Skip to main content
Download PDF

A systematic review of telemedicine for neuromuscular diseases: components and determinants of practice

BMC Digital Health volume 2, Article number: 17 (2024) Cite this article

Abstract

Introduction

Neuromuscular diseases (NMDs) entail a group of mostly inherited genetic disorders with heterogeneous phenotypes impacting muscles, the central or peripheral nervous system. They can lead to severe disabilities and shortened lifespans. Despite their severity, NMDs often lack in public awareness and appropriate medical and social support. Telemedicine can improve patients’ and caregivers’ lives by enhancing continuity of and access to care. The first aim of this systematic review was to summarise the status quo of telemedicine services for patients with NMDs. Secondly, barriers and facilitators of the respective implementation processes should be analysed.

Methods

The databases PubMed, Web of Science and CENTRAL by Cochrane were searched in May 2022. To be truly explorative, any original evidence from any setting was included. Two independent researchers completed the screening process. Data was extracted and analysed using the taxonomy of Bashshur et al. (2011) and the Consolidated Framework for Implementation Research (CFIR).

Results

Fifty-seven original papers were included in the systematic review. The results showed a high representation of teleconsultations and remote monitoring studies. Teleconsultations replaced in person appointments and telemonitoring mostly focused on ventilation. Physical therapy, pulmonology, neurology, and psychology were the most represented medical specialties. We found barriers and facilitators relating to implementation mainly referred to the intervention and the individuals involved. Technical errors and inaccessibility due to a lack of technical devices or the patient’s disability were stated as hindrances. A positive mindset of users as well as patient empowerment were necessary for the adoption of new technology. Technophobia or uncertainty around technology negatively impacted the implementation process.

Discussion

This systematic review provides an overview of the current use of telemedicine in patients with NMDs. The distribution of telemedicine interventions between the defined domains was very heterogenous. Previous research has neglected to fully describe the implementation process of telemedicine for NMDs.

Conclusion

The evidence shows that telemedicine can benefit patients with NMDs in a multitude of ways. Therefore, health policies should endorse and incentivise the uptake of telemedicine by institutions and health care workers. Further research needs to be conducted to confirm the current evidence and close existing research gaps.

Peer Review reports

Introduction

Neuromuscular diseases (NMDs) are a heterogeneous group of disorders, that affect the nerves controlling muscles, leading to muscle weakness, wasting, and other related symptoms [1]. NMDs are often hereditary and have been linked to 500 different affected genes [2, 3]. Most NMDs are classified as rare diseases. The prevalence of NMDs can vary widely and, even for common diagnostic groups, the prevalence ranges between 0.1 to 60 per 100,000 [4]. The onset, cause, and course of the disease vary widely between disorders [5]. While each individual's experience is unique, there are common disability-related challenges faced by patients with NMDs. Acknowledging these commonalities and addressing the unique needs of each person are essential for providing comprehensive care and support to individuals and their families living with NMDs. NMDs are highly complex diseases defined by a degenerative course and progressive muscle weakness as the main symptom. Their impact extends beyond the musculoskeletal system, affecting various organs and systems throughout the body, such as eyes, lungs or the brain [1, 2]. As a result, patients suffer from a reduced quality of life and a significant disease burden [2, 6]. Multidisciplinary care is often considered the optimal approach for providing holistic treatment and symptomatic management for individuals with NMDs [7,8,9,10,11]. The needs of patients during disease progression are ever changing based on disease stage, symptom burden, and personal priorities. General practitioners, specialists, and allied health professionals each bring unique expertise to the care team, allowing for comprehensive, patient-centred care that adapts to changing needs and priorities throughout the course of the disease and ensures continuity and quality of care [1, 12, 13]. Recognising and supporting caregivers is crucial in the care of NMD patients. Most NMD patients receive informal care, often provided by their partner or family members. The caregiver burden increases with the progression of the patient’s disease. In severe cases, it can lead to psychological distress and burnout, a state of physical and emotional exhaustion [14,15,16,17].

Mobile health apps, teleconsultation and telemonitoring have been proven to be useful tools in the management and treatment of chronic diseases such as diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. They have the potential to increase treatment adherence, support self-management, and promote continuity of care [18,19,20]. They have the potential to reduce hospital admissions, decrease mortality rates, and lessen health services usage [21,22,23,24]. The research focus in telemedicine for NMDs varies between disorders. A recent systematic review by Helleman et al. showed telemedicine for ALS patients to be a useful option for remote monitoring, consultations, and follow-ups [25]. From a patient’s perspective it can be time- and cost-saving while reducing stress and fatigue. While telemedicine has demonstrated its value in certain NMDs like ALS, its usage in the care of other NMDs have not been as extensively studied or described.

This systematic review aims to identify telemedicine interventions for patients with NMDs and analyse the barriers and facilitators of the implementation process associated with telemedicine for NMD patients. The taxonomy by Bashshur et al. will be used to standardise terminology and make it easier to categorise and study the various telehealth interventions and services [26]. The term “Telemedicine” will be used as an umbrella term to encompass a broad range of remote healthcare services and technologies. This is done to avoid the potential ambiguities and unclarities that can arise from newer terms like "e-health" or "telehealth". This review will provide an overview of the status quo and will offer recommendations for future innovations.

Methods

This systematic review followed the PRISMA [27] checklist. The study protocol was registered on PROSPERO (ID: CRD42022325481).

Databases and search strategy

For the literature search PubMed, Web of Science, and the Cochrane database CENTRAL were used as sources. If full text could not be found, the authors were contacted. The final search was conducted in May of 2022.

The search strategy consisted of two major themes: Firstly, synonyms for NMDs and secondly, synonyms and subcategories for telemedicine. The full search strings can be found in the supplementary file 1.

Study selection

The study selection was conducted by two reviewers KS and DS. The following inclusion criteria were applied: Studies from any country with any healthcare and insurance system were eligible to maximise the diversity and inclusivity of the evidence base. No restrictions regarding cultural or socio-economic context were made to be truly explorative. Articles were eligible for inclusion if their study population consisted of patients with one or more types of NMDs. Since a single comprehensive list of all NMDs could not be found, the list of NMDs by the Muscular Dystrophy Association (MDA) was used as a reference [28]. If a disease could not be found under the listed disorders, the International Classification of Diseases (ICD) was consulted [29]. No limitations regarding sex, age, race, or nationality were made. All types of telemedicine were eligible for inclusion. The taxonomy by Bashshur et al. was used as a guiding definition [26]. Bashshur uses telemedicine in his paper as the original term for ICT in healthcare. The domains include the following components:

  • Telehealth: Health behavior & education; Health & disease epidemiology; Environmental/Industrial health; Health management & policy.

  • E-health : Electronic health record; Health information; Clinical decision support system; Physician order entry.

  • M-health : Clinical support; Health worker support; Remote data collection; Helplines.

Interventions could be implemented on a national, communal, or institutional level. The users could include patients, caregivers, and healthcare workers. Only primary research was included. Due to the explorative nature of the systematic review, no major restrictions regarding study types were made. Only articles written in English or German were included. Due to the rapid pace of technological progress, only studies from the last ten years were considered. This ensured that the telemedicine interventions were not out-of-date or obsolete.

Studies were excluded if no specific diagnostic group was mentioned. Further reviews, study protocols and commentaries were excluded.

