Skip to main content
  • Study protocol
  • Open access
  • Published:

Effects of Internet of Things-based power cycling and neuromuscular training on pain and walking ability in elderly patients with KOA: protocol for a randomized controlled trial



Osteoarthritis (OA) is a common and highly disabling disease that imposes a heavy burden on individuals and society. Although physical therapy is recommended as an important method to relieve OA symptoms, patients cannot continue treatment after returning home. Research on Internet telerehabilitation for knee osteoarthritis (KOA) can reduce pain and improve patient quality of life, and Internet of Things (IoT)-based telerehabilitation is a new form of delivering rehabilitation. This study will evaluate the effect of telerehabilitation via IoT, as a medium to deliver exercises, on pain and walking in patients with KOA.


This study is a single-blind randomized controlled trial. We will recruit 42 middle-aged and elderly patients with KOA aged ≥ 50 years and randomly divided into power cycling group, neuromuscular exercise group, and control group, and intervention will last for 12 weeks. Outcome measures will be taken at baseline and 4 weeks, 8 weeks, and 12 weeks post-intervention. The pre- and posttreatment differences in knee pain and physical function between participants undergoing power cycling and neuromuscular training and those in the control group will be determined by each scale. The effectiveness will be assessed by the Western Ontario and McMaster Universities Osteoarthritis Index Score (WOMAC) and an 11-point numerical pain rating scale. Walking function and quality of life will be assessed by the timed up and go and walk test, 6-min walk test, and quality of life health status questionnaires.


The findings from this trial will establish the feasibility and effectiveness of IoT-based power cycling and neuromuscular training on elderly patients with KOA in the community. As a result, this trial may help provide experimental evidence for finding a better exercise method suitable for elderly patients with KOA in the community.

Trail registration

Chinese Clinical Trials Registry ChiCTR2200058924. Prospectively registered on 6 May 2022.

Peer Review reports


Osteoarthritis (OA) refers to a chronic degenerative bone and joint disease caused by a variety of factors, and its clinical symptoms include joint pain, deformity, and motor dysfunction [1]. Globally, 9.6% of men and 18.0% of women aged 60 years and older are living with symptomatic osteoarthritis [2, 3]. OA is widely recognized as a major health problem [4], affecting the quality of life of elderly patients and leading to a huge economic burden [5,6,7]. It has been reported that nearly 250 million individuals suffered from OA worldwide, and the overall prevalence of primary OA in people over 40 years old in China is as high as 46.3% [5, 8]. OA prone to occur in the knee, hip, and hand joints, of which the knee joints are the most commonly affected site according to previous report [5]. Epidemiological survey showed that the incidence of knee osteoarthritis (KOA) is approximately 16–17%, with an increasing trend year by year. In addition, patients aged among 50 to 70 are more likely to be affected [6, 7]. Exercise therapy, as a core option for KOA treatment, has been recommended by various guidelines [9,10,11]. Exercise with on-site supervision given by physiotherapists has been proven effective for patients with KOA [5]. However, patients who in rural and remote area, from where the population accounts for a large proportion [8], may have difficulties in getting access to in person guidance when exercising; therefore, to establish a way, prescribing exercises should be incorporated for consideration in the development of a treatment plan that bridges physicians and patients. Telerehabilitation has emerged since 1998 [12]. Telerehabilitation refers to providing patients with continuous rehabilitation services through the use of information and communication technology, with combination with telemedicine [13]. The main contents of telerehabilitation include health education, exercise training, self-management, online follow-up, and regular assessment [13, 14]. Previous study demonstrated the feasibility and safety of telerehabilitation in practical applications [15]. Comparing with in-person forms, telerehabilitation showed no statistic difference in the fields of reducing pain and improving patients’ quality of life [14, 16]. With the popularity of the Internet among the elderly population and the development of modern technology, the platform of telerehabilitation has gradually transitioned from telephone to the Internet and the emerging Internet of Things (IoT) [14, 17]. The Internet of Things refers to a network that is extended on the basis of the Internet, and the user terminal can be extended to anything for information exchange, communication, and more intelligent interactive mode, which is a newly developed form of telerehabilitation. Studies have proven that compared with telephone and DVD digital discs, the guidance of the IoT can improve patients’ self-efficacy [18, 19].

