
==== Front
Eur J Neurol
Eur J Neurol
10.1111/(ISSN)1468-1331
ENE
European Journal of Neurology
1351-5101
1468-1331
John Wiley and Sons Inc. Hoboken

39109838
10.1111/ene.16429
ENE16429
EJoN-24-0463.R2
Original Article
Multiple Sclerosis
Baseline engagement with healthy lifestyles and their associations with health outcomes in people with multiple sclerosis enrolled in an online multimodal lifestyle course
Lifestyle patterns and MS outcomes
Yu et al.
Yu Maggie https://orcid.org/0000-0002-2022-4939
1 maggie.yu@unimelb.edu.au

Neate Sandra 1
Nag Nupur https://orcid.org/0000-0002-0271-0781
1
Bevens William 1
Jelinek George 1
Simpson‐Yap Steve https://orcid.org/0000-0001-6521-3056
1 2
Davenport Rebekah A. 3
Fidao Alex 1 4
Reece Jeanette https://orcid.org/0000-0003-2897-0271
1
1 Neuroepidemiology Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health The University of Melbourne Melbourne Victoria Australia
2 Menzies Institute for Medical Research, University of Tasmania Hobart Tasmania Australia
3 Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences The University of Melbourne Melbourne Victoria Australia
4 Non‐communicable Disease, Environmental Health, Epidemiology and Surveillance, Health Protection Branch, Public Health Division Victorian Department of Health Melbourne Victoria Australia
* Correspondence
Maggie Yu, Neuroepidemiology Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
Email: maggie.yu@unimelb.edu.au

07 8 2024
10 2024
31 10 10.1111/ene.v31.10 e1642912 7 2024
07 3 2024
19 7 2024
© 2024 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Background and Purpose

Healthy lifestyle behaviour modification may improve health outcomes in people with multiple sclerosis (pwMS), but empirical evidence is needed to confirm prior study findings. We developed an online multimodal lifestyle intervention (Multiple Sclerosis Online Course) to examine the impact of lifestyle modification on health outcomes in pwMS via a randomized control trial (RCT). However, the present study specifically analyses baseline data to assess engagement with healthy lifestyles by RCT participants and cross‐sectional associations with health outcomes.

Methods

Baseline engagement with six “healthy lifestyle behaviours” of the intervention course (high‐quality, plant‐based diet; ≥5000 IU/day vitamin D; omega‐3 supplementation; ≥30 min physical activity 5 times/week; ≥30 min/week meditation; and nonsmoking) was examined. Associations between individual versus collective behaviours (individual behaviours summated) and health outcomes (quality of life [QoL]/fatigue/disability) were evaluated using multivariate modelling (linear/log‐binomial/multinomial).

Results

At baseline, 33.7% and 30.0% of participants (n = 857) engaged in one or two healthy behaviours, respectively. In total, engagement with healthy lifestyles by participants was as follows: nonsmoking, 90.7%; omega‐3 supplementation, 34.5%; vitamin D supplementation, 29.8%; physical activity, 29.4%; diet, 10.7%; and meditation, 10.5%. Individual behaviours (nonsmoking/physical activity/diet) were independently associated with better health outcomes. Engagement with multiple behaviours, especially diet and physical activity, was associated with better outcomes; engaging with ≥4 behaviours was associated with a 9.0‐point higher mental QoL and a 9.5‐point higher physical QoL, as well as 23% and 56% lower prevalence of fatigue and moderate disability, respectively.

Conclusions

Baseline engagement with ≥4 healthy behaviours, including diet and physical activity, was associated with better health outcomes.

digital health education
health outcomes
healthy lifestyle behaviours
multiple sclerosis
quality of life
source-schema-version-number2.0
cover-dateOctober 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:20.09.2024
Yu M , Neate S , Nag N , et al. Baseline engagement with healthy lifestyles and their associations with health outcomes in people with multiple sclerosis enrolled in an online multimodal lifestyle course. Eur J Neurol. 2024;31 :e16429. doi:10.1111/ene.16429
==== Body
pmcINTRODUCTION

People with multiple sclerosis (pwMS) frequently experience physical and psychological symptoms and reduced quality of life (QoL). Emerging evidence suggests modification of lifestyle risk factors may play a role in improving MS‐related symptoms and attenuating MS progression. Lifestyle modification, as a strategy to mitigate disease progression and worsening clinical outcomes, herein referred to as adoption of “healthy lifestyles,” encompasses varying activities including a high‐quality diet [1, 2, 3], increased physical activity [4], stress‐reducing activities [5, 6], and omega‐3 [7] and vitamin D supplementation [8].