Data extraction and analysis

The data extraction and analysis were done by DS. From the included studies the following data points were extracted: authors, year of publication, country, included NMDs, intervention type and analysed outcomes. Additionally, barriers and facilitators of the implementation process were collected. The Consolidated Framework for Implementation Research (CFIR) was used to guide the extraction process [30]. The CFIR is an established framework for the analysis of implementation processes. Based on this structure, a detailed coding manual with operationalised definitions for each construct was created. This manual served as a reference guide to ensure that the extraction and coding process was systematic and reproducible.

The data synthesis was done narratively. Since no effect measures were used, a quantitative analysis was not applicable. Firstly, the types of telemedicine interventions were clustered according to the domains described by Bashshur et al., to gain a comprehensive understanding of the current landscape of telemedicine applications [26]. Secondly, the CFIR was used to label quotes on implementation barriers and facilitators [30].

No meta-analysis was conducted as there are no quantitative outcomes to analyse. Further, the heterogeneity of the studies was not assessed. Due to the broad inclusion criteria, a high heterogeneity could be expected. Since the focus of this systematic review lies on the intervention types, rather than on their effectiveness, subgroup analyses were not performed. Equally no sensitivity analyses were conducted. The focus of the systematic review was not to summarise evidence regarding a specific intervention, it was an exploration of the current telemedicine options for patients with NMDs.

Risk of bias

The study protocol stated a risk of bias assessment using the RoB 2 and ROBINS-I tools [31, 32]. This was later changed to the JBI’s critical appraisal tools as they offered a wider selection of checklists [33]. No meta-bias was analyzed since the outcomes of the studies were not a point of interest.

Results

Included studies

Figure 1 depicts the study selection process for the systematic review, including a total of 57 reports. These included four report pairs with interlinked content. Ando et al. published two papers on the Intervention Careportal in 2019 and 2021 [34, 35]. Hobson et al. conducted one study with results disseminated across two publications [36, 37]. Martinet et al. conducted two studies utilising the same intervention but with distinct comparison groups and study populations [38, 39]. Lastly, Sobierajska-Rek et al. and Wasilewska et al. published two articles addressing different subsections of one main study [40, 41]. Studies excluded during the full text screening process can be found in supplementary file 2.

Fig. 1
figure 1

Flow diagram of the identified studies (Source: own depiction)

Full size image

Study characteristics

Table 1 presents an overview of the study characteristics. A total of 25 studies were carried out using a cross-sectional design [34, 35, 40, 42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63]. Additionally, the review included two case series [64, 65] and one case–control study [66]. Among the studies, 16 adopted a cohort study design [41, 67,68,69,70,71,72,73,74,75,76,77,78,79,80,81], while ten employed an experimental design [36,37,38,39, 82,83,84,85,86,87]. The remaining three reports were method papers [88,89,90]. Geographically, the majority of the studies took place in Europe [34,35,36,37,38,39,40,41,42,43, 47,48,49, 53, 58,59,60, 62, 65, 66, 68, 69, 72,73,74, 76, 80,81,82, 84, 87,88,89,90] and the USA [44,45,46, 51, 52, 54, 56, 57, 63, 67, 71, 77,78,79, 85, 86]. Two studies were conducted in Canada [50, 64] and one in each of the following countries: Japan [70], India [75], Brazil [83], and Australia [55]. One study included participants from around the globe [61].

Table 1 Study characteristics
Full size table

A total of twenty-nine studies exclusively focussed on ALS patients [34,35,36,37, 42, 46, 48, 51, 52, 55,56,57, 62, 69,70,71,72,73, 76,77,78,79,80,81, 85,86,87,88,89,90], while another four studies included ALS patients alongside other NMD diagnostic groups [53, 54, 58, 60]. The study outcomes assessed in these studies varied widely. Clinical outcomes, such as physical and cognitive function, as well as mental health, were often used. Further, user satisfaction and utilisation measurements were applied to evaluate interventions. For patient registry studies, epidemiological statistics, including prevalence, were commonly employed as outcome measures.

Risk of bias

While the primary focus has been on exploring the availability of telemedicine interventions for patients with NMDS, it is crucial not to overlook the evaluation of individual study quality and the potential impact of bias. In summary, most studies demonstrated a low risk of bias and employed sound methods and procedures. However, certain limitations, such as the lack of comparison groups, insufficient follow up time, and some inadequate reporting, should be noted. Visual depictions and the complete analysis can be found in supplementary material 3. Three reports were not assessed as they only presented a method paper without empirical results [88,89,90].

Telemedicine domains of included interventions

In the following sections the telemedicine interventions included in the analysis will be examined, guided by the taxonomy by Bashshur et al [26]. According to their definition, telemedicine comprises of three major domains: telehealth, e-health, and m-health. Eight studies were categorised under the telehealth domain, encompassing all traditional public health areas. E-health, mainly describing the online storage of information and supporting tools for physicians, was represented by ten studies. The majority of studies fell within the m-health domain, a rapidly growing field that leverages mobile devices like smartphones and tablets to deliver healthcare services, monitor patients remotely, and support self-management. Given that interventions could encompass elements from different domains, multiple mentions or references to different domains is possible. As stated, there were instances where multiple reports featured identical telemedicine interventions [34,35,36,37,38,39]. In order not to bias the results, identical interventions were counted as one during the analysis of the telemedicine domains and components. The distribution of telemedicine domains is illustrated in Fig. 2a.

Fig. 2
figure 2

Distribution of the telemedicine (a) and telehealth domains (b) (Source: own depiction)

Full size image

Telehealth

The studies within the telehealth domain were mostly epidemiological studies. Six studies described online patient registries for one or more NMDs [43, 44, 47, 56, 58, 72]. The remaining two studies were categorised under health education. One study introduced a blended curriculum focusing on physical examinations for patients with NMDs [45] while another detailed a virtual neuromuscular ultrasound course [61]. The distribution of the telehealth domain can be seen in Fig. 2b.

E-Health

The second smallest domain was e-health (Fig. 3). Within this domain, three studies incorporated electronic health records [69, 89, 90]. Health information was the subject of five studies, with two of these not providing an intervention but instead investigating patients’ computer use and information seeking behaviour [42, 50]. Only two interventions described clinical decision support systems, one supporting physicians during the diagnostic phase [53] and another supporting patients with advanced care planning [71]. A singular app used a function for physician order entries, specifically for nutrition plan entries [86].

Fig. 3
figure 3

Distribution of the e-Health (a) and m-health domains (b) (Source: own depiction)

Full size image

M-Health

Most included studies contained m-health components (Fig. 3). Among the various m-health interventions analysed, helplines represented the smallest category. Specifically, four interventions provided emergency telephone support, and one included useful helpline numbers in their app [35, 60, 81, 89, 90].

The predominant categories within the m-health domain were clinical support and remote data collection. Nine studies reported interventions with synchronous consultations and data collection [40, 51, 57, 62, 65, 73, 75, 81, 85]. To illustrate, Christodoulou et al. conducted telephone-based cognitive-behavioural screening in ALS patients [85], demonstrating how telemedicine can seamlessly combine remote data collection processes with distance consultations. Another example was the remote application of the ALS Functioning Rating Scale during teleconsultations [62]. An alternative approach identified involving clinical support and remote data collection occurring asynchronously, utilising specially designed devices or mobile applications for data collection [35, 66, 68,69,70, 88, 90]. In this approach, clinical consultation was offered either on demand or automatically triggered based on the collected data.