A recent review has put forward a city planning concept, highlighting on constructing a healthy and sustainable city [20]. To achieve this goal, it is indispensable to take medical treatment into consideration, especially for those specific types of chronic non-infectious diseases. For patients with KOA, it takes a lot of time for them to go for doctors or physiotherapists in hospitals on-site, which brings heavy medical and social burdens; therefore, KOA treatment will shift to IoT form, which will be an inevitable trend. Our previous research on the Chinese model of IoT-based KOA rehabilitation showed that IoT rehabilitation can ensure that doctors and therapists keep abreast of the patient’s condition and give appropriate treatment, while also strengthening the communication between doctors and patients [21]. Combining IoT with patient exercise to provide community rehabilitation, giving patients living in remote areas the opportunity to communicate with physicians in real-time, supporting ongoing rehabilitation services for patients with KOA [13, 22], has the potential to increase engagement and outcomes for patients with KOA.

Due to the poor physical function of elderly patients with KOA, low-intensity, slow-moving aerobic exercise should be recommended. Power cycling is a low-load and no-impact form of aerobic exercise that help patients with KOA alleviate pain improve range of motion, muscle strength of lower extremities, and cardiorespiratory endurance [23]. In addition, neuromuscular training, as one of another commonly used clinical exercise for KOA patients, has received increasing attention due to its simple and easy-to-learn exercises. Previous studies have demonstrated that regular power cycling and neuromuscular training can both relieve knee pain and stiffness, improve motor function of lower extremity and patients’ aerobic exercise capacity, and lower extremity strength and quality of life for KOA patients [24,25,26,27,28,29,30]. In summary, we have learned about the benefits and convenience of power cycling, neuromuscular training, and telerehabilitation in the rehabilitation of patients with KOA. To better deliver the above exercises, we need to further clarify how the combination of the IoT and specific rehabilitation therapy can help patients with KOA in the community. The IoT has been widely used in various fields of social life, and the medical field is also being studied in depth. Several studies have been conducted, including projects such as “Join2Move,” “PainCOACH,” and “Help My Knees,” to explore the feasibility and effectiveness of Internet-based rehabilitation for patients with KOA [31, 32]. However, to our knowledge, no studies have demonstrated whether power cycling and neuromuscular training via IoT can effectively reduce pain and promote improvements in physical function and quality of life in patients with KOA in the community.



The primary objective of this study is to compare the effect of IoT-based power cycling and neuromuscular training on the elderly patients with KOA in the community. Whether the intervention of elderly patients with KOA is effective or not provides experimental evidence for finding a better exercise delivery mode for elderly patients with KOA in the community.

Study design

This study is a 12-week single-blind randomized controlled trial. The trial intervention and data collection will be completed from September 2022 to June 2023. The protocol was designed following the Recommendations for Interventional Trials (SPIRIT) and SPIRIT checklist could be found in Additional file 1 [33]. The trial will be conducted in the community in Chengdu, Sichuan Province. Assessments would perform online.

Participants and setting

We will recruit 42 participants at West China Hospital, Sichuan University, in Southwest China. Ethics approval was obtained from Ethics Committee on Biomedical Research, West China Hospital of Sichuan University, and prospectively registered on the Chinese Clinical Trials Registry (ChiCTR2200058924) registered 6 May 2022.

Inclusion criteria

Participants are potentially eligible if they (i) have knee pain and could provide with previous diagnosis certificate from a regular medical institution or are diagnosed with KOA according to the Guidelines for Diagnosis and Treatment of Osteoarthritis in China (2021 Edition) (see Table 1) [8]; (ii) have a I–III Kellgren-Lawrence grade [34]; (iii) are able to use their smartphones to send messages and make video calls using WeChat; and (iv) can communicate in Mandarin and understand Chinese characters.

Table 1 Inclusion and exclusion criteria

Exclusion criteria

Participants who meet any of the following criteria will not be able to participate in this study if they (i) have rheumatoid arthritis, severe osteoporosis, or other pathological conditions at the knee; (ii) are ready for knee arthroplasty or other knee surgery; (iii) receive KOA rehabilitation treatment or participating in other programs related to KOA in hospitals, clinics, or rehabilitation institutions; (iv) undergo uncontrolled hypertension and diabetes (such as fasting blood glucose ≥ 16.7 mmol/L), repeated episodes of hypoglycemia (blood glucose ≤ 3.9 mmol/L), or other diseases that restrict exercise (such as stroke, heart failure), severe anemia, etc.; (v) are with a history of mental disorders; and (vi) are informed that there are other circumstances that are not suitable for exercises without supervision; (vii) are unable to sign an informed consent.

