MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis. MATERIALS AND METHODS: Following a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and patterns in patient sleep and activity. We compute meaningful sequences for patients using clustering and proximity search, displaying these with an interactive visual interface composed of coordinated views. Our evaluation posed both closed and open-ended tasks to participants, utilizing a scoring system to gauge the tool's usability, and effectiveness in supporting insight generation across 15 clinicians, data scientists, and non-experts. RESULTS AND DISCUSSION: We present MS Pattern Explorer, a visual analytics system that helps clinicians better address complex data-centric challenges by facilitating the understanding of activity patterns. It enables innovative analysis that leads to rapid insight generation and contextualization of temporal activity data, both within and between patients of a cohort. Our evaluation results indicate consistent performance across participant groups and effective support for insight generation in MS patient fitness tracker data. Our implementation offers broad applicability in clinical research, allowing for potential expansion into cohort-wide comparisons or studies of other chronic conditions. CONCLUSION: MS Pattern Explorer successfully reduces the signal overload clinicians currently experience with activity data, introducing novel opportunities for data exploration, sense-making, and hypothesis generation.

Positive autobiographical memories to counteract low mood in remitted depression: A longitudinal daily-life investigation.

Positive autobiographical memories (AMs) have the potential to repair low mood, but previously depressed individuals have difficulty leveraging their positive AMs for emotion regulation purposes. We examined whether previously depressed individuals benefit from guided, deliberate recollection of preselected AMs to counteract low mood in daily life, utilizing individuals' smartphones to facilitate recollection. Sixty participants enrolled in 2020 were randomly allocated to retrieval of positive or everyday activity AMs and completed ecological momentary assessment of emotional experience for 3 weeks. Participants first created a pool of six memories for the digital AM diary. This was followed by a training week with two recollection tasks daily and a 2-week follow-up period where the diary could be used spontaneously. The positive condition experienced a greater increase in feelings of happiness and a greater decrease in feelings of sadness from pre- to post-AM recollection. While participants in the positive condition used the AM technique more frequently overall during the 2-week follow-up, the effect of condition was moderated by changes in feelings of sadness. The more participants experienced an emotional benefit during the training week, the more they used it spontaneously. Emotional vividness of untrained positive AMs at the 2-week follow-up differed depending on whether they were assessed before or after the first pandemic lockdown. Residual depressive symptoms decreased in both conditions over the study course, while mental well-being remained unchanged. Strengthening positive, self-affirming AMs in daily life may provide a tool to support regulation of transient low mood in those remitted from depression. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Conversation-based AI for anxiety disorders might lower the threshold for traditional medical assistance: a case report.

Artificial intelligence (AI) offers a wealth of opportunities for medicine, if we also bear in mind the risks associated with this technology. In recent years the potential future integration of AI with medicine has been the subject of much debate, although practical clinical experience of relevant cases is still largely absent. This case study examines a particular patient's experience with different forms of care. Initially, the patient communicated with the conversation (chat) based AI (CAI) for self-treatment. However, over time she found herself increasingly drawn to a low-threshold internal company support system that is grounded in an existing, more traditional human-based care structure. This pattern of treatment May represent a useful addition to existing care structures, particularly for patients receptive to technology.

From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal.

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.

Work Engagement and Well-being Study (SWELL): a randomised controlled feasibility trial evaluating the effects of mindfulness versus light physical exercise at work.