Fourteen studies used clinical support without remote data collection, including home exercise programs [40, 82,83,84], psychological interventions [39, 87] and pure teleconsultation [52, 54, 55, 60, 77,78,79]. In contrast, 12 studies focussed on pure remote data collection without clinical support. This included, accelerometers [74, 80], physical assessments [63, 64, 67, 76] or the assessment of the patient’s nutritional status [86] or disease-related health [37, 46, 89]. Additionally, Cesareo et al. as well as Wasilewska et al. examined remote pulmonary monitoring devices [41, 49].

Barriers and facilitators for the implementation of telemedicine

CFIR was used to assess factors that may facilitate or hinder the implementation of telemedicine. This framework consists of five domains: the inner setting, the outer setting, the implementation process, the intervention characteristics, and the characteristics of the individuals. Relevant information was found in 22 studies, with a predominant focus on patient and carer perspectives [34, 36, 37, 41, 42, 48,49,50,51,52, 55, 62, 63, 66, 69, 73,74,75,76,77,78, 83]. As a result, no information regarding the inner/outer setting or the implementation process was gathered. All statements focused on the intervention characteristics or the characteristics of the individuals. Thus, the following section is structured according to the two domains and their constructs.

Intervention characteristics

A summary of mentioned barriers and facilitators can be seen in Table 2.

Table 2 Barriers and facilitators—intervention characteristics according to CFIR framework (Source: own depiction)
Full size table

General characteristics

This category summarises all barriers and facilitators directly linked to the intervention that could not be categorised elsewhere. The most common barrier encountered during the implementation of telemedicine interventions were malfunctions related to internet connectivity or end devices. Examples included software errors [51], faulty data transmission [34] or a poor internet connection [83]. Additionally, it was reported, that the internet and necessary end devices, such as smartphones, tablets, or computers, were often not available [48, 50, 63].

Relative advantage

A major factor for patients was the reduced time and travel burden [34, 51, 52, 62, 76, 78]. In more advanced stages of the diseases travelling with medical equipment became almost impossible, making telemedicine vital for house-bound patients [78].

Telemonitoring and the remote data collection provided multiple advantages, with patients and caregivers highlighting the timeliness of actions in case of alerts [34, 73]. Continuous monitoring also proved beneficial for in-person visits, as medical staff stated that appointments could be used more efficiently with data being analysed beforehand [69]. Some disadvantages regarding telemedicine were acknowledged. Caregivers and physicians noted the lack of physical evaluation as problematic [51, 52]. Additionally, an emotional distance and a lack of informal encounters between patients and healthcare workers was reported [52, 55].

Adaptability

Patients appreciated the flexibility of online exercise programs, which were easier to integrate into their daily routines [83]. It was seen as important to be able to choose the main form of communication [55, 62]. For example, patients with speech difficulties communicating via E-Mail was preferred.

Complexity

Interventions were easier implemented if participants were thoroughly informed about the telemedicine service and if a computer-literate person was on-site [78]. The duration and frequency of sessions was another major point. Overall, more frequent, and shorter sessions were perceived as less fatiguing [78].

Design and quality

Critical considerations included the presentation, design, and quality of telemedicine products, emphasizing features like accessible closing mechanisms for wearable devices and age-appropriate designs [49, 74].

Cost

From a patient’s perspective telemedicine was cost-saving due to reduced travel [34, 48]. Nevertheless, acquisition costs could be a barrier for some. Institutional perspectives indicated potential savings, ranging from 20 to 89%, depending on the approach, making costs a crucial factor [50, 77].

Characteristics of individuals

The second domain related to the characteristics of individuals. This includes all stakeholders such as patients, caregivers, and healthcare workers. Table 3 depicts the barriers and facilitators relating to the characteristics of individuals.

Table 3 Barriers and facilitators—characteristics of individuals according to CFIR framework (Source: own depiction)
Full size table

Knowledge and Beliefs about the Intervention

The CFIR highlights the importance of an individual’s pre-existing knowledge and beliefs about the intervention [30]. Trust in the intervention was vital for patients using telemonitoring [34, 36, 52, 69, 78]. This includes being confident that the transmitted data was monitored and that providers would act in the case of abnormalities.

Self-efficacy

Easy to use technology was seen as an enabler for telemedicine implementation, as it reassured the user in their abilities. Accordingly, barriers arose if patients could not or did not feel confident in using technological devices [50, 51, 69]. Lack of confidence led patients to use technology on rare occasions and only if deemed necessary [36].

Other personal attributes

Lastly, this category summarises all personal traits of stakeholders that might impact the implementation of the intervention [30]. Younger, higher-educated patients embraced technology more readily [42, 75]. Another enabler was telemonitoring improving patient empowerment, symptom awareness, and communication [34, 36, 51, 69]. However, some found constant disease confrontation challenging [69]. Lastly, a personal connection with medical staff enabled telemedicine use [36].

Discussion

This systematic review presents a comprehensive overview of the current status of telemedicine applications for patients with NMDs. The primary objective was to classify the identified interventions according to the dimensions of telemedicine. While some studies within this review explored the epidemiology of NMDs, and two interventions provided education for clinical staff, it's clear that certain aspects of telemedicine in public health remain under-studied.

E-health, encompassing health information, an electronic health record or physician order entries/treatment instructions, was comparatively underutilised, with only a subset of interventions included. Moreover, decision support systems were rarely investigated. The predominant focus of most interventions was on clinical support and remote data collection.

The second phase of the analysis concentrated on the implementation process, with a specific focus on identifying barriers and facilitators associated with both the intervention itself and the individuals involved. In comparison to traditional care, telemedicine often demonstrated a relative advantage. The high motivation demonstrated by NMD patients and their caregivers in integrating telemedicine into their care plan is a testament to the potential of telemedicine as a transformative force in healthcare.

Telemedicine was often perceived as a resource-saving, less fatiguing alternative, particularly offering increased accessibility for homebound patients. The lack of physical touch and reduced personal connections emerged as significant barriers. Additionally, the accessibility of technology played a pivotal role, as inadequate design hindered some patients from using telemedicine services. The acceptance and uptake of telemedicine services often depended on the readiness of patients and their caregivers to embrace and adapt to new digital solutions. Recognising the importance of patient empowerment, fostering the development of essential skills and confidence in utilising technology is crucial for enabling patients to actively engage in their healthcare.

Clinical and policy implications

The COVID-19 pandemic created an unprecedented opportunity for the development and implementation of telehealth. Disruptions in healthcare access, caused by social distancing and hygiene guidelines, led healthcare practitioners to expand telemedicine services to ensure the continuity of care [91, 92]. This trend extended to the field of neuromuscular disease care as well [48, 62, 91, 93, 94]. The American Academy of Neurology's "Telehealth Position Statement" endorsed telemedicine, citing benefits such as improved access, reduced costs, and enhanced comfort, aligning with findings in this review [95].

Our findings further highlighted important considerations for the successful implementation of telemedicine. Firstly, it is essential to recognise that not all geographic locations are equally suited for telehealth. Remote areas with insufficient internet or cell phone coverage, as well as low-income households with a lack of digital technologies, may encounter difficulties in participating in telemedicine interventions [96]. Secondly, careful selection of the target population is vital, as the attitude and willingness of users significantly impact technology uptake [34, 36, 52, 69, 78]. The acceptance and efficacy of telemedicine interventions are inherently intertwined with diverse cultural attitudes towards healthcare and technology.

