Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study

Luciana Midaglia, Patricia Mulero, Xavier Montalban, Jennifer Graves, Stephen L Hauser, Laura Julian, Michael Baker, Jan Schadrack, Christian Gossens, Alf Scotland, Florian Lipsmeier, Johan van Beek, Corrado Bernasconi, Shibeshih Belachew, Michael Lindemann, Luciana Midaglia, Patricia Mulero, Xavier Montalban, Jennifer Graves, Stephen L Hauser, Laura Julian, Michael Baker, Jan Schadrack, Christian Gossens, Alf Scotland, Florian Lipsmeier, Johan van Beek, Corrado Bernasconi, Shibeshih Belachew, Michael Lindemann

Abstract

Background: Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits.

Objective: The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery.

Methods: People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed.

Results: People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis.

Conclusions: People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings.

Trial registration: ClinicalTrials.gov: NCT02952911; https://ichgcp.net/clinical-trials-registry/NCT02952911.

Keywords: mobile phone; multiple sclerosis; patient adherence; patient satisfaction; smartphone; wearable electronic devices.

Conflict of interest statement

Conflicts of Interest: XM has received speaker honoraria and travel expense reimbursement for participation in scientific meetings, been a steering committee member of clinical trials, or served on advisory boards of clinical trials for Actelion, Biogen, Celgene, Merck, Novartis, Oryzon, Roche, Sanofi Genzyme, and Teva Pharmaceutical. JG has received grants or research support from Biogen, Genentech Inc, and S3 Group and has received compensation for a nonbranded resident and fellow education seminar supported by Biogen. SLH serves on the scientific advisory boards for Annexon, Symbiotix, Bionure, and Molecular Stethoscope, is on the board of trustees for Neurona Therapeutics, and has received travel reimbursement and writing assistance from F Hoffmann–La Roche Ltd for CD20-related meetings and presentations. LJ is an employee of Genentech Inc and a shareholder of F Hoffmann–La Roche Ltd. MB, JS, and CG are employees and shareholders of F Hoffmann–La Roche Ltd. AS, FL, and JvB are employees of F Hoffmann–La Roche Ltd. CB and ML are contractors for F Hoffmann–La Roche Ltd. SB was an employee of F Hoffmann–La Roche Ltd during the completion of the work related to this manuscript. SB is now an employee of Biogen (Cambridge, MA), which was not in any way associated with this study. LM and PM have nothing to disclose.

©Luciana Midaglia, Patricia Mulero, Xavier Montalban, Jennifer Graves, Stephen L Hauser, Laura Julian, Michael Baker, Jan Schadrack, Christian Gossens, Alf Scotland, Florian Lipsmeier, Johan van Beek, Corrado Bernasconi, Shibeshih Belachew, Michael Lindemann. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.08.2019.

Figures

Figure 1
Figure 1
FLOODLIGHT study design. PRO: patient-reported outcome. "a" indicates that active tests were administered weekly or every two weeks (see next figure for schedule). "b" indicates that brain magnetic resonance imaging was performed in people with multiple sclerosis.
Figure 2
Figure 2
FLOODLIGHT active tests and their schedule frequency. DMQ: Daily Mood Question; MSIS-29: Multiple Sclerosis Impact Scale–29; SBT: Static Balance Test; SDMT: Symbol Digit Modalities Test; ST: Symptom Tracker; 2MWT: Two-Minute Walk Test; 5UTT: 5 U-Turn Test.
Figure 3
Figure 3
Adherence of people with multiple sclerosis to active tests for individual participants: number of performed active tests per week [level of activity (light green: high; dark green/grey: low] over individual study weeks [columns]).
Figure 4
Figure 4
Adherence of people with multiple sclerosis to active tests. 2MWT: Two-Minute Walk Test.
Figure 5
Figure 5
Adherence of people with multiple sclerosis to smartphone and smartwatch passive monitoring. Days with more than 4 hours of passive monitoring on a device are considered as adherent.
Figure 6
Figure 6
Adherence of people with multiple sclerosis to active tests and passive monitoring. The results of the time-to-event survival analysis based on the Kaplan–Meier method along the FLOODLIGHT study. The abandoning event was defined as the last week in which the participant was adherent according to the definitions for active tests and passive monitoring. Active tests performed on days of in-clinic visits were not considered in the adherence calculation, to focus the abandoning analysis on the remote use. Participants leaving the study before the terminal visit were considered as censored. 2MWT: Two-Minute Walk Test.
Figure 7
Figure 7
Implications of FLOODLIGHT in people with multiple sclerosis for “impact on daily activities” from the patient satisfaction questionnaire.
Figure 8
Figure 8
Implications of FLOODLIGHT in people with multiple sclerosis for “avoiding one component of FLOODLIGHT" from the patient satisfaction questionnaire. 2MWT: Two-Minute Walk Test.
Figure 9
Figure 9
Implications of FLOODLIGHT in people with multiple sclerosis for “desire to continue using the FLOODLIGHT app” from the patient satisfaction questionnaire.
Figure 10
Figure 10
Implications of FLOODLIGHT in people with multiple sclerosis for “prefer to see results immediately to monitor” from the patient satisfaction questionnaire.

