key: cord-0326107-nhcw00yi authors: Mikolaizak, A. S.; Rochester, L.; Maetzler, W.; Sharrack, B.; Demeyer, H.; Mazza, C.; Caulfield, B.; Garcia-Aymerich, J.; Vereijken, B.; Arnera, V.; Miller, R.; Piraino, P.; Ammour, N.; Gordon, M. F.; Troosters, T.; Yarnall, A. J.; Alcock, L.; Gassner, H.; Winkler, J.; Klucken, J.; Schlenstedt, C.; Watz, H.; Kirsten, A.-M.; Vogiatzis, I.; Chynkiamis, N.; Hume, E.; Megaritis, D.; Nieuwboer, A.; Ginis, P.; Buckley, E.; Brittain, G.; Comi, G.; Leocani, L.; Helbostad, J. L.; Johnsen, L. G.; Taraldsen, K.; Blain, H.; Driss, V.; Frei, A.; Puhan, M. A.; Polhemus, A.; Bosch de Basea, M.; Gimeno, E. title: Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement - the Mobilise-D study protocol. date: 2022-05-26 journal: nan DOI: 10.1101/2022.05.25.22275598 sha: b87d176a71625a6d5a01434172234046ddc64f00 doc_id: 326107 cord_uid: nhcw00yi Background: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions Methods/Design: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinsons Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes 26 to identify, stratify, and monitor disability. This will support the development of widespread, cost-27 effective access to optimal clinical mobility management through personalised healthcare. Further, 28 Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies 29 and health technology assessment bodies to quantify the impact of disease-modifying interventions on 30 mobility. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; 1 Introduction 2 A key challenge for delivering healthcare in ageing societies is the optimal evaluation of mobility, which 3 can be broadly defined as the ability and performance of a person to move about in their environment 4 (1). Central aspects of mobility according to the World Health Organization (WHO) are changing one's 5 body position or location; or transferring from one place to another; carrying, moving or manipulating 6 objects; or walking, running or climbing (2). 7 Walking is the most common and functionally relevant aspect of mobility that is affected by age-8 associated processes and multiple chronic diseases. Walking is a complex activity that requires 9 interactions between the cardiovascular, pulmonary, and musculoskeletal systems as well as widespread 10 brain networks for effective performance (3). Deteriorations in these systems are reflected in walking 11 performance. As such, walking speed is increasingly denominated as the "6 th vital sign of health" (4) and 12 represents an appropriate mobility measure for multiple populations. This is mirrored by strong evidence 13 that mobility outcomes, such as walking speed and physical activity predict morbidity, mortality, falls, 14 cognitive impairment, and disability (5-10). It is, therefore, not surprising that people living with chronic 15 conditions often rate physical mobility -and specifically walking ability -as one of the most important 16 clinical outcome measures (11) (12) (13) (14) (15) . Currently, clinical research and practice mainly rely on patient-reported outcomes (self-18 reported/perceived walking capacity/ability), objective clinical assessments of walking capacity, and 19 subjective clinical assessments (clinician-led evaluation of walking capacity). All assessments are subject 20 to recall and response bias, are often burdensome in their execution (for both patients and assessors), 21 have ceiling or floor effects, Hawthorne effects, and/or other limitations (16) (17) (18) . Fluctuations due to 22 medication and disease exacerbation further reduce reliability and validity due to the intermittent nature 23 of assessment. Moreover, these assessments are regularly conducted in lab-based or clinical environment . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 /2022 doi: medRxiv preprint 1 which do not necessarily reflect the complex environmental determinants of functional mobility in daily 2 life, which severely hampers their ecological validity (16, (19) (20) (21) . Variations in the environment for these 3 measurements can include supervised and controlled settings to measure capacity vs. non-supervised and 4 uncontrolled environments (2). Despite the identified need for quantitative mobility assessment under 5 multiple conditions, inconsistent testing procedures and wide variations in baseline "norms" have 6 prevented the establishment of a widely adopted consensus on walking and mobility outcomes. 7 Consequently, there is no harmonised approach to the measurement and understanding of impaired, real-8 life mobility. 9 Rationale 10 There is a clinical need for mobility assessment to reflect real-world performance. In the last decade, The SPIRIT reporting guidelines have been followed within this manuscript (40), see Figure 1 . This 23 protocol represents version 1.5; March 2022. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 Figure ) ; screening and baseline assessment (T1) must be completed at the respective clinical site; T2 to 2 T5 should also be completed at site, but can be completed at the participants' home under exceptional 3 circumstances to minimise drop-out and loss to follow-up. Table 1 . The disease cohorts have been chosen because they represent different classes of mobility problems 19 relating to low physical activity, different gait disturbances, and frailty, each affecting large groups of 20 European citizens over substantial periods of time. The disease cohorts include a broad and . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. heterogeneous range of subject characteristics with varying chronic care needs, and represent different 2 trajectories of disability. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint  Able to walk 4 meters independently with or without walking aids  Anticipated availability for repeated study visits over 24 months  Ability to consent and comply with any study specific procedures.  Willingness to wear a wearable sensor for mobility monitoring  Able to read and write in first language in the respective country  Occurrence of any of the following within 3 months prior to informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD), active treatment for cancer or other malignant disease, uncontrolled congestive heart disease (NYHA class >3), acute psychosis or major psychiatric disorders or continued substance abuse PD  Aged 18 or over In the PD cohort, participants with mild to moderate disease state will be included (Hoehn & Yahr stage 3 1-3), which is of interest due to: 1) PD being a progressive disease, so the evaluation of a mobility endpoint 4 over the course of two years can provide insight into disease trajectories; 2) PD being a heterogeneous 5 disease, so the assessment of mobility endpoints in this disease can give relevant insight into the 6 sensitivity to change of this endpoint across a broad range of mobility patterns; 3) improving our 7 understanding of the association of mobility disability with falls and the influence of PD-specific symptoms 8 on mobility in particular; and 4) PD being associated with characteristic gait disorders that can validate 9 the Mobilise-D gait algorithm also for "challenging" gait deficits. In the COPD cohort, patients with different COPD severity will be included, from those with mild disease 11 and little burden of disease to those with very severe disease and significant burden and mobility impact. This represents the typical sample of patients recruited in clinical trials investigating pharmacologic and 13 non-pharmacologic interventions. Exacerbations are events that punctuate the disease progression in 14 COPD and their occurrence marks moments of acute deterioration and are often followed by only partial 15 recovery. In the COPD cohort particular attention will be given to these events and how they interact with 16 mobility in particular mobility decline. Mobilise-D will allow the study of mobility worsening over time in 17 relation to other relevant clinical outcomes. In addition, the collection of treatment data will allow 18 identification of patients undergoing pulmonary rehabilitation programs which offers insight in mobility 19 improvement trajectories. The MS population will be based on the revised McDonald's criteria, with mild to moderate disability., Patients will be recruited covering a wide spectrum of overall disability and varying severity of walking 22 impairment, with Expanded Disability Status Scale from 3 -fully ambulatory but with mild to moderate 23 disability in other functional systems -to 6.5 -indicating reduced walking distance and requiring walking 24 aids to walk 20 meters without resting. They will also have had disability progression over the previous . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 two years, to enrich the sample with patients who are likely to progress over the next two years. Patients 2 with a recent relapse (30 days before screening) will be excluded in order to have a reliable mobility 3 measure not impacted by the recovery from a relapse. The PFF cohort will be recruited either during the acute postoperative or subacute phase (max. 52 weeks) 5 following sustaining a hip fracture. All PFF participants are community dwelling at point of enrolment; 6 care home residents will not be included due to their increased burden of comorbidities likely to bias 7 mobility results. Enrolling acute PFF patients allows monitoring of mobility during the recovery phase over 8 the first 12 months providing unprecedented analysis of mobility trajectories over a long period of time 9 in a disease cohort where mobility impairment is the direct consequence of a major trauma. Enrolling 10 subacute PFF participants will allow a longer follow-up of mobility to focus on functional decline in a 11 population with a high prevalence of frailty and sarcopenia. The aim is to study a mixed population of 12 older community-dwelling adults to capture those who received for informal and formal care as well as 13 participants who were independent prior to sustaining a hip fracture. 14 Ethics and regulatory approval . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) 4 A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported 5 outcomes (PRO), observer-reported outcomes (ObsRO), clinician-reported outcomes (ClinRO), The cohort specific primary outcome measures are fall frequency during 24-month follow-up in the PD 19 and MS cohorts, occurrence of moderate to severe COPD exacerbations during the first 12-months follow-20 up in the COPD cohort and admission to a care home at six-months follow-up within the PFF cohort. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) Figure 1 ). Primary and secondary outcomes will be used as the constructs against which the DMO's predictive ability, 6 construct validity, estimates of the Minimum Important Difference, and ability to detect change over 24 7 months will be assessed. Figure 1 includes details of all assessments and outcome and their respective 8 validation use. The secondary outcome measures of special interest for each cohort are as follows: The Expanded Disability Status Scale (EDSS) (45) will be used to assess predictive capacity, construct 16 validity, detect change over 24 months as well as determine the MID amongst in the MS cohort. The EDSS 17 is an ordinal clinical rating scale to quantify disability in MS ranging from normal neurologic examination 18 (0) to death due to MS (10). Forced expiratory volume in one second (expressed as a percentage of predicted norm) (FEV-1) will be 20 used to assess predictive capacity, construct validity, detect change over 24 months as well as determine 21 the MID amongst for the COPD cohort. The spirometry test measuring FEV-1 is used to diagnose and stage 22 COPD. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 The Short Physical Performance Battery (SPPB) (46) will be used to assess predictive capacity, construct 2 validity, detect change over 24 months as well as determine the MID amongst the PFF cohort. The SPPB 3 assesses lower extremity function and mobility, consisting of a static balance task, a five-repetition chair-4 rise test, and a 4-meter walk test. Diaries 6 MS and PD participants will be asked to complete a falls diary and return these on a monthly basis 7 recording the relevant events (exacerbation, change of medication, falls) as and if they occur during each 8 month. COPD participants will be asked to complete exacerbation diaries including medication change, 9 hospitalisation, unplanned doctor's visits, and falls and return diaries at subsequent study visits. Other Environmental factors 12 Given the fact that the CVS will not be carried out under controlled laboratory settings, meteorological 13 variables (such as daily maximum temperature, precipitation, snowfall, and mean wind speed, among 14 others) will be collected to characterise the participants' weather condition exposure and assess the 15 effect of these environmental factors on the digital mobility outcomes. 17 Prior to the design of the CVS, patients' opinions regarding acceptability of wearable devices to measure 18 gait and physical activity were collected and explored. Patients were involved in the design of the study 19 protocol, including the consideration of COVID-19 protocols and risks, and in the review of patient-facing 20 documents to ensure readability and understanding. Throughout the CVS, a dedicated Patient and Public 21 Advisory Group (PPAG) will advise on key topics such as the identification of meaningful mobility 22 outcomes and patient needs and concerns regarding digital technology. Furthermore, the PPAG will co-. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. be undertaken by the Assessor Support Group (ASG). All committees will run for the study duration. 7 Study Management Group 8 The SMG will be responsible for the day-to-day management of the study. The group includes the key 9 individuals responsible for undertaking the study. The SMG will monitor the study progress, ensure 10 protocol adherence, and take appropriate action to safeguard participants and the quality of the study. Data issues and safety will be a priority for the SMG. This is a low risk observational study that does not 12 require a specific Data Monitoring Committee. Study Steering Committee The SSC will provide overall supervision of the study. The SSC has an independent chair and majority 15 independent representation including patient representatives. The SSC will monitor the study progress 16 and is responsible for making top-level decisions. The SSC carries the responsibility for deciding whether 17 the study should be stopped on grounds of safety or efficacy. Assessor Support Group The ASG will be responsible for the day-to-day running of the study and to provide general support to 20 study assessors. The ASG is managed by the study coordinators. Individual cohort leads are responsible 21 for providing cohort specific oversight and support. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. Sample size was calculated separately for each cohort according to its main disease-specific primary 4 outcome, as well as with 24-months changes in LLFDI score as global primary outcome. The estimated 5 sample size aiming for a sample that allowed the disease-specific objectives as well as the global objectives 6 to be met. 7 Cohort specific sample size calculations 8 Sample size calculation for each disease cohort showed that 600 participants will be required in each 9 cohort in order to achieve a statistically significant outcome of the respective hypothesis critical value 10 with a power of 90% and an alpha error of 0.05. Details of the hypotheses and the assumption 11 underpinning these calculations are summarised in Table 2 . . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 1 The proposed statistical method will be amended upon data completion and prior analysis should the 2 data distribution require a more appropriate methodology. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 The statistical analysis will follow a pre-specified step-wise procedure. We will elaborate a Statistical 3 Analysis Plan including the definition of analysis sets, details on data edition (including derivation of new 4 variables), handling of missing data and statistical analysis (including prioritisation of outcomes). Descriptive analysis of main characteristics of patients, including detailed description of COAs and DMOs, 6 will be done by number and percentage for categorical variables, mean and standard deviation for 7 continuous variables with normal distribution, and median and percentiles 25th-75th for continuous 8 variables with non-normal distribution. 