Humans; Rare Diseases; Outcome Assessment, Health Care; Genetics (clinical); Pharmacology (medical); General Medicine
Abstract :
[en] Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.
Disciplines :
Neurology
Author, co-author :
Poleur, Margaux ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Centre de Référence des Maladies Neuromusculaires, Centre Hospitalier Régional de la Citadelle, Boulevard du 12eme de Ligne 1, 4000, Liège, Belgium. margaux.poleur@citadelle.be
Markati, Theodora; MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
Servais, Laurent ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie (CHR) ; MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
Language :
English
Title :
The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.
Committee for Orphan Medicinal Products (COMP). https://www.ema.europa.eu/en/committees/committee-orphan-medicinal-products-comp. Accessed 15 May 2023.
Orphan Drug Act. https://www.fda.gov/industry/designating-orphan-product-drugs-and-biological-products/orphan-drug-act-relevant-excerpts. Accessed 15 May 2023.
Ferreira CR. The burden of rare diseases. Am J Med Genet A. 2019;179:885–92.
Bell SA, Tudur Smith C. A comparison of interventional clinical trials in rare versus non-rare diseases: an analysis of ClinicalTrials.gov. Orphanet J Rare Dis. 2014;9:170.
Alfano LN, Lowes LP, Berry KM, Yin H, Dvorchik I, Flanigan KM, et al. Pilot study evaluating motivation on the performance of timed walking in boys with Duchenne muscular dystrophy. Neuromuscul Disord [Internet]. 2014;24:860.
Rath A, Salamon V, Peixoto S, Hivert V, Laville M, Segrestin B, et al. A systematic literature review of evidence-based clinical practice for rare diseases: what are the perceived and real barriers for improving the evidence and how can they be overcome? Trials. 2017;18:1–11.
European Medical Agency opinion on qualification of digital technology-based methodologies to support approval of medicinal products. https://www.ema.europa.eu/en/documents/other/questions-answers-qualification-digital-technology-based-methodologies-support-approval-medicinal_en.pdf. Accessed on Nov 2021.
Outcome measures and case definition. https://oxfordmedicine.com/view/10.1093/med/9780198732860.001.0001/med-9780198732860-chapter-12#med-9780198732860-chapter-12-div1-1. Accessed June 2022.
Piwek L, Ellis DA, Andrews S, Joinson A. The rise of consumer health wearables: promises and barriers. PLoS Med. 2016;13:e1001953.
Youn B-Y, Ko Y, Moon S, Lee J, Ko S-G, Kim J-Y. Digital biomarkers for neuromuscular disorders: a systematic scoping review. Diagnostics. 2021;11:1275.
Sica M, Tedesco S, Crowe C, Kenny L, Moore K, Timmons S, et al. Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review. PLoS ONE [Internet]. 2021;16:e0246528.
Vienne-Jumeau A, Quijoux F, Vidal P-P, Ricard D. Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: a systematic review and meta-analysis. Ann Phys Rehabil Med [Internet]. 2020;63:138–47.
Ricotti V, Kadirvelu B, Selby V, Festenstein R, Mercuri E, Voit T, et al. Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy. Nat Med [Internet]. 2023;29:95–103.
Bortolani S, Brusa C, Rolle E, Monforte M, de Arcangelis V, Ricci E, et al. Technology outcome measures in neuromuscular disorders: a systematic review. Eur J Neurol [Internet]. 2022;29:1266–78.
Haberkamp M, Moseley J, Athanasiou D, de Andres-Trelles F, Elferink A, Rosa MM, et al. European regulators’ views on a wearable-derived performance measurement of ambulation for Duchenne muscular dystrophy regulatory trials. Neuromuscul Disord [Internet]. 2019;29:514–6.
Jacques MF, Onambele-Pearson GL, Reeves ND, Stebbings GK, Dawson EA, Stockley RC, et al. 12-Month changes of muscle strength, body composition and physical activity in adults with dystrophinopathies. Disabil Rehabil [Internet]. 2022;44:1847–54.
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 [Internet]. 2021;55:799–806.
Lott DJ, Taivassalo T, Senesac CR, Willcocks RJ, Harrington AM, Zilke K, et al. Walking activity in a large cohort of boys with Duchenne muscular dystrophy. Muscle Nerve [Internet]. 2021;63:192–8.
An J, Xie Z, Jia F, Wang Z, Yuan Y, Zhang J, et al. Quantitative coordination evaluation for screening children with Duchenne muscular dystrophy. Chaos [Internet]. 2020;30:023116.
Ganea R, Jeannet P-Y, Paraschiv-Ionescu A, Goemans NM, Piot C, van den Hauwe M, et al. Gait assessment in children with Duchenne muscular dystrophy during long-distance walking. J Child Neurol [Internet]. 2012;27:30–8.
