The effect of structured exercise on non-exercise physical activity and sedentary behaviour in persons with mild Multiple Sclerosis and healthy controls

doi:10.21203/rs.3.rs-3203061/v1

Download PDF

Research Article

The effect of structured exercise on non-exercise physical activity and sedentary behaviour in persons with mild Multiple Sclerosis and healthy controls

https://doi.org/10.21203/rs.3.rs-3203061/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background Exercise interventions fail to increase objective physical activity (PA) in persons with Multiple Sclerosis (PwMS), while they self-report higher exercise participation. This suggests that PwMS change their non-exercise PA (NEPA). We aimed to explore NEPA changes of PwMS and healthy controls (HC), and whether these constrain exercise adaptations.

Methods Twenty-nine mildly-disabled PwMS and 26 HC completed a 10-month home-based running program. The primary outcome was time in different NEPA intensities (light intensity PA [LIPA] and moderate-to-vigorous intensity PA [MVPA]) and in sedentary behaviour ([SB]; total and uninterrupted SB) at baseline (T1), after 5 (T2) and 10 (T3) months of exercise. Data were averaged over days with and without exercise sessions (EX and NONEX days). Secondary outcomes included patient-reported and physical exercise adaptations (fatigue, walking mobility, blood pressure, body composition and cardiorespiratory fitness).

Results A significant reduction in non-exercise MVPA was observed from T1 to T2 (-113±31 min/week, p=0.006) and from T1 to T3 (-95±26 min/week, p=0.007) in PwMS, which approximately matched the weekly exercise duration at those time points. PwMS also increased their uninterrupted SB on NONEX days compared to EX days (+0.7±0.3h, p=0.003). There were no changes in MVPA or SB of HC (group×time effect MVPA: p=0.017; group×EX day effect uninterrupted SB: p=0.003). Secondary outcomes improved similarly in both groups and were not associated with NEPA/SB changes.

Conclusions In contrast to HC, PwMS significantly changed their NEPA and the pattern in which they accumulated SB in response to structured exercise. This might be a necessary behavioural compensation in order to adhere to the exercise intervention and did not constrain patient-reported and physical outcomes. Future research is warranted to unravel the underlying causes and to investigate the effects on other exercise adaptations, such as cardiometabolic health.

Trial registration The present study was registered (December 10, 2019) at clinicaltrials.gov as NCT04191772.

Multiple Sclerosis

exercise

non-exercise physical activity

sedentary behaviour

exercise adaptations

Multiple sclerosis (MS) is an autoimmune, inflammatory and neurodegenerative disorder of the central nervous system, predominantly affecting young to middle-aged adults[1]. Persons with MS (PwMS) manifest with heterogenous symptoms, commonly including spasticity, paralysis, walking difficulties, fatigue and cognitive decline[2]. These primary disease symptoms often limit PwMS to engage in physical activity (PA)[3], making them more sedentary and less active compared to healthy controls (HC)[4]. To date, a plethora of evidence shows the beneficial effects of structured exercise on MS symptoms, cardiorespiratory fitness and the risk of developing comorbidities that are associated with disability and disease progression, such as obesity and hypertension[5–8].

However, it is currently not known yet what happens with the non-exercise PA (NEPA) of PwMS in response to exercise. Changes in NEPA, defined as unstructured and unplanned PA embedded in much of daily life, such as climbing stairs, doing household chores and active transportation[9], can be hypothesised to impact the eventual exercise adaptations. Previous research showed that PwMS self-report to participate in more PA after terminating an exercise intervention, while objective PA levels do not increase[10, 11]. This indeed suggests that PwMS decrease their NEPA in response to exercise, but specific changes during an intervention and the effect on the eventual exercise adaptations are not known yet.

More specifically, the lack of an objective PA increase[10, 11] might be caused by a reduction in the intensity of NEPA (i.e. a shift from moderate-to-vigorous intensity to more light intensity PA), or by an increase in sedentary behaviour (SB). SB is defined as “any waking behaviour in a sitting, reclining or lying posture with an energy expenditure ≤ 1.5 metabolic equivalents”[12]. These changes might have a distinct impact on the eventual exercise adaptations. Furthermore, previous research did not include healthy controls (HC), limiting conclusions on whether NEPA/SB changes are MS-specific or rather a consequence of the exercise protocol. Therefore, the present study primarily aimed to investigate the specific changes in daily NEPA/SB of PwMS and HC during an exercise intervention, and secondarily whether these changes are associated with the eventual exercise adaptations in patient-reported outcomes (fatigue and walking disability) and physical outcomes (blood pressure, body composition and cardiorespiratory fitness).

