Evaluation of a Self-management Programme to Prevent Falls in People With Multiple Sclerosis

Purpose and aims The overall purpose is to evaluate an online fall prevention intervention for ambulatory and non-ambulatory people with multiple sclerosis (PwMS). The intervention incorporates key features of self-management and addresses a variety of fall risk factors. The effects of the intervention on fall rate will be evaluated in a full scale randomised controlled trial (RCT). The hypothesis is that after participating in the intervention, PwMS will identify and self-manage risk factors for falls in their everyday life while taking the daily fluctuations of multiple sclerosis (MS) into consideration (i.e., develop fall prevention behaviours), and thereby reduce the number of falls. Aims: 1. To determine if the fall prevention programme is effective in reducing fall rate in PwMS 6 and 12 months after start of intervention. 2. To determine if the program has an effect on secondary outcomes at 3, 6 and 12 months. 3. To explore how process evaluation components (context, implementation, mechanisms of impact) inform interpretation of outcomes. 4. To evaluate the cost effectiveness of the fall prevention program at 12 months after start of intervention. INTRODUCTION MS is a chronic inflammatory, demyelinating, and neurodegenerative disease with a global prevalence of about 36 per 100,000 population. The prevalence in women is two to four times higher than in men and the usual onset is between 20 and 40 years of age. The disease is typically progressive in nature with consequences that include an increased risk for falls. Up to 71% of PwMS fall each six months (1). Those who report a fall in the past year have an 82% probability of falling again in the six months after a fall and a 56% probability of sustaining an injurious fall (1). Falls among PwMS are associated with injuries, fear of falling and low health-related quality of life (2). Several consequences of MS are known fall risk factors for this population, including impaired balance, reduced walking speed, and impaired cognition (2). The lack of attention given to behavioural and environmental influences on fall risk for PwMS is a major gap in existing evidence. Another major gap in MS falls research is that few studies have explored influences on fall risk among individuals who are non-ambulatory, i.e., only capable of walking a few steps or not at all (3). Furthermore, most interventions aiming to prevent falls among PwMS have excluded non-ambulatory individuals. To date, only two research teams are addressing the fall prevention needs of non-ambulatory PwMS: ours and a team based in the U.S. (4). The importance of targeting a variety of modifiable risk factors through fall prevention programs for PwMS has been highlighted (5). The need for comprehensive approaches to fall prevention that address physical, environmental, and behavioural aspects of falls management has been echoed by Gunn et al (6). Despite the recognition of the value of attention to diverse influences on fall risk, most fall prevention interventions for PwMS focus only on addressing physical impairments, such as compromised balance. For PwMS, who live with an unpredictable disease and daily fluctuations in functioning, the benefits of self-management (7) of the multifactorial fall risks have been highlighted (8). Nevertheless, research on self-management interventions to prevent falls in PwMS is in its infancy. The delivery and settings of previous interventions have been face-to-face in physical locations, supported by online resources, or web-based without any in real-time interactions. Our proposed self-management intervention will be enhanced by digital health technologies with real-time digital meetings and online learning platforms. For PwMS, such delivery will require less time and no expenses for travelling, reduced impact on fatigue, and have the possibility to reach people also in sparsely populated areas. To address the unique fall prevention needs of ambulatory and non-ambulatory PwMS, we have designed an online self-management fall prevention intervention. The intervention is a complex intervention, given its number of interacting components; the number and difficulty of behaviours required by both those delivering and receiving the interventions; and the number of outcomes addressed. The intervention was developed informed by findings from our scoping review (9), social cognitive theory (SCT) (10), and using a co-design method (11), based on design thinking (12), with the goal of enhancing the intervention's quality and relevance to end users. The co-design process (including pre-planning, the workshops, and the refinement phase) captured feedback from various stakeholders (PwMS, the patient organisation Neuro Sweden and healthcare professionals); and resulted in a manualized, online fall prevention programme. The programme is group-based with maximum 8 participants; led by a trained group leader; and performed in eight two-hour sessions, the first seven over seven consecutive weeks and the last final session held one month after the seventh session. The sessions are face-to-face digital meetings by use of a video platform (Zoom Video Communications, Inc.). An online learning platform (BASS) is used to share intervention content including assignments to be completed by participants between sessions, and for asynchronous activities and communication with the group leader that can be shared between participants outside the sessions. The assignments are designed to provide participants with opportunities to practice skills learned during the fall prevention programme. One group leader will lead each programme cycle, i.e., the eight sessions. The intervention focuses on six core self-management skills (7): problem solving, decision-making, using resources, partnership with healthcare providers, taking action, and self-tailoring. The components of the intervention are theoretically grounded in SCT (10) including observational learning, persuasion and support, and opportunities for skill mastery. The pedagogical format is theoretically