In response to criticism that epilepsy care for children has little impact, healthcare professionals and administrators have developed various service models and strategies to address perceived inadequacies.
A systematic review of Medline, EMBASE, CINAHL, SCOPUS and IEEE Xplore up to February 2018 was carried out. Studies of stroke arm interventions were included where more than 50% of the time spent in therapy was initiated and carried out by the participant. Quality of the evidence was assessed using the Cochrane risk of bias tool.
A total of 40 studies (n = 1172 participants) were included (19 randomized controlled trials (RCTs) and 21 before–after studies). Studies were grouped according to no technology or the main additional technology used (no technology n = 5; interactive gaming n = 6; electrical stimulation n= 11; constraint-induced movement therapy n = 6; robotic and dynamic orthotic devices n = 8; mirror therapy n = 1; telerehabilitation n = 2; wearable devices n = 1). A beneficial effect on arm function was found for self-directed interventions using constraint-induced movement therapy (n = 105; standardized mean difference (SMD) 0.39, 95% confidence interval (CI) −0.00 to 0.78) and electrical stimulation (n = 94; SMD 0.50, 95% CI 0.08–0.91). Constraint-induced movement therapy and therapy programmes without technology improved independence in activities of daily living. Sensitivity analysis demonstrated arm function benefit for patients >12 months poststroke (n = 145; SMD 0.52, 95% CI 0.21–0.82) but not at 0–3, 3–6 or 6–12 months.
Self-directed interventions can enhance arm recovery after stroke but the effect varies according to the approach used and timing. There were benefits identified from self-directed delivery of constraint-induced movement therapy, electrical stimulation and therapy programmes that increase practice without using additional technology.
|1.||Han, C, Wang, Q, Meng, PP. Effects of intensity of arm training on hemiplegic upper extremity motor recovery in stroke patients: a randomized controlled trial. Clin Rehabil 2013; 27: 75–81. Google Scholar, SAGE Journals, ISI|
|2.||Hayward, KS, Brauer, SG. Dose of arm activity training during acute and subacute rehabilitation post stroke: a systematic review of the literature. Clin Rehabil 2015; 29: 1234–1243. Google Scholar, SAGE Journals, ISI|
|3.||Pollock, A, Farmer, SE, Brady, MC. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev 2014; 11: CD010820. Google Scholar|
|4.||Bernhardt, J, Chan, J, Nicola, I. Little therapy, little physical activity: rehabilitation within the first 14 days of organized stroke unit care. J Rehabil Med 2007; 39: 43–48. Google Scholar, Crossref, Medline, ISI|
|5.||Clarke, DJ, Burton, LJ, Tyson, SF. Why do stroke survivors not receive recommended amounts of active therapy? Findings from the ReAcT study, a mixed-methods case-study evaluation in eight stroke units. Clin Rehabil. Epub ahead of print 27 March 2018. DOI: 10.1177/0269215518765329. Google Scholar, SAGE Journals|
|6.||Demain, S, Burridge, J, Ellis-Hill, C. Assistive technologies after stroke: self-management or fending for yourself? A focus group study. BMC Health Serv Res 2013; 13: 334. Google Scholar, Crossref, Medline, ISI|
|7.||Higgins, JPT, Green, S. Cochrane handbook for systematic reviews of interventions. Hoboken, NJ: John Wiley & Sons, 2011. Google Scholar|
|8.||Da-Silva, R, Price, CI, Moore, S. A systematic review of self-directed therapy interventions with and without technology for upper limb rehabilitation after stroke, 2016, http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016038619 Google Scholar|
|9.||Review manager (Rev Man) version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014. Google Scholar|