2.1. Study design

The exercise intervention consisted of a 10-month home-based running exercise program (February 2020-December 2020). The study design comprised a non-randomised controlled study. Because there is no data available on NEPA changes in PwMS during an exercise intervention, sample size calculation was based on the change in cardiorespiratory fitness previously reported by our research group with a similar exercise protocol[13]. An estimated effect size of 0.5 resulted in 42 PwMS and 42 HC needed to detect a significant change of 5.2% from pre- to post-intervention with a power of 80% and a two-sided α using a paired t-test and drop-out rate of 10%.

After checking eligibility of participants via mail or phone, participants were invited for the first study visit to assess baseline measurements (T1) (see additional file 1). Primary outcome measures included NEPA on days with and without exercise sessions (EX and NONEX days), and were monitored by accelerometry for 24h/day during 7 consecutive days at T1, after 5 months of intervention (T2) and in the last week of the intervention (T3). Secondary outcome measures included patient-reported outcomes (fatigue and walking impairment) and physical outcomes (blood pressure, body composition and cardiorespiratory fitness), and were measured at T1 and T3 (after the intervention).

2.2. Participants

PwMS and HC (> 18 years) were recruited through online and paper advertisements via the Belgian-based MS-related MoveToSport foundation (Kontich, Belgium) networks. PwMS were included if they had mild disability (Expanded Disability Status Scale score; EDSS < 5), independent of the MS phenotype. Participants were excluded if they experienced an acute MS exacerbation 6 months prior to the start of the study, had contraindications to participate in moderate-to-vigorous intensity PA (MVPA), had medication changes in the last three months, planned or were planning to follow a weight reduction program or weight loss (> 2kg) in the last three months, were pregnant, or had no daily internet access. All participants provided written informed consent prior to participation. The study protocol was approved by the Medical Ethical Committee of Hasselt University (Hasselt, Belgium; CME2019/062) and the Jessa Hospital Hasselt (Belgium), was conducted in accordance with the principles of the Declaration of Helsinki (2013) and is registered at clinicaltrials.gov as NCT04191772 (December 19, 2019).

2.3. Exercise program

Running instructions were communicated weekly via a sports watch (Polar® M430, Polar Electro Oy, Finland) and were based on the maximal heart rate (HR_max) measured during the cycle exercise test (see paragraph 2.4.2.3), increased by 5% to correct for the transfer between cycling and running[14]. Based on baseline cardiorespiratory fitness (reference values from Heyward et al.[15]) and running experience, participants were assigned to a start to run (STR) or experienced run (ER) program (see additional file 1). In the STR program, participants alternated walking and running in the first 3 months until they were able to run continuously for 30min. Hereafter, STR participants started in the ER program. The design of both programs was periodised, and comprised periodically alternating blocks of 3 weeks. The first two weeks of every cycle included one high intensity training ([HIIT]; 20-30min, 80–100%HR_max) and two moderate intensity continuous training ([MICT]; 30-80min, 60–80% HR_max) sessions/week. The third week of every cycle alternatingly comprised only one HIIT or two MICT sessions to allow recuperation. Each exercise session started and ended with a 5-min warm-up/cool-down (50–60% HR_max) period (see additional file 2).

2.4. Measurements

2.4.1. Primary outcome measures: NEPA and SB

A tri-axial activPAL3® monitor (PAL Technologies, Glasgow, Scotland) was used to quantify NEPA and SB for 24h/day during 7 consecutive days. The monitor was waterproofed with a small sleeve and medical-grade adhesive wrapping (Tegaderm; 3M, Saint Paul, MN, USA) and attached on the mid part of the anterior thigh of participants using Tegaderm (3M). The proprietary algorithm in the activPAL software was used to generate second-by-second estimates of total PA and SB. The following outcome measures were used to describe NEPA and SB:

Light intensity PA ([LIPA]; stand + walking at < 99 steps/min[16]), in % of waking time (WT)/day
Moderate-to-vigorous intensity PA ([MVPA]; walking, running and cycling at ≥ 100 steps/min[16]), in % of WT/day
Total SB (% of WT/day)
Uninterrupted SB (time in sedentary bouts > 60min; hours)

These outcomes were averaged for:

All measured days without exercise session (NONEX days), and
All measured days with exercise session (EX days), in which exercise time was removed from the raw data generated by the activPAL algorithm.

Data were only included when there were at least 4 measurement days of 24h/day with minimum 1 weekend day and 2 exercise sessions (for T2 and T3). WT was determined by subtracting sleeping time (determined by the activPAL algorithm and manually checked/corrected with self-reported time-in-bed data[17]) from the total wear time/day.