Servais L, Yen K, Guridi M, Lukawy J, Vissière D, Strijbos P. Stride velocity 95th centile: insights into gaining regulatory qualification of the first wearable-derived digital endpoint for use in Duchenne muscular dystrophy trials. J Neuromuscul Dis [Internet]. 2022;9:335–46.
Davidson ZE, Hughes I, Ryan MM, Kornberg AJ, Cairns AG, Jones K, et al. Effect of a multicomponent nutritional supplement on functional outcomes for Duchenne muscular dystrophy: a randomized controlled trial. Clin Nutr [Internet]. 2021;40:4702–11.
Jeannet P-Y, Aminian K, Bloetzer C, Najafi B, Paraschiv-Ionescu A. Continuous monitoring and quantification of multiple parameters of daily physical activity in ambulatory Duchenne muscular dystrophy patients. Eur J Paediatr Neurol [Internet]. 2011;15:40–7.
Capelini CM, da Silva TD, Tonks J, Watson S, Alvarez MPB, de Menezes LC, et al. Improvements in motor tasks through the use of smartphone technology for individuals with Duchenne muscular dystrophy. Neuropsychiatr Dis Treat [Internet]. 2017;13:2209–17.
Ferrer-Mallol E, Matthews C, Stoodley M, Gaeta A, George E, Reuben E, et al. Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases. Front Pharmacol [Internet]. 2022;13:916714.
Liao Q, He J, Huang K. Physical activities and risk of neurodegenerative diseases: a two-sample Mendelian randomization study. Front Aging Neurosci [Internet]. 2022;14:991140.
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 [Internet]. 2019;266:2387–95.
Rutkove SB, Narayanaswami P, Berisha V, Liss J, Hahn S, Shelton K, et al. Improved ALS clinical trials through frequent at-home self-assessment: a proof of concept study. Ann Clin Transl Neurol [Internet]. 2020;7:1148–57.
Agurto C, Ahmad O, Cecchi GA, Norel R, Pietrowicz M, Eyigoz EK, et al. Analyzing progression of motor and speech impairment in ALS. Annu Int Conf IEEE Eng Med Biol Soc [Internet]. 2019;2019:6097–102.
Londral A, Pinto S, de Carvalho M. Markers for upper limb dysfunction in amyotrophic lateral sclerosis using analysis of typing activity. Clin Neurophysiol [Internet]. 2016;127:925–31.
Pancani S, Tindale W, Shaw PJ, Mazzà C, McDermott CJ. Efficacy of the head up collar in facilitating functional head movements in patients with amyotrophic lateral sclerosis. Clin Biomech [Internet]. 2018;57:114–20.
Meyer T, Spittel S, Grehl T, Weyen U, Steinbach R, Kettemann D, et al. Remote digital assessment of amyotrophic lateral sclerosis functional rating scale: a multicenter observational study. Amyotroph Lateral Scler Front Degener [Internet]. 2022. 10.1080/21678421.2022.2104649. DOI: 10.1080/21678421.2022.2104649
Vogelnik K, Koritnik B, Leonardis L, Dolenc Grošelj L, Saifee TA, Zidar J, et al. Shaky hands are a part of motor neuron disease phenotype: clinical and electrophysiological study of 77 patients. J Neurol [Internet]. 2022;269:4498–509.
Crook-Rumsey M, Musa AM, Iniesta R, Drakakis E, Boutelle MG, Shaw CE, et al. A shortened surface electromyography recording is sufficient to facilitate home fasciculation assessment. Muscle Nerve [Internet]. 2022;66:625–30.
Peterson DS, Moore A, Ofori E. Performance fatigability during gait in adults with Charcot-Marie-Tooth disease. Gait Posture. 2021;85:232–7.
Saifee TA, Pareés I, Kassavetis P, Kaski D, Bronstein AM, Rothwell JC, et al. Tremor in Charcot-Marie-Tooth disease: no evidence of cerebellar dysfunction. Clin Neurophysiol. 2015;126:1817–24.
Hamel J, Creigh PD, Dekdebrun J, Eichinger K, Thornton CA. Remote assessment of myotonic dystrophy type 1: a feasibility study. Muscle Nerve [Internet]. 2022;66:336–9.
Maleki G, Zhuparris A, Koopmans I, Doll RJ, Voet N, Cohen A, et al. Objective monitoring of facioscapulohumeral dystrophy during clinical trials using a smartphone app and wearables: observational study. JMIR Form Res [Internet]. 2022;6:e31775.
O’Connor L, Westerberg E, Punga AR. Pattern of habitual physical exercise in myasthenia gravis patients. J Neuromuscul Dis [Internet]. 2019;6:85–91.
Birnbaum S, Bachasson D, Sharshar T, Porcher R, Hogrel J-Y, Portero P. Free-living physical activity and sedentary behaviour in autoimmune myasthenia gravis: a cross-sectional study. J Neuromuscul Dis [Internet]. 2021;8:689–97.
