scholarly journals eHealth-Based Behavioral Intervention for Increasing Physical Activity in Persons With Multiple Sclerosis: Fidelity Protocol for a Randomized Controlled Trial (Preprint)

2018 ◽  
Author(s):  
Stephanie L Silveira ◽  
Justin McCroskey ◽  
Brooks C Wingo ◽  
Robert W Motl
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Behavior Change ◽  
Behavioral Intervention ◽  
Behavioral Interventions ◽  
Health Behavior Change ◽  
Phase Iii ◽  
Monitoring Plan ◽  
Randomized Controlled ◽  
Study Results

BACKGROUND The rate of physical activity is substantially lower in persons with multiple sclerosis (MS) than in the general population. This problem can be reversed through rigorous and reproducible delivery of behavioral interventions that target lifestyle physical activity in MS. These interventions are, in part, based on a series of phase II randomized controlled trials (RCTs) supporting the efficacy of an internet-delivered behavioral intervention, which is based on social cognitive theory (SCT) for increasing physical activity in MS. OBJECTIVE This paper outlines the strategies and monitoring plan developed based on the National Institutes of Health Behavior Change Consortium (NIH BCC) treatment fidelity workgroup that will be implemented in a phase III RCT. METHODS The Behavioral Intervention for Physical Activity in Multiple Sclerosis (BIPAMS) study is a phase III RCT that examines the effectiveness of an internet-delivered behavioral intervention based on SCT and is supported by video calls with a behavioral coach for increasing physical activity in MS. BIPAMS includes a 6-month treatment condition and 6-month follow-up. The BIPAMS fidelity protocol includes the five areas outlined by the NIH BCC. The study design draws on the SCT behavior-change strategy, ensures a consistent dose within groups, and plans for implementation setbacks. Provider training in theory and content will be consistent between groups with monitoring plans in place such as expert auditing of calls to ensure potential drift is addressed. Delivery of treatment will be monitored through the study website and training will focus on avoiding cross-contamination between conditions. Receipt of treatment will be monitored via coaching call notes and website monitoring. Lastly, enactment of treatment for behavioral and cognitive skills will be monitored through coaching call notes among other strategies. The specific strategies and monitoring plans will be consistent between conditions within the constraints of utilizing existing evidence-based interventions. RESULTS Enrollment began in February 2018 and will end in September 2019. The study results will be reported in late 2020. CONCLUSIONS Fidelity-reporting guidelines provided by the NIH BCC were published in 2004, but protocols are scarce. This is the first fidelity-monitoring plan involving an electronic health behavioral intervention for increasing physical activity in MS. This paper provides a model for other researchers utilizing the NIH BCC recommendations to optimize the rigor and reproducibility of behavioral interventions in MS. CLINICALTRIAL ClinicalTrials.gov NCT03490240; https://www.clinicaltrials.gov/ct2/show/NCT03490240. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/12319

Randomized controlled trial of a behavioral intervention targeting symptoms and physical activity in multiple sclerosis

2013 ◽  
Vol 20 (5) ◽  
pp. 594-601 ◽  
Author(s):  
LA Pilutti ◽  
D Dlugonski ◽  
BM Sandroff ◽  
R Klaren ◽  
RW Motl
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Randomized Controlled Trial ◽  
Sleep Quality ◽  
Behavioral Intervention ◽  
Behavioral Interventions ◽  
Controlled Trial ◽  
Randomized Controlled ◽  
Related Quality ◽  
Lifestyle Physical Activity

