Selukar S; Moriello C; Andersen RE; Reid RER; Mayo NE

Research Article

Austin J Mult Scler & Neuroimmunol. 2015;2(3): 1019.

Lack of Evidence for a Temporal Relationship between Fatigue Perception and Physical Activity in Multiple Sclerosis

Selukar S¹, Moriello C², Andersen RE³, Reid RER³ and Mayo NE^1,2*

¹School of Physical and Occupational Therapy, McGill University, Canada

²Division of Clinical Epidemiology, McGill University, Canada

³Department of Kinesiology and Physical Education, McGill University, Canada

*Corresponding author: Mayo NE, Division of Clinical Epidemiology, McGill University, Royal Victoria Hospital, 687 pine ave west (R4.17), Montreal, Quebec, H3A-1A1, Canada

Received: April 16, 2015; Accepted: July 01, 2015; Published: July 04, 2015

Abstract

There is inconsistent evidence regarding daily variation of fatigue perception and its association with physical activity. While there is evidence that exercise can reduce fatigue it is not clear whether fatigue is a cause or consequence of physical inactivity.

The global aim of this study is to contribute evidence towards the daily variability in fatigue perception and to delineate the temporal sequence with physical activity in people with MS. This is a longitudinal predictive study over two time periods, immediately after trial entry and after the first exercise prescription (1 week later).

Fatigue perception was measured using a visual analogue scale for everyday at 8am, 12pm, 4pm and 9pm. Physical activity was recorded using an accelerometer for two continuous periods of 7 days.

Data was analyzed using Generalized Estimating Equations to adjust the variance for the clustering of measures within person. Odds ratio (OR) was used to identify peak times of the day for fatigue perception.

The results on 40 participants suggested that the odds of having maximum fatigue increased along the course of the day (lowest in morning, highest at night). Compared to 8:00 hours, the OR associated with having maximum fatigue at 21:00 hours was 5.57 (95% CI: 1.00, 2.44). At neither time period did end-of-day fatigue level impact on next day physical activity as measured by number of steps and the same for activity and perception of fatigue the next day. In this sample of low disability MS, there was no evidence linking fatigue and physical activity.

Keywords: Fatigue; Multiple sclerosis; Physical activity

Abbreviations

MS: Multiple Sclerosis; OR: Odds Ratio; CI: Confidence Interval

Introduction

Fatigue is the most studied symptom of Multiple Sclerosis (MS) in terms of measurement, impact, and treatment. A recent taxonomy of fatigue, separates perceived fatigue from performance fatigability [1], the former being measured using questionnaires, the latter measured with tests of muscle output. For patients, fatigue perception is what they report as distressing and is potentially what they can control by regulating activity and rest. Variation in perceived fatigue over time and with activity, and what this may mean in terms of etiology, impact, and treatment, has not been investigated systematically and evidence of a pattern is inconsistent [2-6].

People with MS consider fatigue as a barrier to take part in physical activity [7], yet the benefits of exercise are well established [8-11]. Despite this evidence, persons with MS show poor long term adherence in exercise programs and are reported to have high dropout rates [12]. The question remains, as to whether, exercise programs should be directed to improve physical activity in order to reduce fatigue, or should management of fatigue be considered first to improve physical activity. The relationship between fatigue and every day physical activity is a “chicken or egg” phenomenon, it is not clear whether fatigue is a cause or a consequence of physical inactivity.

The current longitudinal study aims: (i) to estimate the extent of variability in daily fatigue perception and identify the time of day where fatigue perception is the highest, and (ii) to estimate the temporal sequence between fatigue perception and physical activity.

Materials and Methods

Study design

This is a two-period longitudinal predictive study embedded within an ongoing randomized trial of exercise for people with MS “The Role of Exercise in Modifying outcomes of People with Multiple Sclerosis” (MSTEP) [13]. The fatigue monitoring component occurred over the first 14 days from study entry with the time divided into two consecutive periods of 7 days, one prior to exercise prescription and one after exercise prescription. Ethical consideration for this study was obtained from McGill University Health Centre (MUHC) at the Montreal Neurological Hospital.