BACKGROUND: Mindfulness-based programmes (MBPs) are increasingly offered at work, often in online self-guided format. However, the evidence on MBPs' effect on work performance (WP) is inconsistent. OBJECTIVE: This pragmatic randomised controlled feasibility trial assessed procedural uncertainties, intervention acceptability and preliminary effect sizes of an MBP on WP, relative to an alternative intervention. METHODS: 241 employees from eight employers were randomised (1:1) to complete a 4-week, self-guided, online MBP or a light physical exercise programme (LE)(active control). Feasibility and acceptability measures were of primary interest. WP at postintervention (PostInt) was the primary outcome for preliminary assessment of effect sizes. Secondary outcomes assessed mental health (MH) and cognitive processes hypothesised to be targeted by the MBP. Outcomes were collected at baseline, PostInt and 12-week follow-up (12wFUP). Prospective trial protocol: NCT04631302. FINDINGS: 87% of randomised participants started the course. Courses had high acceptability. Retention rates were typical for online trials (64% PostInt; 30% 12wFUP). MBP, compared with the LE control, offered negligible benefits for WP (PostInt (d=0.06, 95% CI -0.19 to 0.32); 12wFUP (d=0.02, 95% CI -0.30 to 0.26)). Both interventions improved MH outcomes (ds=-0.40 to 0.58, 95% CI -0.32 to 0.18); between-group differences were small (ds=-0.09 to 0.04, 95% CI -0.15 to 0.17). CONCLUSION: The trial is feasible; interventions are acceptable. Results provide little support for a later phase trial comparing an MBP to a light exercise control. To inform future trials, we summarise procedural challenges. CLINICAL IMPLICATIONS: Results suggest MBPs are unlikely to improve WP relative to light physical exercise. Although the MBP improved MH, other active interventions may be just as efficacious. TRIAL REGISTRATION NUMBER: NCT04631302.

Exploring the Major Barriers to Physical Activity in Persons With Multiple Sclerosis: Observational Longitudinal Study.

BACKGROUND: Physical activity (PA) represents a low-cost and readily available means of mitigating multiple sclerosis (MS) symptoms and alleviating the disease course. Nevertheless, persons with MS engage in lower levels of PA than the general population. OBJECTIVE: This study aims to enhance the understanding of the barriers to PA engagement in persons with MS and to evaluate the applicability of the Barriers to Health Promoting Activities for Disabled Persons (BHADP) scale for assessing barriers to PA in persons with MS, by comparing the BHADP score with self-reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life, as well as sensor-measured PA. METHODS: Study participants (n=45; median age 46, IQR 40-51 years; median Expanded Disability Status Scale score 4.5, IQR 3.5-6) were recruited among persons with MS attending inpatient neurorehabilitation. They wore a Fitbit Inspire HR (Fitbit Inc) throughout their stay at the rehabilitation clinic (phase 1; 2-4 wk) and for the 4 following weeks at home (phase 2; 4 wk). Sensor-based step counts and cumulative minutes in moderate to vigorous PA were computed for the last 7 days at the clinic and at home. On the basis of PA during the last 7 end-of-study days, we grouped the study participants as active (≥10,000 steps/d) and less active (<10,000 steps/d) to explore PA barriers compared with PA level. PA barriers were repeatedly assessed through the BHADP scale. We described the relevance of the 18 barriers of the BHADP scale assessed at the end of the study and quantified their correlations with the Spearman correlation test. We evaluated the associations of the BHADP score with end-of-study reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life with multivariable regression models. We performed separate regression analyses to examine the association of the BHADP score with different sensor-measured outcomes of PA. RESULTS: The less active group reported higher scores for the BHADP items Feeling what I do doesn't help, No one to help me, and Lack of support from family/friends. The BHADP items Not interested in PA and Impairment were positively correlated. The BHADP score was positively associated with measures of fatigue and depression and negatively associated with self-efficacy and health-related quality of life. The BHADP score showed an inverse relationship with the level of PA measured but not when dichotomized according to the recommended PA level thresholds. CONCLUSIONS: The BHADP scale is a valid and well-adapted tool for persons with MS because it reflects common MS symptoms such as fatigue and depression, as well as self-efficacy and health-related quality of life. Moreover, decreases in PA levels are often related to increases in specific barriers in the lives of persons with MS and should hence be addressed jointly in health care management.

Natural language processing analysis of the theories of people with multiple sclerosis about causes of their disease.

