Adekola Babatunde Ademoyegun, Adebukola Grace Ibitoye

Department of Physiotherapy, Osun State University Teaching Hospital, Osogbo, Nigeria

Keywords: Adherence, home-based exercise, stroke, outcome measure, rehabilitation

Abstract

Objective: Adherence to a home-based exercise program (HEP) is important in the achievement of rehabilitation goals among stroke survivors. Earlier studies have measured adherence to HEPs among this population using non-specific outcome measures, which may not be suitable. In addition, the influence of prescribed exercise parameters on adherence remains unclear. This study assessed adherence to HEPs among stroke survivors using stroke-specific measures of adherence to HEP and determined its correlates.

Materials and Methods: This multicenter cross-sectional study involved 125 consenting Nigerian community-dwelling stroke survivors. Data on HEP adherence, prescribed exercise parameters, patients’ attitudes, perceived/encountered barriers to exercise, stroke-related characteristics, and socio-demographics were assessed. Descriptive and inferential statistics were employed. A P value of <0.050 indicated statistical significance.

Results: The mean age of the participants was 60.26 ± 12.11 years. Findings showed that only approximately 35% of the participants were adherent to their HEP. Among the non-adherent group, 22% had no adherence and 43% had low adherence. The results indicated that stroke severity, pain during exercise, a number of prescribed exercises of ≥3, a number of weekly exercise sessions of ≥7, a duration of the exercise of ≥43 min, and exercise intensity of ≥5 on a 0–10 Borg’s rate of perceived exertion scale were found to be related with non-adherence to the HEP (P < 0.050).

Conclusion: Adherence to HEPs among Nigerian community-dwelling stroke survivors is poor. HEP adherence is associated with stroke severity, pain, and the prescribed exercise parameters. This study highlights the need to consider exercise parameters in HEP prescription to enhance patient adherence.

Introduction

Stroke is one of the main causes of disability globally (1) and usually requires long-term rehabilitation (2). Stroke patients receive medical rehabilitation while admitted to the hospital; however, many stroke survivors do not attain full functional independence by discharge (3). Although some rehabilitation centers offer exercise programs for stroke survivors, these centers are minimal in developing countries (4). Access to these centers is also limited in developed countries (5). Moreover, in developing countries, particularly Nigeria, many stroke survivors depend primarily on the relatively inadequate outpatient appointments provided at public hospitals, which are usually insufficient to meet their health demands. Therefore, prescribed home-based exercise programs (HEPs) have grown in popularity (6). The effectiveness of HEPs has been described and proven in many studies (7,8,9,10,11). However, a patient’s adherence to a prescribed HEP is considered vital to achieving successful rehabilitation outcomes (12).

Adherence has been described as a complex phenomenon, and its accurate assessment reportedly is difficult, especially for stroke survivors (12,13). In addition, the available measures of HEP adherence among stroke survivors are primarily designed for other disease populations–not stroke survivors (12). Evidence has shown that HEP adherence in stroke survivors is influenced by stroke-specific factors that may not be captured by an adherence scale meant for other disease populations (12). Moreover, the outcome measure often deployed in assessing adherence to HEPs among stroke survivors is self-report questionnaires (14), a onetime measurement where patients recall and fill in the details of their previous HEP without considering any exercise parameters. Adherence to an exercise program should include some measures of duration, frequency, intensity, and correct performance of movements (15). Recently, Mahmood et al. (12) developed a 13-item self-administered stroke-specific measure of adherence to home-based exercise (SS-MAHE) for measuring adherence to HEPs after a stroke and tested its reliability.

Knowledge of the rate and correlates of HEP adherence among stroke survivors may help health professionals develop effective strategies to improve HEP adherence in stroke survivors. Several studies have investigated HEP adherence among stroke survivors (16,17,18); unfortunately, the rate of HEP adherence among stroke survivors reported in the literature has been questioned due to the tools that were employed (12). This scenario may hamper effective HEP planning for stroke survivors. Thus, there is a need to have updated data on the rate of HEP adherence among stroke survivors using an adherence scale specific to stroke. To the authors’ knowledge, only one study to date has employed strokespecific measures of HEP adherence (12). In addition, evidence has shown that the number of prescribed exercises may influence HEP adherence (19,20,21). However, information about the influence of prescribed exercise parameters on HEP adherence in stroke patients is sparse.

This study aims to assess HEP adherence rates using the SS-MAHE, and it investigates the factors associated with HEP adherence, especially the prescribed exercise parameters (number of exercises, number of sessions, duration of exercise, intensity of exercise, and time of prescription), among Nigerian communitydwelling stroke survivors.

