Insight

Smart devices in the management of multiple sclerosis
Smart devices in the management of multiple sclerosis
Katrina Mountfort, Freelance Medical Writer for Touch Medical Media, UK

Multiple sclerosis (MS) is the most common autoimmune disorder affecting the central nervous system. The disease course is chronic and unpredictable, often leading to severe disability. The long-term management of the disease is challenging for patients and clinicians, and adherence to treatment is essential to maximise the long-term benefits of therapy.1 However, most immunomodulatory treatments are administered by frequent subcutaneous injections, causing injection stress and pain, or injection site reactions, which lead to non-adherence.2 In addition, MS progression can involve deterioration in cognitive and fine-motor skills, which impair a patient’s ability to perform manual injections and therefore worsen adherence.3

Smart devices can help with many different aspects of living with MS, including maintaining activities and lifestyle, and medication adherence. Auto-injection devices can simplify MS therapy, as well as providing additional features to improve patient comfort and adherence. These include reminder functions, as memory or attention impairment is a major factor in suboptimal adherence,3 adjustable injection speed and depth, optical and acoustic signalling at the beginning and end of the injection process, hidden needle to overcome needle phobia, low-force safety release to ensure the device is positioned on the skin correctly at the time of injection, and an LED display to visualise the injection progress.4,5

The RebiSmart® (Merck Serono) electronic device has been developed for treatment with subcutaneous interferon (IFN) β-1a (Rebif®). A number of short and long-term studies have found that this device improves adherence in patients with relapsing-remitting MS (RRMS).4,6,7 The recent retrospective RELOAD study analysed 258 patients who were using the RebiSmart device and found that treatment adherence was over 90% on average over 3 years. Over the study period, 30.2% of patients achieved an adherence rate of 100%, 80.6% at least 90%, and only 13.2% of patients had suboptimal adherence (<80%). In addition, 59.9% of subjects remained relapse-free.6 The recent multicentre, prospective FUTURE study found that self-administration of IFN using the RebiSmart device resulted in long-term improvements in self- and parent-reported quality of life at home and school among 40 adolescents with MS.8

Other automated injectors include the Betaconnect® (Medicom), which administers IFN β-1b (Betaseron®) and has also been associated with high patient adherence (a study of 107 patients found that only 13.1% injected <80% of doses over 24 weeks and adherence was 80.5% over this period),5 and the mechanical ExtaviPro™ device (Novartis Pharma AG), which also administers IFN β-1b (Extavia®).9 In a recent market survey of 85 participants, 82% of patients using Betaconnect, 67% of the RebiSmart users and 60% of the ExtaviPro users were highly satisfied with their device.10 The most important features of an ideal autoinjector were considered to be ease of the injection process, uncomplicated preparation of the device before use, potential for usage without help from others, easy determination of injection start and stop, and ease of pressing button for the start of the injection.10

Smart devices are also being developed for monitoring signs of disease progression. Mobility impairment is a common symptom of MS that is generally only monitored in the clinic. In a recent study, a novel wireless, skin-mounted motion sensor (BioStampRC®, MC10 Inc.) accurately and precisely measured gait parameters in 45 people with MS across a range of walking impairment levels.11 While the factors that contribute to the progression of MS have not been fully established, environmental factors including exposure to psychological stress are thought to be important.12 Wearable sensors can measure autonomic responses through electrodermal activity and heart rate variability data collected from the wrist, and provide the potential for wearable stress management devices for people with MS.13 Another important factor is visual impairment, which is common in in MS and may be related to neurodegeneration.14 Visual field defects are the most common visual disorder in MS and usually assessed using the Humphrey test, which is time consuming and not widely available outside specialist ophthalmology clinics. A smartphone application has been developed that reliably assessed visual fields in a study of 23 eyes. Such technology has the potential to be a self-monitoring tool for patients with MS.15

Ongoing studies are also exploring the potential for remote monitoring of MS using data from smartphone apps. In the FLOODLIGHT study, 80 patients with MS and 40 healthy controls received a preconfigured smartphone and smartwatch that prompted the user to perform a number of tests, comprising active tests (daily hand motor function, gait and static balance tests; weekly cognitive tests; and patient-reported outcomes) and passive monitoring (continuous assessment of gait and mobility), for 24 weeks. Interim data analysis showed that smartphone-based assessments correlated well with in-clinic tests.16 A recently launched study, ElevateMS, aims to collect sensor-based data from patients when performing physical tasks. Patients will also record symptoms using an iPhone (http://www.elevatems.org/).