Although prior observational studies have identified relationships between individual lifestyle factors and better health outcomes in pwMS [2, 3, 4, 5, 6, 8], few studies have investigated associations between multimodal (collective) lifestyle factors and health outcomes. Of these, the STOP MS study observed 11.3% and 19.5% improvements in QoL among participants attending a multimodal lifestyle educational retreat based on the Overcoming MS program [9], at the 1‐year and 5‐year follow‐up, respectively [10]. Moreover, all healthy lifestyle changes were maintained over a 3‐year period, except physical activity [11]. In the longitudinal Health Outcomes and Lifestyle in a Sample of People With MS (HOLISM) study, engagement with more healthy lifestyle behaviours at baseline was associated with increased physical QoL (pQoL) and mental QoL (mQoL) at 2.5 years (n = 1401) [12], and engagement with specific lifestyle behaviours (e.g., high‐quality diet) was associated with an 11% lower risk of fatigue [13]. At the 7.5‐year HOLISM follow‐up, engagement with ≥3 healthy behaviours was associated with greater mQoL and pQoL than ≤1 behaviour [14]. A recent systematic review of 57 lifestyle self‐management studies in pwMS reported that multimodal lifestyle interventions were associated with higher QoL [15]. Another review on combined wellness interventions (exercise training, meditation, and dietary modifications) in people with progressive MS reported significant improvements in fatigue, depression, and cognitive performance [16]. Furthermore, a recent pilot study found a remotely delivered multimodal lifestyle intervention may improve fatigue and QoL outcomes in recently diagnosed pwMS [17]. Collectively, these studies illustrate the potential additive benefits of lifestyle behaviours for pwMS. However, large randomized controlled trials (RCTs) are needed to provide robust evidence.

To investigate the role of multimodal healthy lifestyle behaviours in health outcomes, we developed the online multimodal healthy lifestyle program (the Multiple Sclerosis Online Course [MSOC]) for pwMS, for testing via a large RCT. The MSOC comprises an intervention course (IC) based fundamentally on the Overcoming MS program [9], which will be compared against a standard‐care course (SCC) providing information regarding lifestyle recommendations sourced from international MS‐related websites [18, 19].

The present study uses baseline data from the MSOC RCT to explore the role of multimodal healthy lifestyle in health outcomes in pwMS preintervention. Specifically, we examined the cross‐sectional relationship between individual and collective healthy lifestyle behaviours and health outcomes, including fatigue, disability, and QoL. Baseline engagement with individual healthy behaviours and combinations of multiple healthy behaviours was also examined in this largely unexplored area of research, which is highly relevant for evaluating the effectiveness of lifestyle interventions.

METHODS

Study design and participants

This study specially analyses baseline data collected as part of the flagship MSOC RCT [18]. Data from IC and SCC participants were combined, as data were collected prior to course commencement. RCT participants were recruited via online platforms (MS society websites, Facebook support groups, and other social media platforms) during five recruitment rounds between June 2022 and July 2023. Eligible recruitment criteria included age ≥ 18 years, a self‐reported clinical diagnosis of MS, and not enrolled in another study or trial. Eligible participants were requested to complete a baseline survey online, capturing information on sociodemographic and clinical variables, patient‐reported outcome measures (PROMs), and lifestyle behaviours. The IC and SCC course content is provided in Table S1.

Demographics and clinical variables

Baseline data included sociodemographic variables (e.g., age, sex, country of residence); clinical variables (MS type categorized into nonprogressive [benign/relapsing–remitting MS] and progressive [primary progressive/secondary progressive/progressive–relapsing MS]); MS duration derived from the diagnosis date and survey completion date; body mass index (BMI; weight/height2) categorized into underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2) according to World Health Organization guidelines; treated comorbidities using the Self‐Administered Comorbidity Questionnaire then dichotomized (≤1/>1); disease‐modifying therapy (DMT) use (no/yes); use of medication such as antifatigue medication (no/yes); and self‐reported ongoing symptoms from relapse for ≤30 days (no/yes).

Patient‐reported outcome measures (PROMs)

QoL was assessed using the Multiple Sclerosis Quality of Life‐54 [20]. Two health composite scores representing mQoL and pQoL were calculated (ranging from 0 [low] to 100 [high]; higher scores represent higher QoL). Fatigue was measured using the 9‐item Fatigue Severity Scale (FSS); FSS >5 indicates clinically significant fatigue [21, 22]. Disability was assessed using Patient‐Determined Disease Steps [23], a self‐reported analog of the Expanded Disability Status Scale widely utilized by neurologists for assessing gait disability; it consists of three categories: normal/mild (0–2), moderate (3–5), and severe (6–8).

Healthy lifestyle behaviours

We examined baseline engagement with six “healthy lifestyle behaviours,” as defined by IC recommendations (based largely on the Overcoming MS program [9]): (i) “Healthy diet” is defined as a high‐quality, plant‐based diet with no meat or dairy consumption. Diet quality was measured using the modified Dietary Habits Questionnaire [24, 25]. Scores in the top quartile were considered high‐quality diet [3]. Participants responding with "I do not consume dairy" and "I do not consume meat" were classified as following a plant‐based diet. Although seafood consumption is recommended in the IC, we did not query seafood consumption; (ii) “Vitamin D supplementation” is defined as ≥5000 IU/day (assessed by frequency and average daily dose); (iii) “Omega‐3 supplementation” is defined by “Do you consume omega‐3 supplements?” (yes/no); (iv) “Regular physical activity” is defined as engaging in moderate‐intensity or vigorous physical activity for ≥30 min/day, ≥3 times/week (yes/no), assessed using International Physical Activity Questionnaire Short Form [26]; (v) “Meditation” is defined as engaging in meditation ≥30 min/week (yes/no) by querying the frequency ("never" to "every day") and average duration (min/day) of meditation practice in the past 12 months; (vi) “Nonsmoking” (yes/no) is defined by querying smoking status as smoking (current smoker) and nonsmoking (never/ex‐smoker).

Engagement with individual healthy lifestyles was assessed by calculating the prevalence of engagement with each healthy lifestyle. Combinations of engagement with multiple healthy lifestyles were also assessed to examine the patterns of engagement with >1 healthy lifestyle behaviour.

