Abstract
Background:
Although rare bone diseases (RBDs) present mobility challenges, there is little consolidated evidence on evaluated mobility measurement tools or how mobility impairments impact daily activities and quality of life (QoL).
Objectives and design:
This systematic literature review investigated: (1) the impacts of mobility impairment on daily activities/QoL; (2) the suitability/comprehensiveness of tools measuring mobility.
Data sources and methods:
MEDLINE/Embase databases (January 19, 2022) and Google (October 19, 2022) were searched for articles published between 2011 and 2022; conference proceedings from 2020 to 2021 were hand-searched. Included articles reported on how mobility impairments impact daily activities/QoL, or the use of tools for measuring mobility, in RBDs. A narrative analysis using descriptive statistics was conducted. Studies were assessed for risk of bias using The Alberta Heritage Foundation for Medical Research Quality Assessment Criteria and National Institute of Health Quality Assessment Tool for Case Series Studies.
Results:
Inclusion criteria were met by 113 articles, investigating 39 RBDs (sample sizes: N = 1–959). Mobility impairments, commonly joint function/gait disturbances, negatively impacted daily activities (n = 47 cohorts; frequently walking (27/47; 57.4%)) and QoL (n = 36 cohorts; commonly pain (30/36; 83.3%; Objective 1). There were 34 functional assessments, 22 questionnaires, and 5 technologies described. Only nine functional assessments/questionnaires were reported to have good validity/reliability/responsiveness for an RBD (not reported for technologies); none comprehensively captured daily living/QoL impacts of mobility impairment. The quality of studies was moderate, though many were case studies/series, which are at inherent risk of bias.
Conclusion:
Few tools comprehensively captured mobility impairments and associated impacts on daily activities/QoL. Consistent reporting of tools’ validity/reliability/responsiveness would support clinicians in selecting methods for use across RBD populations. Used remotely, wearables could support understanding of real-world mobility challenges. Since searches were conducted, additional technologies (e.g., remote gait analysis) have been tested in RBDs, although validation is required.
Protocol PROSPERO registration:
CRD42022311513. Sponsored by Ipsen.
Plain language summary
Rare bone diseases are a group of conditions that affect bones, cartilage, and/or muscles. People with a rare bone disease often have difficulty moving, which may stop them being able to do their usual daily activities (e.g. household chores). As a result, people may have lower quality of life. Yet there is not much research on the impact of movement difficulties across rare bone diseases. Doctors often use questionnaires or clinical assessments to measure movement. Wearable technologies worn at home might help Doctors test movement remotely and reveal day-to-day impacts on daily activities and quality of life. It is important to test and validate these technologies for people with rare bone diseases that will use them. We conducted a literature review to explore how movement difficulties affect the lives of people with rare bone diseases. The second aim was to see how different methods can measure movement across these diseases. We included literature published between 2011–2022 that provided relevant information. The literature showed that difficulty moving negatively impacts people’s lives. Many experience pain and challenges with walking/personal care. There were 22 questionnaires, 34 clinical assessments, and 5 technologies used to measure movement. Some methods were not well-suited for use in particular rare bone diseases. For example, some measurements did not correspond with impacts that individuals described. Only 9 questionnaires/clinical assessments were validated. No technologies had been validated or used outside of the clinic. Researchers could do further tests to see if these tools are suitable for measuring movement in people with rare bone diseases or they could re-use existing remote technologies. These technologies would need to be validated first. The information from remote technologies used at home could help Doctors decide how best to care for people with rare bone diseases.
Background
Rare bone diseases (RBDs) are a group of conditions affecting cartilage, bones, soft tissue, and/or dentin, encompassing skeletal dysplasias and metabolic bone diseases (e.g., fibrodysplasia ossificans progressiva (FOP), osteogenesis imperfecta (OI), and X-linked hypophosphatemia (XLH)).1,2 The list of RBDs continues to evolve; categorizations in the Nosology of Genetic Skeletal Disorders were updated in 2023, increasing the number of distinct conditions from 461 to 771.3,4 Most individuals with RBDs have complex physical health challenges,5,6 including substantial restriction of movement and cumulative disability.6–9 This can negatively impact an individual’s ability to perform daily activities as well as their quality of life (QoL), including emotional and social well-being.6–9 For example, an FOP burden of illness survey demonstrated that loss of joint function, as assessed by Patient-Reported Mobility Assessment (PRMA) score, had a significant, detrimental impact on QoL for individuals with FOP, resulting in decreasing EQ-5D-5L index scores. 10 Loss of joint function also increased the proportion of individuals using assistive devices and adaptations to the home to assist with daily activities. 10
Whilst existing research characterizes the functional, social, and physiological burden of a very limited number of RBDs,8–10 there is a lack of consolidated evidence on the impact of mobility impairment on daily activities and QoL across different RBDs, and the related unmet needs of affected individuals. A variety of functional assessment tools and questionnaires are used in clinical practice and trials to evaluate the functional mobility of individuals with RBDs (e.g., the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and 6-min walk test (6MWT) used as secondary endpoints in XLH clinical trials). 11 Some commonly used functional assessments correlate weakly with existing patient-reported mobility change assessments (i.e., completed without the input of a healthcare professional).12,13 For example, individuals with hypophosphatasia (HPP) self-report greater mobility limitations than those captured by the 6MWT and 10-m walk test (10MWT). 13 Additionally, tools often measure aspects of functional mobility, but have limited use in capturing the wider impacts on daily activities and QoL that individuals experience.14–18 A more comprehensive tool, capable of capturing multiple aspects of QoL in addition to functional mobility, may help alleviate the burden of multiple assessments,19,20 including the need to frequently travel to a clinic for separate assessments, which may be particularly challenging for those with mobility limitations. Furthermore, many tools can only provide a snapshot of mobility challenges at a single point in time. Technological methods used remotely (e.g., wearables), have the potential to measure individuals’ mobility over time in a home environment, in a manner that is more reflective of their experiences in a real-world setting.