Therefore, understanding cultural factors is critical to discern how these variables may influence the successful integration of telehealth programs across diverse patient populations. A systematic analysis of cultural competence would provide valuable insights to refine and customise approaches, meeting the distinctive needs of diverse communities. Such considerations not only enhance the inclusivity of telemedicine but also contribute to its overall effectiveness and acceptance among a broad spectrum of individuals.

As the results have shown, it is vital to adapt telemedicine to the specific and evolving needs of patients with NMDs. These needs not only vary from patient to patient but also change over time as the disease progresses [5]. Therefore, when designing telemedicine technology for patients with NMDs, emphasis should be placed on adaptability, flexibility and accessibility [49, 55, 62, 74, 83].

Designing telemedicine technology that caters for the unique challenges faced by patients with physical disabilities and cognitive impairments is crucial for fostering inclusive healthcare [49, 74]. User interfaces need to incorporate accessibility features, such as voice commands, large fonts, and intuitive navigation, to accommodate individuals with motor challenges or cognitive limitations. Additionally, instructions and information must be presented in various accessible formats, accommodating diverse learning needs [97].

Prioritising plain language and ensuring readability at lower literacy levels is essential. This approach not only makes instructions universally accessible but also empowers all patients to effectively participate in telemedicine interactions. By incorporating these considerations into the design, telemedicine can better serve the needs of patients with NMDs, promoting inclusivity and enhancing the overall effectiveness of healthcare delivery [97].

Health policies and regulatory frameworks play a significant role in influencing the development and adoption of telehealth practices. A nuanced understanding of these regulations, encompassing aspects such as licensure, reimbursement, and liability, is essential for gaining comprehensive insights into the complex landscape that shapes and governs telemedicine [96]. The intricate web of reimbursement policies directly influences the economic viability of telemedicine services, impacting both healthcare providers and patients. By navigating and understanding these policy and regulatory intricacies, stakeholders in the telemedicine ecosystem can strategically address and potentially overcome barriers, facilitating a more widespread and effective implementation of telehealth services [96].

This review reveals that telemedicine interventions for patients with NMDs exist but have yet to realise their full protentional. Firstly, the heavy focus on ALS care should be expanded to encompass all diagnostic groups within the NMD spectrum. Especially the high availability of mHealth applications, which could be seamlessly integrated into care plans. This integration has the potential to enhance continuity of care, simultaneously easing the burden on the healthcare system and reducing appointment frequency for patients [69].

The incorporation of long-term patient data through remote monitoring holds numerous advantages [98, 99]. Continuous data collection could offer enhanced insights into disease progression, thereby improving disease management. Given the degenerative nature of most NMDs, there is a speculation that long-term data could help in detecting early signs of deterioration, facilitating quicker adaption of treatments. Furthermore, detailed information about disease progression could contribute to health prognosis, empowering both patients and healthcare professionals to better plan and coordinate care [98, 99]. It is evident that the full benefits of telemonitoring remain undiscovered, making it an important and interesting area for future research. The exploration of these untapped potentials could significantly advance the effectiveness and scope of telemedicine in the context of NMDs.

Research and evaluation opportunities

The current telemedicine landscape yields promising results, particularly in its role in supporting rare disease research through the establishment of disease registries. These registries systematically collect patient data related to disease progression and treatment, forming the foundation for observational studies [100, 101]. These studies offer critical insights into the management and progression of rare disease, contributing to evidence-based clinical decisions and facilitating the recruitment of participants for clinical trial.

National and international patient registries are pivotal for studying prevalence and incidence, enhancing our understanding of rare diseases like neuromuscular disorders [100, 101]. The establishment of global patient registries becomes especially important for pooling data on rare diseases. International collaborations can help bridge the gap in research for understudied NMDs. By fostering collaboration and sharing data on a global scale, telemedicine-supported registries contribute significantly to advancing our understanding and management of rare diseases.

The results of our systematic review highlight a gap in the research on telemedicine for NMDs. Except for ALS, most NMDs are underrepresented in the current body of literature. Future research should include a more diverse range of diagnostic groups and undertake a comparative analysis of challenges and solutions. This would lead to a higher external validity and faster adaption of telemedicine solutions.

While teleconsultation and remote monitoring for NMDs are well described, other critical domains within telemedicine have received comparatively limited attention. These research gaps should be addressed in the future. Most importantly, implementation science has a critical role in the successful deployment of telemedicine interventions for NMDs. As seen in this systematic review studies, the focus needs to be on patients, caregivers, and health care practitioners, as well as the intervention itself.

It is noteworthy that there is underreporting of crucial aspects, such as the inner and outer settings, as well as the implementation process, in telemedicine interventions for NMDs. Additionally, there is need for research examining the impact of health policies and clinical guidelines on the adoption and implementation of telemedicine. The lack of implementation research has been described in the systematic review by Helleman et al., who analysed telemedicine for ALS patients [25]. Implementation science is needed to improve the efficiency and uptake of future telemedicine interventions for NMDs [102].

While our systematic review focused on highlighting the barriers and facilitators of telemedicine, we fully recognise the importance of addressing the validation challenges associated with digital health data. Future research and healthcare policies should emphasise the need for robust validation processes to ensure the reliability and clinical relevance of digital outcomes in telemedicine interventions.

Limitations

Despite an extensive search string, additional search terms might have yielded more results, especially considering synonyms for neuromuscular diseases. A more specific search for individual diagnostic groups would have been more inclusive, but the sheer number of NMDs made this unfeasible.

The literature databases used represent common sources of clinical evidence, but they may not comprehensively cover health policies, management, and health education related to NMDs, which might be found in other types of databases.

The absence of experimental study designs in the individual studies was notable, with most included studies being cross-sectional or observational. However, as this review aims to provide an overview of interventions, this description suffices.

The majority of included studies are from high-income countries, and the extent of telemedicine utilisation in low- and middle-income countries remains unclear. The variation in target population size and time horizon in NMD research reflects the complexity and rarity of these conditions, suggesting a need for longer follow-up times in future studies to better describe long-term outcomes.

Conclusion

This systematic review offers a comprehensive view of the telemedicine landscape in the context of NMDs. While domains like teleconsultation and telemonitoring have received extensive attention and reporting in the literature, other critical domains, such as decision support tools and informational support, are notably lacking in research and documentation. To further understand, develop and implement telemedicine solutions and to close existing gaps in NMD-specific healthcare provision, policies and guidelines are needed. By actively integrating telemedicine into existing healthcare plans and maintaining a commitment to ongoing updates and improvements, healthcare systems can optimise care delivery, enhance patient outcomes, and ensure that individuals with NMDs receive the high-quality care they deserve. In addition, more high-quality studies are needed to close research gaps concerning the implementation process of telemedicine and prove the respective efficiency and effectiveness in the long run.

Availability of data and materials

Due to the nature of the paper, no primary data was generated. All data analysed during this study are included in this published article and its supplementary information files.