Annoussamy M, Seferian AM, Daron A, Péréon Y, Cances C, Vuillerot C, et al. Natural history of Type 2 and 3 spinal muscular atrophy: 2-year NatHis-SMA study. Ann Clin Transl Neurol [Internet]. 2021;8:359–73.
le Moing A-G, Seferian AM, Moraux A, Annoussamy M, Dorveaux E, Gasnier E, et al. A movement monitor based on magneto-inertial sensors for non-ambulant patients with Duchenne muscular dystrophy: a pilot study in controlled environment. PLoS ONE [Internet]. 2016;11:e0156696.
Gidaro T, Gasnier E, Annoussamy M, Vissing J, Attarian S, Mozaffar T, et al. Home-based gait analysis as an exploratory endpoint during a multicenter phase 1 trial in limb girdle muscular dystrophy type R2 and facioscapulohumeral muscular dystrophy. Muscle Nerve [Internet]. 2022;65:237–42.
Shrader JA, Kats I, Kokkinis A, Zampieri C, Levy E, Joe GO, et al. A randomized controlled trial of exercise in spinal and bulbar muscular atrophy. Ann Clin Transl Neurol [Internet]. 2015;2:739–47.
Gordon MF, Grachev ID, Mazeh I, Dolan Y, Reilmann R, Loupe PS, et al. Quantification of motor function in Huntington disease patients using wearable sensor devices. Digit Biomark. 2019;3:103–15.
Adams JL, Dinesh K, Xiong M, Tarolli CG, Sharma S, Sheth N, et al. Multiple wearable sensors in Parkinson and Huntington disease individuals: a pilot study in clinic and at home. Digit Biomark [Internet]. 2017;1:52–63.
Gil Polo C, Cubo Delgado E, Mateos Cachorro A, Rivadeneyra Posadas J, Mariscal Perez N, Armesto FD. Energy balance in Huntington’s disease. Ann Nutr Metab. 2015;67:267–73.
Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey ER, et al. A longitudinal wearable sensor study in Huntington’s disease. J Huntingtons Dis [Internet]. 2020;9:69–81.
Andrzejewski KL, Dowling A, Stamler D, Felong TJ, Harris DA, Wong C, et al. Wearable sensors in Huntington disease: a pilot study. J Huntingtons Dis [Internet]. 2016;5:199–206.
McLaren B, Drummond SPA, Glikmann-Johnston Y, Loy C, Bellgrove MA, Stout JC, et al. Greater time in bed and less physical activity associate with poorer cognitive functioning performance in Huntington’s disease. Neuropsychology [Internet]. 2021;35:656–67.
Waddell EM, Dinesh K, Spear KL, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE ®: a pilot study of a smartphone application for Huntington’s disease. J Huntingtons Dis. 2021;10:293–301.
Lang C, Gries C, Lindenberg K, Lewerenz J, Uhl S, Olsson C, et al. Monitoring the motor phenotype in Huntington’s disease by analysis of keyboard typing during real life computer use. J Huntingtons Dis. 2021;10:259–68.
Cohen S, Waks Z, Elm JJ, Gordon MF, Grachev ID, Navon-Perry L, et al. Characterizing patient compliance over six months in remote digital trials of Parkinson’s and Huntington disease. BMC Med Inform Decis Mak [Internet]. 2018;18:138.
Lipsmeier F, Simillion C, Bamdadian A, Tortelli R, Byrne LM, Zhang Y-P, et al. A remote digital monitoring platform to assess cognitive and motor symptoms in Huntington disease: cross-sectional validation study. J Med Internet Res [Internet]. 2022;24:e32997.
Keren K, Busse M, Fritz NE, Muratori LM, Gazit E, Hillel I, et al. Quantification of daily-living gait quantity and quality using a wrist-worn accelerometer in Huntington’s disease. Front Neurol [Internet]. 2021;12:719442.
Purcell NL, Goldman JG, Ouyang B, Liu Y, Bernard B, O’Keefe JA. The effects of dual-task cognitive interference on gait and turning in Huntington’s disease. PLoS ONE [Internet]. 2020;15:e0226827.
Dalton A, Khalil H, Busse M, Rosser A, van Deursen R, ÓLaighin G. Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic Huntington’s disease. Gait Posture [Internet]. 2013;37:49–54.
Gasner H, Jensen D, Marxreiter F, Kletsch A, Bohlen S, Schubert R, et al. Gait variability as digital biomarker of disease severity in Huntington’s disease. J Neurol [Internet]. 2020;267:1594–601.
Collett J, Esser P, Khalil H, Busse M, Quinn L, DeBono K, et al. Insights into gait disorders: walking variability using phase plot analysis, Huntington’s disease. Gait Posture [Internet]. 2014;40:694–700.
Desai R, Blacutt M, Youdan G, Fritz NE, Muratori LM, Hausdorff JM, et al. Postural control and gait measures derived from wearable inertial measurement unit devices in Huntington’s disease: recommendations for clinical outcomes. Biomechanics [Internet]. 2022;96:105658.