Material and Methods

Participants

Participants in this multicenter cross-sectional study were community-dwelling stroke survivors recruited from the physiotherapy outpatient clinics of four Nigerian tertiary hospitals including Bowen University Teaching Hospital; Ladoke Akintola University of Technology Teaching Hospital, Ogbomoso; State Specialist Hospital; and Osun State University Teaching Hospital, Osogbo. Included in this study were stroke survivors who were discharged from the hospital stroke unit/ward at least one month before this study, must have been prescribed a protocol for a HEP, and were able to comprehend instructions. However, stroke survivors with other neurological, psychiatric, or disabling musculoskeletal conditions unrelated to stroke, who were aphasic, and those who had cognitive impairment with more than one or two errors on the short portable mental status questionnaire were excluded. Using the Kasiulevicius et al. (22) sample-size formula for a cross-sectional study with a 95% confidence level, 0.05 precision level, and estimated 10% prestudy estimate of stroke proportion, 125 patients were sampled. Thus, a total of 125 community-dwelling stroke survivors were consecutively recruited between February and October 2022. The Ethical Review Board of Bowen University Teaching Hospital, Ogbomoso approved this study (BUTH/REC-341). Furthermore, written informed consent was obtained from all participants.

Assessment of Home-based Exercise Adherence

The SS-MAHE was used to assess adherence to HEPs among the participants. The SS-MAHE is a 13-item self-administered questionnaire for measuring adherence to home-based exercise after a stroke, with adequate reliability and internal consistency, having an inter-rater reliability score of 0.81 and a Cronbach’s alpha of 0.89 (12). Among other measures of adherence to exercise, the SSMAHE is specific to stroke, easy to use, requires less time to fill, and can be easily understood by the participants. The SS-MAHE considers exercise adherence the same as defined by the World Health Organization (WHO): comparing prescribed exercises as recalled by the participants to the actual exercises performed at home. The level of adherence is categorized as no adherence (participants did not adhere to any of the prescribed home-based exercises), low adherence (≤70% adherence to the prescribed dose of home-based exercises), and high adherence (>70% adherence to the prescribed dose of home-based exercises). Furthermore, participants with good adherence levels were categorized as the adherent group, while those with no and low adherence levels were categorized as the non-adherent group (12).

Assessment of Prescribed Home-based Exercise Parameters

The parameters of the prescribed home-based exercises, including the time since discharge, the type of exercises, the number of exercises, the number of weekly sessions, the duration of exercise, and the intensity of the exercises, were assessed. The participants were asked to rate the prescribed exercise intensity on a 0–10 scale of Borg’s rate of perceived exertion (RPE) (23).

Assessment of stroke-related Characteristics

The stroke-related characteristics of the participants, including stroke laterality, stroke type, functional independence, stroke severity, and stroke duration, were assessed. The functional independence of the participants was evaluated by the modified Barthel index (mBI). The mBI has a maximum score of 100, with a higher score indicating higher functional abilities. In this study, participants with an mBI score of >60 were deemed independent in activities of daily living (24,25,26). The mBI is well-validated among stroke survivors in assessing functional abilities (25). The severity of the stroke was obtained using the National Institutes of Health Stroke Scale (NIHSS). The NIHSS measures neurological impairment following a stroke incident. The NIHSS has excellent psychometric properties in assessing stroke severity, and it is useful in predicting patient outcomes (27). The maximum NIHSS score is 42, and by using the total NIHSS score, the participants’ stroke severity was categorized as minor (1–4), moderate (5–15), moderate severe (16–20), and severe (21–42) (28). Stroke duration was classified as early sub-acute (≤3 months), late sub-acute (>3 or ≤6 months), and chronic (>6 months) (29).

Assessment of Patients’ Attitudes and Perceived/ Encountered Barriers to Exercise

The patients’ attitudes and perceived/encountered barriers to exercise, an important component of the psychometric characteristics of adherence measure, were measured by a validated 12-item scale questionnaire (30). This instrument has four factors relating to patients’ attitudes and barriers to exercise: discomfort, barriers, helplessness, and dependence (30). Some of the items in the questionnaire include “the exercises are difficult”, “the exercises are painful”, and “I can do little by myself”. The items are scored on a four-point scale: 1 (“agree”), 2 (“strongly agree”), 3 (“strongly disagree”), and 4 (“disagree”). The options were dichotomized for the purpose of analysis in this study. The options “agree” and “strongly agree” were collapsed and recoded as “yes,” and “strongly disagree” and “disagree” were recorded as “no”.