2.4.2. Secondary outcome measures

2.4.2.1 Exercise session information

Participants tracked their exercise sessions (intensity in % of HR_max and duration in minutes) with a sports watch and uploaded this information via an online Polar account. Exercise session information was then available for the research team via polar.flow.com/coach. Session adherence was calculated as the number of completed exercise sessions compared to the protocol (% of the total prescribed sessions)[18], and content adherence as the total training duration and time per heart rate zone compared to the protocol (% of the prescribed total duration/time per heart rate zone)[18].

2.4.2.2. Patient-reported outcomes

Multidimensional (physical, cognitive and psychosocial) fatigue of the last four weeks was assessed by the Modified Fatigue Impact Scale (MFIS)[19]. Total (range 0–84) and different MFIS dimension scores were reported. In addition, the number of participants with a total MFIS score ≥ 38, which is the cut-off value for MS-related fatigue[20], was reported.

The impact of MS on walking mobility was measured by the 12-item MS Walking Scale (MSWS-12), for which higher scores indicate worse perceived walking ability [0-100][21].

2.4.2.3. Physical outcomes

After sitting on a chair for 10 min in a quiet room with constant temperature (21°C), HR_rest (beats per minute; bpm), systolic and diastolic blood pressure (SBP and DBP; mm Hg) were assessed four times at 5-min intervals with an electronic sphygmomanometer (Omron®, Omron Healthcare, IL, USA) on the dominant arm and documented as the mean value of the final 3 measurements.

Body height was measured to the nearest 0.1cm using a wall-mounted Harpenden stadiometer, with participants barefoot. Body weight (BW; kg) was determined using a digital-balanced weighting scale in underwear to the nearest 0.1kg. Body mass index (BMI) was calculated from weight and height measurements (weight/height²). Whole body fat percentage (fat%; %) and lean tissue mass (kg) were evaluated using a dual energy X-ray absorptiometry (Hologic Series Delphi-A Fan Beam X-ray Bone Densitometer, Vilvoorde, Belgium).

A gradual maximal exercise test was performed on an electronically braked cycle ergometer (eBike Basic®, General Electric GmbH, Bitz, Germany), which provides a valid measure of cardiorespiratory fitness in PwMS with low to mild levels of disability (EDSS ≤ 4.0)[22]. Participants started cycling at 20 (♀) or 30 (♂) watt (W) for 1 minute, followed by workload increases of 10 or 15W/min respectively, while maintaining a cadence of > 70 rounds per minute (rpm). HR was monitored continuously using a Polar chest strap (Polar®, Oy, Finland), oxygen uptake (V̇O₂), expiratory volume and respiratory exchange ratio (RER) were measured breath-by-breath and averaged every 10 seconds using a Metalyzer II^®B (Cortex, Leipzig, Germany). Exercise testing was terminated by volitional exhaustion or failure to maintain 45rpm. The RER (≥ 1.10), maximal heart rate (HR_max; ≥90% of age-predicted HR [220-age]), post-test lactate levels (2 min after termination; ≥8 mmol/L) and perceived exertion (Borg scale; ≥17/20) were used to verify maximal effort. Peak oxygen uptake (V̇O_2peak) relative to total body mass (ml/min/kg), peak workload (W_peak) and the HR recovery 2 min after termination (HR_recov; % of HR_max) were reported. In addition, V̇O_2peak values were compared with the predicted V̇O_2peak values based on age, gender and body weight[23] and presented as %V̇O_2peakPRED.

2.5. Statistical analyses

Statistical analyses were performed using IBM SPSS® version 27.0 (IBM SPSS Statistics for Windows, Chicago, IL, USA). Primary outcome measures were presented as means ± standard error of the mean (SEM), secondary outcome measures as estimated means ± SEM. Data were analysed using a per-protocol (PP) principle, for which all participants with a session adherence < 70% were excluded. After checking the assumptions, baseline differences between groups were checked with a univariate analysis of variance (with gender as covariate for the secondary outcome measures). PA data were analysed using a linear mixed model analysis for repeated measures including main and interaction effects for time (T1, T2, T3), group (MS, HC) and exercise day (EX, NONEX day), participant ID as random effect and an unstructured repeated covariance structure for the residuals. The same model was used for the secondary outcome measures, without the factor for exercise day and the addition of gender as covariate. Model validation was performed with likelihood-ration tests. When a significant main effect was found, Bonferroni-corrected pairwise comparisons were performed. Exercise information was analysed using a univariate analysis of variance with group (MS, HC) as fixed factor. Categorical data were compared between groups (MS, HC) with a chi square test. To investigate whether NEPA changes impacted the secondary outcome measures, partial correlations between NEPA changes (T1 to T3) and changes in secondary outcomes were made for the whole group, corrected for baseline values and gender. Correction for multiple testing was implemented using the Benjamini-Hochberg false discovery rate (FDR) method[24]. To explore explanatory factors for the NEPA changes, partial correlations between NEPA changes (T1 to T3) and demographics (age, EDSS, MS duration), baseline secondary outcomes (total and physical fatigue, walking mobility, V̇O_2peak, fat%) and exercise information (exercise protocol and duration) were made for the whole group and PwMS only. A p-value ≤ 0.05 (2-tailed) was considered statistically significant for all analyses.