Study population

Participants were those agreeing to the trial which recruited from two university hospital MS clinics. To be included, people had to be diagnosed with MS after 1994, aged 19 to 65 years, and capable of walking 100 meters without walking aid (PDDS stage: Early cane). Participants were excluded if they: (i) were already exercising three or more times per week; (ii) had any additional illness that restricted their function; (iii) had experienced a relapse during the past 30 days (included only if they were stable for more than 30 days after relapse); and (iv) showed difficulty reading, understanding, or speaking either English or French. We included only persons diagnosed after 1994 to have a more homogeneous group of people with respect to diagnostic criteria and access to disease modifying therapies (DMT) [14-16].

Measurement

The demographic information such as age, gender, disability status was collected at baseline. The Patient Determined Disease Steps (PDDS) was used as a measure of disability. The PDDS is the patient version of the Expanded Disability Status Scale (EDSS) [17] and shows a high degree of correlation with the clinical measure [18].

Physical activity was measured using a uniaxial accelerometer, ActivPAL^TM. Accelerometers have been shown to have excellent psychometric properties in persons with MS and are considered a feasible and acceptable tool to measure physical activity in this population [19]. Reliability estimates are 0.85 for vigorous activities and 0.90 on rhythmic activities [19]. Information on average number of steps/day, energy expenditure, time spent sitting/lying, standing, stepping, number of transitions, and cadence was recorded.

Fatigue perception was measured using a 0 to 10 Visual Analogue Scale (VAS) with 0 indicating not at all fatigued and 10 worst fatigues. Participants were provided with a diary to record their VAS rating of fatigue perception on four times every day at 08:00, 12:00, 16:00, and 21:00 hours. As the accelerometer did not distinguish between sitting and lying, participants also recorded in this diary when they went to bed and when they got up to provide an estimate of hours spent lying down.

Procedure

Eligible participants were identified from clinic records and were mailed a post-card about the study. Potential participants contacted the study centre directly or were telephoned by the research coordinator to be informed of the study and, for those interested, an appointment was made for an assessment. At this first visit, the consent was obtained, questionnaires and physical performance tests were completed, and an accelerometer was fixed to the thigh. At a second visit, 7 days later, the accelerometer was retrieved, randomization was carried out, the participants were given their exercise program, and a second accelerometer was put in place. This was to be worn for another seven days and then mailed back in a specially designed and addressed envelope. Thus, participants wore the accelerometer continuously, for a total of 14 days, 7 days pre-exercise prescription and 7 days post-exercise prescription. The analysis focused on linking fatigue perception to physical activity the next day and vice versa as shown in Figure 1.

Figure 1: The analysis focused on linking fatigue to physical activity the next day.

    
    
    Figure 1:  The analysis focused on linking fatigue to physical activity the next
day.

Data analysis

Descriptive statistics were calculated to describe the cohort and summarize, across days and time-periods, fatigue values and all physical activity parameters. The distribution of steps/day was also calculated after a log transformation.

The first analysis linked time of day to highest fatigue perception. For fatigue the maximum number of data points per person is 56 (2 time points X 7 days X 4 times of fatigue measurement every day- 8:00, 12:00, 16:00 and 21:00 hours). For each person, the time of the day where the fatigue was highest was identified and coded “1”, and all other time points were coded “0”. The proportion of persondays with the highest fatigue was calculated for each time point. To estimate the impact of time of day on highest fatigue perception, Generalized Estimating Equations (GEE), a generalized linear model that takes into account when estimating regression parameters that observations are correlated within subject [20], was used. The autoregressive correlation structure was used. The estimates (β) were used to compute odds ratio (OR) along with 95% confidence intervals (CI). The odds of fatigue at 12:00, 16:00, and 21:00 hours were estimated relative to the odds of fatigue at 8:00 hours. Single imputation was used for missing data and all statistical assumptions were accounted for.

GEE was also used to link fatigue perception to physical activity and vice versa as shown in Figure 1. The model also included age, gender, and disability (PDDS). All statistical analysis was carried out using Statistical Analysis System (SAS) Version 9.1, function ‘SAS PROC GENMOD’.

Results and Discussion

Table 1 shows characteristics of the sample. The mean age of the 40 participants was 44 years and 75% were women. The majority had a university degree (62%) and 78% had not changed their work status due to their MS. About 42% of participants had mild sensory symptoms, which did not interfere with their activity levels as seen on Patient Determined Disease Steps (PDDS). Also 25% had difficulty walking, and performing physically demanding activities.