Total Health Lifestyle Index Score (THLIS)

To assess multiple healthy lifestyles simultaneously, we employed a composite lifestyle score approach, as utilized in previous studies [12, 13, 14]. To generate a composite score for collective healthy lifestyle engagement, we developed a Total Healthy Lifestyle Index Score (THLIS) by measuring and summating engagement with the six healthy lifestyle behaviours (healthy diet, regular physical activity, vitamin D and omega‐3 supplementation, meditation, and nonsmoking). The THLIS reflects the number of lifestyle recommendations of the IC that participants engaged with, ranging from 0 (no engagement) to 6 (engagement with all behaviours). As the number of participants engaging with 0 and >4 healthy behaviours was limited, 0–1 and 4–6 behaviours were combined, resulting in THLIS categorization into four groups: (i) 0–1, (ii) 2, (iii) 3, and (iv) 4–6 healthy behaviours.

Statistical analysis

To describe the patterns of engagement with healthy lifestyles, we measured the prevalence of engagement with each healthy lifestyle and calculated the proportion of participants engaging with different combinations of healthy lifestyles. To examine associations between (i) individual lifestyle behaviours and (ii) summated behaviours (THLISs) and PROMs (QoL [continuous], fatigue [binary] and disability [categorical]), respectively, we employed linear, log‐binomial, or multinomial regression models, where appropriate. Multivariate models were adjusted for relevant confounders, including age, sex, MS type, current symptoms due to relapse, MS duration, and comorbidity number. To examine independent associations between each lifestyle behaviour and PROMs, we adjusted for engagement with other lifestyle behaviours. QoL models were further adjusted for disability, and fatigue models were further adjusted for disability and antifatigue medication. Of note, due to the limited numbers of participants engaging in a healthy diet (11%) or meditation (10%), we did not have sufficient power to examine associations between specific combinations/patterns of healthy behaviour and health outcomes.

Analyses were conducted using complete cases, with STATA/BE, version 17 (StataCorp, College Station, TX, USA).

RESULTS

Participant characteristics

At baseline (n = 857), median participant age was 47 years (interquartile range [IQR] = 38–55), and 87% were female (Table 1). A large proportion were university educated (66%), employed (55%), and partnered (70%). Participants resided in 52 countries, 33% in the USA/Canada, 28% in Australia/New Zealand, and 13% in the UK. The remaining 26% participants resided across Europe (e.g., Czech Republic 2.2%, Ukraine 2.1%, Germany 1.5%, France 1.3%), the Middle East (e.g., Iran 0.4%), Africa (e.g., South Africa 1.2%), and Asia (e.g., India 0.4%; Table S2).

TABLE 1 Descriptive statistics of the study sample (N = 857).

Characteristic	n (%)	
Demographics characteristics		
Gender, female	748 (87.3%)	
Sex, female	752 (88.3%)	
Age, median, years (IQR)	47.0 (38.0–55.0)	
BMI, low/normal	424 (49.6%)	
Overweight	212 (24.8%)	
Obese	219 (25.6%)	
Education, university	562 (65.6%)	
Employment, working	471 (55.0%)	
Partnered, married, de facto	598 (69.8%)	
Country of residence		
Australia/NZ	236 (27.5%)	
USA/Canada	286 (33.4%)	
UK	113 (13.2%)	
Other	222 (25.9%)	
Health characteristics	
MS type, nonprogressive	614 (71.6%)	
MS duration, median, years (IQR)	6.0 (2.0–14.0)	
Disability, PDDS	
Normal/mild	449 (52.4%)	
Moderate	323 (37.7%)	
Severe	85 (9.9%)	
Clinically significant fatigue, FSS >5	475 (55.4%)	
Comorbidities, ≥1	486 (56.7%)	
Taking medication for MS	581 (67.8%)	
Note: Continuous data were summarized using median and IQR and binary/categorical data using number and percentage.

Abbreviations: BMI, body mass index; FSS, Fatigue Severity Scale; IQR, interquartile range; MS, multiple sclerosis; PDDS, Patient‐Determined Disease Steps.

Median MS duration was 6 years (IQR = 2–14), 22% of participants had progressive MS, and 68% were taking DMTs. Approximately half had low/normal BMI (50%), normal/mild disability (52%), clinically significant fatigue (55%), and ≥1 comorbidity (57%).

Prevalence and patterns of participants' engagement with healthy lifestyle behaviours

At baseline, 4% of participants engaged with none of the defined healthy behaviours, 34% engaged with one, 30% with two, and 21% with three (Table 2). Engagement with >3 healthy behaviours was low (8% with four, 3% with five, <1% with six). Ninety‐one percent of participants engaged with nonsmoking, followed by omega‐3 (35%) and vitamin D supplementation (30%), regular physical activity (29%), healthy diet (11%), and meditation (11%).

TABLE 2 Prevalence of healthy lifestyle behaviour engagement of Multiple Sclerosis Online Course participants at baseline.