With such a large number of RBDs, developing and validating disease-specific mobility measurement tools for every condition would be resource-intensive and costly. Clinicians caring for individuals with a particular RBD may benefit from repurposing tools used for related RBDs (e.g., with similar skeletal phenotypes), or using general mobility measurement tools that have been validated in different RBDs. However, we are not aware of any previous study that has synthesized published tools that have been used to measure mobility in RBDs, and their reported validity, reliability, or responsiveness. Such research could inform the development of more appropriate and comprehensive methods of measuring the impact of mobility impairment in RBDs.
A systematic literature review (SLR) was therefore conducted with two objectives: Objective 1 was to investigate the impacts of mobility impairment on daily activities and QoL for individuals with RBDs; Objective 2 aimed to identify existing tools or those in development for measuring mobility in RBDs, including their reported validity, reliability, or responsiveness in an RBD population. The capability of tools identified in Objective 2 to comprehensively capture the aspects of daily activities and QoL impacted by mobility impairment, as identified in Objective 1, was also synthesized. This is the first SLR, to our knowledge, that collates evidence on the impact of mobility impairment across RBDs and the tools that can measure functional mobility and wider impacts on daily activities and QoL.
Methods
This SLR was conducted in accordance with a pre-specified protocol, which was registered to PROSPERO (CRD42022311513) and written in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 21
Search strategy
Electronic database searches were conducted on January 19, 2022, in MEDLINE and Embase, to identify relevant articles. A single search strategy was used within electronic databases, combining search terms for RBDs with terms for patient perspectives, QoL, and mobility impairment (Objective 1), and/or mobility assessment (Objective 2). The full search strategies used for each online database are provided in Supplemental Additional File 1.
Congress proceedings from the previous 2 years (January 1, 2020–December 31, 2021; Supplemental Additional File 2), and the bibliographies of relevant SLRs and (network) meta-analyses (NMAs) identified during the search of electronic databases were hand-searched to identify any additional relevant articles. Hand searches of Google were conducted on October 19, 2022, to identify any further publications not included in the databases (Supplemental Additional File 3). Database search terms were adapted for searching congress proceedings and Google; full search strategies are provided in Supplemental Additional Files 2 and 3.
Article selection
Identified articles were screened for relevance against pre-specified eligibility criteria (Table 1). Included articles were published between January 1, 2011 and January 18, 2022 (or January 1, 2020 and December 31, 2021 for congress proceedings). Articles that described individuals with RBDs listed in the 2019 Nosology and Classification of Genetic Skeletal Disorders, 3 were included if they reported on relevant outcomes. For Objective 1, relevant outcomes included the impacts of a physiological mobility impairment on daily activities and/or QoL. Mobility impairment was defined as a physiological limitation of a person’s coordination or movement (encompassing gait disturbance, reduced range of joint motion, impaired limb movement, or impaired fine motor movement) or unspecified reduced mobility as reported by the study investigators. The mobility impairment could have occurred as a direct (e.g., ossification of connective tissue restricting range of motion in FOP) or indirect (e.g., bone weakening in XLH causing a gait-disturbing fracture) result of the RBD.22,23 This broad definition was used in order to capture the wide variety of different mobility impairments people with RBDs may experience; the definition was limited to physiological limitations because wider impacts on daily activities (e.g., self-care) would be captured as impacts of mobility impairment as part of Objective 1. For Objective 2, relevant outcomes were the application or development of tool(s) capable of measuring mobility in RBDs (Objective 2). Tools including one or more domains that measure mobility were eligible.
Eligibility criteria used for assessing articles for inclusion in the SLR.
Included articles were published between January 1, 2011 and January 18, 2022 (or January 1, 2020 and December 31, 2021 for congress proceedings).
Or synonyms of the disease.
A mobility impairment in this SLR was defined as a physiological mobility impairment that has caused an impact on daily activities or QoL.
Except if the primary outcomes assessed were mobility-related daily activities.
The assessment of mobility by the mobility measurement tool and not just “physical function,” must have been specified within the article.
With/without assessment of the validity, reliability, or responsiveness of the tool.
SLRs and NMAs were considered relevant at the title/abstract review stage and hand-searched for relevant primary studies, but were excluded during the full-text review stage unless they reported primary research.
See Supplemental Additional File 2 for a list of the congress proceedings that were searched.
Congress proceedings published prior to 2019 identified through Google searches without a corresponding published peer-reviewed article were included.
NA, not applicable; NMA, network meta-analyses; QoL, quality of life; RBD, rare bone disease; SLR, systematic literature review.
