Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BACKGROUND

Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults.

OBJECTIVES

To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y.

METHODS

TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations.

RESULTS

Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%.

CONCLUSIONS

These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Progression events defined by home-based assessment of motor function in multiple sclerosis: protocol of a prospective study

Background

This study relates to emerging concepts of appropriate trial designs to evaluate effects of intervention on the accumulation of irreversible disability in multiple sclerosis (MS). Major starting points of our study are the known limitations of current definitions of disability progression by rater-based clinical assessment and the high relevance of gait and balance dysfunctions in MS. The study aims to explore a novel definition of disease progression using repeated instrumental assessment of relevant motor functions performed by patients in their home setting.

Methods

The study is a prospective single-center observational cohort study with the primary outcome acquired by participants themselves, a home-based assessment of motor functions based on an RGB-Depth (RGB-D) camera, a camera that provides both depth (D) and color (RGB) data. Participants are instructed to perform and record a set of simple motor tasks twice a day over a one-week period every 6 months. Assessments are complemented by a set of questionnaires. Annual research grade assessments are acquired at dedicated study visits and include clinical ratings as well as structural imaging (MRI and optical coherence tomography). In addition, clinical data from routine visits is provided semiannually by treating neurologists. The observation period is 24 months for the primary endpoint with an additional clinical assessment at 27 month to confirm progression defined by the Expanded Disability Status Scale (EDSS). Secondary analyses aim to explore the time course of changes in motor parameters and performance of the novel definition against different alternative definitions of progression in MS. The study was registered at Deutsches Register für Klinische Studien (DRKS00027042).

Discussion

The study design presented here investigates disease progression defined by marker-less home-based assessment of motor functions against 3-month confirmed disease progression (3 m-CDP) defined by the EDSS. The technical approach was chosen due to previous experience in lab-based settings. The observation time per participant of 24, respectively, 27 months is commonly conceived as the lower limit needed to study disability progression. Defining a valid digital motor outcome for disease progression in MS may help to reduce observation times in clinical trials and add confidence to the detection of progression events in MS.

Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review

BACKGROUND

People with physical disabilities and/or chronic diseases tend to have an inactive lifestyle. Monitoring physical activity levels is important to provide insight on how much and what types of activities people with physical disabilities and/or chronic diseases engage in. This information can be used as input for interventions to promote a physically active lifestyle. Therefore, valid and reliable physical activity measurement instruments are needed. This scoping review aims 1) to provide a critical mapping of the existing literature and 2) directions for future research on measurement properties of device-based instruments assessing physical activity behavior in ambulant adults with physical disabilities and/or chronic diseases.

METHODS

Four databases (MEDLINE, CINAHL, Web of Science, Embase) were systematically searched from 2015 to April 16th 2023 for articles investigating measurement properties of device-based instruments assessing physical activity in ambulatory adults with physical disabilities and/or chronic diseases. For the majority, screening and selection of eligible studies were done in duplicate. Extracted data were publication data, study data, study population, device, studied measurement properties and study outcome. Data were synthesized per device.

RESULTS

One hundred three of 21566 Studies were included. 55 Consumer-grade and 23 research-grade devices were studied on measurement properties, using 14 different physical activity outcomes, in 23 different physical disabilities and/or chronic diseases. ActiGraph (n = 28) and Fitbit (n = 39) devices were most frequently studied. Steps (n = 68) was the most common used physical activity outcome. 97 studies determined validity, 11 studies reliability and 6 studies responsiveness.

CONCLUSION

This scoping review shows a large variability in research on measurement properties of device-based instruments in ambulatory adults with physical disabilities and/or chronic diseases. The variability highlights a need for standardization of and consensus on research in this field. The review provides directions for future research.

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

"Bring Your Own Device"-A New Approach to Wearable Outcome Assessment in Trauma

Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients' own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study's screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.

Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis

BACKGROUND

Remote smartphone-based 2-minute walking tests (s2MWTs) allow frequent and potentially sensitive measurements of ambulatory function.