Individual healthy lifestyle behaviour	No. of healthy lifestyle behaviours participants engaged with, n (%)	Total	
0	1	2	3	4	5	6	
Healthy diet	0	1 (0.4)	11 (4.3)	27 (14.9)	29 (42.0)	19 (86.4) a	5 (100.0) a	92 (10.7) a	
Vitamin D	0	8 (2.8)	74 (28.8)	101 (55.8) a	48 (69.6) a	19 (86.4) a	5 (100.0) a	255 (29.8) a	
Omega‐3	0	6 (2.1)	87 (33.9)	114 (63.0) a	62 (89.9) a	22 (100.0) a	5 (100.0) a	296 (34.5) a	
Physical activity	0	8 (2.8)	87 (33.9)	90 (49.7)	43 (62.3) a	19 (86.4) a	5 (100.0) a	252 (29.4) a	
Meditation	0	2 (0.7)	14 (5.5)	35 (19.3)	25 (36.2)	9 (40.9)	5 (100.0) a	90 (10.5) a	
Nonsmoking	0	264 (91.4) a	241 (93.8) a	176 (97.2) a	69 (100.0) a	22 (100.0) a	5 (100.0) a	777 (90.7) a	
Total	34 (4.0)	289 (33.7)	257 (30.0)	181 (21.1)	69 (8.1)	22 (2.6)	5 (0.6)	857 (100.0)	
a Engagement > 50% of the particular lifestyle behaviour subgroup.

When examining engagement with >1 healthy behaviours (that is, THLISs > 1), the most common combinations of engagement with 2–4 healthy behaviours were nonsmoking and either omega‐3 supplementation, vitamin D supplementation, or physical activity (Table 3, Figure S1). Specifically, for two healthy behaviours, participants most commonly engaged with nonsmoking with physical activity (33%), omega‐3 supplementation (31%), or vitamin D supplementation (27%). For three healthy behaviours, the most prevalent combinations were nonsmoking together with omega‐3 and vitamin D supplementation (28%), or omega‐3 supplementation and physical activity (20%). For 4 healthy behaviours, the most prevalent combinations were nonsmoking and omega‐3 supplementation with either vitamin D supplementation and physical activity (25%), or vitamin D supplementation and healthy diet (20%). That is, engagement with a healthy diet was only prevalent in engagement with ≥4 behaviours.

TABLE 3 Number and combinations of participant engagement in healthy lifestyle behaviours.

Healthy lifestyle behaviours engaged in	n (%)	
One lifestyle behaviour	289 (100%)	
Nonsmoking a	264 (91.4%)	
Other	25 (8.7%)	
Two lifestyle behaviours	241 (100%)	
Nonsmoking + physical activity a	80 (33.2%)	
Nonsmoking + omega‐3 a	75 (31.1%)	
Nonsmoking + vitamin D a	64 (26.6%)	
Nonsmoking + meditation	13 (5.4%)	
Other	2 (0.8%)	
Three lifestyle behaviours	181 (100%)	
Nonsmoking + omega‐3 + vitamin D a	50 (27.6%)	
Nonsmoking + omega‐3 + physical activity a	37 (20.4%)	
Nonsmoking + vitamin D + physical activity a	28 (15.5%)	
Nonsmoking + omega‐3 + meditation	15 (6.7%)	
Nonsmoking + physical activity + meditation	13 (7.7%)	
Nonsmoking + vitamin D + healthy diet	12 (0.7%)	
Other	26 (14%)	
Four lifestyle behaviours	69 (100%)	
Nonsmoking + omega‐3 + vitamin D + physical activity a	17 (25.0%)	
Nonsmoking + omega‐3 + vitamin D + healthy diet a	14 (20.3%)	
Nonsmoking + omega‐3 + vitamin D + meditation a	11 (15.9%)	
Nonsmoking + omega‐3 + physical activity + healthy diet a	11 (15.9%)	
Nonsmoking + omega‐3 + physical activity + meditation a	8 (11.6%)	
Nonsmoking + vitamin D + physical activity + meditation	4 (5.8%)	
Other	4 (5.8%)	
Five lifestyle behaviours	22 (100%)	
Nonsmoking + omega‐3 + vitamin D + physical activity + healthy diet a	13 (59.1%)	
Nonsmoking + omega‐3 + vitamin D + healthy diet + meditation a	3 (13.6%)	
Nonsmoking + omega‐3 + physical activity + healthy diet + meditation a	3 (13.6%)	
Nonsmoking + omega‐3 + vitamin D + physical activity + meditation a	3 (13.6%)	
Six lifestyle behaviours	5 (100%)	
Nonsmoking + vitamin D + omega‐3 + physical activity + healthy diet + meditation a	5 (100%)	
Note: Other = other combinations <5% of the subgroup.

a Combinations >10% of the subgroup.

Associations between individual healthy behaviours and PROMs

A healthy diet, physical activity, and nonsmoking were all independently associated with specific health outcomes after adjusting for relevant confounders, including other lifestyle behaviours (Tables 4 and 5). Healthy diet was associated with higher mQoL (adjusted regression coefficient [aβ] = 5.54, 95% confidence interval [CI] = 0.39–10.68) and pQoL (aβ = 6.16, 95% CI = 2.14–10.17), and lower fatigue prevalence (adjusted prevalence ratio [aPR] = 0.70, 95% CI = 0.51–0.97). Physical activity was associated higher mQoL (aβ = 5.80, 95% CI = 2.33–9.27) and pQoL (aβ = 6.24, 95% CI = 3.57–8.91), and lower fatigue prevalence (aPR = 0.84, 95% CI = 0.71–0.99) and moderate (aPR = 0.55, 95% CI = 0.35–0.84) and severe (aPR = 0.42, 95% CI = 0.18–0.99) disability. Nonsmoking was associated with 6.34‐point higher mQoL (95% CI = 0.74–11.94) and 4.43‐point higher pQoL (95% CI = 0.14–8.71). No associations were observed between meditation practice, vitamin D supplementation, or omega‐3 supplementation, and outcomes examined.