grounded in universal design for learning (13) and principles for collaborative learning with blended methods (digital tools and learning platform involved) (14). Universal design for learning emphasizes the importance of designing an intervention to enable participants of various abilities to assimilate the knowledge using different forms of engagement, materials, action, and expression. The blended learning design adds to this by structuring the sessions according to principles for group collaboration in an online setting. Each of those features are reflected in the intervention. To support programme fidelity the group leaders will complete a train-the-trainer program prior to the intervention start. This online education programme consists of asynchronous online modules providing information on: the fall prevention programme aim, structure, and content; theoretical grounding of the fall prevention programme; the role of the group leader; basic knowledge on MS, and on falls in MS; how to use an action plan; and detailed content of the eight session manuals. Each train-the-trainer module ends with a quiz or a reflective question. One module of the train-the-trainer program consists of a synchronous online role-play session. In addition, the group leaders will receive a manual that describes how the program's digital tools are used. The intervention was evaluated in a feasibility study (15) comprising 46 participants: 23 were randomized to participate in the group-based self-management intervention and 23 to the control group. Analyses of feasibility of delivery of the intervention, intervention fidelity, feasibility of outcome measures, and acceptability of the intervention from the perspectives of both the PwMS who participated in the intervention and group leader are ongoing. Findings show that the recruitment process, the data collection procedures, and the intervention procedures are feasible (16). Methods Consistent with the Medical Research Council's recommendations for development and evaluation of complex interventions, the fall prevention programme will be evaluated regarding outcome and process with quantitative and qualitative methods. To ensure patient benefit, MS-specific representatives from the patient organisation Neuro Sweden have been project partners in the programme development and feasibility testing. They will continue to be project partners in this effectiveness study through ongoing collaborative discussions on project procedures, for example development of interview questions. Effect study Aims: To determine if the fall prevention programme is effective in reducing fall rate in PwMS 6 months and 12 months after start of intervention; and To determine if the programme has an effect on secondary outcomes at 3, 6 and 12 months. Participants: Eligible participants will be community dwelling PwMS aged ≥ 18 years, who are able to independently transfer from bed to wheelchair with or without aids but without assistance of another person, have experienced one or more falls during the last year, are able to understand and communicate in Swedish; and have ability to use and access to cell phone and technical devices for online meetings i.e., computers or tablets with internet access. Participants will be recruited from in- and outpatient MS- and rehabilitation clinics, by the patient organization Neuro Sweden and by social media advertisements. Power calculation is based on data from our feasibility pilot RCT study showing that a sample size of 84 participants per group is necessary to achieve adequate statistical power (0.8, alpha=0.05). Based on literature reported a high attrition and drop-out in chronic conditions we aim to recruit up to 240 participants, allowing for up to a 35% drop-out rate. The sample size is based on a Poisson distribution, which aligns with the nature of our primary outcome falls/person/year. Design and procedures: A parallel-group RCT will be conducted. After informed consent and baseline assessments, digitally collected through BASS electronic data capture tools hosted at Karolinska Institutet, participants will be allocated to intervention or control group. Participants (n=240) will be stratified for ambulation level (ambulatory/non-ambulatory) and a 1:1 allocation ratio of blocks of eight will be used. Intervention-group participants will participate in the fall prevention programme. Both intervention and control-group participants will receive a leaflet about fall risk factors and fall prevention. Baseline data and follow-up assessments will be collected and managed using BASS, except the short version of Montreal Cognitive Assessment which will be collected through telephone (see below). . Approximately eight group leaders will be recruited to lead one or more cycles of the fall prevention programme in parallel groups. This will ensure that the outcomes of the programme are not attributed to an individual group leader. The group leaders will not be involved in the usual care of the participants. In both groups, falls will be monitored from baseline via an online short message service (SMS). An SMS will be sent once a week (to avoid recall bias) asking "Have you fallen within the last week?" Participants answering "yes" will receive a questionnaire through BASS with questions on fall related injuries. Fall is defined as "an unexpected event in which the participants come to rest on the ground, floor, or lower level". In line with recommendations for evaluation of fall prevention interventions the monitoring of falls will continue until 12 months after start of intervention. Data-collection: Baseline data include sociodemographic (e.g., age, sex, civil status, work status) and disease-related characteristics (e.g., MS severity); expectations on the programme; self-reported falls in the previous 6 months, health literacy (HLS-EU Q16), depression/anxiety (Hospital Anxiety and Depression Scale), fatigue (Fatigue Severity Scale), cognition (short version of Montreal Cognitive Assessment), and the outcomes. Primary outcome is fall rate (falls/person/year). Secondary outcomes include data on self-management fall prevention