3.1. Participant characteristics

A total of 128 participants were screened for study eligibility, of which 89 were included (Fig. 1). All participants (41 PwMS, 48 HC) completed the baseline measurements, after which 5 HC and 4 PwMS dropped out at T2 and another 2 HC and 1 PwMS at T3, resulting in 36 PwMS and 41 HC completing the intervention. Because the aim of the current study was to explore PA changes of PwMS that effectively perform exercise, only the data of participants with ≥ 70% session adherence (29 PwMS and 26 HC) are presented here. There were no differences in baseline measures or participant characteristics between participants with < 70% or ≥ 70% adherence. Participants were on average 41.3 ± 1.0 years and the majority was female (PwMS: 86.2%, HC: 65.4%, p = 0.111). All PwMS had the relapsing-remitting phenotype, a mean MS duration of 8.2 ± 1.2 years at study enrolment and were only mildly disabled (mean EDSS score 1.4 ± 0.2) (Table 1). A higher percentage of PwMS performed the STR protocol compared to the HC (37.9% vs. 11.5%, p = 0.032).

3.2. Primary outcome measures: NEPA and SB

Besides a longer sleep duration in PwMS (+ 0.6 ± 0.2h, p = 0.001; group effect p < 0.001), which did not change during the intervention (interaction effects p > 0.05; see additional file 3), all other baseline PA and SB characteristics were similar between groups. On both T2 and T3, PwMS significantly reduced their MVPA levels compared to T1 (-1.6 ± 0.5% of WT/day or -113 ± 31 min/week, p = 0.006 and − 1.3 ± 0.4% of WT/day or -95 ± 26 min/week, p = 0.007 respectively), whilst this did not change in HC (-0.1 ± 0.6% of WT/day and + 0.1 ± 0.5% of WT/day, p = 1.000; group×time interaction effect p = 0.017; Fig. 2 and additional file 3). Of the PwMS reducing their MVPA, approximately 50% replaced it with LIPA, 50% with SB. These changes were not significant. Furthermore, PwMS increased their uninterrupted SB on NONEX compared to EX days (+ 0.7 ± 0.3h, p < 0.001), while there was no difference between EX and NONEX days in HC (-0.1 ± 0.2h, p = 0.907; group×EX day interaction effect p = 0.003; Fig. 2). There were no significant correlations between changes in NEPA or SB and demographics, baseline secondary outcomes nor exercise protocol.

3.3. Secondary outcome measures

3.3.1. Exercise session information

The mean exercise duration per session was longer for HC (44 ± 5min vs. 41 ± 5min; p = 0.031), but there were no differences in the total number of exercise sessions (89 ± 14 sessions in 10 months, p = 0.221), mean exercise intensity (78.7 ± 6.1% of HR_max, p = 0.116), session adherence (92.4 ± 13.1% p = 0.234), nor content adherence (94.9 ± 15.1%, p = 0.167 and Fig. 3) between groups. PwMS and HC also did not differ in total exercise time performed during the 1-week T2 and T3 PA assessment (T2 PwMS: 114 ± 9min, HC: 104 ± 8min, p = 0.424 and T3 PwMS: 105 ± 9min, HC: 130 ± 9min, p = 0.053).

3.3.2. Exercise adaptations

At baseline, W_peak and cardiorespiratory fitness (V̇O_2peak and V̇O_2peakPRED) were significantly lower in PwMS than HC and more PwMS reached the cut-off value of MS-related fatigue. These differences between groups did not change after the intervention. Fatigue (-5.8 ± 1.2 points), fat% (-1.0 ± 0.4%), HR_rest (-3 ± 1bpm), W_peak (+ 12.3 ± 1.6W), cardiorespiratory fitness (V̇O_2peak + 2.1 ± 0.5ml/min/kg) and HR_recov (+ 1.7 ± 0.6%) improved similarly in both groups (see Table 2 for p-values of time effects). No significant associations between changes in NEPA/SB and changes in secondary outcome measures were observed.