TABLE 4 Associations between individual lifestyle behaviours and QoL.

Healthy behaviour	Mental QoL	Physical QoL	
Unadjusted model, n = 722	Adjusted model, n = 643	Unadjusted model, n = 704	Adjusted model, n = 632	
β (95% CI)	p	aβ (95% CI)	p	β (95% CI)	p	aβ (95% CI)	p	
Healthy diet	6.84 (1.78, 11.89)	0.008	5.54 (0.39, 10.68)	0.035	7.78 (3.22, 12.34)	0.001	6.16 (2.14, 10.17)	0.003	
Vitamin D	0.734 (−2.64, 4.10)	0.669	0.22 (−3.44, 3.89)	0.973	−0.21 (−3.20, 2.78)	0.889	0.87 (−1.83, 3.58)	0.526	
Omega‐3	1.72 (−1.59, 5.02)	0.308	1.16 (−2.31, 4.62)	0.513	2.70 (−0.23, 5.64)	0.071	1.28 (−1.40, 3.96)	0.349	
Physical activity	6.95 (3.64, 10.27)	<0.001	5.80 (2.33, 9.27)	0.001	9.76 (6.84, 12.69)	<0.001	6.24 (3.57, 8.91)	<0.001	
Meditation	−0.89 (−5.69, 3.91)	0.717	−2.11 (−7.06, 2.83)	0.402	−2.87 (−7.15, 1.41)	0.188	−1.73 (−5.58, 2.12)	0.379	
Nonsmoking	8.02 (2.58, 13.47)	0.004	6.34 (0.74, 11.94)	0.026	6.01 (1.24, 10.79)	0.014	4.43 (0.14, 8.71)	0.043	
Note: Adjusted log‐binomial regression models controlled for age, sex, MS type, current symptoms due to recent relapse, MS duration, number of comorbidities, disability, and other lifestyle behaviours. The sample size in the unadjusted models differ due to the inclusion of participants with complete data on outcomes and confounders. Results in boldface denote significant associations between healthy behaviours and QoL.

Abbreviations: aβ, adjusted β (coefficients); CI, confidence interval; MS, multiple sclerosis; QoL, quality of life.

TABLE 5 Associations between individual lifestyle behaviours and fatigue and disability.

Healthy behaviour	Fatigue	Disability	
FSS > 5	Moderate vs. normal/mild	Severe vs. normal/mild	
Unadjusted model, n = 722	Adjusted model, n = 696	Unadjusted model, n = 722	Adjusted model, n = 643	Unadjusted model, n = 722	Adjusted model, n = 643	
PR (95% CI)	p	aPR (95% CI)	p	PR (95% CI)	p	aPR (95% CI)	p	PR (95% CI)	p	aPR (95% CI)	p	
Healthy diet	0.69 (0.50, 0.94)	0.043	0.70 (0.51, 0.97)	0.043	0.78 (0.45, 1.37)	0.385	0.70 (0.36, 1.38)	0.307	0.79 (0.31, 2.01)	0.616	0.79 (0.24, 2.54)

	0.690	
Vitamin D	0.92 (0.79, 1.07)	0.276	0.88 (0.75, 1.03)	0.127	1.43 (0.99, 2.05)	0.055	1.03 (0.66, 1.62)	0.882	1.76 (1.00, 3.12)	0.051	1.41 (0.65, 3.06)	0.374	
Omega‐3	1.03 (0.89, 1.19)	0.708	1.08 (0.93, 1.25)	0.291	0.86 (0.60, 1.22)	0.390	0.70 (0.45, 1.09)	0.112	0.75 (0.41, 1.34)	0.330	0.53 (0.24, 1.16)

	0.206	
Physical activity	0.80 (0.68, 0.95)	0.009	0.84 (0.71, 0.99)	0.042	0.54 (0.38, 0.78)	0.001	0.55 (0.35, 0.84)	0.006	0.36 (0.18, 0.70)	0.003	0.42 (0.18, 0.99)

	0.047	
Meditation	1.10 (0.90, 1.34)	0.358	1.05 (0.85, 1.29)	0.858	1.39 (0.83, 2.32)	0.217	1.03 (0.66, 1.62)	0.854	1.83 (0.84, 3.99)	0.129	0.89 (0.31, 2.58)

	0.833	
Nonsmoking	0.89 (0.71, 1.07)	0.182	0.96 (0.78, 1.17)	0.726	0.65 (0.37, 1.16)	0.144	0.60 (0.31, 1.17)	0.134	0.76 (0.29, 1.94)	0.559	0.61 (0.28, 1.31)	0.163	
Note: Adjusted log‐binomial regression models controlled for age, sex, MS type, current symptoms due to recent relapse, MS duration, number of comorbidities, antifatigue medication, and other lifestyle behaviours. The sample sizes in the unadjusted and adjusted models differ due to the inclusion of participants with complete data on outcomes and confounders. Results in boldface denote significant associations between healthy behaviours and fatigue and disability.

Abbreviations: aPR, adjusted prevalence ratio; CI, confidence interval; FSS, Fatigue Severity Scale; MS, multiple sclerosis; PR, prevalence ratio.