OBJECTIVE

To investigate the s2MWT on assessment of, and responsiveness to change in ambulatory function in MS.

METHODS

One hundred two multiple sclerosis (MS) patients and 24 healthy controls (HCs) performed weekly s2MWTs on self-owned smartphones for 12 and 3 months, respectively. The timed 25-foot walk test (T25FW) and Expanded Disability Status Scale (EDSS) were assessed at 3-month intervals. Anchor-based (using T25FW and EDSS) and distribution-based (curve fitting) methods were used to assess responsiveness of the s2MWT. A local linear trend model was used to fit weekly s2MWT scores of individual patients.

RESULTS

A total of 4811 and 355 s2MWT scores were obtained in patients (n = 94) and HC (n = 22), respectively. s2MWT demonstrated large variability (65.6 m) compared to the average score (129.5 m), and was inadequately responsive to anchor-based change in clinical outcomes. Curve fitting separated the trend from noise in high temporal resolution individual-level data, and statistically reliable changes were detected in 45% of patients.

CONCLUSIONS

In group-level analyses, clinically relevant change was insufficiently detected due to large variability with sporadic measurements. Individual-level curve fitting reduced the variability in s2MWT, enabling the detection of statistically reliable change in ambulatory function.

Accelerometers in Our Pocket: Does Smartphone Accelerometer Technology Provide Accurate Data?

This study evaluates accelerometer performance of three new state of the art smartphones and focuses on accuracy. The motivating research question was whether accelerator accuracy obtained with these off-the-shelf modern smartphone accelerometers was or was not statistically different from that of a gold-standard reference system. We predicted that the accuracy of the three modern smartphone accelerometers in human movement data acquisition do not differ from that of the Vicon MX motion capture system. To test this prediction, we investigated the comparative performance of three different commercially available current generation smartphone accelerometers among themselves and to a gold-standard Vicon MX motion capture system. A single subject design was implemented for this study. Pearson's correlation coefficients^® were calculated to verify the validity of the smartphones' accelerometer data against that of the Vicon MX motion capture system. The Intraclass Correlation Coefficient (ICC) was used to assess the smartphones' accelerometer performance reliability compared to that of the Vicon MX motion capture system. Results demonstrated that (a) the tested smartphone accelerometers are valid and reliable devices for estimating accelerations and (b) there were not significant differences among the three current generation smartphones and the Vicon MX motion capture system's mean acceleration data. This evidence indicates how well recent generation smartphone accelerometer sensors are capable of measuring human body motion. This study, which bridges a significant information gap between the accuracy of accelerometers measured close to production and their accuracy in actual smartphone research, should be interpreted within the confines of its scope, limitations and strengths. Further research is warranted to validate our arguments, suggestions, and results, since this is the first study on this topic.

Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions

Objectives

The purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions.

Design

A cross-sectional study and repeated measures.

Methods

A total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure.

Results

Based on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 (P < 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, P < 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts.

Conclusions

The smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition.

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.

First Insights into Barriers and Facilitators from the Perspective of Persons with Multiple Sclerosis: A Multiple Case Study

Multiple Sclerosis (MS) is a complex, lifelong disease. Its effects span across different areas of life and vary strongly. In Switzerland, there is an intense discussion on how to optimize quality of care and patient safety. Patients should be more involved in the management of health care to improve the quality of care from the patient's perspective and form a more comprehensive perspective. This multiple-case study explores the question of how persons with MS experience and describe functioning related barriers, facilitating factors, and ethically relevant conflicts. To address this from a comprehensive perspective, the MS core set of the International Classification for Functioning, Disability, and Health (ICF) is used as theoretical framework. To explore barriers, facilitators, and relevant ethical issues, different narrative sources were used for thematic analysis and ICF coding: (a) MS transcripts from DIPEx interviews and (b) an autobiographical book of persons living with MS. Insights that were meaningful for daily practice and education were identified: (a) understanding the importance of environmental circumstances based on narrative sources; (b) understanding the importance of a person's individual life situation, and the ability to switch perspectives in the medical field; (c) respect for PwMS' individuality in health care settings; (d) creating meaningful relationships for disease management and treatment, as well as building trust.