Randomization and allocation

After the participants completed the baseline measurements, they were divided into power cycling group, neuromuscular training group, and control group according to the random allocation principle using a computer-generated random allocation table at a ratio of 1:1:1. Then, according to the random number, the corresponding test envelope is obtained, which includes the form for recording participants' information, the intervention plan, and the plan for emergent event to ensure that each participant was only aware of their own experimental interventions.


Due to the characteristic of this trial, participants and physiotherapists will not be blinded to group allocation and will be aware of the alternative treatment options. The study hypotheses will be blinded to participants. The researcher conducting the data collection and analysis will be blinded to treatment allocation.

Participant timeline

Table 2 shows the assessments at each time point following the SPIRIT statement [33]. Figure 1 demonstrates the flow chart of this trial.

Table 2 Study assessments at specific time points
Fig. 1
figure 1

Flow-chart of the study

Media for intervention prescription

To realize the connection between Internet and things, we built an applet, called Huaxi Rehabilitation Cloud applet, within the WeChat mini programs platform, by which the participants could receive the prescription of the corresponding interventions. To make it more convenient for usage, we invited public and therapists to involve in the design process of this applet and asked for any suggestions during using the applet in daily routine. After several internal discussions, we optimized the final version (Additional file 2) of the applet and applied in our trial.

Power cycling group

Participants in this group will conduct three 30-min cycling sessions per week and twelve weeks in total. Participants will perform exercise with power cycling which are set in community before our study launched. Each time participant warms up before exercises and cool down for 5 min after that. The exercise intensity, measured by the rating of perceived exertion (RPE), scores 12–14 points during cycling, that is, with self-efficacy of feeling a little hard [35]. A physiotherapist will supervise participants through a video call weekly, and new exercise prescription will be delivered when participants feel easy or too hard to perform cycling at the pre-set intensity. Doctors and physiotherapists will conduct telerehabilitation assessments, after which they will send exercise prescriptions through applet according to the assessment results. Then, participants scan the QR code typed on the power cycling through the applet to start exercise. If participants have any discomfort during exercise, they can click “End Early” on mobile phone to stop, and the physiotherapist will automatically receive a feedback, and they will decide whether it is necessary to make a new assessment. Besides, the weekly video call session and progressive exercise prescribed by physiotherapist could be considered as a method to monitor potential dropout issue and prevent the loss of follow-up and reduce the number of subjects who violate the allocation plan.

Neuromuscular training group

Participants allocated in this group will conduct exercises at home three times per week [36]. Details of neuromuscular training exercises could be found in Table 3 [28]. Generally, it comprises weight-bearing training and up-down stepping exercises. After assessments, doctors and physiotherapists will send training videos to participants and have a video call to ensure the exercises are performed correctly. Family members will accompany the participants during each exercise session (including video call with physiotherapists) to ensure safety and to provide any support. Physiotherapists will prescribe planned exercises according to the feedback received from participants weekly. If pain worsen and could not relieve within 24 h after conducting exercises advanced, participants will be asked to continue the former exercises.

Table 3 Details of neuromuscular training exercises

Control group

According to the recommendation by Chinese guideline for diagnosis and treatment of osteoarthritis (2021 edition) and Osteoarthritis Research Society International (OARSI), participants are administrated with glucosamine hydrochloride capsules (pharmaceutical product registration certificate number HC20140008 and produced by Hong Kong Aomei Pharmaceutical Factory), with a dose of 750 mg and 1.5 g/day, to relieve symptoms of KOA [8, 11].

Common interventions

In addition to the specific and individual interventions for three groups mentioned above, we also set three aspects as common rehabilitation interventions and participants in each group will receive the following information:

  • KOA education

    Through applet, information detailing the knowledge of KOA and clinical guidelines were sent three times per week by doctors and physiotherapists. At the same time, participants can also communicate with healthcare providers through Huaxi Rehabilitation Cloud applet to inquire any health conditions about KOA.

  • Self-management

    Information comprise weight management and emotional management.