Trojaniello D, Ravaschio A, Hausdorff JM, Cereatti A. Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects. Gait Posture [Internet]. 2015;42:310–6.
Mannini A, Trojaniello D, della Croce U, Sabatini AM.Hidden Markov model-based strategy for gait segmentation using inertial sensors: application to elderly, hemiparetic patients and Huntington’s disease patients. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society [Internet] 2015;5179–82.
Muratori LM, Quinn L, Li X, Youdan G, Busse M, Fritz NE. Measures of postural control and mobility during dual-tasking as candidate markers of instability in Huntington’s disease. Hum Mov Sci [Internet]. 2021;80:102881.
Porciuncula F, Wasserman P, Marder KS, Rao AK. Quantifying postural control in Premanifest and manifest Huntington disease using wearable sensors. Neurorehabil Neural Repair [Internet]. 2020;34:771–83.
Kegelmeyer DA, Kostyk SK, Fritz NE, Fiumedora MM, Chaudhari A, Palettas M, et al. Quantitative biomechanical assessment of trunk control in Huntington’s disease reveals more impairment in static than dynamic tasks. J Neurol Sci [Internet]. 2017;376:29–34.
Bennasar M, Hicks YA, Clinch SP, Jones P, Holt C, Rosser A, et al. Automated assessment of movement impairment in Huntington’s disease. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2018;26:2062–9.
Mannini A, Trojaniello D, Cereatti A, Sabatini AM. A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington’s disease patients. Sensors (Basel) [Internet]. 2016;16:134.
Lauraitis A, Maskeliunas R, Damaševičius R. ANN and fuzzy logic based model to evaluate Huntington disease symptoms. J Healthc Eng [Internet]. 2018. 10.1155/2018/4581272. DOI: 10.1155/2018/4581272
Shih H-JS, Quinn L, Morgan-Jones P, Long K, Schreier AR, Friel CP. Utilization of wearable activity monitors to support physical activity interventions in neurodegenerative diseases: a feasibility study [Internet]. medRxiv. 2022.
Hatanaka N, Sato K, Hishikawa N, Takemoto M, Ohta Y, Yamashita T, et al. Comparative Gait analysis in progressive supranuclear palsy and Parkinson’s disease. Eur Neurol. 2016;75:282–9.
Baston C, Mancini M, Schoneburg B, Horak F, Rocchi L. Postural strategies assessed with inertial sensors in healthy and Parkinsonian subjects. Gait Posture. 2014;40:70–5.
Pilotto A, Rizzetti MC, Lombardi A, Hansen C, Biggi M, Verzeroli G, et al. Cerebellar rTMS in PSP: a double-blind sham-controlled study using mobile health technology. Cerebellum [Internet]. 2021. 10.1007/s12311-021-01239-6. DOI: 10.1007/s12311-021-01239-6
Djuric-Jovicic M, Petrovic I, Jecmenica-Lukic M, Radovanovic S, Dragasevic-Miskovic N, Belic M, et al. Finger tapping analysis in patients with Parkinson’s disease and atypical parkinsonism. J Clin Neurosci. 2016;30:49–55.
De Vos M, Prince J, Buchanan T, FitzGerald JJ, Antoniades CA. Discriminating progressive supranuclear palsy from Parkinson’s disease using wearable technology and machine learning. Gait Posture. 2020;77:257–63.
Sotirakis C, Conway N, Su Z, Villarroel M, Tarassenko L, FitzGerald JJ, et al. Longitudinal monitoring of progressive supranuclear palsy using body-worn movement sensors. Disorders [Internet]. 2022;37:2263–71.
Panyakaew P, Cho HJ, Lee SW, Wu T, Hallett M. The pathophysiology of dystonic tremors and comparison with essential tremor. J Neurosci. 2020;40:9317–26.
Delrobaei M, Rahimi F, Jackman ME, Atashzar SF, Shahbazi M, Patel R, et al. Kinematic and kinetic assessment of upper limb movements in patients with writer’s cramp. J Neuroeng Rehabil. 2016;13:1–10.
Rudzińska M, Krawczyk M, Wójcik-Pedziwiatr M, Szczudlik A, Wasielewska A. Tremor associated with focal and segmental dystonia. Neurol Neurochir Pol [Internet]. 2013;47:223.
Nieuwhof F, Toni I, Dirkx MF, Gallea C, Vidailhet M, Buijink AWG, et al. Cerebello-thalamic activity drives an abnormal motor network into dystonic tremor [Internet]. medRxiv. 2021. 10.1016/j.nicl.2021.102919. DOI: 10.1016/j.nicl.2021.102919
Elble RJ, Hellriegel H, Raethjen J, Deuschl G. Assessment of head tremor with accelerometers versus gyroscopic transducers. Mov Disord Clin Pract. 2017;4:205–11.
Celletti C, Ferrazzano G, Belvisi D, Ferrario C, Tarabini M, Baione V, et al. Instrumental timed up and go test discloses abnormalities in patients with cervical dystonia. Clin Biomech [Internet]. 2021;90:105493.