On account of the large volume of literature identified, the eligibility criteria were tightened after the full-text review stage (final criteria shown in Table 1), to prioritize the articles of most relevance to the objectives: when assessing articles against Objective 1, if the impact of an RBD on a mobility-related daily activity was specified without mention of a specific physiological mobility impairment, the article was excluded (so included articles had to directly link a specific mobility impairment to an impact on daily activities or QoL), unless the primary study outcomes were mobility-related daily activities. Other excluded articles were those where the QoL impact mentioned was “pain” in relation to a mobility impairment, without the inclusion of any other QoL or daily activity impact. These articles were excluded because it was often not possible to ascertain if the pain was a result of the mobility impairment, or causal of reduced mobility. Additionally, pain is a more established impact of mobility impairment in RBDs and so was determined not to be an outcome of focus.24,25
Each abstract was reviewed against the study eligibility criteria by one reviewer (A.S., E.W., J.J.), in line with the Cochrane Handbook for Systematic Reviews and Interventions. 26 Full-text articles were reviewed by two independent reviewers (A.S., J.J.), and where necessary, a third reviewer (E.W.) was consulted to reach a final decision on whether the study eligibility criteria were met.
Data extraction and quality assessment
Data extraction was performed in line with guidelines from the University of York Centre for Reviews and Dissemination. 27 Data from included articles, such as study characteristics, patient characteristics, and outcomes related to Objectives 1 and/or 2, were extracted by a single individual (A.S., E.W., J.J.) into a pre-specified grid (Supplemental Additional File 4) and independently verified by another reviewer (A.S., J.J.). Articles reporting on the same study were linked, and the quality of included articles was assessed by one reviewer and verified by a second (A.S., J.J.). Where necessary, a third reviewer (E.W.) was consulted to reach a final decision regarding the extracted information and quality assessments (QAs). The Alberta Heritage Foundation for Medical Research (AHFMR) Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields was used to assess quality of all study types identified in Objectives 1 and 2, apart from case studies/case series (due to the number of questions in this checklist that are not applicable to this study design). 28 For case studies/series, the National Institutes of Health (NIH) Quality Assessment Tool for Case Series Studies was used to assess the quality of evidence. 29
Data analysis
A narrative analysis was conducted whereby outcomes data were analyzed in three groups: at the study level for all included articles, for patient cohorts, and at the patient level. Where data were analyzed by article (covering all study types), descriptive statistics were calculated as the proportion or number of articles reporting each variable, with a denominator of the total number of articles (primary and secondary articles reporting on the same study population were included separately). Patient cohort data were analyzed across all study types and were presented as descriptive statistics calculated as the proportion or number of patient cohorts reporting each variable, with a denominator of the total number of patient cohorts across all articles. For patient-level data (from either cohort, cross-sectional, or case studies), descriptive statistics were calculated as the proportion or number of individuals for whom each variable was reported, with a denominator of the total number of individuals across all articles for whom patient-level data were reported.
For tools used to measure mobility, the validity, reliability, or responsiveness were extracted as reported in the article(s) and then categorized as “good,” “uncertain or conflicting evidence,” or “poor” (e.g., high reliability would be categorized as “good”), based on available data across articles which described that tool. If two or more articles conflicted in the reported validity, reliability, or responsiveness of the tool, or if the assessment reported in an article was unclear, the tool was categorized under “uncertain or conflicting evidence.” The psychometric properties of the tools were not critically assessed as part of this SLR.
Results
Characteristics of included articles
A total of 113 articles from database searches, conference proceedings, Google searches, and bibliography searches were prioritized for extraction (Figure 1). The articles reported primarily on cross-sectional studies (n = 35; 31.0%), case studies (n = 32; 28.3%), or cohort studies (n = 30; 26.5%). Other study types included eight randomized controlled trials (7.1%), six single-arm interventional studies (5.3%), and two non-RCTs (1.8%). The sample size of included studies varied considerably from 1 to 959 individuals with an RBD (Supplemental Additional File 5). Across all articles, there were 84 unique individuals with an RBD for whom patient-level data were reported.
PRISMA flowchart of identified articles.
Patient characteristics
In total, 87.6% (99/113) of the included articles reported the age of the patient population, at varying timepoints. For patient cohorts (N = 106), the distribution of mean and median age “at the time of study” is shown in Supplemental Additional File 6; the most reported age group was 31–40 years. Age of individuals for whom patient-level data were reported are presented in Supplemental Additional File 6; the patient age “at the time of study” ranged from 17 to 63 years.
Sex was reported for 85 patient cohorts, including 6011 people, consisting of 60.8% female and 39.2% male individuals. Among the 83 individuals for whom sex was reported across articles, 48.2% (40/83) were female, and 51.8% (43/83) were male. Information regarding the race/ethnicity of included individuals can be found in Supplemental Additional File 7.
Disease characteristics
Included articles reported on 39 different RBDs; XLH was the most studied RBD (n = 24 articles), followed by OI (n = 15 articles), HPP, and FOP (n = 13 articles each; Supplemental Additional File 5). Disease severity was reported in 18 articles, typically based on different disease subtypes. The impact of distinct RBDs on mobility could not be compared with respect to outcomes for individuals with different disease severities.
Mobility impairment was reported for 78/84 of the individuals for whom patient-level data were available. Mobility impairments reported included issues related to joint function, gait disturbance, limb function, and fine motor movement, as well as general mobility limitations (Figure 2).
Proportion of individuals with RBDs with each mobility impairment. (a) Patient cohortsa. (b) Across articlesb.