Abbreviations

ALS:

Amyotrophic lateral sclerosis

ASS:

Anti-synthetase syndrome

BMD:

Becker muscular dystrophy

CFIR:

Consolidated framework for implementation research

CM:

Congenital myopathy

CMD:

Congenital muscular dystrophy

CMT:

Charcot-Marie-tooth disease

DM:

Dermatomyositis

DMD:

Duchenne muscular dystrophy

EDMD:

Emery-Dreifuss muscular dystrophy

FSHD:

Facioscapulohumeral muscular dystrophy

HSP:

Hereditary spastic paraparesis

ICD:

International classification of diseases

JDM:

Juvenile dermatomyositis

LEMS:

Lambert-Eaton-myasthenic-syndrome

LGMD:

Limb-Girdle muscular dystrophy

MyD:

Myotonic dystrophy

MD:

Muscular dystrophy

MDA:

Muscular dystrophy association

MFM:

Myofibrillar myopathies

MG:

Myasthenia gravis

MGSD:

Muscle glycogenosis

MP:

Myopathy

NM:

Necrotizing myositis

NMD:

Neuromuscular disease

OM:

Overlap myositis

PD:

Pompe disease

PM:

Polymyositis

PPS:

Post-Polio syndrome

RCT:

Randomized controlled trial

SBMA:

Spinal and bulbar muscular atrophy

SMA:

Spinal muscular atrophy

TTR-FAP:

Transthyretin familial amyloid polyneuropathy

References

  1. Mary P, Servais L, Vialle R. Neuromuscular diseases: diagnosis and management. Orthopaed Traumatol: Surg Res. 2018;104(1, Supplement):89-S95. Available from: https://www.sciencedirect.com/science/article/pii/S187705681730333X.

    Google Scholar 

  2. Aitken M, Mercer EJ, Mckemey A. Understanding neuromuscular disease care: current state and future prospects. IQVIA Institute; 2018.

  3. Bonne G, Rivier F, Hamroun D. The 2018 version of the gene table of monogenic neuromuscular disorders (nuclear genome). Neuromusc Disord: NMD 2017; 27(12). Available from: https://pubmed.ncbi.nlm.nih.gov/29961566/.

  4. Deenen JC, Horlings CG, Verschuuren JJ, Verbeek AL, van Engelen BG. The epidemiology of neuromuscular disorders: a comprehensive overview of the literature. J Neuromuscul Dis. 2015;2(1):73–85.

    Article  PubMed  Google Scholar 

  5. Feldman EL, Russell JW, Löscher WN, Grisold W, Meng S. Atlas of Neuromuscular diseases: a practical guideline. 3rd ed. Cham: Springer International Publishing; Imprint Springer; 2021. Springer eBook Collection.

    Book  Google Scholar 

  6. Craig BM, Hartman JD, Owens MA, Brown DS. Prevalence and losses in quality-adjusted life years of child health conditions: a burden of disease analysis. Matern Child Health J. 2016;20(4):862–9.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Birnkrant DJ, Bushby K, Bann CM, Alman BA, Apkon SD, Blackwell A, et al. Diagnosis and management of Duchenne muscular dystrophy, part 2: respiratory, cardiac, bone health, and orthopaedic management. Lancet Neurol. 2018;17(4):347–61. Available from: https://pubmed.ncbi.nlm.nih.gov/29395990/.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Birnkrant DJ, Bushby K, Bann CM, Apkon SD, Blackwell A, Brumbaugh D, et al. Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and neuromuscular, rehabilitation, endocrine, and gastrointestinal and nutritional management. Lancet Neurol. 2018;17(3):251–67. Available from: https://pubmed.ncbi.nlm.nih.gov/29395989/.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hulisz D. Amyotrophic lateral sclerosis: disease state overview. The American journal of managed care. 2018;24(15 Suppl):S320–6. Available from: https://pubmed.ncbi.nlm.nih.gov/30207670/.

    PubMed  Google Scholar 

  10. Khan F, Ng L, Amatya B, Brand C, Turner-Stokes L. Multidisciplinary care for Guillain-Barré syndrome. Europ J Phys Rehabil Med. 2011;47(4):607–12. Available from: https://pubmed.ncbi.nlm.nih.gov/21912364/.

    CAS  Google Scholar 

  11. Mercuri E, Sumner CJ, Muntoni F, Darras BT, Finkel RS. Spinal muscular atrophy. Nat Rev Dis Prim. 2022;8(1):52. Available from: https://pubmed.ncbi.nlm.nih.gov/35927425/.

    Article  PubMed  Google Scholar 

  12. Birnkrant DJ, Bushby K, Bann CM, Apkon SD, Blackwell A, Colvin MK, et al. Diagnosis and management of Duchenne muscular dystrophy, part 3: primary care, emergency management, psychosocial care, and transitions of care across the lifespan. Lancet Neurol. 2018;17(5):445–55.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Johnson NE. Myotonic muscular dystrophies. Continuum (Minneap Minn). 2019;25(6):1682–95.

    PubMed  Google Scholar 

  14. Brandt M, Johannsen L, Inhestern L, Bergelt C. Parents as informal caregivers of children and adolescents with spinal muscular atrophy: a systematic review of quantitative and qualitative data on the psychosocial situation, caregiver burden, and family needs. Orphanet J Rare Dis. 2022;17(1):274. Available from: https://pubmed.ncbi.nlm.nih.gov/35854387/.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Landfeldt E, Edström J, Buccella F, Kirschner J, Lochmüller H. Duchenne muscular dystrophy and caregiver burden: a systematic review. Dev Med Child Neurol. 2018;60(10):987–96.

    Article  PubMed  Google Scholar 

  16. Tramonti F, Bonfiglio L, Bongioanni P, Belviso C, Fanciullacci C, Rossi B, et al. Caregiver burden and family functioning in different neurological diseases. Psychol Health Med. 2019;24(1):27–34.

    Article  PubMed  Google Scholar 

  17. de Wit J, Bakker LA, van Groenestijn AC, van den Berg LH, SchrÖder CD, Visser-Meily JMA, et al. Caregiver burden in amyotrophic lateral sclerosis: a systematic review. Palliat Med. 2018;32(1):231–45. Available from: https://pubmed.ncbi.nlm.nih.gov/28671483/.

    Article  PubMed  Google Scholar 

  18. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17(2):e52. Available from: https://pubmed.ncbi.nlm.nih.gov/25803266/.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H. Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. J Med Internet Res. 2017;19(5):e172.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Whitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Med Internet Res. 2016;18(5):e97.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bashshur RL, Shannon GW, Smith BR, Alverson DC, Antoniotti N, Barsan WG, et al. The empirical foundations of telemedicine interventions for chronic disease management. Telemed J e-health. 2014;20(9):769–800.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Eze ND, Mateus C, Cravo Oliveira Hashiguchi T. Telemedicine in the OECD: An umbrella review of clinical and cost-effectiveness, patient experience and implementation. PLos One. 2020;15(8):0237585.

    Article  Google Scholar 

  23. León-Salas B, González-Hernández Y, Infante-Ventura D, de Armas-Castellano A, García-García J, García-Hernández M, et al. Telemedicine for neurological diseases: a systematic review and meta-analysis. Eur J Neurol. 2023;30(1):241–54.

    Article  PubMed  Google Scholar 

  24. Wang H, Yuan X, Wang J, Sun C, Wang G. Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Prim Health Care Res Dev. 2021;22:e48.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Helleman J, Kruitwagen ET, van den Berg LH, Visser-Meily JMA, Beelen A. The current use of telehealth in ALS care and the barriers to and facilitators of implementation: a systematic review. Amyotroph Lateral Scler Frontotemporal Degener. 2020;21(3–4):167–82.