Park J, Yang KY, Lee J, Youn K, Lee J, Chung SG, et al. Objective evaluation of cervical dystonia using an inertial sensor-based system. J Med Biol Eng [Internet]. 2019;39:305–14.
Zhang Z, Cisneros E, Lee HY, Vu JP, Chen Q, Benadof CN, et al. Hold that pose: capturing cervical dystonia’s head deviation severity from video. Ann Clin Transl Neurol [Internet]. 2022;9:684–94.
Mason XL, Cross KA, Arac A, Bordelon Y, Wu AD. Vim-thalamic deep brain stimulation for cervical dystonia and upper-limb tremor: quantification by markerless-3d kinematics and accelerometry. Tremor Other Hyperkinet Mov [Internet]. 2022;12:5.
Hickey A, Gunn E, Alcock L, del Din S, Godfrey A, Rochester L, et al. Validity of a wearable accelerometer to quantify gait in spinocerebellar ataxia type 6. Physiol Meas [Internet]. 2016;37:N105–17.
Shah V, Curtze C, Mancini M, Carlson-Kuhta P, Nutt J, Gomez CM, et al. Inertial sensor algorithms to characterize turning in neurological patients with turn hesitations. IEEE Trans Biomed Eng [Internet]. 2020;68:2615–25.
Thierfelder A, Seemann J, John N, Harmuth F, Giese M, Schüle R, et al. Real-life turning movements capture subtle longitudinal and preataxic changes in cerebellar ataxia. Mov Disord [Internet]. 2022;37:1047–58.
Jin L, Lv W, Han G, Ni L, Sun D, Hu X, et al. Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning. Artif Intell Med [Internet]. 2020;103:101794.
Shah V, Rodriguez-Labrada R, Horak FB, McNames J, Casey H, Hansson Floyd K, et al. Gait variability in spinocerebellar ataxia assessed using wearable inertial sensors. Mov Disord [Internet]. 2021;36:2922–31.
Zhou H, Nguyen H, Enriquez A, Morsy L, Curtis M, Piser T, et al. Assessment of gait and balance impairment in people with spinocerebellar ataxia using wearable sensors. Neurol Sci [Internet]. 2022;43:2589–99.
Velázquez-Pérez L, Rodriguez-Labrada R, González-Garcés Y, Arrufat-Pie E, Torres-Vega R, Medrano-Montero J, et al. Prodromal spinocerebellar ataxia type 2 subjects have quantifiable gait and postural sway deficits. Mov Disord [Internet]. 2021;36:471–80.
Martindale CF, Roth N, Gasner H, Jensen D, Kohl Z, Eskofier BM. Mobile gait analysis using personalised hidden Markov models for hereditary spastic paraplegia patients. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC) [Internet]. 2018; p. 5430–3.
Martindale CF, Strauss M, Gassner H, List J, Muller M, Klucken J, et al. Segmentation of gait sequences using inertial sensor data in hereditary spastic paraplegia. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) [Internet]. 2017; p. 1266–9.
Regensburger M, Spatz IT, Ollenschläger M, Martindale CF, Lindeburg P, Kohl Z, et al. Inertial gait sensors to measure mobility and functioning in hereditary spastic paraplegia: a cross-sectional multicenter clinical study. Neurology [Internet]. 2022;99:E1079–89.
O’Keefe JA, Guan J, Robertson E, Biskis A, Joyce J, Ouyang B, et al. The effects of dual task cognitive interference and fast-paced walking on gait, turns, and falls in men and women with FXTAS. Cerebellum [Internet]. 2021;20:212–21.
O’Keefe JA, Robertson EE, Ouyang B, Carns D, McAsey A, Liu Y, et al. Cognitive function impacts gait, functional mobility and falls in fragile X-associated tremor/ataxia syndrome. Gait Posture [Internet]. 2018;66:288–93.
Mueller A, Paterson E, McIntosh A, Praestgaard J, Bylo M, Hoefling H, et al. Digital endpoints for self-administered home-based functional assessment in pediatric Friedreich’s ataxia. Ann Clin Transl Neurol [Internet]. 2021;8:1845–56.
Corben LA, Tai G, Szmulewicz D, Horne MK, Pathirana PN, Delatycki MB. Developing a clinically meaningful instrumented measure of upper limb function in Friedreich ataxia. Cerebellum [Internet]. 2019;18:13–4.
Pilzak K, Zebrowska A, Sikora M, Hall B, Lakomy O, Kostorz S, et al. Physical functioning and symptoms of chronic fatigue in sarcoidosis patients. Adv Exp Med Biol. 2018;1040:13–21.
Cho PSP, Vasudevan S, Maddocks M, Spinou A, Chamberlain Mitchell S, Wood C, et al. Physical Inactivity in Pulmonary Sarcoidosis. Lung. 2019;
Bahmer T, Watz H, Develaska M, Waschki B, Rabe KF, Magnussen H, et al. Physical activity and fatigue in patients with sarcoidosis. Respiration [Internet]. 2018;95:18–26.