Associations between total healthy behaviours and PROMs

Examining associations between THLISs and PROMs found that engagement with three healthy lifestyle behaviours was associated with higher mQoL (aβ = 4.56, 95% CI = 0.35–8.77) and pQoL (aβ = 5.58, 95% CI = 2.31–8.84), and lower prevalence of moderate disability (aPR = 0.50, 95% CI = 0.29–0.86; Tables 6 and 7). Engagement with 4–6 healthy behaviours was associated with higher mQoL (aβ = 8.96, 95% CI = 3.71–14.20) and pQoL (aβ = 9.51, 95% CI = 5.44–13.58), and lower prevalence of fatigue (aPR = 0.77, 95% CI = 0.59–0.99) and moderate disability (aPR = 0.44, 95% CI = 0.22–0.86).

TABLE 6 Associations between Total Healthy Lifestyle Index Score and QoL.

Healthy behaviour	Mental QoL	Physical QoL	
Unadjusted model, n = 722	Adjusted model, n = 643	Unadjusted model, n = 704	Adjusted model, n = 632	
β (95% CI)	p	aβ (95% CI)	p	β (95% CI)	p	aβ (95% CI)	p	
0–1, Ref.	0.00 (0.00, 0.00)		0.00 (0.00, 0.00)		0.00 (0.00, 0.00)		0.00 (0.00, 0.00)		
2	1.77 (−1.97, 5.51)	0.353	1.73 (−2.17, 5.63)	0.384	2.21 (−1.15, 5.58)	0.196	1.96 (−1.06, 4.98)	0.203	
3	5.88 (1.82, 9.94)	0.005	4.56 (0.35, 8.77)	0.034	7.02 (3.35, 10.69)	<0.001	5.58 (2.31, 8.84)	<0.001	
4–6	12.02 (6.94, 17.09)	<0.001	8.96 (3.71, 14.20)	<0.001	12.61 (8.03, 17.18)	<0.001	9.51 (5.44, 13.58)	<0.001	
Note: Adjusted log‐binomial regression models controlled for age, sex, MS type, current symptoms due to recent relapse, MS duration, number of comorbidities, and disability. The sample sizes in the unadjusted and adjusted models differ due to the inclusion of participants with complete data on outcomes and confounders. Results in boldface denote significant associations between healthy behaviours and QoL.

Abbreviations: aβ, adjusted β (coefficients); CI, confidence interval; MS, multiple sclerosis; QoL, quality of life; Ref., reference.

TABLE 7 Associations between Total Healthy Lifestyle Index Score and fatigue and disability.

Healthy behaviour	Fatigue	Disability	
FSS > 5	Moderate vs. normal/mild	Severe vs. normal/mild	
Unadjusted model, n = 722	Adjusted model, n = 696	Unadjusted model, n = 722	Adjusted model, n = 696	Unadjusted model, n = 722	Adjusted model, n = 696	
PR (95% CI)	p	aPR (95% CI)	p	PR (95% CI)	p	aPR (95% CI)	p	PR (95% CI)	p	aPR (95% CI)	p	
0–1, Ref.	1.00 (0.00, 0.00)		1.00 (0.00, 0.00)		1.00 (0.00, 0.00)		1.00 (0.00, 0.00)		1.00 (0.00, 0.00)		1.00 (0.00, 0.00)

		
2	0.92 (0.79, 1.07)	0.278	0.92 (0.79, 1.07)	0.281	0.89 (0.63, 1.25)	0.507	0.98 (0.61, 1.58)	0.938	0.98 (0.56, 1.71)	0.942	1.28 (0.56, 2.92)	0.561	
3	0.82 (0.68, 0.98)	0.031	0.86 (0.71, 1.04)	0.129	0.77 (0.52, 1.13)	0.180	0.50 (0.29, 0.86)	0.012	0.81 (0.43, 1.54)	0.526	0.58 (0.23, 1.46)	0.247	
4–6	0.73 (0.56, 0.95)	0.017	0.77 (0.59, 0.99)	0.039	0.59 (0.36, 0.97)	0.038	0.44 (0.22, 0.86)	0.017	0.65 (0.28, 1.49)	0.307	0.42 (0.12, 1.41)	0.159	
Note: Adjusted log‐binomial regression models controlled for age, sex, MS type, current symptoms due to recent relapse, MS duration, number of comorbidities, antifatigue medication, and other lifestyle behaviours. The sample sizes in the unadjusted and adjusted models differ due to the inclusion of participants with complete data on outcomes and confounders. Results in boldface denote significant associations between healthy behaviours and fatigue and disability.

Abbreviations: aPR, adjusted prevalence ratio; CI, confidence interval; FSS, Fatigue Severity Scale; MS, multiple sclerosis; PR, prevalence ratio; Ref., reference.

DISCUSSION

We assessed baseline engagement with healthy lifestyle behaviours and their relationships with health outcomes in MSOC participants. Overall, engagement with healthy behaviours was relatively low, with 33.7% of participants engaging in only one behaviour. Nonsmoking was most engaged with, followed by omega‐3 and vitamin D supplementation, regular physical activity, healthy diet, and meditation. The most frequently reported combination of engagement with 2–4 healthy behaviours was nonsmoking with regular physical activity and/or omega‐3 supplementation and/or vitamin D supplementation (not including healthy diet or meditation). Nonsmoking, regular physical activity, and healthy diet were all independently associated with better health outcomes. Engagement with more healthy behaviours, especially healthy diet and physical activity, was associated with better outcomes; engaging with 3 healthy behaviours was associated with 5%–6% higher mQoL and pQoL, whereas engaging with ≥4 behaviours was associated with 9%–10% higher mQoL and pQoL, 56% lower moderate disability prevalence, and 23% lower fatigue prevalence.

