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Abstract 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.

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.

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. Author summary Physical activity (PA) is an important aspect of health and well-being. However, PA is often reduced in persons with multiple sclerosis (MS), a neurodegenerative autoimmune disease which affects physical function, motor control, and energy levels. It is of public health interest to increase PA behavior in this population. However, valid and user-friendly methods for tracking PA are required to quantify PA behavior during patients’ daily lives. So-called “research-grade” wearable devices are used for short-term measurements (for example, 7 days), but offer poor user experience and are therefore not suitable for longer-term PA tracking. It is therefore increasingly common for MS researchers to use “consumer-grade” devices such as Fitbits. However, high-quality evidence of their validity in MS populations is limited. In this study, we compared PA metrics derived from a Fitbit device to multiple, validated research-grade methods. While the PA metrics derived from each method were not equivalent, all exhibited the similar evidence of validity. In some cases, Fitbit outperformed research-grade methods. We posit that PA metrics derived from the Fitbit are now suitable for long-term PA tracking in MS populations, and that the resulting longitudinal data has the potential to progress our understanding of world PA behavior in MS populations.

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.

BackgroundThe 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. ObjectiveWe 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. MethodsWe 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. ResultsA 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. ConclusionsThis 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.