Structured exercise training is a cornerstone of MS treatment, but its effect on NEPA levels of PwMS is unclear. The present data support our hypothesis that PwMS change their NEPA in response to exercise. More specifically, a reduction in MVPA was observed after 5 and 10 months (-113 ± 31 min/week and − 95 ± 26 min/week), which approximately matched the weekly duration of the exercise sessions at those time points (114 ± 9 min/week and 105 ± 9 min/week). Furthermore, PwMS also increased their uninterrupted SB on days without exercise session compared to days with exercise session (+ 0.7 ± 0.3h). These NEPA/SB changes were not seen in HC, indicating that these behavioural changes are MS-specific. Previous systematic reviews also found that healthy adults and overweight/obese persons did not change their NEPA/SB in response to prescribed exercise[25, 26].

It is intriguing to speculate why the present NEPA changes seem to be MS-specific. Firstly, there were no associations between any of the MS characteristics (EDSS score, MS duration and walking mobility) and NEPA/SB changes. It is however important to note that this might also be due to the low variation within the MS characteristics, as the included PwMS were only mildly disabled and had a rather short MS duration. Secondly, fatigue, reported to be the most frequent and debilitating MS symptom[27], was proposed by King et al. as important explanatory factor for inter-individual variability in NEPA changes in healthy persons[28]. In the PwMS of the present study, a large variation in fatigue was measured (range MFIS questionnaire: 2–64 points), but no correlations were present between fatigue at any time point and NEPA or SB changes. Furthermore, fatigue significantly improved with 20.5% in the MS group. This implies that fatigue did not cause the NEPA/SB changes. However, although the MFIS questionnaire assesses multidimensional fatigue, it only captures the average fatigue of the last four weeks. This limits conclusions on acute fatigue and especially on exercise-induced fatigue/tiredness. Smith et al. reported that fatigue does not increase immediately nor 24h after exercise in PwMS, but only a single exercise session was included and the intensity was rather low (Borg scale 12–14/20)[29]. Moreover, an observational study on symptomatic fatigue in PwMS (n: 309) reported that 64% of participants reported worse fatigue after “moderate exercise”, while this was the case for 83% after “vigorous exercise”[30]. Although sub analyses of the current results showed that exercise intensity (HIIT versus MICT sessions) had no effect on the NEPA/SB changes, the overall demand of 2–3 exercise sessions per week at moderate-to-vigorous intensity might require necessary NEPA/SB compensations in order to conserve energy to adhere to the exercise intervention. Such energy conservation strategies are often taught to PwMS to prevent or treat fatigue[31]. However, it might also be true that PwMS implemented dysfunctional energy conservation strategies, which were not adapted accordingly to their improving fatigue and cardiorespiratory fitness levels. This was not investigated in the present study, but this indicates that fatigue and the implementation of energy conservation strategies might need to be monitored and possibly revised throughout an exercise intervention.

Another plausible explanation might be that the current PwMS could not maintain their baseline PA levels in addition to doing more exercise, because their baseline PA was already high (mean ± SD: 41 ± 23min MVPA/day and 9782 ± 2569 steps/day [data not shown]). However, PwMS in previous research had clearly lower baseline PA levels (mean ± SD: 26 ± 18min MVPA/day, 4488 ± 2251 steps/day and 6095 ± 2363 steps/day), but their total PA also did not increase while they self-reported to do more exercise[10, 11]. In a study of Keadle et al.[32], overweight/obese participants had even higher baseline MVPA (mean ± SD 50 ± 17min/day) than the present PwMS, and they were able to further increase this during an exercise intervention when they also received education on SB and self-monitoring of non-exercise MVPA. This might also be possible in PwMS when they receive a multicomponent intervention targeting both exercise PA and NEPA/SB and should be explored in future research.

The exercise intervention effectively improved the cardiorespiratory fitness, body composition, fatigue and resting HR in both groups, with no difference between groups. This is in line with previous periodised exercise interventions in PwMS and HC[13, 33]. However, this might seem contradictory, because PwMS changed their NEPA and SB whilst HC did not. Moreover, the non-exercise MVPA reduction approximately matched the exercise time, which would result in an unaffected net MVPA change. The fact that exercise effects were not constrained, might be explained by a difference in PA intensity. More specifically, the exercise intensity was probably higher (78.7 ± 6.1% of HR_max) compared to that of the reduced non-exercise MVPA, limiting the impact on exercise outcomes. Unfortunately, it is not possible to draw strong conclusions on the exact intensity of the reduced NEPA, because all walking and running activities with ≥ 100 steps/min are classified as MVPA by the activPAL algorithm. It might be relevant to include continuous heart rate monitoring in future research in order to measure the exact NEPA intensities.