|10.||Moher, D, Liberati, A, Tetzlaff, J. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 2009; 62: 1006–1012. Google Scholar, Crossref, Medline, ISI|
|11.||Adie, K, Schofield, C, Berrow, M. Does the use of Nintendo Wii sportsTM improve arm function? Trial of WiiTM in stroke: a randomized controlled trial and economics analysis. Clin Rehabil 2017; 31: 173–185. Google Scholar, SAGE Journals, ISI|
|12.||Brkic, L, Shaw, L, van Wijck, F. Repetitive arm functional tasks after stroke (RAFTAS): a pilot randomised controlled trial. Pilot Feasibil Stud 2016; 2: 50. Google Scholar, Crossref, Medline|
|13.||Brunner, IC, Skouen, JS, Strand, LI. Is modified constraint-induced movement therapy more effective than bimanual training in improving arm motor function in the subacute phase post stroke? A randomized controlled trial. Clin Rehabil 2012; 26: 1078–1086. Google Scholar, SAGE Journals, ISI|
|14.||Dos Santos-Fontes, RL, De Andrade, KN, Sterr, A. Home-based nerve stimulation to enhance effects of motor training in patients in the chronic phase after stroke: a proof-of-principle study. Neurorehabil Neural Repair 2013; 27: 483–490. Google Scholar, SAGE Journals, ISI|
|15.||Gabr, U, Levine, P, Page, SJ. Home-based electromyography-triggered stimulation in chronic stroke. Clin Rehabil 2005; 19: 737–745. Google Scholar, SAGE Journals, ISI|
|16.||Hara, Y, Ogawa, S, Tsujiuchi, K. A home-based rehabilitation program for the hemiplegic upper extremity by power-assisted functional electrical stimulation. Disabil Rehabil 2008; 30: 296–304. Google Scholar, Crossref, Medline, ISI|
|17.||Harris, JE, Eng, JJ, Miller, WC. A self-administered Graded Repetitive Arm Supplementary Program (GRASP) improves arm function during inpatient stroke rehabilitation: a multi-site randomized controlled trial. Stroke 2009; 40: 2123–2128. Google Scholar, Crossref, Medline, ISI|
|18.||Kimberley, TJ, Lewis, SM, Auerbach, EJ. Electrical stimulation driving functional improvements and cortical changes in subjects with stroke. Exp Brain Res 2004; 154: 450–460. Google Scholar, Crossref, Medline, ISI|
|19.||Michielsen, ME, Selles, RW, Van Der Geest, JN. Motor recovery and cortical reorganization after mirror therapy in chronic stroke patients: a phase II randomized controlled trial. Neurorehabil Neural Repair 2011; 25: 223–233. Google Scholar, SAGE Journals, ISI|
|20.||Nijenhuis, SM, Prange-Lasonder, GB, Stienen, AH. Effects of training with a passive hand orthosis and games at home in chronic stroke: a pilot randomised controlled trial. Clin Rehabil 2017; 31: 207–216. Google Scholar, SAGE Journals, ISI|
|21.||Smania, N, Gandolfi, M, Paolucci, S. Reduced-intensity modified constraint-induced movement therapy versus conventional therapy for upper extremity rehabilitation after stroke: a multicenter trial. Neurorehabil Neural Repair 2012; 26: 1035–1045. Google Scholar, SAGE Journals, ISI|
|22.||Standen, PJ, Threapleton, K, Richardson, A. A low cost virtual reality system for home based rehabilitation of the arm following stroke: a randomised controlled feasibility trial. Clin Rehabil 2017; 31: 340–350. Google Scholar, SAGE Journals, ISI|
|23.||Stinear, CM, Barber, PA, Coxon, JP. Priming the motor system enhances the effects of upper limb therapy in chronic stroke. Brain 2008; 131: 1381–1390. Google Scholar, Crossref, Medline, ISI|
|24.||Sullivan, JE, Hurley, D, Hedman, LD. Afferent stimulation provided by glove electrode during task-specific arm exercise following stroke. Clin Rehabil 2012; 26: 1010–1020. Google Scholar, SAGE Journals, ISI|
|25.||Tariah, HA, Almalty, A, Sbeih, Z. Constraint induced movement therapy for stroke survivors in Jordon: a home-based model. Int J Ther Rehabil 2010; 17: 638–646. Google Scholar, Crossref|