Impacts of mobility impairment on the lives of individuals with RBDs
The impact of the identified mobility impairments described above (Figure 2) on daily activities was explored in 47/113 articles, all of which described that mobility impairment negatively impacted daily activities. Similarly, patient-level data (from either cohort, cross-sectional, or case studies) on the impact of mobility impairment on daily activities were provided for 73 of the 78 individuals for whom mobility impairments were reported; all experienced negative effects. The activities most reported to be impacted by mobility impairment were walking and personal care (Table 2). Qualitative evidence was used more frequently than quantitative evidence for documenting an impact on daily activities, and was most commonly patient-reported (n = 14 cohorts; n = 25 individuals), as opposed to physician (n = 1 cohort; n = 2 individuals), family/caregiver-(n = 1 cohort; n = 3 individuals), patient and family/caregiver-(n = 2 cohorts; n = 0 individuals), or physician and patient-(n = 1 cohort; n = 0 individuals) reported. Quantitative evidence was provided for 23 patient cohorts and 13 individuals. Figure 3 provides an overview of the quantitative tests used to evaluate the impacts of mobility impairment on daily activities, of which the 6MWT was the most frequently used (to evaluate walking across all articles). Several mobility aids were reported to have been used to support individuals with their impacted daily activities, including crutches, wheelchairs, walkers, canes, prostheses, braces, splints, orthotics, and walking aids (the type of walking aid was not specified in any of the articles; Supplemental Additional File 8).
Daily activities reported to be negatively impacted in individuals with RBDs due to mobility impairment.
RBDs, rare bone diseases.
Tests used to quantify negative impact of mobility impairment on daily activities. (a) Across articles. (b) Individuals.
The impact of the identified mobility impairments (Figure 2) on QoL was explored in 36/113 articles and for 42 of the 78 individuals for whom mobility impairments were reported. Nearly all articles (36/37) and patient-level data (41/42 individuals) described that mobility impairment negatively impacted QoL. Only one case study described an individual with no QoL detriment due to mobility impairment. 30 The QoL factors most reported to be impacted were pain and fatigue (Table 3). Qualitative evidence was used more frequently than quantitative evidence to document an impact of mobility impairment on QoL and was typically patient-reported (n = 12 cohorts; n = 29 individuals) as opposed to physician-(n = 0 cohorts; n = 0 individuals), family/caregiver-(n = 0 cohorts; n = 1 individuals), or patient and family/caregiver-(n = 1 cohort; n = 0 individuals) reported. Quantitative evidence was provided for 13 patient cohorts and 1 individual with an RBD. Quantitative tests used to evaluate the impact of mobility impairment in patient cohorts are summarized in Figure 4. For one individual, the impact of mobility impairment on QoL was quantified by the 12-item Short Form Survey (SF-12); all other patient-level data included qualitative evidence only.
QoL areas reported to be negatively impacted in individuals with RBDs due to mobility impairment.
QoL, quality of life; RBD, rare bone diseases.
Number of articles using tests to report a negative impact of mobility impairment on QoL.a
Tools in use or development for RBDs, which are capable of measuring mobility
Overall, 61 tools capable of measuring mobility (through one or more domain) were identified, including 22 questionnaires, 34 functional assessments, and 5 technological methods (Figure 5). These tools were mostly reported to have been used in a clinical setting (as opposed to in an individual’s home; 68.9% (42/61); Figure 5).
Identified tools capable of measuring mobility through one or more domains, by type and application setting.
A mixture of disease-specific (27.3% (6/22)) and general (72.7% (16/22)) questionnaires were identified (Table 4), most commonly the WOMAC and EQ-5D (Supplemental Additional File 9). All but one of the disease-specific questionnaires were originally developed to assess mobility in specific RBDs, and the other (the WOMAC) had been repurposed for use in individuals with XLH and nail patella syndrome. Most questionnaires were patient-reported (86.4% (19/22); Supplemental Additional File 9).
Identified questionnaires that measured mobility.
Articles reported a mobility-specific domain for these questionnaires, which could include domains/questions assessing functional impairment related to activities of daily living or disability. For articles without an footnote (a), the questionnaire may assess additional aspects that were not captured in the included articles.
APPT, Adolescent Pediatric Pain Tool; BPI-SF, Brief Pain Inventory Short Form; CAJIS, Cumulative Analogue Joint Involvement Scale; CHAQ, Childhood Health Assessment Questionnaire; FAQ, Functional Assessment Questionnaire; FIM, functional independence measure; FOP, Fibrodysplasia Ossificans Progressiva; FOP-PFQ, FOP-Physical Function Questionnaire; HAQ, Health Assessment Questionnaire; HAQ-DI, Health Assessment Questionnaire-Disability Index; HIPS, Hypophosphatasia Impact Patient Survey; HOST, Hypophosphatasia Outcomes Study Telephone Interview; HPP, Hypophosphatasia; HRQoL, Health-Related Quality of Life; ICF, International Classification of Functioning, Disability and Health; LEFS, Lower Extremity Functional Scale; MPS, mucopolysaccharidoses; MPS-HAQ, Mucopolysaccharidoses Health Assessment Questionnaire; NPS, Nail Patella Syndrome; NR, not reported; OI, osteogenesis imperfecta; PEDI, Pediatric Evaluation of Disability Inventory; PedsQL, Pediatric Quality of Life; PODCI, Pediatric Outcomes Data Collection Instrument; POSNA, Pediatric Orthopedic Society of North America; PRMA, Patient-Reported Mobility Assessment; PROMIS, Patient-Reported Outcome Measure Information System; RBD, rare bone diseases; SF-36, 36-item Short Form Survey; VAS, Visual Analogue Scale; WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index; XLH, X-linked hypophosphatemia.