References

    1. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS) Neurology. 1983 Nov;33(11):1444–1452.
    1. Bradshaw MJ, Farrow S, Motl RW, Chitnis T. Wearable biosensors to monitor disability in multiple sclerosis. Neurol Clin Pract. 2017 Aug;7(4):354–362. doi: 10.1212/CPJ.0000000000000382.
    1. Lavorgna L, Brigo F, Moccia M, Leocani L, Lanzillo R, Clerico M, Abbadessa G, Schmierer K, Solaro C, Prosperini L, Tedeschi G, Giovannoni G, Bonavita S. e-Health and multiple sclerosis: An update. Mult Scler. 2018 Nov;24(13):1657–1664. doi: 10.1177/1352458518799629.
    1. Busis N. Mobile phones to improve the practice of neurology. Neurol Clin. 2010 May;28(2):395–410. doi: 10.1016/j.ncl.2009.11.001.
    1. Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, Cheng W, Fernandez-Garcia I, Siebourg-Polster J, Jin L, Soto J, Verselis L, Boess F, Koller M, Grundman M, Monsch AU, Postuma RB, Ghosh A, Kremer T, Czech C, Gossens C, Lindemann M. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. 2018 Dec;33(8):1287–1297. doi: 10.1002/mds.27376.
    1. Prasad S, Ramachandran R, Jennings C. Development of smartphone technology to monitor disease progression in multiple sclerosis. Neurology. 2012 Apr 22;78(Meeting Abstracts 1):P01.144. doi: 10.1212/WNL.78.1_MeetingAbstracts.P01.144.
    1. Bove R, White CC, Giovannoni G, Glanz B, Golubchikov V, Hujol J, Jennings C, Langdon D, Lee M, Legedza A, Paskavitz J, Prasad S, Richert J, Robbins A, Roberts S, Weiner H, Ramachandran R, Botfield M, De Jager PL. Evaluating more naturalistic outcome measures: a 1-year smartphone study in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2015 Dec;2(6):e162. doi: 10.1212/NXI.0000000000000162.
    1. Boukhvalova AK, Kowalczyk E, Harris T, Kosa P, Wichman A, Sandford MA, Memon A, Bielekova B. Identifying and quantifying neurological disability via smartphone. Front Neurol. 2018;9:740. doi: 10.3389/fneur.2018.00740. doi: 10.3389/fneur.2018.00740.
    1. Balto JM, Kinnett-Hopkins DL, Motl RW. Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis. Mult Scler J Exp Transl Clin. 2016;2:2055217316634754. doi: 10.1177/2055217316634754.
    1. Haase R, Schultheiss T, Kempcke R, Thomas K, Ziemssen T. Modern communication technology skills of patients with multiple sclerosis. Mult Scler. 2013 Aug;19(9):1240–1241. doi: 10.1177/1352458512471882.
    1. Rudick RA, Miller D, Bethoux F, Rao SM, Lee J, Stough D, Reece C, Schindler D, Mamone B, Alberts J. The Multiple Sclerosis Performance Test (MSPT): an iPad-based disability assessment tool. J Vis Exp. 2014 Jun 30;(88):e51318. doi: 10.3791/51318.
    1. Gholami F, Trojan DA, Kovecses J, Haddad WM, Gholami B. A Microsoft Kinect-based point-of-care gait assessment framework for multiple sclerosis patients. IEEE J Biomed Health Inform. 2017 Dec;21(5):1376–1385. doi: 10.1109/JBHI.2016.2593692.
    1. Kos D, Raeymaekers J, Van Remoortel A, D'hooghe MB, Nagels G, D'Haeseleer M, Peeters E, Dams T, Peeters T. Electronic visual analogue scales for pain, fatigue, anxiety and quality of life in people with multiple sclerosis using smartphone and tablet: a reliability and feasibility study. Clin Rehabil. 2017 Sep;31(9):1215–1225. doi: 10.1177/0269215517692641.