BACKGROUND: While potential risk factors for multiple sclerosis (MS) have been extensively researched, it remains unclear how persons with MS theorize about their MS. Such theories may affect mental health and treatment adherence. Using natural language processing techniques, we investigated large-scale text data about theories that persons with MS have about the causes of their disease. We examined the topics into which their theories could be grouped and the prevalence of each theory topic. METHODS: A total of 486 participants of the Swiss MS Registry longitudinal citizen science project provided text data on their theories about the etiology of MS. We used the transformer-based BERTopic Python library for topic modeling to identify underlying topics. We then conducted an in-depth characterization of the topics and assessed their prevalence. RESULTS: The topic modeling analysis identifies 19 distinct topics that participants theorize as causal for their MS. The topics most frequently cited are Mental Distress (31.5%), Stress (Exhaustion, Work) (29.8%), Heredity/Familial Aggregation (27.4%), and Diet, Obesity (16.0%). The 19 theory topics can be grouped into four high-level categories: physical health (mentioned by 56.2% of all participants), mental health (mentioned by 53.7%), risk factors established in the scientific literature (genetics, Epstein-Barr virus, smoking, vitamin D deficiency/low sunlight exposure; mentioned by 47.7%), and fate/coincidence (mentioned by 3.1%). Our study highlights the importance of mental health issues for theories participants have about the causes of their MS. CONCLUSIONS: Our findings emphasize the importance of communication between healthcare professionals and persons with MS about the pathogenesis of MS, the scientific evidence base and mental health.

Multiple sclerosis (MS) is a disease that affects the brain and spinal cord, causing a wide range of symptoms. Our study investigated what people living with the disease think causes MS. We analyzed the replies given by 486 people who were questioned about their MS to look for patterns in the responses. We identified 19 distinct themes, notably mental and work-related stress, genetics, and dietary factors, which we grouped into 4 categories: physical health, mental health, established scientific risk factors, and chance. We found that mental health problems were viewed as a key factor for MS. Our work highlights the need for healthcare professionals to have transparent conversations with people with MS about what is known about the disease course and potential causes. In addition, it highlights the importance of fully informing and supporting people with MS regarding their mental health.

Efficacy and moderators of efficacy of cognitive behavioural therapies with a trauma focus in children and adolescents: an individual participant data meta-analysis of randomised trials.

BACKGROUND: Existing clinical trials of cognitive behavioural therapies with a trauma focus (CBTs-TF) are underpowered to examine key variables that might moderate treatment effects. We aimed to determine the efficacy of CBTs-TF for young people, relative to passive and active control conditions, and elucidate putative individual-level and treatment-level moderators. METHODS: This was an individual participant data meta-analysis of published and unpublished randomised studies in young people aged 6-18 years exposed to trauma. We included studies identified by the latest UK National Institute of Health and Care Excellence guidelines (completed on Jan 29, 2018) and updated their search. The search strategy included database searches restricted to publications between Jan 1, 2018, and Nov 12, 2019; grey literature search of trial registries ClinicalTrials.gov and ISRCTN; preprint archives PsyArXiv and bioRxiv; and use of social media and emails to key authors to identify any unpublished datasets. The primary outcome was post-traumatic stress symptoms after treatment (<1 month after the final session). Predominantly, one-stage random-effects models were fitted. This study is registered with PROSPERO, CRD42019151954. FINDINGS: We identified 38 studies; 25 studies provided individual participant data, comprising 1686 young people (mean age 13·65 years [SD 3·01]), with 802 receiving CBTs-TF and 884 a control condition. The risk-of-bias assessment indicated five studies as low risk and 20 studies with some concerns. Participants who received CBTs-TF had lower mean post-traumatic stress symptoms after treatment than those who received the control conditions, after adjusting for post-traumatic stress symptoms before treatment (b=-13·17, 95% CI -17·84 to -8·50, p<0·001, τ2=103·72). Moderation analysis indicated that this effect of CBTs-TF on post-traumatic stress symptoms post-treatment increased by 0·15 units (b=-0·15, 95% CI -0·29 to -0·01, p=0·041, τ2=0·03) for each unit increase in pre-treatment post-traumatic stress symptoms. INTERPRETATION: This is the first individual participant data meta-analysis of young people exposed to trauma. Our findings support CBTs-TF as the first-line treatment, irrespective of age, gender, trauma characteristics, or carer involvement in treatment, with particular benefits for those with higher initial distress. FUNDING: Swiss National Science Foundation.

Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis.

Background: Consumer-grade fitness trackers offer exciting opportunities to study persons with chronic diseases in greater detail and in their daily-life environment. However, attempts to bring fitness tracker measurement campaigns from tightly controlled clinical environments to home settings are often challenged by deteriorating study compliance or by organizational and resource limitations. Objectives: By revisiting the study design and patient-reported experiences of a partly remote study with fitness trackers (BarKA-MS study), we aimed to qualitatively explore the relationship between overall study compliance and scalability. On that account, we aimed to derive lessons learned on strengths, weaknesses, and technical challenges for the conduct of future studies. Methods: The two-phased BarKA-MS study employed Fitbit Inspire HR and electronic surveys to monitor physical activity in 45 people with multiple sclerosis in a rehabilitation setting and in their natural surroundings at home for up to 8 weeks. We examined and quantified the recruitment and compliance in terms of questionnaire completion and device wear time. Furthermore, we qualitatively evaluated experiences with devices according to participants' survey-collected reports. Finally, we reviewed the BarKA-MS study conduct characteristics for its scalability according to the Intervention Scalability Assessment Tool checklist. Results: Weekly electronic surveys completion reached 96%. On average, the Fitbit data revealed 99% and 97% valid wear days at the rehabilitation clinic and in the home setting, respectively. Positive experiences with the device were predominant: only 17% of the feedbacks had a negative connotation, mostly pertaining to perceived measurement inaccuracies. Twenty-five major topics and study characteristics relating to compliance were identified. They broadly fell into the three categories: "effectiveness of support measures", "recruitment and compliance barriers", and "technical challenges". The scalability assessment revealed that the highly individualized support measures, which contributed greatly to the high study compliance, may face substantial scalability challenges due to the strong human involvement and limited potential for standardization. Conclusion: The personal interactions and highly individualized participant support positively influenced study compliance and retention. But the major human involvement in these support actions will pose scalability challenges due to resource limitations. Study conductors should anticipate this potential compliance-scalability trade-off already in the design phase.

Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis.

Tools for monitoring daily physical activity (PA) are desired by persons with multiple sclerosis (MS). However, current research-grade options are not suitable for longitudinal, independent use due to their cost and user experience. Our objective was to assess the validity of step counts and PA intensity metrics derived from the Fitbit Inspire HR, a consumer-grade PA tracker, in 45 persons with MS (Median age: 46, IQR: 40-51) undergoing inpatient rehabilitation. The population had moderate mobility impairment (Median EDSS 4.0, Range 2.0-6.5). We assessed the validity of Fitbit-derived PA metrics (Step count, total time in PA, time in moderate to vigorous PA (MVPA)) during scripted tasks and free-living activity at three levels of data aggregation (minute, daily, and average PA). Criterion validity was assessed though agreement with manual counts and multiple methods for deriving PA metrics via the Actigraph GT3X. Convergent and known-groups validity were assessed via relationships with reference standards and related clinical measures. Fitbit-derived step count and time in PA, but not time in MVPA, exhibited excellent agreement with reference measures during scripted tasks. During free-living activity, step count and time in PA correlated moderately to strongly with reference measures, but agreement varied across metrics, data aggregation levels, and disease severity strata. Time in MVPA weakly agreed with reference measures. However, Fitbit-derived metrics were often as different from reference measures as reference measures were from each other. Fitbit-derived metrics consistently exhibited similar or stronger evidence of construct validity than reference standards. Fitbit-derived PA metrics are not equivalent to existing reference standards. However, they exhibit evidence of construct validity. Consumer-grade fitness trackers such as the Fitbit Inspire HR may therefore be suitable as a PA tracking tool for persons with mild or moderate MS.

Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals.

BACKGROUND: Physical activity is central to maintaining the quality of life for patients with complex chronic conditions and is thus at the core of neurorehabilitation. However, maintaining activity improvements in daily life is challenging. The novel Stay With It program aims to promote physical activity after neurorehabilitation by cultivating self-monitoring skills and habits. OBJECTIVE: We examined the implementation of the Stay With It program at the Valens Rehabilitation Centre in Switzerland using the normalization process theory framework, focusing on 3 research aims. We aimed to examine the challenges and facilitators of program implementation from the perspectives of patients and health care professionals. We aimed to evaluate the potential of activity sensors to support program implementation and patient acceptance. Finally, we aimed to evaluate patients' engagement in physical activity after rehabilitation, patients' self-reported achievement of home activity goals, and factors influencing physical activity. METHODS: Patients were enrolled if they had a disease that was either chronic or at risk for chronicity and participated in the Stay With It program. Patients were assessed at baseline, the end of rehabilitation, and a 3-month follow-up. The health care professionals designated to deliver the program were surveyed before and after program implementation. We used a mixed methods approach combining standardized questionnaires, activity-sensing data (patients only), and free-text questions. RESULTS: This study included 23 patients and 13 health care professionals. The diverse needs of patients and organizational hurdles were major challenges to program implementation. Patients' intrinsic motivation and health care professionals' commitment to refining the program emerged as key facilitators. Both groups recognized the value of activity sensors in supporting program implementation and sustainability. Although patients appreciated the sensor's ability to monitor, motivate, and quantify activity, health care professionals saw the sensor as a motivational tool but expressed concerns about technical difficulties and potential inaccuracies. Physical activity levels after patients returned home varied considerably, both within and between individuals. The self-reported achievement of activity goals at home also varied, in part because of vague definitions. Common barriers to maintaining activity at home were declining health and fatigue often resulting from heat and pain. At the 3-month follow-up, 35% (8/23) of the patients withdrew from the study, with most citing deteriorating physical health as the reason and that monitoring and discussing their low activity would negatively affect their mental health. CONCLUSIONS: Integrating aftercare programs like Stay With It into routine care is vital for maintaining physical activity postrehabilitation. Although activity trackers show promise in promoting motivation through monitoring, they may lead to frustration during health declines. Their acceptability may also be influenced by an individual's health status, habits, and technical skills. Our study highlights the importance of considering health care professionals' perspectives when integrating new interventions into routine care.

Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis.

The emergence of new digital technologies has enabled a new way of doing research, including active collaboration with the public ('citizen science'). Innovation in machine learning (ML) and natural language processing (NLP) has made automatic analysis of large-scale text data accessible to study individual perspectives in a convenient and efficient fashion. Here we blend citizen science with innovation in NLP and ML to examine (1) which categories of life events persons with multiple sclerosis (MS) perceived as central for their MS; and (2) associated emotions. We subsequently relate our results to standardized individual-level measures. Participants (n = 1039) took part in the 'My Life with MS' study of the Swiss MS Registry which involved telling their story through self-selected life events using text descriptions and a semi-structured questionnaire. We performed topic modeling ('latent Dirichlet allocation') to identify high-level topics underlying the text descriptions. Using a pre-trained language model, we performed a fine-grained emotion analysis of the text descriptions. A topic modeling analysis of totally 4293 descriptions revealed eight underlying topics. Five topics are common in clinical research: 'diagnosis', 'medication/treatment', 'relapse/child', 'rehabilitation/wheelchair', and 'injection/symptoms'. However, three topics, 'work', 'birth/health', and 'partnership/MS' represent domains that are of great relevance for participants but are generally understudied in MS research. While emotions were predominantly negative (sadness, anxiety), emotions linked to the topics 'birth/health' and 'partnership/MS' was also positive (joy). Designed in close collaboration with persons with MS, the 'My Life with MS' project explores the experience of living with the chronic disease of MS using NLP and ML. Our study thus contributes to the body of research demonstrating the potential of integrating citizen science with ML-driven NLP methods to explore the experience of living with a chronic condition.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Engagement in volunteering activities by persons with multiple sclerosis in Switzerland.