    (a) Weight management: obesity is one of the risk factors of KOA, so here, we emphasize weight control [1]. Participants need to upload their height and weight to the applets every week, and the system will automatically generate a body mass index (BMI). When BMI > 24 kg/m2, the doctor will remind the participant to control their weight and send information about weight management and healthy eating. (b) Emotional management: patients with chronic pain and activity limitation are at a high risk of depression [37], and in this study, physical therapists push brochures to participants and communicate and guide them. Ensure adequate sleep (7–8 h a day), chat with friends every day, take a deep breath when troubled, listen to soothing light music when irritable, etc.

  • Exercise education

Participants should do at least 150 min of moderate-intensity (70% of maximum heart rate) aerobic activity per week, such as brisk walking, swimming (freestyle is best), and cycling. Excessive exercise should be avoided, and knee pads and other auxiliary devices should be used to support and protect the diseased joints. You can try team sports under the guidance of a special person, such as group hiking and group gymnastics., in mutual supervision and encouragement, to increase the fun of sports.

Outcome assessments

Outcomes will be measured at baseline (T0) and at 4 (T1), 8 (T2), and 12 (T3) weeks after the allocation. All participants will be assessed by researchers blinded to group tasks.

Primary outcome measures

(I) The Western Ontario and McMaster Universities Index (WOMAC) score will be used to assess knee pain, stiffness and physical function. The WOMAC is a self-assessed health status scale for KOA patients consisting of 24 items in three subscales of pain (5 items), stiffness (2 items), and physical function (17 items). All items are scored from 0 (asymptomatic) to 4 (very severe), and the total score ranges from 0 to 96, with higher scores indicating more severe symptoms [38, 39]. The test–retest reliability of the scale was 0.83, and the validity was 0.77 [40]. (ii) The 11-point numerical pain rating scale (11-NRS) was used to assess knee pain 48 h before the test. The 11-NRS is a standard tool for the study of chronic pain. A score of 0 indicates no pain, and 10 indicates severe pain [41].

Secondary outcome measures

(I) Lower limb strength and balance determined by timed up and go (TUG) test. Record the time the following process takes: stand up from a standard seat (seat height is approximately 46 cm, armrest height is 65 cm), walk 3 m away then turn around, walk back to the chair, and sit down [42,43,44]. (ii) 6-min walk test (6MWT): In an outdoor closed corridor (30 m), participants were allowed to walk back and forth. The timer is set to 6 min, and total walking distance is measured. Participants are allowed to slow down and stop to rest if necessary but are encouraged to continue walking [42, 45]. (iii) Short Form 36 Item Health Survey (SF-36) includes 8 dimensions: physical function, physiological function, physical pain, general health, vitality, social function, emotional function, and mental health, with a total of 36 questions. The total score ranges from 0 to 100. A higher total score indicates a higher quality of life [46, 47].

Safety measure

Since the majority of the interventions are physical exercises, there is no anticipated harm and compensation for trial participation. Participants will be monitored for any adverse events during the intervention, which will be collected and recorded on the case report form (CRF). All adverse events and abnormal laboratory parameters will be followed up until normal, abnormal clinical significance or return to the pretreatment state.

Sample size

The calculation is carried out according to the unified standard of clinical trial reporting and the calculation requirements of the sample size of noninferiority experiments [41, 48]. Based on the previous research results of our research team and comprehensive consideration of previous experience [28], the reduction in pain score in the WOMAC scale was used to assess the improvement of KOA symptoms. The average pain scores in patients with KOA were 1.65, 1.7, and 0 respectively [36, 49]. We set a to 0.05 and test power 1-β to 90%. Calculation via G*Power 3.1 software. A total of 33 participants is required for the calculation. With allowance for a dropout rate of 20%, each group requires 14 enrolled participants.

Statistical analysis

Statistical analysis is performed using the SPSS software (25.0). Significance level will be set at 0.05 for all statistical analysis. Intention-to-treat principle will be followed. Baseline comparability between groups of categorical variable data was determined using the chi-square test or other suitable nonparametric tests. Quantitative data will be expressed as the mean (SD), if it follows normal distribution and homogeneity of variance; otherwise, median (IQR) is given. For statistical inference, if the data obeyed a normal distribution and homogeneity of variance and satisfied spherical symmetry, variance analysis of repeated measurements was used; otherwise, the Kruskal–Wallis H rank sum test will be conducted. For data that recorded from drop-out participants, maximum likelihood estimation will be used to impute missing data, including any lacks for primary and secondary outcomes. The data collected are kept by people not involved in the trial intervention and by the online platform. The datasets analyzed during the current study and statistical code are available from the corresponding author on reasonable request, as it is the full protocol.