|26.||Turton, AJ, Cunningham, P, van Wijck, F. Home-based reach-to-grasp training for people after stroke is feasible: a pilot randomised controlled trial. Clin Rehabil 2017; 31: 891–903. Google Scholar, SAGE Journals, ISI|
|27.||Wolf, SL, Sahu, K, Bay, RC. The HAAPI (Home Arm Assistance Progression Initiative) trial: a novel robotics delivery approach in stroke rehabilitation. Neurorehabil Neural Repair 2015; 29: 958–968. Google Scholar, SAGE Journals, ISI|
|28.||Zondervan, DK, Augsburger, R, Bodenhoefer, B. Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: a randomized controlled trial. Neurorehabil Neural Repair 2015; 29: 395–406. Google Scholar, SAGE Journals, ISI|
|29.||Burridge, JH, Lee, ACW, Turk, R. Telehealth, wearable sensors, and the internet: will they improve stroke outcomes through increased intensity of therapy, motivation, and adherence to rehabilitation programs? J Neurol Phys Ther 2017; 41(suppl. 3): S32–S38. Google Scholar, Crossref, Medline|
|30.||Alon, G, McBride, K, Ring, H. Improving selected hand functions using a noninvasive neuroprosthesis in persons with chronic stroke. J Stroke Cerebrovas Dis 2002; 11: 99–106. Google Scholar, Crossref, Medline|
|31.||Alon, G, Sunnerhagen, KS, Geurts, ACH. A home-based, self-administered stimulation program to improve selected hand functions of chronic stroke. Neurorehabilitation 2003; 18: 215–225. Google Scholar, Medline, ISI|
|32.||Burridge, JH, Turk, R, Merrill, D. A personalized sensor-controlled microstimulator system for arm rehabilitation poststroke. Part 2: objective outcomes and patients’ perspectives. Neuromodulation 2011; 14: 80–88. Google Scholar, Crossref, Medline|
|33.||Brown, EV, McCoy, SW, Fechko, AS. Preliminary investigation of an electromyography-controlled video game as a home program for persons in the chronic phase of stroke recovery. Arch Phys Med Rehabil 2014; 95: 1461–1469. Google Scholar, Crossref, Medline|
|34.||Langan, J, Delave, K, Phillips, L. Home-based telerehabilitation shows improved upper limb function in adults with chronic stroke: a pilot study. J Rehabil Med 2013; 45: 217–220. Google Scholar, Crossref, Medline, ISI|
|35.||Lee, HS, Kim, JU. The effect of self-directed exercise using a task board on pain and function in the upper extremities of stroke patients. J Phys Ther Sci 2013; 25: 963–967. Google Scholar, Crossref, Medline|
|36.||Mawson, S. The SMART rehabilitation system for stroke self-management: issues and challenges for evidence-based health technology research. J Phys Ther Educ 2011; 25: 48–53. Google Scholar, Crossref|
|37.||Mouawad, MR, Doust, CG, Max, MD. Wii-based movement therapy to promote improved upper extremity function post-stroke: a pilot study. J Rehabil Med 2011; 43: 527–533. Google Scholar, Crossref, Medline, ISI|
|38.||Niama Natta, DD, Alagnide, E, Kpadonou, GT. Feasibility of a self-rehabilitation program for the upper limb for stroke patients in Benin. Ann Phys Rehabil Med 2015; 58: 322–325. Google Scholar, Crossref, Medline|
|39.||Nijenhuis, SM, Prange, GB, Amirabdollahian, F. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil 2015; 12: 89. Google Scholar, Crossref, Medline, ISI|
|40.||Page, SJ, Levine, P. Modified constraint-induced therapy extension: using remote technologies to improve function. Arch Phys Med Rehabil 2007; 88: 922–927. Google Scholar, Crossref, Medline, ISI|
|41.||Page, SJ, Levine, P, Hill, V. Mental practice–triggered electrical stimulation in chronic, moderate, upper-extremity hemiparesis after stroke. Am J Occup Ther 2015; 69: 1–88. Google Scholar|
|42.||Pickett, TC, Fritz, SL, Ketterson, TU. Telehealth and constraint-induced movement therapy (CIMT): an intensive case study approach. Clin Gerontol 2007; 31: 5–20. Google Scholar, Crossref|