Chu B, O’Connor DM, Wan M, Barnett I, Shou H, Judson M, et al. Quality of Life and physical activity in 629 individuals with sarcoidosis: prospective, cross-sectional study using smartphones (sarcoidosis App). JMIR Mhealth Uhealth [Internet]. 2022;10:e38331.
Korenromp IHE, Heijnen CJ, Vogels OJM, Van Den Bosch JMM, Grutters JC. Characterization of chronic fatigue in patients with sarcoidosis in clinical remission. Chest. 2011;140:441–7.
Stephens SL, Tremblay MS, Faulkner G, Beyene J, Nguyen TH, Koohsari S, et al. Validity of the stage of exercise scale in children with rheumatologic conditions. J Rheumatol [Internet]. 2016;43:2189–98.
Battaglia S, Bellia M, Serafino-Agrusa L, Giardina A, Messina M, Cannizzaro F, et al. Physical capacity in performing daily activities is reduced in scleroderma patients with early lung involvement. Clin Respir J. 2017;11:36–42.
Kraan CM, Date P, Rattray A, Sangeux M, Bui QM, Baker EK, et al. Feasibility of wearable technology for ‘real-world’ gait analysis in children with Prader–Willi and Angelman syndromes. J Intellect Disabil Res [Internet]. 2022;66:717–25.
Cimolin V, Pau M, Cau N, Leban B, Porta M, Capodaglio P, et al. Changes in symmetry during gait in adults with Prader–Willi syndrome. Comput Methods Biomech Biomed Engin [Internet]. 2020;23:1094–101.
Castner DM, Tucker JM, Wilson KS, Rubin DA. Patterns of habitual physical activity in youth with and without Prader–Willi syndrome. Res Dev Disabil [Internet]. 2014;35:3081–8.
Woods SG, Knehans A, Arnold S, Dionne C, Hoffman L, Turner P, et al. The associations between diet and physical activity with body composition and walking a timed distance in adults with Prader–Willi syndrome. Food Nutr Res [Internet]. 2018. 10.29219/fnr.v62.1343. DOI: 10.29219/fnr.v62.1343
Duran AT, Wilson KS, Castner DM, Tucker JM, Rubin DA. Association between physical activity and bone in children with Prader–Willi syndrome. J Pediatric Endocrinol Metab [Internet]. 2016;29:819–26.
Bellicha A, Coupaye M, Hocquaux L, Speter F, Oppert J-M, Poitou C. Increasing physical activity in adult women with Prader–Willi syndrome: a transferability study. J Appl Res Intellect Disabil [Internet]. 2020;33:258–67.
Rubin DA, Wilson KS, Dumont-Driscoll M, Rose DJ. Effectiveness of a parent-led physical activity intervention in youth with obesity. Med Sci Sports Exerc [Internet]. 2019;51:805–13.
Hamed A, Curran C, Gwaltney C, DasMahapatra P. Mobility assessment using wearable technology in patients with late-onset Pompe disease. NPJ Digit Med [Internet]. 2019;2:70.
Hobson-Webb LD, Zwelling PJ, Raja SS, Pifer AN, Kishnani PS. Quantitative muscle ultrasound and electrical impedance myography in late onset Pompe disease: a pilot study of reliability, longitudinal change and correlation with function. Mol Genet Metab Rep [Internet]. 2021;28:100785.
Mumford RA, Mahon L, Jones S, Bigger B, Canal M, Hare DJ. Actigraphic investigation of circadian rhythm functioning and activity levels in children with mucopolysaccharidosis type III (Sanfilippo syndrome). J Neurodev Disord [Internet]. 2015;7:1–11.
Vallim J, Amaral F, Cipolla-Neto J, D’Almeida V. Rhythmic changes in Fabry disease: inversion and non-oscillatory pattern in 6-sulfatoxymelatonin daily profile. Chronobiol Int. 2019;36:470–80.
Davies EH, Johnston J, Toro C, Tifft CJ. A feasibility study of mHealth and wearable technology in late onset GM2 gangliosidosis (Tay-Sachs and Sandhoff Disease). Orphanet J Rare Dis [Internet]. 2020;15:1–16.
El-Masri S, Malpas CB, Evans A, Walterfang M. Clinical correlates of movement disorders in adult Niemann–Pick type C patients measured via a personal KinetiGraph. Neurol Sci [Internet]. 2022;43:6339–47.
Downs J, Leonard H, Wong K, Newton N, Hill K. Quantification of walking-based physical activity and sedentary time in individuals with Rett syndrome. Dev Med Child Neurol [Internet]. 2017;59:605–11.
Stahlhut M, Downs J, Wong K, Bisgaard A-M, Nordmark E. Feasibility and effectiveness of an individualized 12-week ‘uptime’ Participation (U-PART) Intervention in girls and women with Rett syndrome. Phys Ther [Internet]. 2020;100:168–79.