Additionally, the present PwMS were only mildly disabled and already very physically fit at baseline (on average > 100% of their predicted cardiorespiratory fitness). It could be hypothesised that more disabled or less fit PwMS show larger NEPA/SB compensations in response to exercise, which may in turn have negative effects on the eventual exercise adaptations. Furthermore, other relevant physical exercise outcomes that were not measured, might have been impacted. In a study of Keadle et al.[34], the effect of changes in NEPA in overweight/obese participants was also assessed on fasting lipids and insulin sensitivity. Interestingly, insulin sensitivity only improved within participants who performed exercise and also reduced their SB (-5%; which was replaced with MVPA and LIPA), not in participants who only performed exercise (with no changes in NEPA). It might be especially relevant to monitor the effect of NEPA and SB changes on insulin sensitivity in PwMS in future research, because they already have a higher risk of developing insulin resistance (x2.48 compared to HC)[35], which is associated with a worsening of disability[8, 35, 36]. Furthermore, the current findings also show that PwMS significantly increased their uninterrupted SB in response to exercise. This has already been shown to be negatively associated with insulin sensitivity in a large sample of HC (n: 4935), independent of total MVPA and SB[37]. Hence, measures of insulin sensitivity should be included in future NEPA research in PwMS.

The present study has several strengths. Firstly, PA and SB were objectively assessed for seven consecutive days on three different time points throughout the study, and a distinction was made between days with and without exercise. Secondly, the Polar watches provided objective adherence rates in a home-based setting, which enabled a PP-analysis with participants that effectively trained. This allows us to conclude that the observed NEPA/SB changes occur when PwMS effectively perform the prescribed exercise sessions. Furthermore, a post-hoc power calculation showed a similar effect size for the observed improvement in cardiorespiratory fitness as what was a priori calculated (although the eventual sample size was smaller). Lastly, this is the first study that compared NEPA changes between PwMS and HC. An unfortunate limitation of the study is that T2 and T3 measures were conducted during the COVID-19 pandemic. Although the PA assessments at both time points occurred during periods with only minor COVID-related restrictions, PwMS might have been more afraid to get infected due to the autoimmune nature of their disease, and as such reduced their social and/or outdoor activities to a greater extent than HC. It is however important to note that our findings are in line with studies that were conducted before the COVID-19 pandemic. Furthermore, there was no randomisation in the present study, but our findings in probably very motivated PwMS highlight the relevance of further research to NEPA/SB changes with more rigorous study designs. Finally, the intervention consisted of a running program, which limits the generalisation of results to more disabled PwMS with walking impairments. Future research on NEPA changes in more deconditioned and/or disabled PwMS with other (non-weight bearing) intervention types such as (recumbent) cycling is warranted.

PwMS reduced their weekly non-exercise MVPA with a duration approximately matching the exercise duration, and they accumulated their SB in longer bouts on days without exercise session. This might be a necessary MS-specific, behavioural compensation in order to adhere to the exercise intervention and did not constrain patient-reported and physical outcomes. Future research is warranted to unravel the underlying causes and to investigate the effects on other exercise adaptations, such as cardiometabolic health.

MS Multiple Sclerosis

PwMS Persons with Multiple Sclerosis

PA Physical activity

NEPA Non-exercise physical activity

HC Healthy controls

LIPA Light intensity physical activity

MVPA Moderate-to-vigorous intensity physical activity

SB Sedentary behaviour

EX days Days with exercise session

NONEX days Days without exercise session

EDSS Expanded disability status scale score

HIIT High intensity interval training

MICT Moderate intensity continuous training

HR_maxMaximal heart rate

HR_restResting heart rate

HR_recovHeart rate recovery

STR program Start to run program

ER program Experienced run program

MFIS Modified fatigue impact scale

MSWS-12 12-item MS walking scale

SBP Systolic blood pressure

DBP Diastolic blood pressure

BW Body weight

BMI Body mass index

RPM Rounds per minute

RER Respiratory exchange ratio

V̇O_2peakPeak oxygen uptake

V̇O_2peakPREDPredicted peak oxygen uptake

W_peakPeak workload

PP Per protocol

Ethics approval and consent to participate

The study protocol was approved by the Medical Ethical Committee of Hasselt University (Hasselt, Belgium; CME2019/062) and the Jessa Hospital Hasselt (Belgium), was conducted in accordance with the principles of the Declaration of Helsinki (2013) and is registered at clinicaltrials.gov as NCT04191772 (December 19, 2019).

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are stored in a permanent repository and are available from the corresponding author on reasonable request.

Competing interests

The authors report that there are no competing interests to declare.

Funding

This work was supported by the Flemish Fund for Scientific Research (FWO Vlaanderen; 11E9221N). The funding source was not involved in the preparation of the article.