    Article  CAS  PubMed  Google Scholar 

  26. Bashshur R, Shannon G, Krupinski E, Grigsby J. The taxonomy of telemedicine. Telemed J E-health 2011;17(6). Available from: https://pubmed.ncbi.nlm.nih.gov/21718114/.

  27. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Muscular Dystrophy Association. Find a neuromuscular disease | Muscular Dystrophy Association; 2018 [cited 2022 Mar 28.526Z]. Available from: https://www.mda.org/disease/list.

  29. ICD-11 for Mortality and Morbidity Statistics; 2022 [cited 2022 Mar 31.331Z]. Available from: https://icd.who.int/browse11/l-m/en.

  30. Damschroder LJ, Aron DC, Keith RE, Kirsh, SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009;4. Available from: https://pubmed.ncbi.nlm.nih.gov/19664226/.

  31. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

    Article  PubMed  Google Scholar 

  32. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Critical Appraisal Tools | JBI; 2022 [cited 2022 Sep 8]. Available from: https://jbi.global/critical-appraisal-tools.

  34. Ando H, Ashcroft-Kelso H, Halhead R, Chakrabarti B, Young CA, Cousins R, et al. Experience of telehealth in people with motor neurone disease using noninvasive ventilation. Disabil Rehabil Assist Technol. 2021;16(5):490–6.

    Article  PubMed  Google Scholar 

  35. Ando H, Ashcroft-Kelso H, Halhead R, Young CA, Chakrabarti B, Levene P, et al. Incorporating self-reported questions for telemonitoring to optimize care of patients with MND on noninvasive ventilation (MND OptNIVent). Amyotroph Lateral Scler Frontotemporal Degener. 2019;20(5–6):336–47.

    Article  PubMed  Google Scholar 

  36. Hobson E, Baird W, Bradburn M, Cooper C, Mawson S, Quinn A, et al. Process evaluation and exploration of telehealth in motor neuron disease in a UK specialist centre. BMJ Open. 2019;9(10):e028526. Available from: https://pubmed.ncbi.nlm.nih.gov/31640994/.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hobson EV, Baird WO, Bradburn M, Cooper C, Mawson S, Quinn A, et al. Using telehealth in motor neuron disease to increase access to specialist multidisciplinary care: a UK-based pilot and feasibility study. BMJ Open. 2019;9(10):e028525. Available from: https://bmjopen.bmj.com/content/9/10/e028525.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Martínez O, Amayra I, López-Paz JF, Lázaro E, Caballero P, García I, et al. Effects of teleassistance on the quality of life of people with rare neuromuscular diseases according to their degree of disability. Front Psychol. 2021;12:637413.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Martínez O, Jometón A, Pérez M, Lázaro E, Amayra I, López-Paz JF et al. Effectiveness of teleassistance at improving quality of life in people with neuromuscular diseases. Spanish J Psychol 2014;17. Available from: https://pubmed.ncbi.nlm.nih.gov/26055393/.

  40. Sobierajska-Rek A, Mański Ł, Jabłońska-Brudło J, Śledzińska K, Ucińska A, Wierzba J. Establishing a telerehabilitation program for patients with Duchenne muscular dystrophy in the COVID-19 pandemic. Wien Klin Wochenschr. 2020;133(7–8):344–50. Available from: https://link.springer.com/article/10.1007/s00508-020-01786-8.

    PubMed  PubMed Central  Google Scholar 

  41. Wasilewska E, Sobierajska-Rek A, Małgorzewicz S, Soliński M, Szalewska D, Jassem E. Is it possible to have home e-monitoring of pulmonary function in our patients with duchenne muscular dystrophy in the covid-19 pandemic?-A one center pilot study. Int J Environ Res Public Health 2021;18(17).

  42. Abdulla S, Vielhaber S, Machts J, Heinze H-J, Dengler R, Petri S. Information needs and information-seeking preferences of ALS patients and their carers. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15(7–8):505–12.

    Article  PubMed  Google Scholar 

  43. Ambrosini A, Calabrese D, Avato FM, Catania F, Cavaletti G, Pera MC, et al. The Italian neuromuscular registry: a coordinated platform where patient organizations and clinicians collaborate for data collection and multiple usage. Orphanet J Rare Dis. 2018;13(1):176.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Anil R, Kumar A, Alaparthi S, Sharma A, Nye JL, Roy B, et al. Exploring outcomes and characteristics of myasthenia gravis: Rationale, aims and design of registry - The EXPLORE-MG registry. J Neurol Sci. 2020;414:116830.

    Article  CAS  PubMed  Google Scholar 

  45. Benjamin JC, Groner J, Walton J, Noritz G, Gascon GM, Mahan JD. A blended curriculum to improve resident physical exam skills for patients with neuromuscular disability. MedEdPORTAL. 2019;15:10792.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Berry JD, Paganoni S, Carlson K, Burke K, Weber H, Staples P, et al. Design and results of a smartphone-based digital phenotyping study to quantify ALS progression. Ann Clin Transl Neurol. 2019;6(5):873–81.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bettio C, Salsi V, Orsini M, Calanchi E, Magnotta L, Gagliardelli L, et al. The Italian National Registry for FSHD: an enhanced data integration and an analytics framework towards smart health care and precision medicine for a rare disease. Orphanet J Rare Dis. 2021;16(1):470.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Capozzo R, Zoccolella S, Musio M, Barone R, Accogli M, Logroscino G. Telemedicine is a useful tool to deliver care to patients with Amyotrophic Lateral Sclerosis during COVID-19 pandemic: results from Southern Italy. Amyotroph Lateral Scler Frontotemporal Degener. 2020;21(7–8):542–8.

    Article  CAS  PubMed  Google Scholar 

  49. Cesareo A, Nido SA, Biffi E, Gandossini S, D’Angelo MG, Aliverti A. A Wearable device for breathing frequency monitoring: a pilot study on patients with muscular dystrophy. Sensors (Basel) 2020;20(18).

  50. Climans SA, Piechowicz C, Koopman WJ, Venance SL. Survey of Canadian myotonic dystrophy patients’ access to computer technology. Can J Neurol Sci. 2017;44(5):567–71.

    Article  PubMed  Google Scholar 

  51. Geronimo A, Simmons Z. Evaluation of remote pulmonary function testing in motor neuron disease. Amyotroph Lateral Scler Frontotemporal Degener. 2019;20(5–6):348–55.

    Article  PubMed  Google Scholar 

  52. Geronimo A, Wright C, Morris A, Walsh S, Snyder B, Simmons Z. Incorporation of telehealth into a multidisciplinary ALS Clinic: feasibility and acceptability. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18(7–8):555–61.

    Article  PubMed  Google Scholar 

  53. Grigull L, Lechner W, Petri S, Kollewe K, Dengler R, Mehmecke S, et al. Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. BMC Med Inform Decis Mak. 2016;16:31.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hooshmand S, Cho J, Singh S, Govindarajan R. Satisfaction of telehealth in patients with established neuromuscular disorders. Front Neurol. 2021;12:667813.

    Article  PubMed  PubMed Central  Google Scholar 

  55. James N, Power E, Hogden A, Vucic S. Patients’ perspectives of multidisciplinary home-based e-Health service delivery for motor neurone disease. Disabil Rehabil Assist Technol. 2019;14(7):737–43.

