Res 166:61–70

    Article  PubMed  Google Scholar 

  • Wenzelburger R, Kopper F, Frenzel A, Stolze H, Klebe S, Brossmann A, Kuhtz-Buschbeck J, Golge M, Illert M, Deuschl G (2005) Hand coordination following capsular stroke. Brain 128:64–74

    Article  PubMed  Google Scholar 

  • Winstein CJ, Merians AS, Sullivan KJ (1999) Motor learning after unilateral brain damage. Neuropsychologia 37:975–987

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Physiology and Kinesiology, University of Florida, 132 C, Motor Behavior Laboratory, Gainesville, FL, 32611, USA

    Sagar K. Naik & Neha Lodha

  2. Brain Rehabilitation Research Center, Malcolm Randall VA Medical Center, Gainesville, FL, USA

    Carolynn Patten

  3. Department of Physical Therapy, University of Florida, Gainesville, FL, USA

    Carolynn Patten

  4. Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, 32611, USA

    Carolynn Patten

  5. Departments of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL, USA

    Stephen A. Coombes

  6. Department of Applied Physiology and Kinesiology, University of Florida, 132, Motor Behavior Laboratory, Gainesville, FL, 32611, USA

    James H. Cauraugh

  7. Center of Exercise Science, University of Florida, Gainesville, FL, 32611, USA

    James H. Cauraugh

Authors
  1. Sagar K. Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolynn Patten
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neha Lodha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephen A. Coombes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. James H. Cauraugh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sagar K. Naik.

Appendix: stair-stepping calculations

Appendix: stair-stepping calculations

Stair-stepping

Interruptions in force production during the force tracking task were termed steps. We operationally defined a step using two criteria:

  1. (a)

    Temporal—if the performed rate of force modulation lagged the criterion rate by 50% or more as defined by

  2. (b)

    constant force production—less than 2% change in absolute force over the relevant epoch

A step was confirmed only when both these conditions were satisfied simultaneously.

Step detection—temporal component

Force data were sampled at 100 Hz (i.e., 100 samples/1000 ms) offering the following criteria for step detection.

 

Criterion rate of force production

Temporal epoch, 1% MVC (ms)

Lag (ms)

Samples

5% MVC/s

200

≥100

10

10% MVC/s

100

≥50

5

20% MVC/s

50

≥25

2.5 (3)

Example: 5% MVC/s rate of force production means change of 1% of MVC force requires 100/5 = 20 samples (i.e., 20 × 10 = 200 ms). Therefore, applying temporal definition of step, defines lag as (200 × 50)/100 = 100 ms. Similarly, 20% MVC/s rate of force production implies that 100/20 = 5 samples (i.e., 5 × 10 = 50 ms) are required to change 1% MVC force, which defines temporal component as (50 × 50)/100 = 25 ms.

Step detection—constant force component

In Fig. 6a,

Fig. 6
figure 6

Illustration of algorithm to quantify stair-stepping, including identification of steps and assessment of pause duration. Representative trials from stroke participant performed at a 5% MVC/s and b 20% MVC/s rates of force production

Full size image

Point 1: At time 2250 ms, force produced was 34.04 N

Point 2: At time 2580 ms, force produced was 34.56 N

Step number: Change of produced force from arrow 1 to 2 is 0.52, less than 2% of absolute force (0.52) at arrow 1 (34.04 N). The time lag is 330 ms. Based on our operational definitions, this phase met both criteria and was therefore defined as a step.

Pause duration: Point 3 represents time between points 1 and 2, which was termed as pause duration for this step (330 ms).

Similarly, Fig. 6b demonstrates representative step and pause duration for 20% MVC/s rate of force production.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Naik, S.K., Patten, C., Lodha, N. et al. Force control deficits in chronic stroke: grip formation and release phases. Exp Brain Res 211, 1–15 (2011). https://doi.org/10.1007/s00221-011-2637-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00221-011-2637-8

Keywords

Search

Navigation