Table 1: Key characteristics of study participants (n=40).




  
    Variables
    Mean (SD)
      N (%)
  
  
    Age (years) 
    44 (9.5)
  
  
    Women 
    30 (75%)
  
  
    Education 
     
  
  
    High School
    3 (8%)
  
  
    Junior College/ Technical School
    11 (30%)
  
  
    University
    23 (62%)
  
  
    Employment 
     
  
  
    No change due to MS
    29 (78%)
  
  
    Began to work fewer hours
    8 (22%)
  
  
     
     
  
  
    6MWT(m) 
    503.6 (108)
  
  
    Gait Speed (m/sec) 
    1.39 (0.30)
  
  
    <0.8m/sec 
    1 (3%)
  
  
    0.8-1.0m/sec 
    5 (13%)
  
  
    >1.0 m/sec 
    31 (84%)
  
  
     
     
  
  
    EQ VAS 
    73.4 (13.3)
  
  
     
     
  
  
    PDDS (score) 
     
  
  
    Normal (0)
    17 (42.5%)
  
  
    Mild Disability (1)
    7 (17.5%)
  
  
    Moderate Disability(2)
    7 (17.5%)
  
  
    Gait Disability/Early cane (3/4)
    9 (22.5%)



Table 1:  Key characteristics of study participants (n=40).

Table 2 presents mean fatigue perception scores at four different time points during the day, averaged over seven days, pre- and postexercise prescription. Across all persons, at all time points and days, the full range (0-10) of fatigue scores was observed (data not shown). At 8:00 hours, the average fatigue for the pre-exercise prescription period was 1.8 out of 10 (SD: 2.36). There was a trend for increasing fatigue perception over time, both pre- and post-exercise prescription. To express the increase in fatigue perception over time, the time period of highest fatigue perception was identified for each personday, and expressed as a percent. With 40 participants and 14 days of data collection, the total number of person-days is 560 (40x14). The highest fatigue perception was reported at 8:00 hours for 15.2%. In contrast, the highest fatigue perception was reported at 21:00 hours for 34% of person-days. The odds of having highest fatigue at 12:00, 16:00 and 21:00 hours, relative to the odds at 8:00 hours increased with later time points. At 21:00 hours the odds ratio (OR) was 5.70 (95% CI: 2.78 – 11.69).

Table 2: Mean values of fatigue perception at four time points (averaged over days), pre- and post-exercise prescription, and proportion of person-days per time point with highest level of fatigue perception.




  
    Time of    measurement
    Fatigue Pre- exercise prescription
      Mean (SD)
    Fatigue Post- exercise prescription
      Mean (SD)
    (%person-days of highest fatigue)
    Odds Ratio*
    95%CI
  
  
    08:00 hours
    1.8 (2.36)
    1.7 (2.13)
    15.2%
    Referent
     
  
  
    12:00 hours
    1.8 (2.03)
    2.3 (2.21)
    18.0%
    0.91
    0.49, 1.69
  
  
    16:00 hours
    2.7 (2.35)
    2.9 (2.38)
    32.8%
    2.84
    1.44, 5.61
  
  
    21:00 hours
    3.1 (2.78)
    3.0 (2.57)
    34.0%
    5.70
    2.78, 11.69
  
  
    *Odds ratio derived from GEE to    account for repeated measures of fatigue



Table 2:  Mean values of fatigue perception at four time points (averaged over days), pre- and post-exercise prescription, and proportion of person-days per time point
with highest level of fatigue perception.

Table 3 shows the average steps/day pre- and post-exercise prescription, which was 6245 and 7317, respectively. The SD was large illustrating a non-normal distribution and, hence, the median, 25%ile (Q1), 75%ile (Q3), and range are presented. To meet the assumptions of the future regression analysis, using steps/day as the outcome variable predicted by fatigue perception the day before (Figure 1), it was log transformed and the values are presented. The median steps/ day for the two time periods did not differ statistically (902; 95% CI: -808, 1130). Also shown are the other parameters of activity obtained from the accelerometer. The results showed that all parameters were stable over this short time period.