BACKGROUND: Informal and formal volunteering engagement is a proxy for social integration and may have beneficial effects for physical and mental well-being in persons with multiple sclerosis (pwMS). As literature on the topic among the pwMS is lacking, this study aimed to determine frequency and type of volunteering performed by pwMS and to identify factors associated with volunteering. METHODS: Cross-sectional, self-reported data of 615 pwMS participating in the Swiss Multiple Sclerosis Registry were analyzed using descriptive statistics to determine frequency and type of volunteering engagement. Univariable and multivariable generalized linear models with binomial distribution and log link function were used to identify factors associated with volunteering. Age, sex, employment status and gait disability were added to the multivariable model as fixed confounders. Sociodemographic, health-, work- and daily activity-related factors were included in the analysis. RESULTS: About one third (29.4%) of participants reported engagement in volunteering activities, most often through charities (16.02%) and cultural organizations (14.36%). In the multivariable model, participants who had a university degree were more likely to volunteer than those with lower level of education (RR = 1.48 95% CI [1.14; 1.91]). The ability to pursue daily activities (as measured by the EQ-5D subscale) was strongly associated with participation in volunteering among pwMS. Compared with pwMS who had no or only slight limitations in daily activities, those with severe problems were markedly less likely to engage in volunteering (RR = 0.41, 95% CI [0.21; 0.80]) . Finally, pwMS who reported caring for and supporting their family (i.e., being a homemaker) were more likely to engage in volunteering activities than those who did not (RR = 1.52, 95% CI [1.15; 2.01]). CONCLUSION: Nearly one in three pwMS engaged in diverse volunteering activities. Having a university degree, being less limited in daily activities and being a homemaker increased the probability of pursuing volunteering activities. Contingent on individual-level motivations, resources or physical abilities, pwMS who experience challenges in performing daily activities or social barriers should be made aware of barrier-free offers of socially inclusive and volunteering activities, often provided by the national MS societies and health leagues.

Methodological heterogeneity biases physical activity metrics derived from the Actigraph GT3X in multiple sclerosis: A rapid review and comparative study.

Background: Physical activity (PA) is reduced in persons with multiple sclerosis (MS), though it is known to aid in symptom and fatigue management. Methods for measuring PA are diverse and the impact of this heterogeneity on study outcomes is unclear. We aimed to clarify this impact by comparing common methods for deriving PA metrics in MS populations. Methods: First, a rapid review of existing literature identified methods for calculating PA in studies which used the Actigraph GT3X in populations with MS. We then compared methods in a prospective study on 42 persons with MS [EDSS 4.5 (3.5-6)] during a voluntary course of inpatient neurorehabilitation. Mixed-effects linear regression identified methodological factors which influenced PA measurements. Non-parametric hypothesis tests, correlations, and agreement statistics assessed overall and pairwise differences between methods. Results: In the rapid review, searches identified 421 unique records. Sixty-nine records representing 51 eligible studies exhibited substantial heterogeneity in methodology and reporting practices. In a subsequent comparative study, multiple methods for deriving six PA metrics (step count, activity counts, total time in PA, sedentary time, time in light PA, time in moderate to vigorous PA), were identified and directly compared. All metrics were sensitive to methodological factors such as the selected preprocessing filter, data source (vertical vs. vector magnitude counts), and cutpoint. Additionally, sedentary time was sensitive to wear time definitions. Pairwise correlation and agreement between methods varied from weak (minimum correlation: 0.15, minimum agreement: 0.03) to perfect (maximum correlation: 1.00, maximum agreement: 1.00). Methodological factors biased both point estimates of PA and correlations between PA and clinical assessments. Conclusions: Methodological heterogeneity of existing literature is high, and this heterogeneity may confound studies which use the Actigraph GT3X. Step counts were highly sensitive to the filter used to process raw accelerometer data. Sedentary time was particularly sensitive to methodology, and we recommend using total time in PA instead. Several, though not all, methods for deriving light PA and moderate to vigorous PA yielded nearly identical results. PA metrics based on vertical axis counts tended to outperform those based on vector magnitude counts. Additional research is needed to establish the relative validity of existing methods.

