Smartphone-based assessment of fatigue and fatigability in multiple sclerosis
ECTRIMS Online Library. Lam K. 09/12/19; 279160; P800
Ka Hoo Lam
Ka Hoo Lam
Contributions
Abstract

Abstract: P800

Type: Poster Sessions

Abstract Category: Clinical aspects of MS - Clinical assessment tools

K.H. Lam1, P. Van Oirschot2, B. Den Teuling2, V. De Groot3, B.M.J. Uitdehaag1, J. Killestein1

1Department of Neurology, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, 2MS sherpa BV, Nijmegen, 3Department of Rehabilitation Medicine, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands

Introduction: Fatigue is one the most reported and disabling symptom in multiple sclerosis (MS). Therefore, measuring and monitoring fatigue is of importance for patient care and clinical trials. A smartphone-based assessment of fatigue and fatigability in a day-to-day setting has been developed.
Objectives: To evaluate validity and reliability of smartphone-based measurements of fatigue and fatigability in MS.
Methods: In a prospective observational study MS patients and healthy control subjects are recruited. At baseline and two-weeks follow-up, clinical fatigue measures are assessed: Checklist Individual Strength (CIS20-r), Fatigue Severity Scale (FSS), and Modified Fatigue Impact Scale (MFIS). Smartphone-based measurements are performed daily and includes: a 7-point Likert fatigue rating score and a two-minute eye movement fatigability task captured with the front-facing camera. Eye movement parameters includes: saccadic initiation time, amplitude and velocity. Participants performed the fatigue and fatigability measurements before and after a two-minute walk test or a SDMT task, which were used to induce motor and cognitive fatigue, respectively.
Results: With ongoing subject inclusion, currently 33 MS patients and 10 healthy controls are included. Both groups were similar in age, sex and level of education. Mean digital fatigue ratings significantly correlated with CIS (ρ = 0.654), FSS (ρ = 0.659), and MFIS (ρ = 0.656) at baseline. Test-retest intraclass correlation coefficient in stable participants (n = 31) was 0.735. Cognitive fatigability did not correlate with the MFIS cognitive component (ρ = 0.266, p = 0.096), while motor fatigability significantly correlated with the MFIS physical component (ρ = 0.415, p = 0.008). Extraction and analyses of eye movement parameters are pending.
Conclusions: As our preliminary results demonstrate moderate concurrent validity and good reliability, day-to-day digital fatigue ratings potentially enables fatigue assessment in novel contexts. Additional data acquisition and analyses are pending and will provide more insight into smartphone-based measurement of fatigability.
Disclosure: K.H. Lam has nothing to disclose.
P. van Oirschot is an employee of MS sherpa BV (industry partner).
B. den Teuling is an employee of MS sherpa BV (industry partner).
V. de Groot has nothing to disclose.
B.M.J. Uitdehaag has received personal compensation for consulting from Biogen Idec, Genzyme, Merck Serono, Novartis, Roche and TEVA.
J. Killestein has accepted speaker and consultancy fees from Merck, Biogen, Teva, Genzyme, Roche and Novartis.
The study received financial support from TKI Life Sciences & Health, Stichting MS Research, and Biogen.

Abstract: P800

Type: Poster Sessions

Abstract Category: Clinical aspects of MS - Clinical assessment tools

K.H. Lam1, P. Van Oirschot2, B. Den Teuling2, V. De Groot3, B.M.J. Uitdehaag1, J. Killestein1

1Department of Neurology, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, 2MS sherpa BV, Nijmegen, 3Department of Rehabilitation Medicine, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands

Introduction: Fatigue is one the most reported and disabling symptom in multiple sclerosis (MS). Therefore, measuring and monitoring fatigue is of importance for patient care and clinical trials. A smartphone-based assessment of fatigue and fatigability in a day-to-day setting has been developed.
Objectives: To evaluate validity and reliability of smartphone-based measurements of fatigue and fatigability in MS.
Methods: In a prospective observational study MS patients and healthy control subjects are recruited. At baseline and two-weeks follow-up, clinical fatigue measures are assessed: Checklist Individual Strength (CIS20-r), Fatigue Severity Scale (FSS), and Modified Fatigue Impact Scale (MFIS). Smartphone-based measurements are performed daily and includes: a 7-point Likert fatigue rating score and a two-minute eye movement fatigability task captured with the front-facing camera. Eye movement parameters includes: saccadic initiation time, amplitude and velocity. Participants performed the fatigue and fatigability measurements before and after a two-minute walk test or a SDMT task, which were used to induce motor and cognitive fatigue, respectively.
Results: With ongoing subject inclusion, currently 33 MS patients and 10 healthy controls are included. Both groups were similar in age, sex and level of education. Mean digital fatigue ratings significantly correlated with CIS (ρ = 0.654), FSS (ρ = 0.659), and MFIS (ρ = 0.656) at baseline. Test-retest intraclass correlation coefficient in stable participants (n = 31) was 0.735. Cognitive fatigability did not correlate with the MFIS cognitive component (ρ = 0.266, p = 0.096), while motor fatigability significantly correlated with the MFIS physical component (ρ = 0.415, p = 0.008). Extraction and analyses of eye movement parameters are pending.
Conclusions: As our preliminary results demonstrate moderate concurrent validity and good reliability, day-to-day digital fatigue ratings potentially enables fatigue assessment in novel contexts. Additional data acquisition and analyses are pending and will provide more insight into smartphone-based measurement of fatigability.
Disclosure: K.H. Lam has nothing to disclose.
P. van Oirschot is an employee of MS sherpa BV (industry partner).
B. den Teuling is an employee of MS sherpa BV (industry partner).
V. de Groot has nothing to disclose.
B.M.J. Uitdehaag has received personal compensation for consulting from Biogen Idec, Genzyme, Merck Serono, Novartis, Roche and TEVA.
J. Killestein has accepted speaker and consultancy fees from Merck, Biogen, Teva, Genzyme, Roche and Novartis.
The study received financial support from TKI Life Sciences & Health, Stichting MS Research, and Biogen.

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