Table 3: Steps/day (overall and log transformed), and median values for physical activity parameters, averaged over all days and time points, pre- and post-exercise prescription.




  
    Variables (n=40)
    Pre-Exercise Prescription (n= 262 person-days)
    Post-Exercise Prescription (n= 238 person-days)
     
  
  
    Overall Steps/day
      Mean (SD)
      Median
      Q1 – Q3
      Range
     
      6245 (3682)
      5846
      3486 – 8348
      0 – 17656
     
      7317 (4314)
      6748
      4182 – 9880
      0 – 20826
     
  
  
    Overall Log Steps/day
      Mean (SD)
      Median
      Q1 – Q3
      Range
     
      8.58 (0.81)
      8.69
      8.22 – 9.03
      1.79 – 9.78
     
      8.56 (1.33)
      8.82
      8.37 – 9.20
      0.69 – 9.94
     
  
  
     
    Median [IQR]
    Difference (95% CI)
  
  
    Steps/day
    5846 (4862)
    6748 (5698)
    902 (-808, +1130)
  
  
    Energy Expenditure (MET.h) Time spent
    32.9 (2.0)
    33.4 (2.4)
    0.5 (-0.66, +0.42)
  
  
    Sleeping (hours)
    8.5 (1.6)
    8.2 (1.6)
    -0.3 (-3, +1.6)
  
  
    Sitting (hours)*
    9.7 (3.5)
    9.6 (3.7)
    - 0.1 (-0.24, +0.09)
  
  
    Standing    (hours)
    4.2 (2.7)
    4.4 (2.8)
    0.2 (-0.75, +0.57)
  
  
    Stepping (hours)
    1.3 (1.0)
    1.5 (1.1)
    0.2 (-0.17, +0.14)
  
  
    Transitioning    (sit to stand)
    53.0 (33)
    55.0 (32)
    2 (-3.68, +2.14)
  
  
    Cadence (steps/min)
      Low (0-90)
      Moderate (90-100)
      High (110-140)
     
      2665 (2242)
      1616 (1572)
      483 (1672)
     
      2925 (2248)
      1891 (1842)
      972 (2562)
     
      260 (-623, +291)
      275 (-301, +485)
      489 (-581, +494)
  
  
    *Subtracted average hours for sleep
      Inter Quartile Range (IQR): is the difference    between the value at the 25%ile (Q1) and 75%ile (Q3). E.g. the median for EE    is 32.9 with IQR of 2.0. The 25%ile is 30.9 and the 75%ile is 34.9.



Table 3:  Steps/day (overall and log transformed), and median values for physical activity parameters, averaged over all days and time points, pre- and post-exercise
prescription.

Table 4 shows the impact of end-of-day fatigue perception on physical activity (log steps/day) the next day adjusted for age, gender, and disability. The estimates in this table show the log transformed values for steps/day. For the pre-exercise period, there was no effect of end-of-day fatigue on next-day steps/day (95% CI: -0.04, 0.05). Participants, over 40 years walked an average of 5482 steps/day (SD: 3715), and those under 40 years, walked 6594 steps/day (SD: 4114). There was no difference in average steps/day by age (95% CI: (-0.69, 0.18), nor gender (95% CI: 0.21, 0.34). However, for people with gait disability, there was a statistically significant effect with the higher fatigue perception predicting lower next-day (log) steps/day (-0.48; 95% CI: (-0.96, -0.03). For the post-exercise prescription period, endof- day fatigue perception did not impact on next-day steps/day (95% CI: -0.01, 0.11). Also there was no difference on steps/day by age, gender, or disability.

Table 4: Results of the regression analysis, pre- and post-exercise prescription, linking end-of-day fatigue perception, age, gender, and disability to physical activity (log steps/day) the next day.