Of the 10 questionnaires for which validity, reliability, or responsiveness were described, 7 (31.8%) were categorized as “good” (Table 4). These were the Brief Pain Inventory-Short Form, functional independence measure (FIM), FOP-physical function questionnaire (FOP-PFQ), international classification of functioning, disability, and health (ICF) self-report questionnaire, PRMA, patient-reported outcome measure information system, and WOMAC. However, these questionnaires had only been evaluated in 6 out of the 39 studied RBDs. No information on validity, reliability, or responsiveness was reported for the other 12 questionnaires.
Of the 34 identified functional assessments, the 6MWT and Cumulative Analogue Joint Involvement Scale (CAJIS) were most used (Supplemental Additional File 10). Most functional assessments of mobility were for general use across diseases (79.4% (27/34)); only three were disease-specific (8.8% (3/34)), and the specificity of four functional assessments was not reported (11.8% (4/34); Table 5). One of the disease-specific functional assessments, the modified performance-oriented mobility assessment-gait (mPOMA-G), had been modified to be relevant to HPP. The remaining two functional assessments were developed for specific RBDs (CAJIS for FOP and clubfoot assessment protocol (CAP) for idiopathic clubfoot). The operator of five assessments was specified in the included articles; three of these assessments had been operated by a physiotherapist (gross motor function measure-88, mPOMA-G, and pediatric gait, arms, legs, and spine (pGALS)) and two were operated by a physician (CAP and CAJIS). A modified CAJIS was operated by patients in one study. 46 The operator was not specified for other functional assessments (Table 5).
Identified functional assessment tools that measured mobility.
10MWT, 10-m Walk Test; 1MWT, 1-min Walk Test; 2MWT, 2-min Walk Test; 3MSC, 3-min Stair Climb Test; 6MWT, 6-min Walk Test; 9MWT, 9-min Walk Test; ABS, Antley-Bixler syndrome; AHA, assisting hand assessment; BAMF, Brief Assessment of Motor Function; BOT-2, Bruininks-Oseretsky Test of Motor Proficiency, Second Edition; CAJIS, Cumulative Analogue Joint Involvement Scale; CAP, Clubfoot Assessment Protocol; CODAS, Cerebral, Ocular, Dental, Auricular, Skeletal Anomalies; CPE, clinical problem evaluation; CSHS, cutaneous skeletal hypophosphatemia syndrome; DDST, Denver Developmental Screening Test; FAQ, Functional Assessment Questionnaire; FDI, functional disability inventory; FMS, Functional Mobility Scale; FOP-PFQ, Fibrodysplasia Ossificans Progressiva—Physical Function Questionnaire; GDI, gait deviation index; GMFCS, Gross Motor Function Classification System; GMFM, Gross Motor Function Measure; GMQ, Gross Motor Quotient; HME, Hereditary Multiple Exostoses; HOS, Holt-Oram syndrome; HPP, hypophosphatasia; MED, multiple epiphyseal dysplasia; mPOMA-G, Modified Performance-Oriented Mobility Assessment-Gait; MPS, mucopolysaccharidosis; NR, not reported; OI, osteogenesis imperfecta; PDMS-2, Peabody Developmental Motor Scales, Second Edition; PEDI, Pediatric Evaluation of Disability Inventory; pGALS, pediatric gait, arms, legs, and spine; RBD, rare bone diseases; SPPB, short performance physical battery; T-GAP, Thumb Grasp and Pinch Assessment; TUG, timed up and go; VAS, Visual Analogue Scale; XLH, X-linked hypophosphatemia.
Only two functional assessments were reported to have “good” validity/reliability: mPOMA-G was shown to be reliable and valid in children with HPP, and pGALS demonstrated good inter- and intra-observer consistency in individuals with mucopolysaccharidoses (MPS) in the included studies (Table 5). In individuals with OI and HPP, the 6MWT and 10MWT results were reported to correlate poorly with characteristics of individuals with RBDs. There was no information on validity, reliability, or responsiveness reported for 27/34 of the functional assessments identified.
Five technological methods for assessing mobility were described across identified articles, used in a clinical setting only. These included three measuring gait (3D movement analysis assessed via VICON, objective gait scores (gait deviation index and foot deviation index), and GAITRite™ Electronic Walkway), and two assessing gross motor function (handheld dynamometry using MicroFET®2 and activity monitoring using Actigraph®; Table 6). These technological methods had only been used in five RBDs (OI, XLH, HPP, MPS type 1, and idiopathic clubfoot), despite all being tools intended for general use and applicable in conditions other than RBDs (e.g., use of Actigraph in sarcopenia). 112 MicroFET2 and Actigraph were operated by physiotherapists, whereas the operator was not specified for any other technological method in the included articles. None of the included articles reported on the validity, reliability, or responsiveness of these technological methods in RBDs (Table 6).
Identified technological methods that assessed mobility.
No information on the validity, reliability, or responsiveness of technological methods to assess mobility in rare diseases was identified.
FDI, foot deviation index; GDI, gait deviation index; HPP, hypophosphatasia; MPS, mucopolysaccharidoses; NR, not reported; OI, osteogenesis imperfecta; RBD, rare bone diseases; XLH, X-linked hypophosphatemia.
The ability of the identified tools to assess the impacts of mobility impairment on daily activities and QoL identified in Objective 1 is presented in Table 7. None of the nine tools for assessing mobility that were reported as having “good” validity, reliability, or responsiveness in RBDs comprehensively assessed the daily activity/QoL impacts of mobility impairment identified in Objective 1.