9 We will test construct validity (convergent and known-groups). For convergent validity, we will test the 10 correlation (Pearson or Spearman, depending on variables distribution) between DMOs and related 11 constructs. A table of expected correlations for each DMO-construct combination will be built based on 12 existing literature. For known-group validity, we will use one-way ANOVA test and pairwise comparisons 13 of means between groups a priori expected to have differences in DMOs values. To test predictive capacity 14 of DMOs against the disease-specific and global outcomes we will estimate the association between 15 baseline DMO levels and each outcome using multivariable regression models (specific models depending 16 on outcome distribution) adjusting for confounders. Non-linear associations will be tested using 17 generalised additive models and appropriate transformation of variables will be done consequently. 18 Secondary analyses will include use of DMO changes over time as predictors, and adjusting for baseline 19 levels as sensitivity analysis. The ability of each DMOs to predict disease-specific and global outcomes will 20 be compared to that of traditional predictors and predictive scores. To quantify the ability to detect change of DMOs, we will calculate the change (between baseline and 22 different follow-up periods) and the standardised response mean (SRM) in (1) groups defined by the self-23 reported change in mobility, (2) groups defined according to having had a clinically relevant event (e.g., . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 an exacerbation in COPD, a fall in Parkinson) during follow-up, and (3) groups defined according to 2 clinically relevant changes in anchors. We will establish the MID by triangulation using anchor-and distribution-based estimates. 4 To describe the real-world walking behaviour we will extract walking behaviour metrics during the course 5 of the day and week and model them using advanced statistical analysis such as cumulative probability 6 function, detrended fluctuation analysis or entropy analysis. All study data will be transferred to a centrally managed data management platform. Error! Reference Figure . Where electronic data capture is 16 not feasible, paper case report forms are used (e.g., falls diaries). Data from paper forms is entered into 17 web forms on e-Science Central (e-SC), and the signed and dated paper forms will be scanned and 18 uploaded to the platform. The Mobilise-D e-SC platform has been implemented using Amazon Web Services located within the European Union. 20 Direct access will be granted to authorised representatives from the Sponsor, host institution, and the 21 regulatory authorities to permit study-related monitoring, audits, and inspections-in line with participant 22 consent. Clinical sites will be responsible for archiving all study documents for a period of time that is in 23 keeping with institutional or national guidelines that pertain to that site. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 against expectations for site and disease cohort. Where recruitment falls below a pre-defined percentage, 7 mitigation plans identified by sites will be actioned. All data quality monitoring will be performed centrally, 8 for example on data within study databases, uploaded documentation or by self-assessment checklists at 9 site. Any data queries identified through central monitoring will be sent to each site in form of a monthly 10 report. On-site visits will only be triggered if serious issues are identified at a site level. There is no non-11 serious adverse event reporting planned for this study. However, any Serious Adverse Events (SAEs) that 12 occur during the study visits must be reported to the sponsor and will be recorded on a central SAE log. Staff training 14 A comprehensive training package has been developed, including a core manual to support delivery of 15 the protocol robustly and reliably. In addition, four cohort-specific manuals are prepared in which the 16 cohort-specific assessments are addressed in the same manner as in the core manual, i.e., administration, 17 scoring, and troubleshooting of assessments are described. A third manual describes the exact procedures 18 of instrumented physical assessments as well as the 7-day mobility assessment. A series of eleven webinar 19 sessions has been designed and will be held on a weekly basis to cover all relevant aspects of the manual, 20 including additional parts concerning data entry platforms, data management procedures, and practical 21 training sessions. All study personnel assigned to the clinical work package are invited to participate in the 22 webinars, i.e., assessors, site coordinators, and site lead across the project. Several background topics are 23 addressed, for which expert members of the Mobilise-D consortium are invited as speakers. An additional . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 1 webinar per cohort will be held to address the specific issues related to the four different cohorts. To 2 ensure that all current and future assessors receive the same training and information all webinars are 3 recorded and stored online. Alongside the webinars, nine videos have been produced to describe 4 measurement procedures of several assessments. Covid mitigation 6 As Europe is being severely affected by the COVID-19 pandemic, there have been and will be major 7 impacts in the execution of the study. From a study procedure perspective, all activities have been 8 transferred to a virtual/remote working environment including all assessor training being held via 9 webinars and all sites monitored remotely. Two main impacts may be a slower recruitment rate and an 10 increased drop-out rate. The reporting and monitoring of enrolment of new