  
    Parameter
    Steps/day
      Mean (SD)
    Log    (Steps/day)
      Mean (SD)
    Estimates*    (95%CI)
  
  
     
    Pre-Exercise    Prescription
  
  
    Fatigue
      8:00,12:00,16:00 hours
     
     
    Referent
  
  
    21:00 hours
     
     
    0.003 (-0.04, 0.05)
  
  
    Age
     
     
     
  
  
    >40 years
    5482 (3715)
    8.48 (0.89)
    Referent
  
  
    ≤40 years
    6594 (4114)
    8.77 (0.55)
    -0.25 (-0.69, 0.18)
  
  
     
      Gender
     
     
     
  
  
    Women
    5710 (3984)
    8.56 (0.87)
    Referent
  
  
    Men
    6378 (3399)
    8.62 (0.53)
    0.06 (0.21, 0.34)
  
  
     
      Disability (PDDS)
     
     
     
  
  
    Normal
    6662 (3924)
    8.60 (0.62)
    Referent
  
  
    Mild disability
    5583 (4261)
    8.33 (1.83)
    -0.26 (-0.87, 0.36)
  
  
    Moderate disability
    4939 (4859)
    8.38 (0.68)
    -0.12 (-0.54, 0.31)
  
  
    Gait disability/early cane
    5157 (2705)
    8.40 (0.36)
    -0.49 (-0.96,    -0.03)
  
  
     
    Post-Exercise    Prescription
  
  
    Fatigue
     
     
     
  
  
    8:00,12:00,16:00 hours
     
     
    Referent
  
  
    21:00 hours
     
     
    0.08 (0.02, 0.14)
  
  
    Age
     
     
     
  
  
    >40 years
    5990 (4204)
    8.48 (1.36)
    Referent
  
  
    ≤40 years
    6695 (5714)
    8.76 (1.24)
    -1.01 (-1.9, -0.11)
  
  
     
      Gender
     
     
     
  
  
    Women
    6486 (4611)
    8.77 (0.67)
    Referent
  
  
    Men
    5156 (5222)
    7.68 (2.55)
    -1.40 (-3.6, 0.80)
  
  
    Disability    (PDDS)
     
     
     
  
  
    Normal
    6460 (4941)
    8.45 (1.41)
    Referent
  
  
    Mild disability
    5133 (5207)
    8.19 (0.37)
    0.43 (-0.26, 1.11)
  
  
    Moderate disability
    8745 (5392)
    8.53 (0.74)
    0.44 (-0.22, 1.10)
  
  
    Gait disability/early cane
    5392 (3264)
    8.51 (0.34)
    0.86 (-0.39, 2.10)
  
  
    *Estimates    derived from GEE to account for repeated measure of steps/day; all estimates    are adjusted for the other variables in the model. PDDS=Patient Determined    Disease Steps



Table 4:  Results of the regression analysis, pre- and post-exercise prescription,
linking end-of-day fatigue perception, age, gender, and disability to physical
activity (log steps/day) the next day.

Table 5 presents the distribution of average fatigue perception, pre- and post-exercise prescription and the results of the regression analysis, relating physical activity throughout the day to next-day fatigue perception considering also age, gender, and disability. For the pre-exercise period, there was no effect of physical activity throughout the day on next-day fatigue perception (95% CI: -0.00, 0.00). Post-exercise prescription, physical activity throughout the day did not impact on next-day fatigue perception (95% CI: -0.00, 0.00). Also there was no effect of age, gender, and disability on fatigue perception levels was maintained.

Table 5: Results of the regression analysis, pre- and post-exercise prescription, linking physical activity throughout the day, age, gender, and disability to fatigue perception the next day.




  
    Parameter
    Mean (SD)
    Estimates* (95% CI)
  
  
     
    Pre-Exercise Prescription
  
  
    1000 Steps/day
     
    0.00 (-0.00, 0.01)
  
  
    Age
     
     
  
  
    >40 years
    2.47 (1.97)
    Referent
  
  
    ≤40 years
       
    2.35 (1.86)
    0.07 (-0.67, 0.81)
  
  
    Gender
     
     
  
  
    Women
    2.34 (2.05)
    Referent
  
  
    Men
    2.44 (1.40)
    -0.08 (-0.72, 0.56)
  
  
    Disability (PDDS)
     
     
  
  
    Normal
    2.08 (1.76)
    Referent
  
  
    Mild disability
    2.41 (1.87)
    0.39 (-0.29, 1.06)
  
  
    Moderate    disability
    3.32 (2.96)
    1.32 (-0.35, 2.99)
  
  
    Gait disability/early cane
    2.58 (1.49)
    0.01 (-0.84, 0.86)
  