Authors’ contributions

IN contributed to the recruitment of participants, executed the experiments, contributed to the acquisition, analysis, and interpretation of data for the work and drafted the manuscript; WMAF and JS executed the experiments, contributed to the acquisition, analysis, or interpretation of data for the work and critically revised the manuscript; PVA contributed to the recruitment of participants, interpretation of the data and critically revised the manuscript; HHCMS contributed to the conception or design of the work and critically revised the manuscript; BOE contributed to the conception or design of the work and critically revised the manuscript. All agree to be accountable for all aspects of work ensuring integrity and accuracy. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank all study participants.

Tullman MJ. Overview of the epidemiology, diagnosis, and disease progression associated with multiple sclerosis. Am J Manag Care. 2013 Feb;19(2 Suppl):S15-20.
Noseworthy JH, Lucchinetti C, Rodriguez M, et al. Multiple Sclerosis. New England Journal of Medicine. 2000 2000/09/28;343(13):938-952.
Asano M, Duquette P, Andersen R, et al. Exercise barriers and preferences among women and men with multiple sclerosis. Disabil Rehabil. 2013 Mar;35(5):353-61.
Macdonald E, Buchan D, Cerexhe L, et al. Accelerometer measured physical activity and sedentary time in individuals with multiple sclerosis versus age matched controls: A systematic review and meta-analysis. Mult Scler Relat Disord. 2022 Dec 9;69:104462.
Dalgas U, Langeskov-Christensen M, Stenager E, et al. Exercise as Medicine in Multiple Sclerosis-Time for a Paradigm Shift: Preventive, Symptomatic, and Disease-Modifying Aspects and Perspectives. Curr Neurol Neurosci Rep. 2019 Nov 13;19(11):88.
Latimer-Cheung AE, Pilutti LA, Hicks AL, et al. Effects of exercise training on fitness, mobility, fatigue, and health-related quality of life among adults with multiple sclerosis: a systematic review to inform guideline development. Arch Phys Med Rehabil. 2013 Sep;94(9):1800-1828.e3.
Ewanchuk BW, Gharagozloo M, Peelen E, et al. Exploring the role of physical activity and exercise for managing vascular comorbidities in people with multiple sclerosis: A scoping review. Mult Scler Relat Disord. 2018 Nov;26:19-32.
Marrie RA, Rudick R, Horwitz R, et al. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology. 2010 Mar 30;74(13):1041-7.
Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985 Mar-Apr;100(2):126-31.
Coulter EH, Bond S, Dalgas U, et al. The effectiveness of interventions targeting physical activity and/or sedentary behaviour in people with Multiple Sclerosis: a systematic review. Disabil Rehabil. 2020 Mar;42(5):594-612.
Casey B, Coote S, Hayes S, et al. Changing Physical Activity Behavior in People With Multiple Sclerosis: A Systematic Review and Meta-Analysis. Arch Phys Med Rehabil. 2018 Oct;99(10):2059-2075.
Tremblay MS, Aubert S, Barnes JD, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017 Jun 10;14(1):75.
Keytsman C, Van Noten P, Spaas J, et al. Periodized home-based training: A new strategy to improve high intensity exercise therapy adherence in mildly affected patients with Multiple Sclerosis. Mult Scler Relat Disord. 2019 Feb;28:91-97.
Roecker K, Striegel H, Dickhuth HH. Heart-rate recommendations: transfer between running and cycling exercise? Int J Sports Med. 2003 Apr;24(3):173-8.
Heyward V. Advanced fitness assessment and exercise prescription: Human kinetics Champaign. IL; 2006.
Tudor-Locke C, Sisson SB, Collova T, et al. Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol. 2005 Dec;30(6):666-76.
Courtney JB, Nuss K, Lyden K, et al. Comparing the activPAL software's Primary Time in Bed Algorithm against Self-Report and van der Berg's Algorithm. Meas Phys Educ Exerc Sci. 2021;25(3):212-226.
Dennett R, Madsen LT, Connolly L, et al. Adherence and drop-out in randomized controlled trials of exercise interventions in people with multiple sclerosis: A systematic review and meta-analyses. Multiple Sclerosis and Related Disorders. 2020 2020/08/01/;43:102169.
Kos D, Kerckhofs E, Nagels G, et al. Assessing fatigue in multiple sclerosis: Dutch modified fatigue impact scale. Acta Neurol Belg. 2003 Dec;103(4):185-91.
Flachenecker P, Kümpfel T, Kallmann B, et al. Fatigue in multiple sclerosis: a comparison of different rating scales and correlation to clinical parameters. Mult Scler. 2002 Dec;8(6):523-6.