Data collection and monitoring

An independent Data Safety Monitoring Committee will be assembled to assess the safety and validity of the execution of the trial as well as to ensure the best interests of participants will be observed at all times. The committee will be comprised by a chairman, one independent statisticians and two physiotherapists, who will be responsible for (1) monitoring participants’ safety at all times during the trial; (2) noting any reported adverse events, making medical decisions and providing essential management to control the situation; and (3) reviewing participant recruitment and withdrawal and mark down the reasons. Once 10, 50, and 80% of the sample size is reached, a data quality audit will be performed during the trial being conducted. Further, data will be stored in encrypted spreadsheets on secured servers hosted by the West China Hospital of Sichuan University, in which any potential risk of omissions and errors will be regularly scrutinized, and then exported to a statistical software for analysis by a statistician blinded to group allocation. All data collected in this trial will be restricted to the principal investigator and specific members of the research team using the backend of the database or servers. A monitoring committee (The Good Clinical Practice Unit at West China Hospitals) will follow and oversee this trial. The monitor will visit the trial site before trial commencement and, after that, have a visit of the trial site twice per month. The monitor who is in charge will check trial procedures, including the completion rate and quality of prescribed exercises, safety assessments, and data recording, and complete the verification procedures of raw data and participant confidentiality. For the enrollment and follow-up of participants, a trial steering committee will manage the trial process and check day-to-day. Any suspension of participation caused by low adherence or voluntary quit by the influence of the COVID-19 pandemic will be recorded.

Ethics approval and dissemination

The trial will be conducted in accordance with the Declaration of Helsinki. Ethics approval was granted by the Clinical Trial and Biomedical Ethics Committee of the West China Hospital, Sichuan University (ethics reference: 2021 (1696)). The study was registered on the Chinese Registry website (ChiCTR2200058924). Informed consent will be provided to all participants and signed prior to the trial. All study-related information of subjects will be protected during the trial. On the consent form, participants will be asked if they agree to collect and use of their data should they choose to withdraw from the trial, and this trial does not involve any biological specimens. The consent form is available, on request, from the corresponding author.

The results of this study will be published at local, national, or international rehabilitation conferences and submitted as manuscripts to peer-reviewed journals. The main findings of the study will also be shared with all participants and disseminated to researchers, health service providers, health care professionals, and the public via the courses.


This study aims to investigate the effect of Internet-based power cycling or neuromuscular training on pain and walking function in KOA patients. Unlike traditional research, we enroll eligible participants through outpatient clinic, communities, and the Internet. Our rehabilitation setting will focus primarily on the community. After allocation, we will provide them with health education and a personalized exercise training plan. The entire process will use online video connections and platforms to implement interventions. The exercise process requires community administrators and family members to supervise and provide any support. During telerehabilitation, participants will have opportunities to communicate with their peers in a community setting, thereby promoting willingness and motivation to participate through mutual encouragement [50]. Besides, performing neuromuscular training at home saves patients’ time commuting to hospitals. During this process, community staffs can participate in the professional training of constructing community KOA network rehabilitation to improve the whole-cycle rehabilitation treatment for patients with KOA, which plays an active role in improving community rehabilitation and reducing the burden on individuals and society. The findings of this study may help develop an evidence-based online exercise program to manage symptoms and function in patients with KOA in the community. However, the proposed study also has some potential limitations. First of all, during the implementation of the research plan, participants may not be recruited on time due to the impact of the epidemic, we will expand the online recruitment channels for this. Secondly, there is no evidence-based quantitative tool to record the frequency and details of family members’ companionship during exercise; we will make a patient exercise card-level adverse event table and ask family members and community administrators to make detailed records of the frequency and details of exercise companionship and adverse events.

Trial status

Recruitment will be started on 1 September 2022, and it is estimated to have a total duration of 10 months. This protocol is the version number 5 dated 2 June 2022.

Availability of data and materials

Not applicable.





Knee osteoarthritis


Internet of Things


Osteoarthritis Index Scores from Western Ontario and McMaster Universities


11-Point numerical pain rating scale


Timed up and go


6-Min walking test


The MOS item short from health survey


Osteoarthritis Research Society International


Standard deviation


Interquartile range


Analysis of variance


Quick response


  1. Whittaker JL, Truong LK, Dhiman K, Beck C. Osteoarthritis year in review 2020: rehabilitation and outcomes. Osteoarthritis Cartilage. 2021;29(2):190–207.