Examining healthy behaviour engagement patterns by pwMS enables speculation as to why pwMS preferentially engage with certain healthy behaviours but not others. For instance, our findings suggest information dissemination based on robust findings by clinicians and public awareness campaigns is a critical component of healthy behaviour engagement by pwMS. That is, the high frequency of nonsmoking in the study cohort likely reflects the widespread dissemination by public awareness campaigns and neurologists of the negative impacts of smoking, and strong evidence‐based findings related to MS progression [27, 28]. Similarly, high rates of vitamin D supplementation may reflect the promotion of supplementation by MS societies and neurologists, albeit at lower doses than the IC (<5000 IU/day) [8]. In contrast, low engagement with a healthy diet defined by the IC could reflect inconsistencies in the literature regarding MS‐related dietary recommendations, contributing to uncertainties regarding the best diet for pwMS [29, 30], and highlighting the need for robust studies to clarify the role of diet in disease progression.

Examining patterns of healthy lifestyle engagement enables us to discuss the importance of identifying potential facilitators and barriers to healthy lifestyle engagement, which may help inform the design and implementation of lifestyle interventions. For example, preferential engagement in certain healthy lifestyles may reflect the “ease” of engagement in certain lifestyles compared with other lifestyles. That is, high rates of engagement with omega‐3 and vitamin D supplementation could indicate supplementation is an “easier” lifestyle to engage with than more “difficult” lifestyles including healthy diet and meditation. However, although regular physical activity could be perceived as more “difficult” to adopt than supplementation, similar rates of engagement were observed, which may reflect other contributing factors promoting physical activity engagement, such as the related benefits associated with physical activity [31].

In contrast, lower rates of engagement could be explained by well‐recognized barriers to healthy lifestyle engagement. For instance, low rates of engagement with a healthy diet could be due to the stringency of the high‐quality plant‐based diet, or food preferences [32, 33], suggesting extra resources or support may facilitate increased engagement with this intervention. With regard to reduced physical activity engagement rates, previously reported barriers to physical activity engagement include fear and apprehension [34], and fatigue and physical issues [35], which may also require directed strategies to increase engagement. Likewise, barriers to meditation previously identified include difficulties learning meditation skills, low perceived benefit, and perceived conflict with cultural/religious beliefs [36], which may need certain strategies to facilitate engagement.

Furthermore, examining engagement with combinations of multiple healthy lifestyles provides a greater understanding of engagement with healthy lifestyles by different subpopulations of pwMS. For instance, engagement with a healthy diet and meditation was predominantly only observed in participants engaging with ≥4 healthy behaviours, and even this engagement was low. These findings indicate that engaging with these two behaviours was restricted to a small subset of pwMS potentially committed to adopting a complete multimodal healthy lifestyle. Subsequently, consideration needs to be given to interventions aimed at modifying one behaviour, such as diet or meditation, as these may only be preferentially adopted by a subset of pwMS who also engage with other healthy behaviours. Therefore, examining engagement with multimodal lifestyle behaviours is likely to be an important consideration even when assessing interventions that aim to induce only one lifestyle behaviour change.

We found certain individual healthy behaviours were independently associated with certain health outcomes. Nonsmoking, a healthy diet, and regular physical activity were associated with higher mQoL and pQoL, with a healthy diet and regular physical activity further associated with lower fatigue prevalence, and regular physical activity also associated with lower disability prevalence. These findings support prior cross‐sectional [37, 38], and prospective [3, 39, 40] studies identifying associations between a high‐quality diet and better health outcomes. Similarly, physical activity is associated with greater health‐related QoL and less fatigue in pwMS [31, 41, 42], and nonsmoking with greater health‐related QoL [43].

Engagement with 4–6 healthy lifestyles, including regular physical activity and a healthy diet, had the strongest association with better health outcomes (higher QoL, lower fatigue, and lower disability). Although cross‐sectional in study design, these findings support prior prospective studies identifying the benefits of accumulative versus individual healthy lifestyles for health outcomes; engagement with ≥3 healthy behaviours in the HOLISM cohort was associated with greater QoL at 7.5 years, with the strongest association observed with five healthy behaviours [14], and engagement with combinations of certain healthy behaviours (smoking cessation, high‐quality nutrition, no/low alcohol consumption, and physical activity [SNAP] scores exceeding 3/5) was associated with a 12% lower risk of fatigue at 2.5 years [13].

The synergistic effects of following more healthy lifestyle behaviours may be attributable to the additive effects of beneficial lifestyle‐induced biological mechanisms. For instance, both a plant‐based diet and physical activity improve cardiovascular health and help reduce the risk of obesity and diabetes, which are associated with reduced QoL [44]. Other lifestyles that act to reduce neural inflammation include regular meditation and a plant‐based diet [45], which may improve symptoms in pwMS, whereas smoking may increase inflammation and exacerbate symptoms [28].