Background: Exercise training is beneficial, but most persons with multiple sclerosis (MS) are sedentary and physically inactive. This has prompted a new focus on the promotion of lifestyle physical activity in MS. We previously designed, tested, and refined a behavioral intervention delivered through the Internet that successfully increased lifestyle physical activity in MS, but have not evaluated the effects on secondary symptomatic and health-related quality of life (HRQOL) outcomes. Objective: We conducted a 6-month randomized controlled trial (RCT) that examined the efficacy of an Internet-delivered, behavioral intervention for improving outcomes of fatigue, depression, anxiety, pain, sleep quality, and HRQOL in 82 ambulatory persons with MS. The secondary aim was to replicate previous results regarding change in free-living physical activity. Results: There was a significant and positive effect of the intervention on fatigue severity ( p=.001, ηρ2=.15) and its physical impact ( p=.008, ηρ2=.09), depression ( p=.006, ηρ2=.10), and anxiety ( p=.006, ηρ2=.10). There were non-significant improvements in pain ( p=.08, ηρ2=.04), sleep quality ( p=.06, ηρ2=.05), and physical HRQOL ( p=.06, ηρ2=.05). We replicated our previous results by demonstrating an increase in self-reported physical activity ( p=.001, ηρ2=.13). Conclusions: Our results support behavioral interventions targeting lifestyle physical activity as an alternative approach for managing symptoms in MS.

scholarly journals Randomized controlled trial of an e-learning designed behavioral intervention for increasing physical activity behavior in multiple sclerosis

2017 ◽  
Vol 3 (4) ◽  
pp. 205521731773488 ◽  
Author(s):  
Robert W Motl ◽  
Elizabeth A Hubbard ◽  
Rachel E Bollaert ◽  
Brynn C Adamson ◽  
Dominique Kinnett-Hopkins ◽  
...  
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Randomized Controlled Trial ◽  
Behavioral Intervention ◽  
Controlled Trial ◽  
Analysis Of Covariance ◽  
Learning Approaches ◽  
Positive Effects ◽  
Randomized Controlled ◽  
E Learning

Background Internet-delivered, behavioral interventions represent a cost-effective, broadly disseminable approach for teaching persons with multiple sclerosis (MS) the theory-based skills, techniques, and strategies for changing physical activity. Objectives This pilot, randomized controlled trial examined the efficacy of a newly developed Internet website based on e-learning approaches that delivered a theory-based behavior intervention for increasing physical activity and improving symptoms, walking impairment, and neurological disability. Methods Participants with MS ( N = 47) were randomly assigned into behavioral intervention ( n = 23) or waitlist control ( n = 24) conditions delivered over a six-month period. Outcomes were administered before and after the six-month period using blinded assessors, and data were analyzed using analysis of covariance in SPSS. Results There was a significant, positive intervention effect on self-reported physical activity ( P = 0.05, [Formula: see text] = 0.10), and non-significant improvement in objectively measured physical activity ( P = 0.24, [Formula: see text] = 0.04). There were significant, positive effects of the intervention on overall ( P = 0.018, [Formula: see text] = 0.13) and physical impact of fatigue ( P = 0.003, [Formula: see text] = 0.20), self-reported walking impairment ( P = 0.047, [Formula: see text] = 0.10), and disability status ( P = 0.033, [Formula: see text] = 0.11). There were non-significant improvements in fatigue severity ( P = 0.10, [Formula: see text] = 0.06), depression ( P = 0.10, [Formula: see text] = 0.07) and anxiety ( P = 0.06, [Formula: see text] = 0.09) symptoms, and self-reported disability ( P = 0.10, [Formula: see text] = 0.07). Conclusions We provide evidence for the efficacy of an Internet-based behavioral intervention with content delivered through interactive video courses grounded in e-learning principles for increasing physical activity and possibly improving secondary outcomes of fatigue, depression, anxiety, and walking impairment/disability in persons with MS.

scholarly journals Does the Effect of a Physical Activity Behavioral Intervention Vary by Characteristics of People with Multiple Sclerosis?