    Article  CAS  Google Scholar 

  2. Tarride JE, Haq M, O’Reilly DJ, Bowen JM, Xie F, Dolovich L, et al. The excess burden of osteoarthritis in the province of Ontario, Canada. Arthritis Rheum. 2012;64(4):1153–61.

    Article  Google Scholar 

  3. Brosseau L, Wells GA, Pugh AG, Smith CA, Rahman P, Àlvarez Gallardo IC, et al. Ottawa Panel evidence-based clinical practice guidelines for therapeutic exercise in the management of hip osteoarthritis. Clin Rehabil. 2016;30(10):935–46.

    Article  Google Scholar 

  4. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323–30.

    Article  Google Scholar 

  5. Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet (London, England). 2019;393(10182):1745–59.

    Article  CAS  Google Scholar 

  6. Zhang W, Moskowitz RW, Nuki G, Abramson S, Altman RD, Arden N, et al. OARSI recommendations for the management of hip and knee osteoarthritis, Part II: OARSI evidence-based, expert consensus guidelines. Osteoarthritis Cartilage. 2008;16(2):137–62.

    Article  CAS  Google Scholar 

  7. Liu Q, Wang S, Lin J, Zhang Y. The burden for knee osteoarthritis among Chinese elderly: estimates from a nationally representative study. Osteoarthritis Cartilage. 2018;26(12):1636–42.

    Article  CAS  Google Scholar 

  8. The Joint Surgery Branch of the Chinese Orthopaedic Association, The Subspecialty Group of Osteoarthritis,Chinese Association of Orthopaedic Surgeons, The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Editorial Office of Chinese Journal of Orthopaedics,Chinese guideline for diagnosis and treatment of osteoarthritis (2021 edition). Chin J Orthop. 2021; 41(18): 1291–1314.

  9. Kolasinski SL, Neogi T, Hochberg MC, Oatis C, Guyatt G, Block J, et al. 2019 American College of Rheumatology/Arthritis Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee. Arthritis Rheumatol. 2020;72(2):220–33.

    Article  Google Scholar 

  10. Bruyère O, Honvo G, Veronese N, Arden NK, Branco J, Curtis EM, et al. An updated algorithm recommendation for the management of knee osteoarthritis from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO). Semin Arthritis Rheum. 2019;49(3):337–50.

    Article  Google Scholar 

  11. Bannuru RR, Osani MC, Vaysbrot EE, Arden NK, Bennell K, Bierma-Zeinstra SMA, et al. OARSI guidelines for the non-surgical management of knee, hip, and polyarticular osteoarthritis. Osteoarthritis Cartilage. 2019;27(11):1578–89.

    Article  CAS  Google Scholar 

  12. Burns RB, Crislip D, Daviou P, Temkin A, Vesmarovich S, Anshutz J, et al. Using telerehabilitation to support assistive technology. Assist Technol. 1998;10(2):126–33.

    Article  CAS  Google Scholar 

  13. Theodoros D, Russell T. Telerehabilitation: current perspectives. Stud Health Technol Inform. 2008;131:191–209.

    Google Scholar 

  14. Dias JF, Oliveira VC, Borges PRT, Dutra F, Mancini MC, Kirkwood RN, et al. Effectiveness of exercises by telerehabilitation on pain, physical function and quality of life in people with physical disabilities: a systematic review of randomised controlled trials with GRADE recommendations. Br J Sports Med. 2021;55(3):155–62.

    Article  Google Scholar 

  15. van Egmond MA, van der Schaaf M, Vredeveld T, Vollenbroek-Hutten MMR, van Berge Henegouwen MI, Klinkenbijl JHG, et al. Effectiveness of physiotherapy with telerehabilitation in surgical patients: a systematic review and meta-analysis. Physiotherapy. 2018;104(3):277–98.

    Article  Google Scholar 

  16. Hauber RP, Jones ML. Telerehabilitation support for families at home caring for individuals in prolonged states of reduced consciousness. J Head Trauma Rehabil. 2002;17(6):535–41.

    Article  Google Scholar 

  17. Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, et al. 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):S32–8.

    Article  Google Scholar 

  18. Allen KD, Sanders LL, Olsen MK, Bowlby L, Katz JN, Mather RC 3rd, et al. Internet versus DVD decision aids for hip and knee osteoarthritis. Musculoskeletal Care. 2016;14(2):87–97.

