Background:

Stroke is the leading preventable cause of disability in the US, however a superior rehabilitation technique has yet to be established to best aid this population in recovery.  The idea of self-control or regulation, referring to the ability to monitor and control our own behaviors, emotions and thoughts in accordance to the demand of the situation, has become a topic of discussion.   Recent literature in 2015 found that absolute self-controlled feedback in addition to limited self-controlled feedback has been successful in enhancing motor learning in the upper extremity on young, healthy adults ^6,7.  In a study by Lim et al, novice Taekwondo students performed significantly better in acquisition and retention when they controlled their feedback scheduling for a serial task, suggesting that having the ability to regulate feedback can have a positive impact both initially and throughout learning⁷.  Furthermore, Tsai et al took it one step further and found that by decreasing the amount of self-controlled feedback received, participants had to increase attention to decisions about feedback and use of feedback, leading to greater acquisition, retention and overall learning⁶.  In spite of these positive correlations in healthy populations, high quality studies with consistent outcome measures and adequate population sizes addressing this therapeutic intervention have scarcely begun to graze the surface of stroke rehabilitation.

Clinical Case

The patient of interest is 65-year-old male who recently suffered a R MCA stroke. PMH includes hypocholesteremia, HTN, BMI: 28, family history of prostate cancer.  His PLOF was independence in all ADLs and IADLs and recreational activity and he worked as a Grounds and Maintenance manager for National parks.

• Key Relevant Exam Findings:
  • LUE PROM WFL; LUE 1/5 globally.
  • Impaired sensation to light touch and proprioception globally on LUE as well as flaccid paralysis
  • Max A x1 to perform upper and lower body dressing
• FIM on IE: 69.
• Patient Goals: Return to PLOF (driving the RV, working in the national parks, playing bridge)

PICO Question

“Does self-controlled feedback impact upper extremity motor re-learning in adult patients following a stroke”?

Search Strategy

Results

Systematic Reviews
Molier et al, 2010 & Subramanian et al, 2010.

• Population, Interventions, Outcomes: The Moiler SR assessed 23 studies, and the Subramanian SR assessed 9 with an overlap of 6 studies. For both of the SRs, the interventions were combinations of aspects of feedback, being nature, timing, and frequency, and types of feedback such as auditory, visual, or sensory.  Outcome measures were classified into two major themes that will be used throughout the rest of the studies.  Motor functions are objective measures whereas motor activities are more quality of mov’t.
• Results: Nature of feedback, meaning KR or KP, was used in all studies assessed by Molier et al with more evidence suggesting a positive effect on MF. Furthermore, Subramanian found that 3 of the 9 studies saw improvements in MF and MA when KP was used. Delivery was only described in one study with no reference to frequency or timing in any article.  Additionally, there were no definitive results on benefits of types of feedback (For example visual combined with auditory feedback) due to multiple types being assessed at once.

Intervention 1
Liu et al, 2016.

• Population, Interventions, Outcomes: The Lui study followed 86 first-stroke survivors who where divided into SR-mCIMT, mCIMT or a control group. Over 2 weeks, participants performed 10 daily talks for 10, 1 hours sessions.   Motor activity outcomes were assessed across groups.
• Results: In all Motor Activities, SR-mCIMT was significantly better than control groups, however, in ¾ outcomes assessed, mCIMT was also significantly superior to control groups.

Intervention 2
Popović et al, 2014.

• Population, Interventions, Outcomes: The Popovic study followed 20 stroke survivors who participated in either no feedback or feedback mediated exercise 5x/wk for 3 weeks. The feedback was presented in a way to make the individual be competitive within themselves and against others in the study.  For example, a positive auditory cue was given when a trial was performed successfully. Motor activities and intrinsic factors outcomes were recorded over both groups.
• Results:
  • Received Therapy Time: All patients in FME reached metric maximum time of treatment; NFE improved but did not reach maximum.
  • Intrinsic Motivation Inventory: significant results for only interest/enjoyment and perceived confidence subscales (p< 0.01 for FME).
  • Modified Drawing Test: Smoothness improvement coefficients and speed were significantly different (P<0.01) in FME.
  • Positive correlation between scores on the perceived competence subscale and: the smoothness metric scores (p < 0.05), and pt. speed P < 0.05). Speed metric is also positively correlated with RTT (p < 0.05).

Intervention 3
Boyd et al, 2006.