2015 ◽  
Vol 17 (2) ◽  
pp. 65-72 ◽  
Author(s):  
Robert W. Motl ◽  
Deirdre Dlugonski ◽  
Lara A. Pilutti ◽  
Rachel E. Klaren
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Behavioral Intervention ◽  
Behavioral Interventions ◽  
Weight Status ◽  
Interindividual Variability ◽  
Vigorous Physical Activity ◽  
Normal Weight ◽  
Analysis Of Covariance ◽  
Disability Status

Background: Behavioral interventions have significantly increased physical activity in people with multiple sclerosis (MS). Nevertheless, there has been interindividual variability in the pattern and magnitude of change. This study documented the efficacy and variability of a behavioral intervention for changing physical activity and examined the possibility that efficacy varied by the characteristics of individuals with MS. Methods: Eighty-two people with MS were randomly assigned to one of two conditions: behavioral intervention (n = 41) or waitlist control (n = 41). We collected information before the study on MS type, disability status, weight status based on body-mass index, and current medications. Furthermore, all participants completed the Godin Leisure Time Exercise Questionnaire and the abbreviated International Physical Activity Questionnaire and wore an accelerometer for 1 week to measure minutes of moderate-to-vigorous physical activity before and after the 6-month intervention period. Results: Analysis of covariance (ANCOVA) indicated that participants in the behavioral intervention had significantly higher levels of physical activity than control participants after the 6-month period (P < .001). There was substantial interindividual variability in the magnitude of change, and ANCOVA indicated that MS type (relapsing vs. progressive) (P < .01), disability status (mild vs. moderate) (P < .01), and weight status (normal weight vs. overweight/obese) (P < .05) moderated the efficacy of the behavioral intervention. Conclusions: The behavioral intervention was associated with improvements in physical activity, particularly for those with mild disability, relapsing-remitting MS, or normal weight status.

scholarly journals Addressing Physical Activity Behavior in Multiple Sclerosis Management

2019 ◽  
Vol 22 (4) ◽  
pp. 178-186
Author(s):  
Sarah J. Donkers ◽  
Sarah Oosman ◽  
Stephan Milosavljevic ◽  
Kristin E. Musselman
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Health Care ◽  
Clinical Practice ◽  
Behavior Change ◽  
Health Care Providers ◽  
Behavioral Interventions ◽  
System Level ◽  
Change Strategies ◽  
Care Providers

Abstract Background: Although physical activity (PA) is considered the most important nonpharmaceutical intervention for persons with multiple sclerosis (MS), less than 20% of people with MS are engaging in sufficient amounts to accrue benefits. Promotion of PA is most effective when combined with additional behavior change strategies, but this is not routinely done in clinical practice. This study aimed to increase our understanding of current practice and perspectives of health care providers (HCPs) in Canada regarding their use of interventions to address PA behavior in MS management. Investigating HCPs’ perspectives on implementing PA behavior change with persons with MS will provide insight into this knowledge-to-practice gap. Methods: Semistructured focus groups were conducted with 31 HCPs working with persons with MS in Saskatchewan, Canada. Based on interpretive description, data were coded individually by three researchers, who then collaboratively developed themes. Analysis was inductive and iterative; triangulation and member reflections were used. Results: Five themes were established: 1) prescribing, promoting, and impacting wellness with PA; 2) coordinating communication and continuity in practice; 3) timely access to relevant care: being proactive rather than reactive; 4) enhancing programming and community-based resources; and 5) reconciling the value of PA with clinical practice. Conclusions: The HCPs value PA and want more support with application of behavior change strategies to deliver PA behavioral interventions, but due to the acute and reactive nature of health care systems they feel this cannot be prioritized in practice. Individual- and system-level changes are needed to support consistent and effective use of PA behavioral interventions in MS.

scholarly journals Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change

2017 ◽  
Vol 45 (3) ◽  
pp. 331-348 ◽  
Author(s):  
Artur Direito ◽  
Deirdre Walsh ◽  
Moohamad Hinbarji ◽  
Rami Albatal ◽  
Mark Tooley ◽  
...  
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Behavioral Intervention ◽  
Intervention Mapping ◽  
Health Behavior Change ◽  
Smartphone Application ◽  
Sedentary Behaviors ◽  
Mhealth Intervention ◽  
Optimal Behavior ◽  
The Individual

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals’ behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.

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