|43.||Sivan, M, Gallagher, J, Makower, S. Home-based computer assisted arm rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. J Neuroeng Rehabil 2014; 11: 163. Google Scholar, Crossref, Medline, ISI|
|44.||Sullivan, JE, Hedman, LD. Effects of home-based sensory and motor amplitude electrical stimulation on arm dysfunction in chronic stroke. Clin Rehabil 2007; 21: 142–150. Google Scholar, SAGE Journals, ISI|
|45.||Turk, R, Burridge, JH, Davis, R. Therapeutic effectiveness of electric stimulation of the upper-limb poststroke using implanted microstimulators. Arch Phys Med Rehabil 2008; 89: 1913–1922. Google Scholar, Crossref, Medline|
|46.||Wittmann, F, Held, JP, Lambercy, O. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil 2016; 13: 75. Google Scholar, Crossref, Medline|
|47.||Wittmann, F, Lambercy, O, Gonzenbach, RR. Assessment-driven arm therapy at home using an IMU-based virtual reality system. In: Proceedings of the 2015 IEEE international conference on rehabilitation robotics (ICORR), Singapore, 11–14 August 2015, pp.707–712. New York: IEEE. Google Scholar|
|48.||Zhang, H, Austin, H, Buchanan, S. Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using RUPERT. In: Proceedings of the IEEE international conference on rehabilitation robotics, Zurich, 29 June–1 July 2011. Google Scholar, Crossref|
|49.||Da-Silva, RH, Frederike van, W, Shaw, F. Prompting arm activity after stroke: a clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function. J Rehabil Assis Technol Eng 2018; 5: 1–8. Google Scholar|
|50.||Chen, J, Nichols, D, Brokaw, EB. Home-based therapy after stroke using the hand spring operated movement enhancer (HandSOME). IEEE Trans Neural Syst Rehabil Eng 2017; 25: 2305–2312. Google Scholar, Crossref, Medline|
|51.||Fryer, CE, Luker, JA, McDonnell, MN. Self management programmes for quality of life in people with stroke. Cochrane Database Syst Rev 2016; 8: CD010442. Google Scholar|
|52.||Wray, F, Clarke, D, Forster, A. Post-stroke self-management interventions: a systematic review of effectiveness and investigation of the inclusion of stroke survivors with aphasia. Disabil Rehabil 2018; 40: 1237–1251. Google Scholar, Crossref, Medline|
|53.||Krakauer, JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol 2006; 19: 84–90. Google Scholar, Crossref, Medline, ISI|
|54.||Korpershoek, C, van der Bijl, J, Hafsteinsdottir, TB. Self-efficacy and its influence on recovery of patients with stroke: a systematic review. J Adv Nurs 2011; 67: 1876–1894. Google Scholar, Crossref, Medline, ISI|
|55.||Jones, F, Riazi, A. Self-efficacy and self-management after stroke: a systematic review. Disabil Rehabil 2011; 33: 797–810. Google Scholar, Crossref, Medline, ISI|
|56.||Brown, E, Cairns, P. A grounded investigation of game immersion. In: Proceedings of the extended abstracts of the 2004 conference on human factors in computer systems, Vienna, 24–29 April 2004, pp.1297–1300. New York: ACM Press. Google Scholar|
|57.||Wade, D. Rehabilitation – a new approach. Part four: a new paradigm, and its implications. Clin Rehabil 2016; 30: 109–118. Google Scholar, SAGE Journals, ISI|
|58.||Farmer, SE, Durairaj, V, Swain, I. Assistive technologies: can they contribute to rehabilitation of the upper limb after stroke? Arch Phys Med Rehabil 2014; 95: 968–985. Google Scholar, Crossref, Medline, ISI|
|59.||Hibbard, JH, Stockard, J, Mahoney, ER. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res 2004; 39: 1005–1026. Google Scholar, Crossref, Medline, ISI|
Mobile health app developers increasingly are interested in supporting the daily self-care of people with chronic conditions. The purpose of this study was to review mobile applications (apps) to promote epilepsy self-management. It investigates the following:
We conducted the review in Fall 2017 and assessed apps on the Apple App Store that related to the terms “epilepsy” and “seizure”. Inclusion criteria included apps (adult and pediatric) that, as follows, were:
Exclusion criteria included apps that were designed for dissemination of publications, focused on healthcare providers, or were available in other languages. The search resulted in 149 apps, of which 20 met the selection criteria. A team reviewed each app in terms of three sets of criteria:
Most apps were for adults and free. Common SM domains for the apps were treatment, seizure tracking, response, and safety. A number of epilepsy apps existed, but many offered similar functionalities and incorporated few SM domains. The findings underline the need for mobile apps to cover broader domains of SM and behavioral change techniques and to be evaluated for outcomes.
12:04 December 3, 2016
“PAUSE” — for Personalized Internet Assisted Underserved Self-management for Epilepsy — is a tablet-based tool customized for each patient to help them stay healthy and reduce the need for emergency services.
Epilepsy is a chronic neurological disorder characterized by abnormal brain activity and seizures that affects more than 65 million people worldwide. About one-third have difficulty controlling their seizures even with medication. Seizures can interfere with work, relationships, and the ability to live independently.
While children and older adults are most likely to have epilepsy, it impacts people of all ages, races, backgrounds and lifestyles. Every patient is different and has their own individual needs.
“The PAUSE program is based on the coordinated care model,” says Dr. Dilip Pandey, associate professor of neurology and rehabilitation in the UIC College of Medicine and a lead investigator on the PAUSE project. “The health care provider identifies information the patient can use to build self-management skills, and also asks each patient what they want to learn about their epilepsy, whether it’s medication management, avoiding seizure triggers, issues around driving – whatever they want to know about.
“Then, we program the PAUSE tablet to include the corresponding educational modules, containing information provided by the Epilepsy Foundation website,” Pandey said. “This allows us to create a personalized self-management education program for each patient.”
Patients take the PAUSE tablet home with them for 10 to 12 weeks and review the information at their own pace. The tablets also allow the patient to video-conference with the research staff to receive individualized assistance.
Approximately 90 patients have been referred to participate in the PAUSE program so far. Pandey plans to enroll about 100 patients from the UIC neurology clinic and another 100 patients referred through the Epilepsy Foundation of Greater Chicago.
PAUSE is one of five UIC projects supported by the Illinois Prevention Research Center, part of the UIC Institute for Health Research and Policy. The IPRC is funded by a grant from the U.S. Centers for Disease Control and Prevention to conduct innovative public health prevention research. The PAUSE study is also a part of the Managing Epilepsy Well Network, which is coordinated by the Prevention Research Center at Dartmouth College.
Dr. Jeffrey Loeb, the John S. Garvin Endowed Chair in Neurology at UIC, is a co-principal investigator on the PAUSE study.
University of Illinois
Continue —> Process evaluation of the Restore4stroke Self-Management intervention ‘Plan Ahead!’: a stroke-specific self-management interventionClinical Rehabilitation – Nienke S Tielemans, Vera PM Schepers, Johanna MA Visser-Meily, Jolanda CM van Haastregt, Wendy JM van Veen, Haike E van Stralen, Caroline M van Heugten, 2016
Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions.
Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices.
A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover.
On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.
Neurologic rehabilitation has been testing a motor learning theory for the past quarter century that may be wearing thin in terms of leading to more robust evidence-based practices. The theory has become a mantra for the field that goes like this. Repetitive practice of increasingly challenging task-related activities assisted by a therapist in an adequate dose will lead to gains in motor skills, mostly restricted to what was trained, via mechanisms of activity-dependent induction of molecular, cellular, synaptic, and structural plasticity within spared neural ensembles and networks.
This theory has led to a range of evidence-based therapies, as well as to caricatures of the mantra (eg, a therapist says to patient, “Do those plasticity reps!”). A mantra can become too automatic, no longer apt to be reexamined as a testable theory. A recent Cochrane review of upper extremity stroke rehabilitation found “adequately powered, high-quality randomized clinical trials (RCTs) that confirmed the benefit of constraint-induced therapy paradigms, mental practice, mirror therapy, virtual reality paradigms, and a high dose of repetitive task practice.”1 The review also found positive RCT evidence for other practice protocols. However, they concluded, no one strategy was clearly better than another to improve functional use of the arm and hand. The ICARE trial2 for the upper extremity after stroke found that both a state-of-the-art Accelerated Skill Acquisition Program (motor learning plus motivational and psychological support strategy) compared to motor learning-based occupational therapy for 30 hours over 10 weeks led to a 70% increase in speed on the Wolf Motor Function Test, but so did usual care that averaged only 11 hours of formal but uncharacterized therapy. In this well-designed RCT, the investigators found no apparent effect of either the dose or content of therapy. Did dose and content really differ enough to reveal more than equivalence, or is the motor-learning mantra in need of repair?
Walking trials after stroke and spinal cord injury,3–8 such as robot-assisted stepping and body weight-supported treadmill training (BWSTT), were conceived as adhering to the task-oriented practice mantra. But they too have not improved outcomes more than conventional over-ground physical therapy. Indeed, the absolute gains in primary outcomes for moderate to severely impaired hemiplegic participants after BWSTT and other therapies have been in the range of only 0.12 to 0.22 m/s for fastest walking speed and 50 to 75 m for 6-minute walking distance after 12 to 36 training sessions over 4 to 12 weeks.3,9 These 15% to 25% increases are just as disappointing when comparing gains in those who start out at a speed of <0.4 m/s compared to >0.4 to 0.8 m/s.3
Has mantra-oriented training reached an unanticipated plateau due to inherent limitations? Clearly, if not enough residual sensorimotor neural substrate is available for training-induced adaptation or for behavioral compensation, more training may only fail. Perhaps, however, investigators need to reconsider the theoretical basis for the mantra, that is, whether they have been offering all of the necessary components of task-related practice, such as enough progressively difficult practice goals, the best context and environment for training, the behavioral training that motivates compliance and carryover of practice beyond the sessions of formal training, and blending in other physical activities such as strengthening and fitness exercise that also augment practice-related neural plasticity? These questions point to new directions for research….
Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.
The aim of the study was to explore the types of self-management strategies prescribed; the number of strategies and the overall length of time allocated to self-management prescription, by consultation type and by injury location, in physiotherapy consultations.
A cross-sectional, observational study of 113 physiotherapist–patient consultations was undertaken. Regression analyses were used to determine whether consultation type and injury location were associated with the number of strategies prescribed and the length/fraction of time spent on self-management.
A total of 108 patients (96%) were prescribed at least one self-management strategy – commonly exercise and advice. The mean length of time spent on self-management was 5.80 min. Common injury locations were the neck (n = 40) and lower back (n = 39). No statistically significant associations were observed between consultation type or injury location for either outcome (number of strategies and the length/fraction of time allocated to self-management prescription).