Socio-demographic Characteristics

The socio-demographic information of the participants, including age, gender, marital status, income level, employment, and education status, was obtained with a self-developed proforma. Income was categorized as low or high based on Nigerian minimum wage, while education up to the university level was considered high. Any education lower than that was considered low education.

Statistical Analysis

The descriptive statistics of frequency, percentage, mean, and standard deviation were used to summarize the data. The independent t-test and chi-square test of association were used to investigate the differences in the adherent and non-adherent groups. The alpha level was set at P < 0.050. Data analysis was conducted with SPSS software version 21.0 (SPSS Inc., Chicago, Illinois, USA).

Results

The mean age of the participants was 60.26 ± 12.11 years, and 88 patients (66.4%) were male. Findings showed that only about 35% of the participants were adherent to their HEPs. Among the nonadherent group, 22% did not adhere at all to the prescribed HEP, and 43% had low adherence (Figures 1, 2). The results of the Chisquare test of association of the adherent status of the participants with the categorical variables including socio-demographics, strokerelated characteristics, and the patients’ attitudes and perceived/ encountered barriers to exercise are presented in Tables 1 and 2. The results indicated that there was a significant association between the adherent status of the participants and stroke severity (χ2 : 11.622; P = 0.003) and pain during exercise (χ2 : 9.259; P = 0.002). Table 3 shows the results of the independent t-test showing the difference in adherence status with respect to the prescribed exercise parameters. The results demonstrate that each of the prescribed number of exercises (t = 2.853; P = 0.005), number of weekly sessions (t = 5.261; P < 0.001), duration of exercise (t = 2.035; P = 0.044), and exercise intensity (t = 4.141; P < 0.001) was significantly higher for the non-adherent group.





Discussion

This study assessed adherence to HEPs among Nigerian stroke survivors using the SS-MAHE. It also investigated the correlates of HEPs among stroke survivors, especially the influence of the prescribed HEP parameters on adherence. The results showed that there was a high rate of non-adherence to HEPs among Nigerian stroke survivors. Poor adherence to HEPs among stroke survivors was associated with stroke severity, pain, and the parameters of the prescribed HEP including the number, session, duration, and intensity of the prescribed exercises.

This study’s findings showed that only 35% of stroke survivors were adherent to their HEP. This is like the findings from Mahmood et al. (12) (28%) who investigated the prevalence of HEP adherence among 92 Indian community-dwelling stroke survivors. In contrast, some studies, including Ogwumike et al. (16) (55.8%), Miller et al. (17) (63.5%), and Babbar et al. (18) (63.5%), reported a higher rate of adherence to HEPs among stroke survivors. This disparity may be because these studies employed a non-specific MAHE among stroke survivors. This discrepancy in adherence rates seems apparent as only this current study and that of Mahmood et al. (12) presented similar rates of HEP adherence among stroke survivors. This may be because these two studies employed stroke-specific measures of HEP adherence. Literature has described the rate of HEP adherence by stroke survivors as poor; however, data from the present study and from Mahmood et al. (12) indicated that the rate of HEP adherence in stroke patients may be even poorer than what was previously reported. These findings suggest an apparent overestimation of HEP adherence among this cohort in the literature, possibly due to the use of a non-specific adherence scale and/or non-consideration of exercise adherence based on the WHO’s definition.