Tools containing at least one mobility domain capable of assessing daily activities and QoL factors impacted by mobility impairment.
Indicates tools that are considered to have “good” validity, reliability, or responsiveness for the assessment of mobility.
Tools that evaluate running have been categorized as suitable to assess the ability to participate in sporting activities.
10MWT, 10-m walk test; 1MWT, 1-min walk test; 2MWT, 2-min walk test; 3MSC, 3-min stair climb test; 6MWT, 6-min walk test; 9MWT, 9-min walk test; AHA, assisting hand assessment; APPT, Adolescent Pediatric Pain Tool; BAMF, Brief Assessment of Motor Function; BOT-2, Bruininks-Oseretsky test of motor proficiency, Version 2; BPI-SF, Brief Pain Inventory Short Form; CAJIS, Cumulative Analogue Joint Involvement Scale; CAP, clubfoot assessment protocol; CHAQ, Childhood Health Assessment Questionnaire; CMD, Charnley Modification Of The Merle D’aubigné Postel Grading System; CPE, clinical problem evaluation; DDST, Denver developmental screening test; FAQ, functional assessment questionnaire; FDI, functional disability inventory; FIM, functional independence measure; FMS, Functional Mobility Scale; FOP-PFQ, Fibrodysplasia Ossificans Progressiva-Physical Function Questionnaire; GDI, Gait Deviation Index; GMFCS, Gross Motor Function Classification System; GMFM, gross motor function measure; HAQ, health assessment questionnaire; HIPS, hypophosphatasia impact patient survey; HOST, hypophosphatasia outcomes study telephone interview; ICF, international classification of functioning, disability, and health; LEFS, Lower Extremity Functional Scale; mPOMA-G, Modified Performance Oriented Mobility Assessment-Gait; MPS-HAQ, mucopolysaccharidoses health assessment questionnaire; PDMS-2, Peabody Developmental Motor Scales, Second Edition; PEDI, pediatric evaluation of disability inventory; PedsQL, pediatric quality of life; pGALS, pediatric gait, arms, legs, and spine; PODCI, pediatric outcomes data collection instrument; POSNA-PODCI, pediatric orthopedic society of North America pediatric outcomes data collection instrument; PRMA, patient-reported mobility assessment; PROMIS, patient-reported outcome measure information system; QoL, quality of life; SF-36, 36-item Short Form Survey; SPPB, short performance physical battery; T-GAP, Thumb grasp and pinch assessment; TUG, timed up and go; WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index.
Quality assessment
The results of the AHFMR QA checklist are shown in Supplemental Additional File 11, and results of the NIH Quality Assessment Tool for Case Series Studies are shown in Supplemental Additional File 12. Results of the AHFMR checklist indicate that the quality of evidence across studies was moderate; individual QA scores for each study are provided in Supplemental Additional File 5. However, most studies only partially defined outcome measures; for example, studies using standard tools to measure outcomes such as mobility provided limited detail on the tools’ use. Most notably, survey-based studies often provided incomplete descriptions of questionnaire/interview content and questionnaire response options. The quality of studies was also low with regard to controlling for confounding factors. There were 32/103 unique studies which were case studies/series; these study types are associated with inherent bias and therefore lower quality than other study designs, so were assessed separately (Supplemental Additional File 12). The quality of case studies/series was mixed. Although most studies clearly described the study objective and study population, there was varied reporting on the comparability of cases and appropriateness of outcomes; no studies clearly described any statistical methods used.
Discussion
This is the first SLR, to our knowledge, that collates evidence on the wide-ranging impacts of mobility impairments in RBDs, and tools being used or in development for RBDs to measure mobility.
In this SLR, mobility impairment was shown to negatively impact daily activities of individuals with RBDs, commonly including walking and personal care.8,9,12,115–117 Whilst this finding is consistent with prior understanding of the impact of mobility impairment, it was author-reported, and may not have been proven in clinical practice. Other reported impacts of mobility impairment included participation in school/employment and social/sporting activities and individuals’ self-esteem and mental well-being.10,12,25,115,118 These impacts of mobility impairment are consistent with wider aspects included in the ICF Framework on Mobility. 119
As the relationship between pain and mobility impairment is complex, pain alone was not considered sufficient as a mobility impairment for Objective 1 of this SLR; articles had to distinguish a physiological mobility impairment from pain, and pain was then extracted as an impact on QoL. Pain associated with mobility impairment was reported for nearly all individuals, highlighting the well-characterized association between pain and mobility limitations,120–122 for example, joint restriction causing pain or pain avoidance resulting in limited mobility.
The extent of the impact of mobility impairment on daily activities and QoL was not commonly reported. Although some articles noted that individuals required mobility aids to assist with daily activities, no articles described the duration or frequency of participants’ mobility aid use. In addition, identified articles did not distinguish between the use of manual and motorized mobility aids, such as wheelchairs. This is an important distinction to allow healthcare resource use and unmet needs to be properly assessed, for example, unequal access to different mobility aids (e.g., due to the higher cost of motorized vs manual wheelchairs).