patients will be tracked 11 through weekly meetings. Contingency plans for all cohorts are in place to expand recruitment sites (e.g., other clinics or hospitals within the geographical area) and recruitment pathways (e.g., via registries, 13 clinics, other forms of advertisement) within each country, and also to commence recruitment of cohorts 14 at sites primarily recruiting a different cohort. Dissemination policy 16 We will seek to publish all results from this clinical validation study in open-access, peer-reviewed 17 international journals, and disseminate findings at scientific and non-scientific conferences and events. Main results will also be shared on the project website and spread to various stakeholders. Authorship 19 eligibility will follow the International Committee of Medical Journal Editors. The algorithms and procedures which result from the consortium efforts will be made publicly available 21 at the end of the Mobilise-D project, enhancing future research and development activities. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) Digital methods that consistently and accurately measure the extent and nature of mobility are now 3 within reach. Once implemented, they will become a benchmark for new interventions, improving patient 4 outcomes in a manner that will extend well beyond the cohorts studied in Mobilise-D. The assessment battery including feasibility advice will be made available during the final year of the capacity of mobility and their actual performance in a real-world setting, which will provide meaningful, 5 robust, harmonized, and objective measures for regulatory evaluation of disease interventions using a 6 similar framework across different diseases. Our low-cost technology has the promise to pervade clinical practice, with every healthcare centre able 8 to offer inexpensive digital assessment systems, linked to a centralized system directly connected to 9 healthcare databases. Our implementation of Mobilise-D will support advances in both therapy and care 10 provision, with an opportunity for improved and more informed decision-making in assessing the efficacy 11 of new interventions. Mobilise-D will provide the first-ever systematic approach to mobility determination that is standardised, is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 2 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101/2022.05.25.22275598 doi: medRxiv preprint 1 Supporting information 2 S1 SPIRIT checklist 3 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 Mobility limitations in the 15 Medicare population: prevalence and sociodemographic and clinical correlates International Classification of Functioning, Disability and Health: ICF Functional Neuroanatomy for Posture and Gait Control White paper: "walking speed: the sixth vital sign Evaluation of physiological 24 workload assessment methods using heart rate and accelerometry for a smart wearable system European Respiratory Society statement on physical activity in COPD Physical activity is the strongest 4 predictor of all-cause mortality in patients with COPD: a prospective cohort study Patient perception of bodily functions 7 in multiple sclerosis: gait and visual function are the most valuable Measuring outcomes in Parkinson's disease: a 9 multi-perspective concept mapping study Identifying priority 11 outcomes that influence selection of disease-modifying therapies in MS Priority setting partnership 14 to identify the top 10 research priorities for the management of Parkinson's disease The PROactive innovative 17 conceptual framework on physical activity Free-living monitoring of Parkinson's disease: 19 Lessons from the field Optimal recall periods for patient-reported 21 outcomes: challenges and potential solutions Need for Free-living and laboratory gait 24 characteristics in patients with multiple sclerosis The First Frontier: Digital Biomarkers for 26 Neurodegenerative Disorders Deterioration of specific 28 aspects of gait during the instrumented 6-min walk test among people with multiple sclerosis Physical activity 31 monitoring in COPD: compliance and associations with clinical characteristics in a multicenter study Large Scale Population 34 Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study Long-term efficacy and effectiveness of a behavioural and community-based exercise intervention 38 (Urban Training) to increase physical activity in patients with COPD: a randomised controlled trial Moving forward on gait measurement: toward a more refined 41 approach Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease 47 Drug Development through Regulatory Science Developing and adopting safe and effective digital biomarkers to 2 improve patient outcomes Toward a 4 Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson's Patients Using 5 Digital Mobility Outcomes Digital technologies for 7 medicines: shaping a framework for success Biomarkers as drug development tools: discovery, validation, qualification and use A users guide to measurement in medicine Biomarkers and surrogate markers: an FDA perspective Walking on common ground: a cross-disciplinary scoping 17 review on the clinical utility of digital mobility outcomes A multi-centric observational study for the technical validation of 19 real-world monitoring of gait Daily Function Assessment in Parkinson's Disease Using Capacity, Perception, and Performance 22 Measures Long-term 24 unsupervised mobility assessment in movement disorders Mobility endpoints 26 in marketing authorisation of drugs: what gets the European medicines agency moving? 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