• Population, Interventions, Outcomes: The Boyd study followed 30 participants, 10 Basal Gangliar strokes, 10 Sensorimtor cortex strokes, and 10 healthy controls. Participants performed serial reaction time and continuous tracking tasks for three days with retention testing on the 4^th   Participants were either given explicit or not given explicit information during the intervention.  Motor activities and intrinsic factors outcomes were assessed in all populations, both with or without explicit knowledge provided.
• Results: Retention: Implicit learning for both continuous and discrete tasks was interrupted by EI for stroke groups, but not HC (p= 0.028); these interferences and benefits were retained at retention.
  • Effect of Task: Overall the groups performed slightly better in CT (p= 0.004) than SR (p= 0.029), with EI continuing to negatively impact the stroke groups.
  • Overall, all EI groups demonstrated at least minimal implicit learning for both tasks, suggesting that learning was not stopped, but limited by EI.

Limitations

As far as limitations are concerned, the obvious were present such as limited high quality studies, small sample sizes, inconsistent control groups and lack of blinding.  Several studies had 1 month follow-ups, however that seems inadequate given the nature of stroke recovery.  In addition, there was a wide variety of interventions, outcome measures and combinations of feedback provided simultaneously, making it challenging to generalize for this populations.

Clinical Bottom Line

At this time, there is limited evidence that directly demonstrates or suggests that self-controlled feedback has a positive correlation on upper extremity motor relearning in adult stroke patients.

However, what can be established is that:

• Individuals suffering from stroke do not process explicit information as do healthy aged matched controls; in fact, it interferes with implicit learning
• Location, chronicity, and severity may play a large role in how feedback is processes

Case Application

First and foremost, it is important to determine the individual’s learning preference and readiness to begin learning after stroke because it is crucial to have them be an active participant in their rehabilitation to remain motivated. The Popovic article demonstrates how positive feedback and competition has a positive correlation with problem solving. The Boyd on the other hand article nicely demonstrates how explicit information can negatively impact motor relearning, so it’s important to not over cue the patient during treatment.   Given the summative results of the selected literature, the Lui article with SR-mCIMT has the most clinical significance and application of the usage of self-controlled feedback so I would prescribe self-regulated mCIMT in combination with feedback mediated exercise for 1 hr/day for 5 days a week for the duration of the pt.’s rehab stay to best aid and facilitate motor relearning.

References

• Molier BI, Asseldonk EHFV, Hermens HJ, Jannink MJA. Nature, timing, frequency and type of augmented feedback; does it influence motor relearning of the hemiparetic arm after stroke? A systematic review. Disability and Rehabilitation. 2010;32(22):1799-1809. doi:10.3109/09638281003734359.
• Subramanian SK, Massie CL, Malcolm MP, Levin MF. Does Provision of Extrinsic Feedback Result in Improved Motor Learning in the Upper Limb Poststroke? A Systematic Review of the Evidence. Neurorehabilitation and Neural Repair. 2009;24(2):113-124. doi:10.1177/1545968309349941.
• Liu, K. P. Y., Balderi, K., Leung, T. L. F., Yue, A. S. Y., Lam, N. C. W., Cheung, J. T. Y., Fong, S. S. M., Sum, C. M. W., Bissett, M., Rye, R. and Mok, V. C. T. (2016), A randomized controlled trial of self-regulated modified constraint-induced movement therapy in sub-acute stroke patients. Eur J Neurol, 23: 1351–1360. doi:10.1111/ene.13037.
• Popović MD, Kostić MD, Rodić SZ, Konstantinović LM. Feedback-Mediated Upper Extremities Exercise: Increasing Patient Motivation in Poststroke Rehabilitation. BioMed Research International. 2014;2014:1-11. doi:10.1155/2014/520374.
• Boyd LA, Winstein CJ. Explicit information interferes with implicit motor learning of Both continuous and discrete movement tasks after stroke. Journal of Neurologic Physical Therapy. 2006;30(2):46–57. doi:10.1097/01.npt.0000282566.48050.9b.
• Tsai M-J, Jwo H. Controlling absolute frequency of feedback in a self-controlled situation enhances motor learning. Perceptual and Motor Skills. 2015;121(3):746–758. doi:10.2466/23.pms.121c28x7.
• Lim S, Ali A, Kim W, Kim J, Choi S, Radlo SJ. Influence of self-controlled feedback on learning a serial motor skill 1 , 2. Perceptual and Motor Skills. 2015;120(2):462–474. doi:10.2466/23.pms.120v13x3.
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