In this study, the results showed that poor HEP adherence in stroke survivors was associated with the prescribed exercise parameters. Specifically, the following parameters were associated with non-adherence: a prescribed number of exercises of ≥3, a number of weekly exercise sessions of ≥7, a duration of exercise ≥43 min, and an exercise intensity RPE of ≥5 (0–10 scale). Exercise prescriptions for stroke survivors are said to be similar to medication prescriptions in that dose is imperative (31). This describes the importance of exercise parameters such as type of exercise, number of sessions, duration of exercise, and exercise intensity. Some researchers have recommended intensive taskspecific exercises for more than 45 min a day and two to five days a week for motor recovery after stroke (2,32). However, the American Heart Association/American Stroke Association (AHA/ASA) recommends 20–60 min per session, for 3–5 days per week, and an intensity of RPE 11–14 (6–20 scale) (31). Meanwhile, previous studies of service members (19), elderly patients (20), and patients with back and neck pain (21) have shown that the number of exercises is associated with adherence to home-based exercise. Henry et al. (20) showed that elderly patients adhere better to two exercises rather than five, while patients with back and neck pain have better adherence to three to six exercises (21). Eckard et al. (19) reported that service members have a higher adherence rate to their HEPs when two or fewer exercises are prescribed, compared with four or more, suggesting that the higher the number of exercises prescribed, the lesser the HEP adherence rate of adherence. This is similar to the findings of this study. The number of prescribed weekly exercise sessions among the non-adherent stroke survivors was higher than the recommendations for weekly exercise sessions. This study’s authors opined that the prescription of a higher number of exercises was responsible for the higher number of weekly exercise sessions patients are required to complete at home. Similarly, in this study, patients whose prescribed exercise intensity exceeded a moderate level were non-adherent to their HEP. The exercise intensity recommended for stroke survivors is RPE 11–14 on a 6–20 Borg’s scale (31), which is comparable to the RPE 3–4 (moderate intensity) on a 0–10 Borg’s scale found for the adherent group in this study. The prescribed high number of exercises, sessions, and intensity may have overwhelmed the stroke survivors, and coupled with easy fatigability often seen in stroke patients (33), might have contributed to poor HEP adherence. Mahmood et al. (12) reported deficiencies in the parameters of the prescribed HEPs for stroke survivors among physiotherapists in India. They suggested that health professionals may be contributing to the high rate of non-adherence to HEPs among stroke survivors through their exercise prescriptions (12). Therefore, clinicians may improve HEP adherence in this cohort through the correct application of recommended exercise prescriptions in stroke patients.

A closer look at the duration of exercise prescribed showed that it was within the recommended parameters, and yet, most of the stroke survivors did not adhere to their HEPs. In this study, an exercise duration of ≥43 min, which is within the recommended ≥45 min (2, 32) and 20–60 min (31), was associated with non-adherence, indicating that the parameters of the exercise prescription recommended for stroke survivors in the literature may not totally apply to HEPs. There may be a need to consider many associated factors with home exercise, including the patient’s clinical conditions, caregivers, environments, socio-demographics, self-efficacy, and psychosocial issues, before prescribing HEP parameters for stroke survivors. Thus, there may be a need for the formulation of separate exercise prescription guidelines for HEPs following stroke. Furthermore, clinicians may need to perform graded or submaximal exercise testing prior to prescription and should endeavor to individualize the prescription of the HEP to the stroke survivors based on clinical and non-clinical factors. The AHA/ASA guidelines recognize the importance of variation in exercise prescriptions for stroke survivors (31). Reports have also shown that factors associated with HEP adherence among stroke survivors are multifaceted and include many intrinsic and extrinsic factors, including caregiverrelated issues (3,26).

This study identified a strong association between stroke severity and adherence to home-based exercise, suggesting that the higher the severity of the stroke, the lesser the rate of adherence. Participation in exercise and physical activity is said to be influenced by a wide range of clinical factors, including stroke severity (31); therefore, more attention should be placed on the severity of the stroke in prescribing HEPs to improve adherence. Stroke severity is linked to the level of functional independence of stroke survivors. Previously, researchers have pointed out that stroke survivors with a high level of functional dependence often depend on their caregivers to perform their HEP; this has been found to influence adherence rates (3,26). Furthermore, in this study, pain during exercise was associated with poor adherence to HEPs. Pain has been identified as the most common physical symptom reported by caregivers of stroke survivors as a barrier to HEP adherence (26). This is consistent with previous studies that have identified pain as a barrier to HEPs in stroke patients (17,18,34). Pain after a stroke episode is common, debilitating, compromises rehabilitation outcomes, and can persist up to five years after a stroke (35,36). Despite its negative effect on stroke clinical outcomes, post-stroke pain (PSP) is often under-reported, under-diagnosed, and under-treated in rehabilitation (35,36).

It can be inferred from the results of this study that pain interfered with HEP adherence because patients with PSP had low HEP adherence. This is not unexpected as pain has been reported to hinder the rehabilitation process and outcomes. Pain during exercise is said to arise mainly from the presence of spasticity, sensory disturbance, and joint stiffness in patients with stroke (16,37), not necessarily the exercise itself. In fact, recent reviews and expert opinions have provided evidence that exercise helps in reducing PSP (38,39). However, despite the benefits of exercise for PSP, clinicians may need to streamline home-based exercise prescriptions for stroke survivors with PSP to enhance HEP adherence. Ademoyegun et al. (26) recommended incorporating cognitive behavior model strategies and ensuring graded exercise that increases over time when prescribing HEPs for stroke survivors with PSP.