Synthesis of the impacts of mobility impairment was somewhat limited by variability in how mobility was assessed, and outcomes were reported. Identified articles largely reported daily activities and QoL outcomes qualitatively, and in instances where quantified, a large variety of tests were used, limiting comparison between studies. The sample size of included studies also ranged greatly, from 1 to 959 individuals, with larger sample sizes used in registry and survey-based studies. Though expected due to the rarity of these conditions, the very small sample size of some studies limits the conclusions that may be drawn, and the applicability of findings to the wider population of individuals with that RBD. The inclusion of case studies/series did, however, provide valuable patient-level information often not reported in large cohort studies, though utility was limited due to inconsistent reporting of outcomes between studies. Additionally, there was heterogeneity between how individuals with RBDs were reported to experience the impacts of mobility impairments, even among those with the same RBD. This is reflective of the wide range of disease severities and symptoms experienced by individuals with RBDs.123,124
The large number of identified tools (N = 61) used in RBDs for measuring mobility through one or more domains is likely reflective of the heterogeneous nature of different RBDs.4,14,15 Yet, only 9 of the 61 identified tools had been assessed through validation studies and were reported to have “good” validity, reliability, or responsiveness, and none of the technological methods had been evaluated through this lens in RBDs. Additionally, variability in methodologies used to assess reliability, validity, or responsiveness limited comparability between different functional assessments and between questionnaires. Since the time that this SLR was conducted, multiple relevant studies reporting on the same tools used to measure mobility in RBDs (e.g., 6MWT, TUG test, SPPB, HAQ-DI, VAS, WeeFIM) have been published.125–131 Of note, only one of the identified articles reported that the tool used (6MWT) was a valid and reliable indicator of physical function in the population being studied (HPP). 130 In addition to the above, the use of a separate technological method was reported by Oder et al., 132 which involved 3D movement analysis for assessment of upper limb movement (similar to the 3D movement analysis used to measure gait found in this SLR)81,113 in individuals with OI. However, consistent with the findings from this SLR, the paper did not comment on the validity, reliability, or responsiveness of this technology. 132 A recently published SLR (2025) examined the use of fully instrumented gait analysis (FGA) in individuals with RBDs; 23 of the 24 identified studies specified the motion analysis system used, which was most commonly the VICON (n = 10) followed by the Cleveland motion analysis protocol (n = 6; not identified in this SLR). 133 Though the 2025 SLR concluded that FGA may improve understanding of gait alterations in people with RBDs, the heterogeneity between studies (e.g., in test subject factors or interpretation by clinicians) and lack of reporting of accuracy/reliability of analysis systems limited the generalizability of results and conclusions that could be drawn. 133 The recent literature therefore substantiates the need identified in our SLR for high-quality studies that consistently evaluate the validity, reliability, and responsiveness of tools for measuring mobility in RBDs. This would inform selection of tool(s) to repurpose for different RBDs; for example, in a condition with the same etiology as the RBD that a tool has been previously validated.
The current SLR highlighted a range of factors that could be considered when selecting a tool for measuring mobility, including whether use of a general versus disease-specific tool is most appropriate. General tools are likely to have been used more widely than disease-specific tools and may therefore present the opportunity to modify a well-validated tool, for use in a specific RBD or population. For example, the POMA-G, a clinical gait assessment tool for adults, has already been modified to increase its sensitivity to HPP-related impairments in children, evidencing how tools may be adapted for use in a particular RBD. 60 However, given the heterogeneous nature of RBDs, disease-specific tools may more appropriately account for different etiologies, disease severities, and mobility symptoms unique to each RBD, than a general tool.4,14,15,134
The feasibility of using certain data collection methods in individuals with different symptoms or at different disease stages should also be considered when selecting a tool. For example, limited ambulatory status may impede individuals from participating in functional assessments, and restricted upper limb mobility may prevent someone from completing self-reported questionnaires. The status of children or people with cognitive impairments may also limit individuals’ ability to complete questionnaires or use digital technologies. People with mobility impairments may also experience difficulties using public transport or traveling as a passenger (e.g., due to difficulty transferring oneself while sitting), or driving a car, and therefore struggle to attend clinical sites for assessment.8,119,135 Therefore, mobility measurement tools that could be operated remotely by an individual with an RBD and/or caregiver, as opposed to a physician, may help to reduce the individual and healthcare system burden. Many of the functional assessments and questionnaires identified in this SLR were only able to capture individuals’ mobility at one timepoint (e.g., 6MWT); consequently, the results of these assessments rarely correlate with an individual’s physical characteristics.12,13 In particular, the remote use of a simple, reproducible technological method would allow longitudinal, real-world data to be captured, and therefore would provide a more realistic picture of the difficulties an individual faces over time. Of the tools identified in this SLR that could be used in a remote setting (questionnaires and functional assessments only), none were reported to have “good” validity, reliability, or responsiveness; no technological methods had been used remotely. The lack of routine use of technologies to measure mobility in RBDs may be a result of advancements in digital capabilities being more recent, or the cost and expertise required to develop, acquire, and utilize technological methods.136,137 Of note, since this SLR was conducted, a study by Fink et al. 15 has been published, examining built-in smartphone sensors (accelerometer and gyroscope) in a free-living environment to detect changes in gait patterns in individuals with RBDs. This readily available methodology may facilitate remote mobility assessment; however, further research into the generalizability of these findings across a wider range of RBDs and the impact of factors such as walking surface and carrying position of the smartphone was recommended by the authors. 15 Remote technological methods used to measure mobility in other conditions could also be explored to understand their appropriateness for assessing mobility impairment beyond gait in RBDs.138,139 For example, the SV95C assessment is a qualified digital endpoint captured by a wearable device for use in place of standard walking tests in Duchenne Muscular Dystrophy. 