In summary, smart devices are having a significant impact on the management of MS, contributing to patient convenience, leading to improved treatment adherence, and offering the potential to improve patient monitoring in the future.

References

1. Steinberg SC, Faris RJ, Chang CF, et al. Impact of adherence to interferons in the treatment of multiple sclerosis: a non-experimental, retrospective, cohort study. Clin Drug Investig. 2010;30:89–100.
2. Brandes DW, Callender T, Lathi E, O’Leary S. A review of disease-modifying therapies for MS: maximizing adherence and minimizing adverse events. Curr Med Res Opin. 2009;25:77–92.
3. Lugaresi A, Rottoli MR, Patti F. Fostering adherence to injectable disease-modifying therapies in multiple sclerosis. Expert Rev Neurother. 2014;14:1029–42.
4. Lugaresi A, Florio C, Brescia-Morra V, et al. Patient adherence to and tolerability of self-administered interferon β-1a using an electronic autoinjection device: a multicentre, open-label, phase IV study. BMC Neurol. 2012;12:7.
5. Kleiter I, Lang M, Jeske J, et al. Adherence, satisfaction and functional health status among patients with multiple sclerosis using the BETACONNECT® autoinjector: a prospective observational cohort study. BMC Neurol. 2017;17:174.
6. Fernandez O, Arroyo R, Martínez-Yélamos S, et al. Long-term adherence to IFN beta-1a treatment when using RebiSmart® device in patients with relapsing-remitting multiple sclerosis. PLoS One. 2016;11:e0160313.
7. Bayas A, Ouallet JC, Kallmann B, et al. Adherence to, and effectiveness of, subcutaneous interferon β-1a administered by RebiSmart® in patients with relapsing multiple sclerosis: results of the 1-year, observational SMART study. Expert Opin Drug Deliv. 2015;12:1239–50.
8. Ghezzi A, Bianchi A, Baroncini D, et al. A multicenter, observational, prospective study of self- and parent-reported quality of life in adolescent multiple sclerosis patients self-administering interferon-β1a using RebiSmartTM —the FUTURE study. Neurol Sci. 2017;38:1999–2005.
9. Hoffmann FA, Trenova A, Llaneza MA, et al. Patient satisfaction with ExtaviProTM 30G, a new auto-injector for administering interferon β-1b in multiple sclerosis: results from a real-world, observational EXCHANGE study. BMC Neurol. 2017;17:156.
10. Limmroth V, Reischl J, Mann B, et al. Autoinjector preference among patients with multiple sclerosis: results from a national survey. Patient Prefer Adherence. 2017;11:1325–34.
11. Moon Y, McGinnis RS, Seagers K, et al. Monitoring gait in multiple sclerosis with novel wearable motion sensors. PLoS One. 2017;12:e0171346.
12. Briones-Buixassa L, Milà R, Mª Aragonès J, et al. Stress and multiple sclerosis: a systematic review considering potential moderating and mediating factors and methods of assessing stress.Health Psychol Open. 2015;2:2055102915612271.
13. Lopez Martinez D, Picard R. Wearable technologies for multiple sclerosis: the future role of wearable stress measurement in improving quality of life. Presented at: Second International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems (SPWID 2016), Valencia, Spain, 22–26 May, 2016. Proceedings available at: spwid_2016_full.pdf (p26–7; accessed 6 February 2017)
14. Martínez-Lapiscina EH, Fraga-Pumar E, Gabilondo I, et al. The multiple sclerosis visual pathway cohort: understanding neurodegeneration in MS. BMC Res Notes. 2014;7:910.
15. Dubuisson N, Paterson, A., Turner, B. et al. Self-monitoring visual function via a smartphone application. J Neurolog Sci. 2017;381 Suppl. 479.
16. Mulero P, Midaglia L, Montalban X, et al. Interim analysis from FLOODLIGHT: a prospective pilot study to evaluate the feasibility of conducting remote patient monitoring with the use of digital technology in patients with multiple sclerosis. Presented at: 7th Joint ECTRIMS–ACTRIMS Meeting, Paris, France, 25–28 October, 2017. Absract P1229.