Autobiographical memory style and clinical outcomes following mindfulness-based cognitive therapy (MBCT): An individual patient data meta-analysis.

The ability to retrieve specific, single-incident autobiographical memories has been consistently posited as a predictor of recurrent depression. Elucidating the role of autobiographical memory specificity in patient-response to depressive treatments may improve treatment efficacy and facilitate use of science-driven interventions. We used recent methodological advances in individual patient data meta-analysis to determine a) whether memory specificity is improved following mindfulness-based cognitive therapy (MBCT), relative to control interventions, and b) whether pre-treatment memory specificity moderates treatment response. All bar one study evaluated MBCT for relapse prevention for depression. Our initial analysis therefore focussed on MBCT datasets only(n = 708), then were repeated including the additional dataset(n = 880). Memory specificity did not significantly differ from baseline to post-treatment for either MBCT and Control interventions. There was no evidence that baseline memory specificity predicted treatment response in terms of symptom-levels, or risk of relapse. Findings raise important questions regarding the role of memory specificity in depressive treatments.

Association of age and disease duration with comorbidities and disability: A study of the Swiss Multiple Sclerosis Registry.

BACKGROUND: While comorbidities increase with age, duration of multiple sclerosis (MS) leads to disability accumulation in persons with MS. The influence of ageing vis-a-vis MS duration remains largely unexplored. We studied the independent associations of ageing and MS duration with disability and comorbidities in the Swiss MS Registry participants. METHODS: Self-reported data was cross-sectionally analyzed using confounder-adjusted logistic regression models for 6 outcomes: cancer, type 2 diabetes (T2D), hypertension, cardiac diseases, depression, and having at least moderate or severe gait disability. Using cubic splines, we explored non-linear changes in risk shapes. RESULTS: Among 1615 participants age was associated with cardiac diseases (OR 1.05, 95% CI [1.02, 2.08]), hypertension (OR 1.08, 95% CI [1.06, 2.10]), T2D (OR 1.10, 95%CI [1.05, 1.16]) and cancer (OR 1.04, 95% CI [1.01, 1.07]). MS duration was not associated with comorbidities, except for cardiac diseases (OR 1.03, 95% CI [1.00, 1.06]). MS duration and age were independently associated with having at least moderate gait disability (OR 1.06, 95% CI [1.04, 1.07]; OR 1.04, 95% CI [1.02, 1.05], respectively), and MS duration was associated with severe gait disability (OR 1.05, 95% CI [1.03, 1.08]). The spline analysis suggested a non-linear increase of having at least moderate gait disability with age. CONCLUSIONS: Presence of comorbidities was largely associated with age only. Having at least moderate gait disability was associated with both age and MS duration, while having severe gait disabity was associated with MS duration only.

Electronic Health Diary Campaigns to Complement Longitudinal Assessments in Persons With Multiple Sclerosis: Nested Observational Study.