140 Additionally, the Mobilise-D study has validated algorithms for wrist-based gait detection (using wearable devices) in several disease areas associated with mobility limitations, such as Parkinson’s disease. 141 Such wearable devices may facilitate easy data collection over time, reduce rater bias associated with judgment-based assessments, and minimize patient burden.142,143 However, data quality should be considered due to potential inconsistencies in wearable data collection and sensor variability; clinical validation is required to regulate data quality and ensure the data are clinically relevant. 144 In addition, caution should be taken to ensure wearables are accessible to different populations, such as those with low digital literacy, with limited socio-economic resources, 144 or those who might have restricted ability to operate wearable devices (e.g., due to limited fine motor movement). Although a potentially complex and lengthy process, the validation of remote/wearable technologies for use in RBDs nonetheless presents an opportunity to better understand the extent and impacts of mobility impairment in individuals with RBDs.145,146
The functional limitations experienced by individuals with RBDs can be influenced both by mobility restrictions and other confounding factors such as the fatigue, reduced muscle strength, and pain seen in some RBDs.12,48,49,147 Many individuals experience fatigue, which can impact endurance and mobility; in a recently published survey of 2312 individuals with OI, 67% of individuals reported fatigue. 18 Muscle degeneration (e.g., in FOP) 148 and reduced muscle power have been observed in individuals with RBDs; for example, diminished ankle power in individuals with XLH correlating with gait quality. 149 Additionally, motor neurons control skeletal muscle activity and can therefore influence mobility, demonstrated by the progressive loss of movement in individuals with motor neuron disease. 150 Furthermore, changes to the central nervous system structure and function have been demonstrated in relation to pain in individuals with fibrous dysplasia/McCune-Albright syndrome, 151 and the associated pain may limit mobility.120–122 Skeletal health can also be compromised in RBDs due to abnormal muscle-bone cross-talk resulting in increased bone fragility and fracture risk. 152 Moreover, bone fragility in certain RBDs can result from reduced bone mass or defects in bone matrix composition or mineralization. 153 Such pathologies can lead to frailty characterized by weakness and slowness (e.g., slow walking speed),8,14,133,154 which may not be captured by a single tool used to measure mobility. Mobility impairments also have widespread impacts on daily living and QoL (as demonstrated by the findings of Objective 1), which were not comprehensively captured by any of the tools identified in Objective 2 (Table 7). It may not be possible for a single tool to capture all the identified aspects of mobility, confounding physiological factors, and impacted daily activities/QoL. Nevertheless, tool(s) validated for RBDs that capture multiple mobility limitations and impacted daily activities, and characterize the relationship between them, would be beneficial to limit the burden conferred by multiple assessments and further understanding of the extent to which mobility restrictions impact individuals with RBDs.
Strengths and limitations of the SLR methodology
Consistent with the Cochrane Handbook for Systematic Reviews and Interventions, 26 this review used a pre-specified protocol, with full and transparent reporting of the eligibility criteria and review stages. Electronic database searches were supplemented by searches of reference lists of relevant SLRs and NMAs, as well as congress proceedings and Google searches, ensuring additional evidence outside of the published literature would be captured. The risk of selection bias was minimized by two reviewers independently assessing each full-text article against the SLR’s eligibility criteria.
The searches were conducted in 2022, and an updated 2023 Nosology and Classification of Genetic Skeletal Disorders has since been published. 4 To ensure evidence of particular relevance published in the last 3 years on new tools capable of measuring mobility in RBDs has been captured, relevant articles were identified and incorporated in the Discussion of this publication.15,123–131
This SLR included a range of study designs, ensuring all available insights were captured including patient-level insights from case studies; this was particularly valuable in this SLR given the low patient numbers and limited evidence available for some RBDs. The quality of studies assessed via the AHFMR checklist (i.e., all studies minus case studies/series) was moderate. However, nearly a third of included studies were case studies/series, which required separate QA due to the inherent bias associated with this study design. Assessment using the NIH checklist indicated that the quality of case studies/series was moderate; however, this should be interpreted in the context of these studies being of lower quality than other study designs. A critical appraisal of the psychometric properties of the tools identified in this SLR was not independently conducted since most papers did not report the data that would be required; instead, the validity, reliability, or responsiveness of these tools was extracted if reported. Therefore, an independent critical appraisal, for example, using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist, would be beneficial to verify findings and evaluate tools for which no validation study was found.
Conclusion
Overall, this SLR collated evidence that mobility challenges in RBDs can severely limit individuals’ daily activities and negatively impact their QoL. Although a large variety of tools capable of measuring mobility in RBDs were identified, only 9/61 were reported to have good validity, reliability, or responsiveness, highlighting the need for further research to modify and validate these tools for individuals with specific RBDs. None of the tools reported to have good validity, reliability, or responsiveness was capable of comprehensively capturing the range of mobility impairments and impacted daily activities and QoL factors identified, or measuring mobility remotely. Adaptable and easy-to-use tools such as wearables, that can remotely and longitudinally measure mobility and/or related daily activities in a real-world setting, would more accurately capture the challenges associated with mobility impairment, whilst conferring minimal burden on the individual with the RBD. Technologies such as smartphone sensors have recently shown promise for remotely measuring mobility challenges like gait disturbance, but would require adaptation and validation for specific RBDs.
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Footnotes
Appendix
Acknowledgements
The authors thank Alice Slade, BSc, of Costello Medical, for medical writing support, which was sponsored by Ipsen in accordance with Good Publication Practice guidelines.
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References
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