  
     
    Post-Exercise prescription
  
  
    1000 Steps/day
     
    0.00 (-0.00, 0.01)
  
  
    Age
     
     
  
  
    >40 years
    2.86 (1.92)
    Referent
  
  
    ≤40 years
       
    1.74 (2.03)
    0.17 (-0.90, 1.25)
  
  
    Gender
     
     
  
  
    Women
    1.71 (0.02)
    Referent
  
  
    Men
    1.67 (1.83)
    -0.19 (-1.11, 0.74)
  
  
    Disability (PDDS)
     
     
  
  
    Normal
    2.36 (2.08)
    Referent
  
  
    Mild    disability
    1.68 (1.91)
    -0.69 (-1.80, 0.42)
  
  
    Moderate disability
    4.03 (2.14)
    -0.03 (-1.70, 1.64)
  
  
    Gait    disability/early cane
    2.55 (1.57)
    -0.77 (-1.93, 0.39)
  
  
    *Estimates derived from GEE to    account for repeated measure of fatigue; all estimates are adjusted for the    other variables in the model
      PDDS: Patient Determined Disease    Steps



Table 5:  Results of the regression analysis, pre- and post-exercise prescription,
linking physical activity throughout the day, age, gender, and disability to fatigue
perception the next day.

Discussion and Conclusion

This study confirmed that fatigue perception in MS varies during the day, being lowest in morning and highest at night. People with MS were much more likely to report their highest fatigue perception at 21:00 hours (OR: 5.57; 95%CI: 1.00, 2.44) than at 8:00 hours (Table 2).

Few studies have addressed the longitudinal course of MS fatigue perception. We were able to find five studies that addressed this temporal sequence and salient features of these studies are presented in Table 6. This topic has been addressed over 4 decades with the largest study [21] comprising 102 people. The measures of fatigue perception varied, however, four out of five studies concluded that fatigue perception was highest in the afternoon, and one study demonstrated that fatigue perception was highest in morning; two studies did not record fatigue perception later than the afternoon. The analysis linking fatigue perceptions to time were mainly qualitative. The previous evidence was insufficient to establish a definite pattern in fatigue perception variability because of a number of measurement limitations. In contrast to this, our study which was designed to address these methodological limitations, we found that fatigue perception was highest at night (Table 2). Our results were included in Table 6 for comparison purposes.

Table 6: Literature review on diurnal variation of fatigue in MS.




  
    Author (Year)
    N 
    Fatigue measures 
    Results 
    Drawback 
  
  
    Krupp (1988)
    32
    Direct question
      (When do you experience your highest    fatigue?)
    Fatigue highest in afternoon
    No analysis
      No measurement
  
  
    Morris (2002)
    14
    Visual Analogue Scale (10:00 and 15:00 hours    for  one day)
    Fatigue increases from morning to afternoon
      (t[13]=-3.14, p=0.008) 
    No recording of fatigue in evening or night
  
  
    Schwid (2002)
    23
    Rochester Fatigue Diary (fatigue measured    every hour for seven days)
    High fatigue in morning and it decreases in    afternoon
    No analysis
  
  
    Mills
      (2008)
    40
    Semi-structured Interview
    Fatigue highest in afternoon
    No measurement
  
  
    Feys
      (2012)
    102
    Rochester Fatigue Diary (9:00, 12:00, 15:00    hours for one day)
    High fatigue at 12:00 and 15:00 hours    compared to 9:00 hours
      (F[2,100]=38.1;    p<0.0001) 
    No recording of fatigue in evening or night
  
  
    Selukar
      (2015)
    40
    VAS 0 – 10 (08:00, 12:00, 16:00, 21:00)
    Highest fatigue 21:00 (OR: 5.57; 95%CI: 1.00,2.44)
    Sample size small, time frame limited to 14    days



Table 6:  Literature review on diurnal variation of fatigue in MS.

The second aim of our study was to estimate the temporal relationship between fatigue perception and next-day physical activity measured using an accelerometer. The average step count pre-exercise prescription was 6245 ± 3682. This was similar to the step count reported by Dlugonski et al. on 645 people with MS (5903 ± 3185) [22] and similar to accumulated data from Motl et al. [23] for people with PDDS of 0 to 2, the majority of our patients.