Suresha PB, O’Leary H, Tarquinio DC, Hehn J, Clifford GD. Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement [Internet]. medRxiv. 2022. 10.1371/journal.pone.0266351. DOI: 10.1371/journal.pone.0266351
Stahlhut M, Hill K, Bisgaard A-M, Jensen AK, Andersen M, Leonard H, et al. Measurement of sedentary behaviors or ‘downtime’ in Rett syndrome. J Child Neurol [Internet]. 2017;32:1009–13.
Downs J, Leonard H, Jacoby P, Brisco L, Baikie G, Hill K. Rett syndrome: establishing a novel outcome measure for walking activity in an era of clinical trials for rare disorders. Disabil Rehabil [Internet]. 2015;37:1992–6.
Earnest T, Shephard E, Tye C, McEwen F, Woodhouse E, Liang H, et al. Actigraph-measured movement correlates of attention-deficit/hyperactivity disorder (ADHD) symptoms in young people with tuberous sclerosis complex (TSC) with and without intellectual disability and autism spectrum disorder (ASD). Brain Sci [Internet]. 2020;10:1–14.
Bornstein A, Hedström A, Wasling P. Actigraphy measurement of physical activity and energy expenditure in narcolepsy type 1, narcolepsy type 2 and idiopathic hypersomnia: a Sensewear Armband study. J Sleep Res [Internet]. 2021;30:e13038.
Padua L, Pazzaglia C, Pareyson D, Schenone A, Aiello A, Fabrizi GM, et al. Novel outcome measures for Charcot−Marie−Tooth disease: validation and reliability of the 6-min walk test and StepWatch™ Activity Monitor and identification of the walking features related to higher quality of life. Eur J Neurol [Internet]. 2016;23:1343–50.
Pazzaglia C, Padua L, Pareyson D, Schenone A, Aiello A, Fabrizi GM, et al. Are novel outcome measures for Charcot-Marie-Tooth disease sensitive to change? The 6-minute walk test and StepWatch TM activity monitor in a 12-month longitudinal study. Neuromuscul Disord [Internet]. 2019;29:310–6.
Corben LA, Nguyen KD, Pathirana PN, Horne MK, Szmulewicz DJ, Roberts M, et al. Developing an instrumented measure of upper limb function in Friedreich ataxia. Cerebellum [Internet]. 2021;20:430–8.
Goldsack JC, Dowling A, Samuelson D, Patrick-Lake B, Clay I. Evaluation, acceptance, and qualification of digital measures: from proof of concept to endpoint. Digit Biomark [Internet]. 2021;5:53–64.
Poleur M, Ulinici A, Daron A, Schneider O, Farra F, Demonceau M, et al. Normative data on spontaneous stride velocity, stride length, and walking activity in a non-controlled environment. Orphanet J Rare Dis. 2021;16:1–10.
Kadirvelu B, Gavriel C, Nageshwaran S, Chan JPK, Nethisinghe S, Athanasopoulos S, et al. A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia. Nat Med [Internet]. 2023;29:86–94.
Sienko S, Buckon C, Bagley A, Fowler E, Heberer K, Staudt L, et al. Kinematic changes in gait in boys with Duchenne Muscular Dystrophy: utility of the Gait Deviation Index, the gait profile score and the gait variable scores. Gait Posture [Internet]. 2022;100:157–64.
Heberer K, Fowler E, Staudt L, Sienko S, Buckon CE, Bagley A, et al. Hip kinetics during gait are clinically meaningful outcomes in young boys with Duchenne muscular dystrophy. Gait Posture [Internet]. 2016;48:159–64.
de Bie E, Oskarsson B, Joyce NC, Nicorici A, Kurillo G, Han JJ. Longitudinal evaluation of upper extremity reachable workspace in ALS by Kinect sensor. Amyotroph Lateral Scler Frontotemporal Degener [Internet]. 2017;18:17–23.
Han JJ, Kurillo G, Abresch RT, de Bie E, Nicorici A, Bajcsy R. Reachable workspace in facioscapulohumeral muscular dystrophy (FSHD) by Kinect. Muscle Nerve [Internet]. 2015;51:168–75.
Kimura S, Ozasa S, Nomura K, Yoshioka K, Endo F. Estimation of muscle strength from actigraph data in Duchenne muscular dystrophy. Pediatr Int [Internet]. 2014;56:748–52.
Berntsen KS, Edvardsen E, Hansen BH, Flatø B, Sjaastad I, Sanner H. Cardiorespiratory fitness in long-term juvenile dermatomyositis: A controlled, cross-sectional study of active/inactive disease. Rheumatology (United Kingdom). 2019;58:492–501.
Siegel BI, Cakmak A, Reinertsen E, Benoit M, Figueroa J, Clifford GD, et al. Use of a wearable device to assess sleep and motor function in Duchenne muscular dystrophy. Muscle Nerve [Internet]. 2020;61:198–204.