BACKGROUND: Electronic health diaries hold promise in complementing standardized surveys in prospective health studies but are fraught with numerous methodological challenges. OBJECTIVE: The study aimed to investigate participant characteristics and other factors associated with response to an electronic health diary campaign in persons with multiple sclerosis, identify recurrent topics in free-text diary entries, and assess the added value of structured diary entries with regard to current symptoms and medication intake when compared with survey-collected information. METHODS: Data were collected by the Swiss Multiple Sclerosis Registry during a nested electronic health diary campaign and during a regular semiannual Swiss Multiple Sclerosis Registry follow-up survey serving as comparator. The characteristics of campaign participants were descriptively compared with those of nonparticipants. Diary content was analyzed using the Linguistic Inquiry and Word Count 2015 software (Pennebaker Conglomerates, Inc) and descriptive keyword analyses. The similarities between structured diary data and follow-up survey data on health-related quality of life, symptoms, and medication intake were examined using the Jaccard index. RESULTS: Campaign participants (n=134; diary entries: n=815) were more often women, were not working full time, did not have a higher education degree, had a more advanced gait impairment, and were on average 5 years older (median age 52.5, IQR 43.25-59.75 years) than eligible nonparticipants (median age 47, IQR 38-55 years; n=524). Diary free-text entries (n=632; participants: n=100) most often contained references to the following standard Linguistic Inquiry and Word Count word categories: negative emotion (193/632, 30.5%), body parts or body functioning (191/632, 30.2%), health (94/632, 14.9%), or work (67/632, 10.6%). Analogously, the most frequently mentioned keywords (diary entries: n=526; participants: n=93) were "good," "day," and "work." Similarities between diary data and follow-up survey data, collected 14 months apart (median), were high for health-related quality of life and stable for slow-changing symptoms such as fatigue or gait disorder. Similarities were also comparatively high for drugs requiring a regular application, including interferon beta-1a (Avonex) and glatiramer acetate (Copaxone), and for modern oral therapies such as fingolimod (Gilenya) and teriflunomide (Aubagio). CONCLUSIONS: Diary campaign participation seemed dependent on time availability and symptom burden and was enhanced by reminder emails. Electronic health diaries are a meaningful complement to regular structured surveys and can provide more detailed information regarding medication use and symptoms. However, they should ideally be embedded into promotional activities or tied to concrete research study tasks to enhance regular and long-term participation.

Feelings of loneliness, COVID-19-specific-health anxiety and depressive symptoms during the first COVID-19 wave in Swiss persons with multiple sclerosis.

The aim of our study was to investigate whether self-reported feeling of loneliness (FoL) and COVID-19-specific health anxiety were associated with the presence of depressive symptoms during the first coronavirus disease 2019 (COVID-19) wave. Questionnaires of 603 persons of the Swiss Multiple Sclerosis Registry (SMSR) were cross-sectionally analyzed using descriptive and multivariable regression methods. The survey response rate was 63.9%. Depressive symptoms were assessed by the Beck Depression Inventory-Fast Screen (BDI-FS). COVID-19-specific health anxiety and FoL were measured using two 5-item Likert scaled pertinent questions. High scoring FoL (2.52, 95% confidence interval (CI) (2.06-2.98)) and/or COVID-19 specific health anxiety (1.36, 95% CI (0.87-1.85)) were significantly associated with depressive symptoms. Further stratification analysis showed that the impact of FoL on depressive symptoms affected all age groups. However, it was more pronounced in younger PwMS, whereas an impact of COVID-19 specific health anxiety on depressive symptoms was particularly observed in middle-aged PwMS. FoL and COVID-19-specific health anxiety were age-dependently associated with depressive symptoms during the first COVID-19 wave in Switzerland. Our findings could guide physicians, health authorities, and self-help groups to better accompany PwMS in times of public health crises.

The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing.

BACKGROUND: The increasing availability of "real-world" data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the "gold standard" for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. OBJECTIVE: We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. METHODS: We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the "Linguistic Inquiry and Word Count" software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning-topic modeling; and (5) results interpretation and validation. RESULTS: A topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: "contacts/communication;" "social environment;" "work;" and "errands/daily routines." Notably, the sentiment analysis revealed that the "contacts/communication" group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19-related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. CONCLUSIONS: This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.