    1. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011 Feb;69(2):292–302. doi: 10.1002/ana.22366. doi: 10.1002/ana.22366.
    1. Smith A. The symbol-digit modalities test: A neuropsychologic test of learning and other cerebral disorders. Seattle: Helmuth J, editor. Learning disorders. Seattle: Special Child Publications; 1968. pp. 83–91.
    1. Smith A. Symbol Digits Modalities Test: Manual. Los Angeles: Western Psychological Services; 1982.
    1. Rao SM, Losinski G, Mourany L, Schindler D, Mamone B, Reece C, Kemeny D, Narayanan S, Miller DM, Bethoux F, Bermel RA, Rudick R, Alberts J. Processing speed test: validation of a self-administered, iPad-based tool for screening cognitive dysfunction in a clinic setting. Mult Scler. 2017 Dec;23(14):1929–1937. doi: 10.1177/1352458516688955.
    1. Berg KO, Wood-Dauphinee SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992;83 Suppl 2:S7–S11.
    1. Penner IK, Raselli C, Stöcklin M, Opwis K, Kappos L, Calabrese P. The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult Scler. 2009 Dec;15(12):1509–1517. doi: 10.1177/1352458509348519.
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–613.
    1. Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol. 2013 Apr 25;13:37. doi: 10.1186/1471-2377-13-37.
    1. Hobart J, Cano S. Improving the evaluation of therapeutic interventions in multiple sclerosis: the role of new psychometric methods. Health Technol Assess. 2009 Feb;13(12):1–177. doi: 10.3310/hta13120. doi: 10.3310/hta13120.
    1. Hobart J, Lamping D, Fitzpatrick R, Riazi A, Thompson A. The Multiple Sclerosis Impact Scale (MSIS-29): a new patient-based outcome measure. Brain. 2001 May;124(Pt 5):962–973. doi: 10.1093/brain/124.5.962.
    1. Riazi A, Hobart JC, Lamping DL, Fitzpatrick R, Thompson AJ. Multiple Sclerosis Impact Scale (MSIS-29): reliability and validity in hospital based samples. J Neurol Neurosurg Psychiatry. 2002 Dec;73(6):701–704. doi: 10.1136/jnnp.73.6.701.
    1. Cheng W, Scotland A, Lipsmeier F, Kilchenmann T, Jin L, Schjodt-Eriksen J, Wolf D, Zhang-Schaerer Y, Garcia I, Siebourg-Polster J, Soto J, Verselis L, Martin-Facklam M, Boess F, Koller M, Grundman M, Monsch A, Postuma R, Ghosh A, Kremer T, Taylor K, Czech C, Gossens C, Lindemann M. Human activity recognition from sensor-based large-scale continuous monitoring of Parkinson's disease patients. Proceedings of the 2nd IEEE/ACM International Conference on Connected Health; July 17-19, 2017; Philadelphia. 2017.
    1. Wang Y, Bohannon RW, Kapellusch J, Garg A, Gershon RC. Dexterity as measured with the 9-Hole Peg Test (9-HPT) across the age span. J Hand Ther. 2015;28(1):53–59. doi: 10.1016/j.jht.2014.09.002.
    1. D'hooghe M, Van Gassen G, Kos D, Bouquiaux O, Cambron M, Decoo D, Lysandropoulos A, Van Wijmeersch B, Willekens B, Penner I, Nagels G. Improving fatigue in multiple sclerosis by smartphone-supported energy management: the MS TeleCoach feasibility study. Mult Scler Relat Disord. 2018 May;22:90–96. doi: 10.1016/j.msard.2018.03.020.

Source: PubMed

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