Kelly M, Lavrov A, Garcia-Gancedo L, Parr J, Hart R, Chiwera T, et al. The use of biotelemetry to explore disease progression markers in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener. 2020;21:563–73.
Jimenez-Moreno AC, Charman SJ, Nikolenko N, Larweh M, Turner C, Gorman G, et al. Analyzing walking speeds with ankle and wrist worn accelerometers in a cohort with myotonic dystrophy. Disabil Rehabil. 2019;41:2972–8.
Pinto AJ, Yazigi Solis M, de Sá Pinto AL, Silva CA, Maluf Elias Sallum A, Roschel H, et al. Physical (in)activity and its influence on disease-related features, physical capacity, and health-related quality of life in a cohort of chronic juvenile dermatomyositis patients. Semin Arthritis Rheum. 2016;46:64–70.
Arteaga D, Donnelly T, Crum K, Markham L, Killian M, Burnette WB, et al. Assessing Physical Activity Using Accelerometers in Youth with Duchenne Muscular Dystrophy. J Neuromuscul Dis. 2020;7:331–42.
Killian M, Buchowski MS, Donnelly T, Burnette WB, Markham LW, Slaughter JC, et al. Beyond ambulation: Measuring physical activity in youth with Duchenne muscular dystrophy. Neuromuscular Disorders. 2020;30:277–82.
Vieira FG, Venugopalan S, Premasiri AS, McNally M, Jansen A, McCloskey K, et al. A machine-learning based objective measure for ALS disease severity. NPJ Digit Med. 2022;5.
Menotti F, Laudani L, Damiani A, Macaluso A. Amount and intensity of daily living activities in Charcot-Marie-Tooth 1A patients. Brain Behav. 2014;4:14–20.
Mohammadi-Ghazi R, Nguyen H, Mishra RK, Enriquez A, Najafi B, Stephen CD, Gupta AS, Schmahmann JD, Vaziri A. Objective assessment of upper-extremity motor functions in spinocerebellar ataxia using wearable sensors. Sensors (Basel). 2022;22(20):7993.
Bachasson D, Moraux A, Ollivier G, Decostre V, Ledoux I, Gidaro T, et al. Relationship between muscle impairments, postural stability, and gait parameters assessed with lower-trunk accelerometry in myotonic dystrophy type 1. Neuromuscul Disord. 2016;26:428–35.
Geronimo A, Martin AE, Simmons Z. Inertial sensing of step kinematics in ambulatory patients with ALS and related motor neuron diseases. J Med Eng Technol. 2021;45:486–93.
van der Geest A, Essers JMN, Bergsma A, Jansen M, de Groot IJM. Monitoring daily physical activity of upper extremity in young and adolescent boys with Duchenne muscular dystrophy: A pilot study. Muscle Nerve. 2020;61:293–300.
Belluscio V, Bergamini E, Salatino G, Marro T, Gentili P, Iosa M, et al. Dynamic balance assessment during gait in children with Down and Prader-Willi syndromes using inertial sensors. Hum Mov Sci. 2019;63:53–61.
Statland JM, Karanevich A, Bruetsch A, Huisinga J. A pilot study of the responsiveness of wireless motion analysis in facioscapulohumeral muscular dystrophy. Muscle Nerve. 2019;60:590–4.
Huisinga J, Bruetsch A, Mccalley A, Currence M, Herbelin L, Jawdat O, et al. An instrumented timed up and go in facioscapulohumeral muscular dystrophy. Muscle Nerve. 2018;57:503–6.
Ramdharry GM, Pollard AJ, Grant R, Dewar EL, Laurá M, Moore SA, et al. A study of physical activity comparing people with Charcot-Marie-Tooth disease to normal control subjects. Disabil Rehabil. 2017;39:1753–8.
Gaßner H, Jensen D, Marxreiter F, Kletsch A, Bohlen S, Schubert R, et al. Gait variability as digital biomarker of disease severity in Huntington’s disease. J Neurol. 2020;267:1594–601.
Fujii T, Takeshita E, Iwata Y, Yajima H, Nozaki F, Mori M, et al. Cumulative jerk as an outcome measure in nonambulatory Duchenne muscular dystrophy. Brain Dev. 2019;41:796–802.
Fowler EG, Staudt LA, Heberer KR, Sienko SE, Buckon CE, Bagley AM, et al. Longitudinal community walking activity in Duchenne muscular dystrophy. Muscle Nerve. 2018;57:401–6.
Subramony SH, Kedar S, Murray E, Protas E, Xu H, Ashizawa T, et al. Objective home-based gait assessment in spinocerebellar ataxia. J Neurol Sci. 2012;313:95–8.
Knak KL, Sheikh AM, Witting N, Vissing J. Physical activity in myotonic dystrophy type 1. J Neurol. 2020;267:1679–86.