Although this study employed well-validated tools, their selfreporting nature may introduce reporting bias, memory lapses, and social desirability, which may serve as limitations. In addition, this study did not investigate the impact of the type of exercise prescribed on the rate of adherence. However, a previous study reported no significant association between the type of exercise and HEP adherence (19). Finally, the associations found in this study cannot be taken as cause and effect because this is a crosssectional study. Prospective studies may be needed to consolidate this study’s findings.

Conclusion

The rate of adherence to HEPs among Nigerian communitydwelling stroke survivors is poor. Poor HEP adherence is associated with stroke severity, pain, and prescribed exercise parameters. This study highlights the importance of streamlining exercise dosage when prescribing HEPs to stroke survivors to facilitate adherence.

Ethics Committee Approval

The Ethical Review Board of Bowen University Teaching Hospital, Ogbomoso approved this study (BUTH/REC-341).

Peer Review

Externally and internally peer-reviewed.

Author Contributions

Surgical and Medical Practices: A.B.A., A.G.I., Concept: A.B.A., A.G.I., Data Collection or Processing: A.B.A., A.G.I., Analysis or Interpretation: A.B.A., A.G.I., Literature Search: A.B.A., A.G.I., Writing: A.B.A., A.G.I.

Conflict of Interest

No conflict of interest was declared by the authors.

Financial Disclosure

The authors declared that this study received no financial support.

References

  1. Han Y, Liu Y, Zhang X, et al. Chinese family caregivers of stroke survivors: determinants of caregiving burden within the first six months. J Clin Nurs 2017;26:4558-4566.
  2. Winstein CJ, Stein J, Arena R, et al. Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2016;47:98-169.
  3. Scorrano M, Ntsiea V, Maleka D. Enablers and barriers of adherence to home exercise programmes after stroke: caregiver perceptions. Int J Ther Rehabil 2018;25:353-364.
  4. Pandian JD, Srikanth V, Read SJ, Thrift AG. Poverty and stroke in India: a time to act. Stroke 2007;38:3063-3069.
  5. Lynch EA, Mackintosh S, Luker JA, Hillier SL. Access to rehabilitation for patients with stroke in Australia. Med J Aust 2019;210:21-26.
  6. Siemonsma P, Döpp C, Alpay L, Tak E, Meeteren Nv, Chorus A. Determinants influencing the implementation of home-based stroke rehabilitation: a systematic review. Disabil Rehabil 2014;36:2019-2030.
  7. Ashari A, Hamid TA, Hussain MR, Hill KD. Effectiveness of individualized home-based exercise on turning and balance performance among adults older than 50 yrs: a randomized controlled trial. Am J Phys Med Rehabil 2016;95:355-365.
  8. Anar SÖ. The effectiveness of home-based exercise programs for low back pain patients. J Phys Ther Sci 2016;28:2727-2730.
  9. Anwer S, Alghadir A, Brismee JM. Effect of home exercise program in patients with knee osteoarthritis: a systematic review and meta-analysis. J Geriatr Phys Ther 2016;39:38-48.
  10. Latham NK, Harris BA, Bean JF, et al. Effect of a home-based exercise program on functional recovery following rehabilitation after hip fracture: a randomized clinical trial. JAMA 2014;311:700-708.
  11. Hill KD, Hunter SW, Batchelor FA, Cavalheri V, Burton E. Individualized home-based exercise programs for older people to reduce falls and improve physical performance: a systematic review and meta-analysis. Maturitas 2015;82:72-84.
  12. Mahmood A, Solomon JM, English C, et al. Measurement of adherence to home-based exercises among community-dwelling stroke survivors in India. Physiother Res Int 2019;25:e1827.
  13. Levy T, Laver K, Killington M, Lannin N, Crotty M. A systematic review of measures of adherence to physical exercise recommendations in people with stroke. Clin Rehabil 2019;33:535-545.
  14. Bailey DL, Holden MA, Foster NE, et al. Defining adherence to therapeutic exercise for musculoskeletal pain: A systematic review. Br J Sports Med 2020;54:326-331.
  15. Frost R, Levati S, McClurg D, Brady M, Williams B. What Adherence measures should be used in trials of home-based rehabilitation interventions? A systematic review of the validity, reliability, and acceptability of measures. Arch Phys Med Rehabil 2017;98:1241-1256.
  16. Ogwumike OO, Badru UM, Adeniyi AF. Factors influencing adherence to home-based exercise by stroke survivors in North-West Nigeria. Int J Ther Rehabil 2014. doi 10.5455/ijtrr.00000023.
  17. Miller KK, Porter RE, DeBaun-Sprague E, Puymbroeck MV, Schmid AA. Exercise after stroke: patient adherence and beliefs after discharge from rehabilitation. Top Stroke Rehabil 2017;24:142-148.
  18. Babbar P, Kumar KV, Joshua PTA, Chakrapani M, Misri ZK. Adherence to home-based neuro-rehabilitation exercise program in stroke survivors. Bangladesh J Med Sci 2021;20:145-153.
  19. Eckard P, Lopez J, Kaus A, Aden J. Home exercise program compliance of service members in the deployed environment: an observational cohort study. Mil Med 2015;180:186-191.
  20. Henry KD, Rosemond C, Eckert LB. Effect of number of home exercises on compliance and performance in adults over 65 years of age. Phys Ther 1999;79:270-277.
  21. Medina-Mirapeix F, Escolar-Reina P, Gascón-Cánovas JJ. Predictive factors of adherence to frequency and duration components in home exercise programs for neck and low back pain: an observational study. BMC Musculoskelet Disord 2009;10:155.
  22. Kasiulevicius V, Sapoka V, Filipaviciute R. Sample size calculation in epidemiological studies. Gerontologija 2006;7:225-231.
  23. Borg G. Borg’s Perceived Exertion and Pain Scales. Champaign, IL: Human Kinetics, 1998:104.
  24. Shan S, Vanclay F, Cooper B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol 1989;42:703-709.
  25. Lee SY, Kim DY, Sohn MK, et al. Determining the cut-off score for the Modified Barthel Index and the modified ranking scale for assessment of functional independence and residual disability after stroke. PLoS One 2020;15:e0226324.
  26. Ademoyegun AB, Mbada CE, Afolabi OE, Adelowokan OI, Adeoye BM, Awotidebe TO. Caregivers’ perception of enablers and barriers to home exercise programme (HEP) adherence among Nigerian stroke survivors: a qualitative study. J Phys Educ 2021;10:14-27.
  27. Frankel MR, Morgenstern LB, Kwiatkowski T, et al. Predicting prognosis after stroke: a placebo group analysis from the National Institute of Neurological Disorders and Stroke rt-PA Stroke Trial. Neurology 2000;55:952-959.
  28. Hage V. “The NIH stroke scale: a window into neurological status”. Nursing Spectrum, 2011;24:44-49.
  29. Bernhardt J, Hayward KS, Kwakkel G, et al. Agreed definitions and a shared vision for new standards in stroke recovery research: the stroke recovery and rehabilitation roundtable taskforce. Int J Stroke 2017;12:444-450.
  30. Sluijs EN, Kok GJ, van der Zee J. Correlates of exercise compliance in physical therapy. Phys Ther 1993;73:771-786.
  31. Billinger SA, Arena R, Bernhardt J, et al. Physical activity and exercise recommendations for stroke survivors: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45:2532-2553.
  32. Hebert D, Lindsay MP, McIntyre A, et al. Canadian stroke best practice recommendations: stroke rehabilitation practice guidelines, update 2015. Int J Stroke 2016;11:459-484.
  33. Skogestad IJ, Kirkevold M, Larsson P, et al. Post-stroke fatigue: an exploratory study with patients and health professionals to develop a patient-reported outcome measure. J Patient Rep Outcomes 2021;5:35.
  34. Sluijs EM, Knibbe JJ. Patient compliance with exercise: different theoretical approaches to short term and long-term compliance. Patient Educ Couns 1991;17:191-204.
  35. Liampas A, Velidakis N, Georgiou T, et al. Prevalence and management challenges in central post-stroke neuropathic pain: a systematic review and meta-analysis. Adv Ther 2020;37:3278-3291.
  36. Westerlind E, Singh R, Persson HC, Sunnerhagen KS. Experienced pain after stroke: a cross-sectional 5-year follow-up stud. BMC Neurol 2020;20:4.
  37. Paci M, Nannetti L, Taiti P, Baccini M, Rinaldi L. Shoulder subluxation after stroke: relationships with pain and motor recovery. Physiother Res Int 2007;12:95-104.
  38. Ma Y, Luo J, Wang XQ. The effect and mechanism of exercise for post-stroke pain. Front Mol Neurosci 2022;15:1074205.
  39. Zhang YH, Hu HY, Xiong YC, et al. Exercise for neuropathic pain: a systematic review and expert consensus. Front Med (Lausanne) 2021;8:756940.