    Article  PubMed  Google Scholar 

  56. Malek AM, Stickler DE, Antao VC, Horton DK. The National ALS Registry: a recruitment tool for research. Muscle Nerve. 2014;50(5):830–4.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Pulley MT, Brittain R, Hodges W, Frazier C, Miller L, Matyjasik-Liggett M, et al. Multidisciplinary amyotrophic lateral sclerosis telemedicine care: The store and forward method. Muscle Nerve. 2019;59(1):34–9.

    Article  PubMed  Google Scholar 

  58. Roy AJ, van den Bergh P, van Damme P, Doggen K, van Casteren V. Early stages of building a rare disease registry, methods and 2010 data from the Belgian Neuromuscular Disease Registry (BNMDR). Acta Neurol Belg. 2015;115(2):97–104.

    Article  PubMed  Google Scholar 

  59. Sobierajska-Rek A, Mański Ł, Jabłońska-Brudło J, Śledzińska K, Wasilewska E, Szalewska D. Respiratory telerehabilitation of boys and young men with Duchenne muscular dystrophy in the COVID-19 Pandemic. Int J Environ Res Public Health 2021;18(12).

  60. Spiliopoulos KC, Kasdaglis N, Veltsista D, Lykouras D, Lagadinou M, Chroni E. Teleneurology in a center for neuromuscular diseases during the COVID-19 pandemic. Acta Neurol Belg. 2022;122(3):721–4.

    Article  PubMed  Google Scholar 

  61. Tawfik EA, van Alfen N, Cartwright MS, Inkpen P, Kerasnoudis A, Lieba-Samal D, et al. Virtual neuromuscular ultrasound courses during COVID-19 pandemic: Leveraging technology to enhance learning opportunities. Muscle Nerve. 2021;65(1):29–33.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Vasta R, Moglia C, D’Ovidio F, Di Pede F, de Mattei F, Cabras S, et al. Telemedicine for patients with amyotrophic lateral sclerosis during COVID-19 pandemic: an Italian ALS referral center experience. Amyotroph Lateral Scler Frontotemporal Degener. 2021;22(3–4):308–11.

    Article  CAS  PubMed  Google Scholar 

  63. White MK, Leffler M, Rychlec K, Jones C, McSherry C, Walker L, et al. Adapting traditional content validation methods to fit purpose: an example with a novel video assessment and training materials in Duchenne muscular dystrophy (DMD). Qual Life Res. 2019;28(11):2979–88.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Menon D, Alnajjar S, Barnett C, Vijayan J, Katzberg H, Fathi D, et al. Telephone consultation for myasthenia gravis care during the COVID-19 pandemic: Assessment of a novel virtual myasthenia gravis index. Muscle Nerve. 2021;63(6):831–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Portaro S, Calabrò RS, Bramanti P, Silvestri G, Torrisi M, Conti-Nibali V, et al. Telemedicine for Facio-Scapulo-Humeral Muscular Dystrophy: A multidisciplinary approach to improve quality of life and reduce hospitalization rate? Disabil Health J. 2017;11(2):306–9.

    Article  PubMed  Google Scholar 

  66. Trucco F, Pedemonte M, Racca F, Falsaperla R, Romano C, Wenzel A, et al. Tele-monitoring in paediatric and young home-ventilated neuromuscular patients: A multicentre case-control trial. J Telemed Telecare. 2019;25(7):414–24.

    Article  PubMed  Google Scholar 

  67. Contesse MG, Sapp ATL, Apkon SD, Lowes LP, Dalle Pazze L, Leffler MG. Reliability and construct validity of the Duchenne Video Assessment. Muscle Nerve. 2021;64(2):180–9.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Garuti G, Bagatti S, Verucchi E, Massobrio M, Spagnolatti L, Vezzani G, et al. Pulmonary rehabilitation at home guided by telemonitoring and access to healthcare facilities for respiratory complications in patients with neuromuscular disease. Eur J Phys Rehabil Med. 2013;49(1):51–7. Available from: https://pubmed.ncbi.nlm.nih.gov/22820817/.

    CAS  PubMed  Google Scholar 

  69. Helleman J, van Eenennaam R, Kruitwagen ET, Kruithof WJ, Slappendel MJ, van den Berg LH, et al. Telehealth as part of specialized ALS care: feasibility and user experiences with “ALS home-monitoring and coaching.” Amyotroph Lateral Scler Frontotemporal Degener. 2020;21(3–4):183–92.

    Article  CAS  PubMed  Google Scholar 

  70. Kamei T, Yamamoto Y, Kanamori T, Nakayama Y, Porter SE. Detection of early-stage changes in people with chronic diseases: A telehome monitoring-based telenursing feasibility study. Nurs Health Sci. 2018;20(3):313–22.

    Article  PubMed  Google Scholar 

  71. Levi BH, Simmons Z, Hanna C, Brothers A, Lehman E, Farace E, et al. Advance care planning for patients with amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18(5–6):388–96.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Longinetti E, Regodón Wallin A, Samuelsson K, Press R, Zachau A, Ronnevi L-O, et al. The Swedish motor neuron disease quality registry. Amyotroph Lateral Scler Frontotemporal Degener. 2018;19(7–8):528–37.

    Article  PubMed  Google Scholar 

  73. de Marchi F, Sarnelli MF, Serioli M, de Marchi I, Zani E, Bottone N, et al. Telehealth approach for amyotrophic lateral sclerosis patients: the experience during COVID-19 pandemic. Acta Neurol Scand. 2021;143(5):489–96.

    Article  PubMed  Google Scholar 

  74. McErlane F, Davies EH, Ollivier C, Mayhew A, Anyanwu O, Harbottle V, et al. Wearable technologies for children with chronic illnesses: an exploratory approach. Ther Innov Regul Sci. 2021;55(4):799–806.

    Article  PubMed  Google Scholar 

  75. Naveen R, Sundaram TG, Agarwal V, Gupta L. Teleconsultation experience with the idiopathic inflammatory myopathies: a prospective observational cohort study during the COVID-19 pandemic. Rheumatol Int. 2020;41(1):67–76.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Newton J, Jayaprakash K, Glasmacher SA, McEleney A, Bethell A, Fraser E, et al. Excellent reliability of the ALSFRS-R administered via videoconferencing: a study of people with motor neuron disease in Scotland. J Neurol Sci. 2020;416:116991.

    Article  PubMed  Google Scholar 

  77. Paganoni S, van de Rijn M, Drake K, Burke K, Doyle M, Ellrodt AS, et al. Adjusted cost analysis of video televisits for the care of people with amyotrophic lateral sclerosis. Muscle Nerve. 2019;60(2):147–54.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Roman A, Baylor C, Johnson L, Barton M. Expanding Availability of Speech-Generating Device Evaluation and Treatment to People With Amyotrophic Lateral Sclerosis (pALS) Through Telepractice: Perspectives of pALS and Communication Partners. Am J Speech Lang Pathol. 2021;30(5):2098–114.

    Article  PubMed  Google Scholar 

  79. Selkirk SM, Washington MO, McClellan F, Flynn B, Seton JM, Strozewski R. Delivering tertiary centre specialty care to ALS patients via telemedicine: a retrospective cohort analysis. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18(5–6):324–32.