behaviours assessed by the Falls Control Scale; a direct measure of fear of falling; falls self-efficacy assessed by the Mobility Inclusive Short Falls Efficacy Scale International, Falls Efficacy Scale-International (ambulatory participants) and the Spinal Cord Injury Fall Concern Scale (non-ambulatory participants); a direct measure of activity curtailment; perceived impact of MS assessed by the Multiple Sclerosis Impact Scale; and health status by the EuroQol-5D-5L. Data will be collected at baseline, directly after the last session, and 6 and 12 months after start of intervention. Analyses: Intention-to-treat and per protocol analyses will be performed. Per protocol analyses will include participants who fulfil content adherence, i.e., completion of at least two action plans. Descriptive statistics will be used to present data. Linear mixed models analyses will be used for the primary outcome and normally distributed secondary outcomes. Non-parametric methods will be used for between- and within-group analyses for categorical and non-normally distributed secondary outcomes. Primary endpoint is 6 months after start of intervention. In addition, analyses of sustainability will be performed after 12 months. Analyses will be performed and reported in accordance with the Consolidated Standards of Reporting Trials guidelines for RCT. A data management plan is established and stored at the Research Data Office at Karolinska Institutet. Process evaluation Aim: To explore how process evaluation components (context, implementation, mechanisms of impact) inform interpretation of outcomes. Design: A mixed methods process evaluation. Participants: a) A purposively selected sample of intervention-group participants (n=20-30) to represent variation in e.g., age, sex, ambulation level, MS severity, and expectations on the programme. b) All group leaders. Data collection: a) Semi-structured individual interviews with participants will be conducted 2- weeks, and 6- and 12-months after the end of intervention. The interviews will cover experiences of participating in the study, contextual factors and mechanisms of impact. Data from the program platform will be collected on intervention participant online activities, e.g., the action plans. b) Background information (e.g. sex, age, profession, work-related knowledge and experience). Structured questions collected after train-the-trainer and after each session covering perceived preparedness and confidence in facilitating the group, fidelity to program manual, interactions with group members, technical issues. Semi-structured interviews conducted after train-the-trainer and after the last session covering the group leaders' perspectives on experiences of participation, contextual factors, implementation, and mechanisms of impact. The interviews will be conducted face-to-face or through Zoom. Analysis: Qualitative content analysis (16) and descriptive statitics. Health economic evaluation Aim: To evaluate the cost effectiveness of the fall prevention programme at 12 months. Participants: All intervention and control participants. Data collection: Participants answering "yes" on the weekly SMS about falls will receive a short questionnaire through BASS with questions related to fall related injuries i.e., healthcare utilization, municipal care utilization, sickness absence, medications, support from significant others, and technical aids including walking aids. Estimations of resources needed for the fall prevention program will be collected related to group facilitators i.e., time for participating in preparatory training, for conducting the programme, and time for preparation in between group sessions. To estimate the monetary value of resource use, both for participants in the RCT as well as for resources needed to conduct the intervention, cost data will be retrieved from the cost per patient (KPP) database held at the Swedish Association of local authorities and regions (SKR), regional pricelists as well as from Statistics Sweden (SCB). Data on health-related quality of life will be measured using the EQ-5D-5L at all time-points. Analyses: The PwMS participating in the new fall prevention program will be compared to the control group using intention-to-treat analysis. Descriptive and comparative statistics will be used for analyses of between and within group differences in health-related quality of life and resource use of participants as well resources needed for conducting the intervention. Significance In contrast to previous studies of fall prevention interventions, the present is based both on pedagogical theories and on theories on behavior change; is directed to both ambulatory and non-ambulatory PwMS; and includes an interactive online design that facilitates participation from people with fatigue and those in rural areas. Thereto, most previous programmes are focused on physical activity, whereas this programme takes the multifactorial risk factors of MS falls into consideration. Through this, participants will learn strategies that they can apply in their everyday life in different fall risk areas and alter strategies as the MS progresses. If the programme is effective in reducing fall rate, it will be of great clinical relevance to healthcare providers working with PwMS. This, as well as the use of co-design in the development and use of a manualized intervention to support programme fidelity will facilitate implementation into existing healthcare for PwMS. We have MS-specific representatives from the patient organisation Neuro Sweden as partners which ensures the patient benefit and facilitates the dissemination of the new knowledge nationally. If effective, we will make the programme publicly available so that it can be implemented in the Swedish MS care. The generated new knowledge can also be used in Swedish national clinical guidelines for treatment of PwMS. Key references 1. Cameron MH, Thielman E, Mazumder R, Bourdette D. Predicting falls in people with multiple sclerosis: fall history is as accurate as more complex measures. Mult Scler Int. 2013;2013:496325. 2. 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