Wearable technology in orthopedic trauma surgery - An AO trauma survey and review of current and future applications

The use of wearable sensors to track activity is increasing. Therefore, a survey among AO Trauma members was conducted to provide an overview of their current utilization and determine future needs and directions. A cross sectional expert opinion survey was administered to members of AO Trauma. Respondents were surveyed concerning their experience, subspeciality, current use characteristics, as well as future needs concerning wearable technology. Three hundred and thirty-three survey sets were available for analysis (Response Rate 16.2%). 20.7% of respondents already use wearable technology as part of their clinical treatment. The most prevalent technology was accelerometry combined with smartphones (75.4%) to measure general patient activity. To facilitate the use of wearable technology in the future, the most pressing issues were cost, patient compliance and validity of results. Wearable activity monitors are currently being used in trauma surgery. Surgeons employing these technologies mostly measure simple activity or activity associated parameters. Cost was the greatest perceived barrier to implementation. Further research, especially concerning the interpretation of the outcome values obtained, is required to facilitate wearable activity monitoring as an objective patient outcome measurement tool.

Data-driven phenotyping of preoperative functional decline patterns in patients undergoing lumbar decompression and lumbar fusion using smartphone accelerometry

OBJECTIVE

Treatment of degenerative lumbar spine pathologies typically escalates to surgical intervention when symptoms begin to significantly impair patients’ functional status. Currently, surgeons rely on subjective patient assessments through patient-reported outcome measures to estimate the decline in patient wellness and quality of life. In this analysis, the authors sought to use smartphone-based accelerometry data to provide an objective, continuous measurement of physical activity that might aid in effective characterization of preoperative functional decline in different lumbar spine surgical indications.

METHODS

Up to 1 year of preoperative activity data (steps taken per day) from 14 patients who underwent lumbar decompression and 15 patients who underwent endoscopic lumbar fusion were retrospectively extracted from patient smartphones. A data-driven algorithm was constructed based on 10,585 unique activity data points to identify and characterize the functional decline of patients preceding surgical intervention. Algorithmic estimation of functional decline onset was compared with reported symptom onset in clinical documentation across patients who presented acutely (≤ 5 months of symptoms) or chronically (> 5 months of symptoms).

RESULTS

The newly created algorithm identified a statistically significant decrease in physical activity during measured periods of functional decline (p = 0.0020). To account for the distinct clinical presentation phenotypes of patients requiring lumbar decompression (71.4% acute and 28.6% chronic) and those requiring lumbar fusion (6.7% acute and 93.3% chronic), a variable threshold for detecting clinically significant reduced physical activity was implemented. The algorithm characterized functional decline (i.e., acute or chronic presentation) in patients who underwent lumbar decompression with 100% accuracy (sensitivity 100% and specificity 100%), while characterization of patients who underwent lumbar fusion was less effective (accuracy 26.7%, sensitivity 21.4%, and specificity 100%). Adopting a less-permissive detection threshold in patients who underwent lumbar fusion, which rendered the algorithm robust to minor fluctuations above or below the chronically decreased level of preoperative activity in most of those patients, increased functional decline classification accuracy of patients who underwent lumbar fusion to 66.7% (sensitivity 64.3% and specificity 100%).

CONCLUSIONS

In this study, the authors found that smartphone-based accelerometer data successfully characterized functional decline in patients with degenerative lumbar spine pathologies. The accuracy and sensitivity of functional decline detection were much lower when using non–surgery-specific detection thresholds, indicating the effectiveness of smartphone-based mobility analysis in characterizing the unique physical activity fingerprints of different lumbar surgical indications. The results of this study highlight the potential of using activity data to detect symptom onset and functional decline in patients, enabling earlier diagnosis and improved prognostication.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.