    Article  PubMed  Google Scholar 

  80. van Eijk RPA, Bakers JNE, Bunte TM, de Fockert AJ, Eijkemans MJC, van den Berg LH. Accelerometry for remote monitoring of physical activity in amyotrophic lateral sclerosis: a longitudinal cohort study. J Neurol. 2019;266(10):2387–95.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Vitacca M, Comini L, Assoni G, Fiorenza D, Gilè S, Bernocchi P, et al. Tele-assistance in patients with amyotrophic lateral sclerosis: long term activity and costs. Disabil Rehabil Assist Technol. 2012;7(6):494–500.

    Article  PubMed  Google Scholar 

  82. Alexanderson H, Munters LA, Dastmalchi M, Loell I, Heimbürger M, Opava CH, et al. Resistive home exercise in patients with recent-onset polymyositis and dermatomyositis—a randomized controlled single-blinded study with a 2-year followup. J Rheumatol. 2014;41(6):1124–32.

    Article  PubMed  Google Scholar 

  83. Astley C, Sieczkowska SM, Marques IG, Ihara BP, Lindoso L, Lavorato SSM, et al. Home-based exercise program for adolescents with juvenile dermatomyositis quarantined during COVID-19 pandemic: a mixed methods study. Pediatr Rheumatol Online J. 2021;19(1):159.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Bankolé L-C, Millet GY, Temesi J, Bachasson D, Ravelojaona M, Wuyam B, et al. Safety and efficacy of a 6-month home-based exercise program in patients with facioscapulohumeral muscular dystrophy: A randomized controlled trial. Medicine (Baltimore). 2016;95(31): e4497.

    Article  PubMed  Google Scholar 

  85. Christodoulou G, Gennings C, Hupf J, Factor-Litvak P, Murphy J, Goetz RR, et al. Telephone based cognitive-behavioral screening for frontotemporal changes in patients with amyotrophic lateral sclerosis (ALS). Amyotroph Lateral Scler Frontotemporal Degener. 2016;17(7–8):482–8.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Wills AM, Garry J, Hubbard J, Mezoian T, Breen CT, Ortiz-Miller C et al. Nutritional counseling with or without mobile health technology: a randomized open-label standard-of-care-controlled trial in ALS. BMC Neurol 2019; 19.

  87. de Wit J, Beelen A, Drossaert CHC, Kolijn R, van den Berg LH, SchrÖder CD, et al. Blended psychosocial support for partners of patients with ALS and PMA: results of a randomized controlled trial. Amyotroph Lateral Scler Frontotemporal Degener. 2020;21(5–6):344–54.

    Article  PubMed  Google Scholar 

  88. Palumbo A, Ielpo N, Calabrese B, Corchiola D, Garropoli R, Gramigna V et al. SIMpLE: A mobile cloud-based system for health monitoring of people with ALS. Sensors (Basel) 2021; 21(21).

  89. Ricci G, Baldanzi S, Seidita F, Proietti C, Carlini F, Peviani S, et al. A mobile app for patients with Pompe disease and its possible clinical applications. Neuromusc Disord: NMD. 2018;28(6):471–5.

    Article  PubMed  Google Scholar 

  90. Scalvini S, Bernocchi P, Zanelli E, Comini L, Vitacca M. Maugeri centre for telehealth and telecare: a real-life integrated experience in chronic patients. J Telemed Telecare. 2018;24(7):500–7.

    Article  PubMed  Google Scholar 

  91. El-Hassar L, Amara A, Sanson B, Lacatus O, Amir Belhouchet A, Kroneman M, et al. Telemedicine in neuromuscular diseases during covid-19 pandemic: ERN-NMD European survey. J Neuromuscul Dis. 2022;Preprint(Preprint):1–12.

    Google Scholar 

  92. Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health. 2020;20(1):1193.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Bombaci A, Abbadessa G, Trojsi F, Leocani L, Bonavita S, Lavorgna L. Telemedicine for management of patients with amyotrophic lateral sclerosis through COVID-19 tail. Neurol Sci. 2021;42(1):9–13.

    Article  PubMed  Google Scholar 

  94. Giannotta M, Petrelli C, Pini A. Telemedicine applied to neuromuscular disorders: focus on the COVID-19 pandemic era. Acta myologica: myopathies and cardiomyopathies. 2022;41(1):30–6. Available from: https://pubmed.ncbi.nlm.nih.gov/35465343/.

    CAS  Google Scholar 

  95. Hatcher-Martin JM, Busis NA, Cohen BH, Wolf RA, Jones EC, Anderson ER, et al. American academy of neurology telehealth position statement. Neurology. 2021;97(7):334–9. Available from: https://n.neurology.org/content/97/7/334.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Kruse CS, Williams K, Bohls J, Shamsi W. Telemedicine and health policy: a systematic review. Health Policy Technol. 2021;10(1):209–29.

    Article  Google Scholar 

  97. Phuong J, Ordóñez P, Cao J, Moukheiber M, Moukheiber L, Caspi A, et al. Telehealth and digital health innovations: a mixed landscape of access. PLOS Digit Health. 2023;2(12):e0000401.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Beswick E, Fawcett T, Hassan Z, et al. A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease. J Neurol. 2022;269:6254–68.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Serrano LP, Maita KC, Avila FR, Torres-Guzman RA, Garcia JP, Eldaly AS, Haider CR, Felton CL, Paulson MR, Maniaci MJ, Forte AJ. Benefits and challenges of remote patient monitoring as perceived by health care practitioners: a systematic review. Permanente J. 2023;27(4):100.

    Article  Google Scholar 

  100. Lacaze P, Millis N, Fookes M, Zurynski Y, Jaffe A, Bellgard M, Winship I, McNeil J, Bittles AH. Rare disease registries: a call to action. Intern Med J. 2017;47(9):1075–9.

    Article  PubMed  Google Scholar 

  101. Kölker S, Gleich F, Mütze U, Opladen T. Rare disease registries are key to evidence-based personalized medicine: highlighting the European experience. Front Endocrinol. 2022;4(13):832063.

    Article  Google Scholar 

  102. Peters DH, Adam T, Alonge O, Akua Agyepong I, Tran N. Implementation research: what it is and how to do it. BMJ 2013;347. Available from: https://www.bmj.com/content/347/bmj.f6753.full

Download references

Acknowledgements

Not applicable.

Funding

The authors did not receive any funding for this paper.

Author information

Authors and Affiliations

  1. Chair of Healthcare Management and Health Services Research, University of Bayreuth, Bayreuth, Germany

    Deniz Senyel, Katja Senn & Klaus Nagels

  2. Chair of Digital Health and Innovation, Department of Public Health, School of Psychology and Public Health, La Trobe University, Melbourne, Australia

    Deniz Senyel & James Boyd

Authors
  1. Deniz Senyel
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Katja Senn
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. James Boyd
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Klaus Nagels
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

DS and KS prepared the study protocol as well as performed the literature search and study selection. DS conducted the data extraction and analysis. The report was written by DS with contributions by KS. JB and KN supervised the complete process. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Deniz Senyel.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare 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 http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senyel, D., Senn, K., Boyd, J. et al. A systematic review of telemedicine for neuromuscular diseases: components and determinants of practice. BMC Digit Health 2, 17 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s44247-024-00078-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s44247-024-00078-9

Keywords

BMC Digital Health

ISSN: 2731-684X

Contact us