A major strength of our study includes the large, diverse international study cohort with comparable participant characteristics to other MS cohorts [46, 47]. However, although the nonsmoking prevalence was similar to other MS cohorts (84%–88%) [12, 28, 46], engagement with IC‐defined healthy behaviours was lower. In particular, the frequencies of omega‐3 (35%) and vitamin D supplementation (30%) were lower than STOP MS and HOLISM [11, 14]. Similarly, the proportion of pwMS engaging with regular physical activity (29%) was lower than two Northern American MS cohorts [46, 47]. Furthermore, although pwMS have expressed high interest in the role of diet in MS [38], we found engagement with a healthy diet was low, but this may be due to dietary strictness, individual preferences, or diet recommendation uncertainties [34, 35, 36, 37], as discussed. However, Balto et al. also found that 86% of pwMS did not adhere to healthy dietary guidelines, and poor diet was the most common risk factor in the SNAP score [46]. Therefore, study findings indicate that a large proportion of pwMS may be following a low‐quality diet, validating the value of interventions to improve diet and nutrition in pwMS.

With the growing interest in self‐management strategies, particularly online lifestyle interventions [48, 49, 50], the MSOC was developed to encourage and empower pwMS to embrace multiple healthy lifestyle behaviours, and potentially improve future well‐being and health outcomes [18]. The current baseline findings provide important insights for subsequent MSOC RCT analyses. Given the low baseline engagement in healthy lifestyle behaviours, with only 11% of participants engaging in >3 healthy behaviours, this RCT has significant potential to increase engagement and assess its impact on health outcomes. Follow‐up data will allow us to explore the effects of lifestyle changes and behaviour combinations in detail. Additionally, participants with higher baseline engagement may show less observable effect due to a ceiling effect, as they might already be experiencing benefits from these behaviours. However, subgroup analyses will determine whether the intervention has a more pronounced effect among those with lower versus higher baseline engagement.

This study has several limitations. First, the reliance on self‐reported measures introduces potential biases, such as recall and social desirability bias, which may affect the accuracy of reported behaviours and outcomes. Second, we were unable to fully assess the healthy diet recommendations of the IC, as we did not query seafood consumption. Consequently, the "healthy diet" cohort includes both vegetarians and pescatarians, introducing variability in dietary patterns. Third, the cohort may be subject to selection bias, as data were analysed only from participants who completed the baseline survey, potentially overrepresenting pwMS with a greater interest in lifestyle behaviour modification. Although this aligns with our aim of exploring behaviour engagement among those enrolled in digital health interventions and RCTs, caution is needed when generalizing findings to the broader MS population. Additionally, the cross‐sectional nature of this study precludes establishing causal relationships and determining the sequence of lifestyle behaviour adoption and maintenance, and it is susceptible to reverse causality, where people with better health may be more likely to engage in healthy behaviours. Future longitudinal studies would allow for a more robust examination of causal pathways and the optimal combinations and sequences of healthy behaviours for greater health benefits.

In conclusion, we report novel findings of the nuanced patterns of engagement in healthy lifestyle behaviours by pwMS. Our results provide evidence supporting the value of pwMS engaging with multimodal healthy lifestyle behaviours, particularly a healthy diet and regular physical activity, with the best health outcomes observed when engaging with ≥4 behaviours. Findings are potentially relevant to common queries in clinical practice regarding how pwMS can improve their future outcomes via facilitating self‐management through lifestyle modifications. The insights into lifestyle behaviour engagement and associations with health outcomes provide a platform for future studies to further investigate the relationship between combinations of behaviours and health outcomes, ultimately improving MS care.

AUTHOR CONTRIBUTIONS

Maggie Yu: Conceptualization; methodology; data curation; formal analysis; validation; visualization; project administration; writing – review and editing; writing – original draft. Sandra Neate: Methodology; supervision; resources; funding acquisition; writing – original draft; writing – review and editing; investigation. Nupur Nag: Conceptualization; writing – review and editing; methodology. William Bevens: Investigation; project administration; methodology; writing – review and editing. George Jelinek: Investigation; resources; funding acquisition; writing – review and editing. Steve Simpson‐Yap: Methodology; writing – review and editing. Rebekah A. Davenport: Writing – review and editing. Alex Fidao: Methodology; writing – review and editing. Jeanette Reece: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing; visualization; supervision.

FUNDING INFORMATION

The Neuroepidemiology Unit also receives funding from anonymous philanthropic donors. The funders had no role in the study design, analysis, interpretation, writing of the manuscript, or decision to publish the results.

CONFLICT OF INTEREST STATEMENT

G.J. receives royalties for his books, Overcoming Multiple Sclerosis and Recovering from Multiple Sclerosis. G.J. and S.N. receive royalties for their book, The Overcoming MS Handbook, and previously received remuneration for conducting educational workshops for people with MS. None of the other authors has any conflict of interest to disclose.

ETHICS STATEMENT

The study protocol was approved by the University of Melbourne Human Research Ethics Committee (22140). The trial was registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12621001605886; 25 November 2021). Participants were provided with a participant information statement, and written informed consent was obtained from all participants in the RCT.

Supporting information

Figure S1.

Table S1.

Table S2.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the generous contribution of the individuals voluntarily participating in the MSOC study, our industry partners, JMA Creative, who created the web‐platform and developed content, and the Overcoming MS Charity (UK), which financially supported the technical development of the MSOC via JMA Creative.

DATA AVAILABILITY STATEMENT

The raw data may not be shared due to conditions approved by our institutional ethics committee, all data are stored as reidentifiable information at the University of Melbourne in the form of password‐protected computer databases, and only the listed investigators have access to the data. Access to deidentified aggregate group data may be requested through J.R. at the completion of the study.
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