Hobart JC, Riazi A, Lamping DL, et al. Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12). Neurology. 2003 Jan 14;60(1):31-6.
Heine M, Hoogervorst ELJ, Hacking HGA, et al. Validity of Maximal Exercise Testing in People With Multiple Sclerosis and Low to Moderate Levels of Disability. Physical Therapy. 2014;94(8):1168-1175.
Myers J, Kaminsky LA, Lima R, et al. A Reference Equation for Normal Standards for VO(2) Max: Analysis from the Fitness Registry and the Importance of Exercise National Database (FRIEND Registry). Prog Cardiovasc Dis. 2017 Jun-Jul;60(1):21-29.
Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing [https://doi.org/10.1111/j.2517-6161.1995.tb02031.x]. Journal of the Royal Statistical Society: Series B (Methodological). 1995 1995/01/01;57(1):289-300.
Washburn RA, Lambourne K, Szabo AN, et al. Does increased prescribed exercise alter non-exercise physical activity/energy expenditure in healthy adults? A systematic review. Clin Obes. 2014 Feb;4(1):1-20.
Fedewa MV, Hathaway ED, Williams TD, et al. Effect of Exercise Training on Non-Exercise Physical Activity: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Sports Med. 2017 Jun;47(6):1171-1182.
Rottoli M, La Gioia S, Frigeni B, et al. Pathophysiology, assessment and management of multiple sclerosis fatigue: an update. Expert Rev Neurother. 2017 Apr;17(4):373-379.
King NA, Caudwell P, Hopkins M, et al. Metabolic and behavioral compensatory responses to exercise interventions: barriers to weight loss. Obesity (Silver Spring). 2007 Jun;15(6):1373-83.
Smith RM, Adeney-Steel M, Fulcher G, et al. Symptom Change With Exercise Is a Temporary Phenomenon for People With Multiple Sclerosis. Archives of Physical Medicine and Rehabilitation. 2006 2006/05/01/;87(5):723-727.
Freal JE, Kraft GH, Coryell JK. Symptomatic fatigue in multiple sclerosis. Archives of physical medicine and rehabilitation. 1984 1984/03//;65(3):135-138.
Blikman LJ, Huisstede BM, Kooijmans H, et al. Effectiveness of Energy Conservation Treatment in Reducing Fatigue in Multiple Sclerosis: A Systematic Review and Meta-Analysis. Archives of Physical Medicine and Rehabilitation. 2013 2013/07/01/;94(7):1360-1376.
Kozey-Keadle S, Staudenmayer J, Libertine A, et al. Changes in sedentary time and physical activity in response to an exercise training and/or lifestyle intervention. J Phys Act Health. 2014 Sep;11(7):1324-33.
Keytsman C, Van Noten P, Verboven K, et al. Periodized versus classic exercise therapy in Multiple Sclerosis: a randomized controlled trial. Multiple Sclerosis and Related Disorders. 2021 2021/04/01/;49:102782.
Kozey Keadle S, Lyden K, Staudenmayer J, et al. The independent and combined effects of exercise training and reducing sedentary behavior on cardiometabolic risk factors. Appl Physiol Nutr Metab. 2014 Jul;39(7):770-80.
Oliveira SR, Simão AN, Kallaur AP, et al. Disability in patients with multiple sclerosis: influence of insulin resistance, adiposity, and oxidative stress. Nutrition. 2014 Mar;30(3):268-73.
Pagano G, Cavallo-Perin P, Cassader M, et al. An in vivo and in vitro study of the mechanism of prednisone-induced insulin resistance in healthy subjects. J Clin Invest. 1983 Nov;72(5):1814-20.
Carson V, Wong SL, Winkler E, et al. Patterns of sedentary time and cardiometabolic risk among Canadian adults. Preventive Medicine. 2014 2014/08/01/;65:23-27.

No competing interests reported.

Download PDF

Editorial decision: Major revision
18 Sep, 2023
Reviews received at journal
08 Sep, 2023
Reviewers agreed at journal
18 Aug, 2023
Reviewers agreed at journal
10 Aug, 2023
Reviewers invited by journal
28 Jul, 2023
Editor assigned by journal
27 Jul, 2023
Submission checks completed at journal
27 Jul, 2023
First submitted to journal
25 Jul, 2023

You are reading this latest preprint version

The effect of structured exercise on non-exercise physical activity and sedentary behaviour in persons with mild Multiple Sclerosis and healthy controls

Status:

Version 1

Abstract

Figures

1. Background

2. Methods

2.1. Study design

2.2. Participants

2.3. Exercise program

2.4. Measurements

2.4.1. Primary outcome measures: NEPA and SB

2.4.2. Secondary outcome measures

2.4.2.1 Exercise session information

2.4.2.2. Patient-reported outcomes

2.4.2.3. Physical outcomes

2.5. Statistical analyses

3. Results

3.1. Participant characteristics

3.2. Primary outcome measures: NEPA and SB

3.3. Secondary outcome measures

3.3.1. Exercise session information

3.3.2. Exercise adaptations

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1