Physiotherapists regularly spend time prescribing self-management strategies such as exercise, advice, and the use of heat or ice to patients receiving treatment linked to a range of injury locations. This suggests that self-management is considered to be an important adjunct to in-clinic physiotherapy. The practice implications of this are that clinicians should reflect on how self-management strategies can be used to maximize patient outcomes, and whether the allocation of consultation time to self-management is likely to optimize patient adherence to each strategy.
Source: An observational study of Australian private practice physiotherapy consultations to explore the prescription of self-management strategies – Peek – 2017 – Musculoskeletal Care – Wiley Online Library
Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope.
We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement.
Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.
Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.
Background: In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges.
Objective: Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home.
Methods: A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a “smart insole” to facilitate measurement of walking activities in a free-living, nonrestrictive environment.
Results: Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants’ affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use.
Conclusion: Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants.
The global incidence of stroke is set to escalate from 15.3 million to 23 million by 2030 . In the United Kingdom, stroke is the largest cause of disability [ ] resulting in a cost to the economy of £8.9 billion a year [ ]. It is estimated that following a stroke, only 15% of people will gain complete recovery for both the upper and lower extremities [ ]. Walking and mobility are prominent challenges for many survivors who report the importance of mobility therapy [ ]. Nevertheless, rehabilitative service needs cannot always be met and therefore initiatives that focus on patient-centered care promoting long-term self-management remain at the forefront of service redesign [ ].
The adoption of technological solutions allows for patient and carer empowerment and a paradigm shift in control and decision-making to one of a shared responsibility. It also has the potential to reduce the burden for care professionals, and support the development of new interventions . Incorporating technology into the daily lives of stroke survivors can be achieved by maintaining high levels of usability, acceptance, engagement, and removing any associated stigma involved with the use of assistive technology [ ].
Technological aids for poststroke motor recovery hitherto have required the use of expensive, complex, and cumbersome apparatus that have typically necessitated the therapist to be present during use [, ]. Recently, inexpensive, wearable, commercially-available sensors have become a more viable option for independent home-based poststroke rehabilitation [ , ]. A systematic review by Powell et al [ ] identified a number of wearable lower-limb devices that have been trialed, such as robotics [ – ], virtual reality [ ], functional electrical stimulation (FES) [ , ], electromyographic biofeedback (EMG-BFB) [ , ], and transcutaneous electrical nerve stimulation [ ]. Of the identified trials exploring improvements in the International Classification of Functioning (ICF) domain of activities and participation, only 1 [ ] found significant improvements. Studies that adopt a positivist randomized controlled trial paradigm often fail to give sufficient consideration as to how intervention components interact [ ]. Indeed, creating and developing technological solutions for complex long-term conditions is challenging and requires multiple stakeholder input [ ].
The Self-management supported by Assistive, Rehabilitation and Telecare Technologies consortium explored rehabilitation for stroke survivors focusing initially on the use of wearable sensors to support upper limb feedback on the achievement of functional goals [– ]. User interface design, the practicalities surrounding deployment, and the ability of the participants to interact with the technology were explored [ ].
The intervention model for the stroke system was based around a rehabilitation paradigm underpinned by theories of motor relearning and neuroplastic adaptation, motivational feedback, self-efficacy, and knowledge transfer [– ]. In order to enhance and strengthen previous research, a realist evaluation [ ] was adopted to evaluate the final personalized self-management rehabilitation system (PSMrS) prototype in order to gain an insight into the value, usability, and potential impact on an individual’s ability to self-manage their rehabilitation following a stroke [ ].
The aim of this work was to understand the conditions under which technology-based rehabilitation would have an impact (outcome) on the motor behavior of the user—more specifically what would work for whom, in what context, and in what respect utilizing a realist evaluation framework . This paper addresses this by focusing on the impact smart insole technology has on participants at home. The impacts are assessed by analyzing a participants’ gait over time, which are then presented and discussed.
Futhermore, the rehabilitation system, its architecture, and technical components are presented along with the evaluation of the prototype with regards to the performance and usability of the system in the homes of stroke survivors.