We were concerned that wearing the accelerometer for the first time would increase exercise engagement a participants knew they are being monitored and, as a result, there would be little additional change after exercise prescription. Although the difference in average steps/day pre- and post-exercise prescription was not statistically different, out of 560 person-days, 212 person-days (38%) showed an increase in steps/day by more than 800, which is considered clinically meaningful in MS [24]. While they had only just been given their exercise program, this shows that some people respond early to exercise recommendation and that the novelty of the device may not have been such a stimulus to exercise. While, there is evidence from a systematic review for the monitoring effect [25], the studies included used pedometers, devices that give immediate feedback unlike the accelerometers we used, that do not.

The results did not show any association between fatigue perception and physical activity over time indicating that in this sample of persons, fatigue perception at night was not associated with physical activity the next day and vice versa. Previous crosssectional literature showed weak correlation [21,26-29]. In contrast to the studies conducted in past, the current study used a longitudinal design, fatigue perception was measured several times in a day, physical activity was measured directly, and appropriate statistical approach was used to deal with this non-independence of data.

Evidence supports the role of physical activity in people with MS. In a meta-analysis on 39 randomized controlled trials, Kuspinar et al indicated that exercise reduces fatigue (ES=0.6). In contrast to this, it was previously believed that physical activity will induce fatigue due to rise in body temperature [30]; our study did not find evidence that being physically active has any negative effect on fatigue perception.

This study has several limitations. According to the taxonomy of fatigue, it has the components of perceived fatigue and performance fatigability [1]. This study was only based on the measure of perceived fatigue. For future studies, it is recommended that performance fatigability should also be assessed while measuring perceived fatigue. This study followed up activity levels only after one week postexercise prescription, for the purposes of looking at relationships before alteration with a new exercise program. These results should be confirmed with long term exercise programs. In addition, we only related fatigue on one day to physical activity the next day and vice versa but not at a more granular level such as fatigue after activity or activity after highest time fatigue. While this would be interesting, it is beyond the scope of this paper.

In conclusion, this study contributed evidence for variation of daily fatigue in people with MS. Fatigue perception is lowest in the morning, goes on increasing as the day progresses, and is highest at night. Our study concluded that, over this relatively short time-frame, fatigue and physical activity were independent of each other. This finding should be reassuring to people with MS who wish to increase physical activity but fear negative impacts on fatigue perception.

The current study has several clinical implications. For clinicians, knowing the pattern of daily fatigue perception could be beneficial to provide an idea as to when and how often should this symptom be assessed throughout the day. The above results are also beneficial for providing an effective self-management program. People with MS should be made aware of this pattern of fatigue. This would help them to effectively use techniques like energy conservation and activity pacing. Also since this study confirmed the diurnal variation in fatigue it is recommended for future research that fatigue scores should not be averaged throughout the day as this might not provide true estimates of this construct. These results should be considered before designing a physical activity intervention, and people with MS should be recommended to participate in the exercise programs despite their levels of fatigue perception. With exercise, in long term follow up, it is anticipated that there would not only be an increase in physical activity, but also a reduction in fatigue perception.

References

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Citation: Selukar S, Moriello C, Andersen RE, Reid RER and Mayo NE. Lack of Evidence for a Temporal Relationship between Fatigue Perception and Physical Activity in Multiple Sclerosis. Austin J Mult Scler & Neuroimmunol. 2015;2(3): 1019.

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Variables	Mean (SD) N (%)
Age (years)	44 (9.5)
Women	30 (75%)
Education
High School	3 (8%)
Junior College/ Technical School	11 (30%)
University	23 (62%)
Employment
No change due to MS	29 (78%)
Began to work fewer hours	8 (22%)

6MWT(m)	503.6 (108)
Gait Speed (m/sec)	1.39 (0.30)
<0.8m/sec	1 (3%)
0.8-1.0m/sec	5 (13%)
>1.0 m/sec	31 (84%)

EQ VAS	73.4 (13.3)

PDDS (score)
Normal (0)	17 (42.5%)
Mild Disability (1)	7 (17.5%)
Moderate Disability(2)	7 (17.5%)
Gait Disability/Early cane (3/4)	9 (22.5%)