    Article  Google Scholar 

  19. Uesugi Y, Koyanagi J, Takagi K, Yamaguchi R, Hayashi S, Nishii T. Exercise therapy interventions in patients with hip osteoarthritis: comparison of the effects of DVD and website-based interventions. JMIR Rehabil Assist Technol. 2018;5(1):e10.

    Article  Google Scholar 

  20. Boeing G, Higgs C, Liu S, Giles-Corti B, Sallis JF, Cerin E, et al. Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities. Lancet Glob Health. 2022;10(6):e907–18.

    Article  Google Scholar 

  21. Suhang X, Chengsen J, Lin Y, Qiu L, Qian W, Liqiong W. Chinese model of knee osteoarthritis rehabilitation based on the Internet of Things. Nat Med J China. 2019;99(31):2404–8.

    Google Scholar 

  22. Hallensleben C, van Luenen S, Rolink E, Ossebaard HC, Chavannes NH. eHealth for people with COPD in the Netherlands: a scoping review. Int J Chron Obstruct Pulmon Dis. 2019;14:1681–90.

    Article  Google Scholar 

  23. Buddhadev HH, Crisafulli DL, Suprak DN, San Juan JG. Individuals with knee osteoarthritis demonstrate interlimb asymmetry in pedaling power during stationary cycling. J Appl Biomech. 2018;34(4):306–11.

    Article  Google Scholar 

  24. Luan L, Bousie J, Pranata A, Adams R, Han J. Stationary cycling exercise for knee osteoarthritis: a systematic review and meta-analysis. Clin Rehabil. 2021;35(4):522–33.

    Article  Google Scholar 

  25. Keogh JWL, Grigg J, Vertullo CJ. Is Home-Based, High-intensity interval training cycling feasible and safe for patients with knee osteoarthritis?: Study Protocol for a Randomized Pilot Study. Orthop J Sports Med. 2017;5(3):2325967117694334.

    Google Scholar 

  26. Kabiri S, Halabchi F, Angoorani H, Yekaninejad S. Comparison of three modes of aerobic exercise combined with resistance training on the pain and function of patients with knee osteoarthritis: a randomized controlled trial. Phys Ther Sport. 2018;32:22–8.

    Article  Google Scholar 

  27. Keogh JW, Grigg J, Vertullo CJ. Is high-intensity interval cycling feasible and more beneficial than continuous cycling for knee osteoarthritic patients? Results of a randomised control feasibility trial. PeerJ. 2018;6:e4738.

    Article  Google Scholar 

  28. Liang Q, He C-Q, Jiang J-L, et al. The effects of neuromuscular exercise program on pain and physical function in patients with knee osteoarthritis. West China Med J. 2018;33(10):1247–51.

    Google Scholar 

  29. Clausen B, Holsgaard-Larsen A, Roos EM. An 8-week neuromuscular exercise program for patients with mild to moderate knee osteoarthritis: a case series drawn from a registered clinical trial. J Athl Train. 2017;52(6):592–605.

    Article  Google Scholar 

  30. Liu J, Chen L, Chen X, Hu K, Tu Y, Lin M, et al. Modulatory effects of different exercise modalities on the functional connectivity of the periaqueductal grey and ventral tegmental area in patients with knee osteoarthritis: a randomised multimodal magnetic resonance imaging study. Br J Anaesth. 2019;123(4):506–18.

    Article  Google Scholar 

  31. Rini C, Porter LS, Somers TJ, McKee DC, DeVellis RF, Smith M, et al. Automated Internet-based pain coping skills training to manage osteoarthritis pain: a randomized controlled trial. Pain. 2015;156(5):837–48.

    Article  Google Scholar 

  32. Brooks MA, Beaulieu JE, Severson HH, Wille CM, Cooper D, Gau JM, et al. Web-based therapeutic exercise resource center as a treatment for knee osteoarthritis: a prospective cohort pilot study. BMC Musculoskelet Disord. 2014;15:158.

    Article  Google Scholar 

  33. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158(3):200–7.

    Article  Google Scholar 

  34. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16(4):494–502.

    Article  CAS  Google Scholar 

  35. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377–81.

    Article  CAS  Google Scholar 

  36. Bennell KL, Kyriakides M, Metcalf B, Egerton T, Wrigley TV, Hodges PW, et al. Neuromuscular versus quadriceps strengthening exercise in patients with medial knee osteoarthritis and varus malalignment: a randomized controlled trial. Arthritis Rheumatol (Hoboken, NJ). 2014;66(4):950–9.

    Article  Google Scholar 

  37. Dobkin BH. A rehabilitation-Internet-of-Things in the home to augment motor skills and exercise training. Neurorehabil Neural Repair. 2017;31(3):217–27.

    Article  Google Scholar 

  38. Bellamy N. WOMAC: a 20-year experiential review of a patient-centered self-reported health status questionnaire. J Rheumatol. 2002;29(12):2473–6.

    Google Scholar 

  39. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15(12):1833–40.

    CAS  Google Scholar 

  40. Faucher M, Poiraudeau S, Lefevre-Colau MM, Rannou F, Fermanian J, Revel M. Assessment of the test-retest reliability and construct validity of a modified WOMAC index in knee osteoarthritis. Joint Bone Spine. 2004;71(2):121–7.

    Article  Google Scholar 

  41. Piaggio G, Elbourne DR, Altman DG, Pocock SJ, Evans SJ. Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. JAMA. 2006;295(10):1152–60.

    Article  CAS  Google Scholar 

  42. Bennell K, Dobson F, Hinman R. Measures of physical performance assessments: Self-Paced Walk Test (SPWT), Stair Climb Test (SCT), Six-Minute Walk Test (6MWT), Chair Stand Test (CST), Timed Up & Go (TUG), Sock Test, Lift and Carry Test (LCT), and Car Task. Arthritis Care Res (Hoboken). 2011;63(Suppl 11):S350–70.

    Article  Google Scholar 

  43. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142–8.

    Article  CAS  Google Scholar 

  44. Dobson F, Hinman RS, Roos EM, Abbott JH, Stratford P, Davis AM, et al. OARSI recommended performance-based tests to assess physical function in people diagnosed with hip or knee osteoarthritis. Osteoarthritis Cartilage. 2013;21(8):1042–52.

    Article  CAS  Google Scholar 

  45. Steffen TM, Hacker TA, Mollinger L. Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds. Phys Ther. 2002;82(2):128–37.

    Article  Google Scholar 

  46. McConnell S, Kolopack P, Davis AM. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC): a review of its utility and measurement properties. Arthritis Rheum. 2001;45(5):453–61.

    Article  CAS  Google Scholar 

  47. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473–83.

    Article  Google Scholar 

  48. Jones B, Jarvis P, Lewis JA, Ebbutt AF. Trials to assess equivalence: the importance of rigorous methods. BMJ. 1996;313(7048):36–9.

    Article  CAS  Google Scholar 

  49. Hu kun. Effects of different exercises on middle-aged and elderly community adults with knee osteoarthritis: a randomized controlled trial[D].Fujian university of traditional Chinese medicine. 2017.

  50. Wang L, Xie S, Bao T, Zhu S, Liang Q, Wang X, et al. Exercise and education for community-dwelling older participants with knee osteoarthritis: a video-linked programme protocol based on a randomised controlled trial. BMC Musculoskelet Disord. 2021;22(1):470.

    Article  Google Scholar 

Download references


The authors are grateful for colleagues from West China Hospital in providing critical comments on study design and sample size calculations and help in developing statistical analysis plan.


This study is supported by the National Natural Science Foundation (81972146 to Chengqi He), the Department of Science and Technology of Sichuan Province (2020YJ0210 to Chengqi He), and West China Hospital of Sichuan University (ZYGD18018 to Chengqi He). The funders played no role in the design, conduct, or reporting of this study.

Author information

Authors and Affiliations



Study design: YZ, CH, SX; funding acquisition: CH; project administration: YZ, CH, XW, LW, RZ, YF; writing: YZ, KS. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chengqi He.

Ethics declarations

Ethics approval and consent to participate

Ethics approval was obtained from Ethics Committee on Biomedical Research, West China Hospital of Sichuan University on 20 January 2022 with the approved number 2021–1696. The relevant sponsor has reviewed the study protocol and consent form.

Consent for publication

All authors gave consent for publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xie, S., Wang, X. et al. Effects of Internet of Things-based power cycling and neuromuscular training on pain and walking ability in elderly patients with KOA: protocol for a randomized controlled trial. Trials 23, 1009 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: