Assessing a novel way to measure step count while walking using a custom mobile phone application

System for Tracking and Evaluating Performance (Step-App®): validation and clinical application of a mobile telemonitoring system in patients with knee and hip total arthroplasty. A prospective cohort study

BACKGROUND

Technological advances and digital solutions have been proposed to overcome barriers to sustainable rehabilitation programs in patients with musculoskeletal disorders. However, to date, standardized telemonitoring systems able to precisely assess physical performance and functioning are still lacking.

AIM

To validate a new mobile telemonitoring system, named System for Tracking and Evaluating Performance (Step-App®), to evaluate physical performance in patients undergone knee and hip total arthroplasty.

DESIGN

Prospective cohort study.

METHODS

A consecutive series of older adults with knee and hip total arthroplasty participated in a comprehensive rehabilitation program. The Step-App®, a mobile telemonitoring system, was used to remotely monitor the effects of rehabilitation, and the outcomes were assessed before (T0) and after the rehabilitation treatment (T1). The primary outcomes were the 6-Minute Walk Test (6MWT), the 10-Meter Walk Test (10MWT), and the 30-Second Sit-To-Stand Test (30SST).

RESULTS

Out of 42 patients assessed, 25 older patients were included in the present study. The correlation analysis between the Step-App® measurements and the traditional in-person assessments demonstrated a strong positive correlation for the 6MWT (T0: r2=0.9981, P<0.0001; T1: r2=0.9981, P<0.0001), 10MWT (T0: r2=0.9423, P<0.0001; T1: r2=0.8634, P<0.0001), and 30SST (T0: r2=1, P<0.0001; T1: r2=1, P<0.0001). The agreement analysis, using Bland-Altman plots, showed a good agreement between the Step-App® measurements and the in-person assessments.

CONCLUSIONS

Therefore, we might conclude that Step-App® could be considered as a validated mobile telemonitoring system for remote assessment that might have a role in telemonitoring personalized rehabilitation programs for knee and hip replacement patients.

CLINICAL REHABILITATION IMPACT

Our findings might guide clinicians in remote monitoring of physical performance in patients with musculoskeletal conditions, providing new insight into tailored telerehabilitation programs.

Tracking the Fatigue Status after a Resistance Exercise through Different Parameters

The purpose of the study was to investigate the sensitivity of back squat bar velocity, isometric mid-thigh pull, heart rate variability parameters, perceived recovery scale and step counts for tracking the muscular fatigue time-course (reduction in countermovement jump [CMJ] performance) after strenuous acute lower limb resistance exercise. Sixteen healthy men performed heart rate variability assessment, perceived recovery scale, CMJ, back squat bar velocity, isometric mid-thigh pull, and daily step counts before and 24 h, 48 h and 72 h post a strenuous acute lower limb resistance exercise (8×10 repetitions). The CMJ height decreased at 24 and 48 h after exercise session (p≤0.017), evidencing the muscular fatigue. The perceived recovery scale presented lower values compared to baseline until 72 h after exercise session (p<0.001 for all). The heart rate variability parameters and step counts were not significantly different across time. At 24 h post, only mean force of mid-thigh pull was decreased (p=0.044), while at 48 h post, only peak force of mid-thigh pull was decreased (p=0.020). On the last day (72 h), only bar velocity (mean) presented reduction (p=0.022). Therefore, the perceived recovery scale was the only variable sensible to tracking muscular fatigue, i. e. presenting a similar time-course to CMJ height.

Quantifying Steps During a Timed Up and Go Test Using a Wearable Sensor System: A Laboratory-Based Validation Study in Healthy Young and Older Volunteers

Mobility is an important factor in maintaining health and independence in an aging population. Facilitating community-dwelling older adults to independently identify signs of functional decline could help reduce disability and frailty development. Step-count from a body-worn sensor system was compared with a criterion measure in healthy young (n = 10) and healthy older adults (n = 10) during a Timed Up and Go test under different conditions. Spearman's rank correlation coefficient indicated strong agreement between the sensor-obtained step-count and that of the criterion measure in both age groups, in all mobility tests. A body-worn sensor system can provide objective, quantitative measures of step-count over short distances in older adults. Future research will examine if step-count alone can be used to identify functional decline and risk of frailty.Clinical Relevance-This demonstrates the correlation between step-count derived from a wearable sensor and a criterion measure over a short distance in older adults.

A Wearable Walking Gait Speed-Sensing Device using Frequency Bifurcations of Multi-Resonator Inductive Link

This paper describes a wearable inductive sensing system to monitor (i.e., sense and estimate) walking gait speed. This proposed design relies on the multi-resonance inductive link to quantify the angle of the human legs for calculating the speed of walking. The walking gait speed can be used to estimate the frailty in elderly patients with cancer. We have designed, optimized, and implemented a multi-resonator sensor unit to precisely measure the angle between human legs during walking. The couplings between resonators change by lateral displacements due to walking, and a reading coil senses the frequency bifurcations, corresponding to the changes in angle between legs. The proposed design is optimized using ANSYS HFSS and implemented using copper foil. The Specific Absorption Rate, SAR, in the human body is calculated 0.035 W/kg using the developed HFSS model. The operating frequency range of the proposed sensor is from 25 MHz to 46 MHz, and it can measure angles up to 90° (-45° to +45°). The measured resolution for estimating the angle shows the capability of the sensor for calculating the walking speed with a resolution of less than 0.1 m/s.

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions

OBJECTIVE

Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making.

MATERIALS AND METHODS

Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains.

RESULTS

Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings.

CONCLUSION

The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility.

TRIAL REGISTRATION

clinicaltrials.gov, NCT03490253.

Walking Speed Reliably Measures Clinically Significant Changes in Gait by Directional Deep Brain Stimulation

Introduction: Although deep brain stimulation (DBS) often improves levodopa-responsive gait symptoms, robust therapies for gait dysfunction from Parkinson's disease (PD) remain a major unmet need. Walking speed could represent a simple, integrated tool to assess DBS efficacy but is often not examined systematically or quantitatively during DBS programming. Here we investigate the reliability and functional significance of changes in gait by directional DBS in the subthalamic nucleus. Methods: Nineteen patients underwent unilateral subthalamic nucleus DBS surgery with an eight-contact directional lead (1-3-3-1 configuration) in the most severely affected hemisphere. They arrived off dopaminergic medications >12 h preoperatively and for device activation 1 month after surgery. We measured a comfortable walking speed using an instrumented walkway with DBS off and at each of 10 stimulation configurations (six directional contacts, two virtual rings, and two circular rings) at the midpoint of the therapeutic window. Repeated measures of ANOVA contrasted preoperative vs. maximum and minimum walking speeds across DBS configurations during device activation. Intraclass correlation coefficients examined walking speed reliability across the four trials within each DBS configuration. We also investigated whether changes in walking speed related to modification of step length vs. cadence with a one-sample t-test. Results: Mean comfortable walking speed improved significantly with DBS on vs. both DBS off and minimum speeds with DBS on (p < 0.001, respectively). Pairwise comparisons showed no significant difference between DBS off and minimum comfortable walking speed with DBS on (p = 1.000). Intraclass correlations were ≥0.949 within each condition. Changes in comfortable walk speed were conferred primarily by changes in step length (p < 0.004). Conclusion: Acute assessment of walking speed is a reliable, clinically meaningful measure of gait function during DBS activation. Directional and circular unilateral subthalamic DBS in appropriate configurations elicit acute and clinically significant improvements in gait dysfunction related to PD. Next-generation directional DBS technologies have significant potential to enhance gait by individually tailoring stimulation parameters to optimize efficacy.

Development and Pilot Study of an iOS Smartphone Application for Perioperative Functional Capacity Assessment

BACKGROUND

Functional capacity assessment plays a core role in the preoperative evaluation. The Duke Activity Status Index (DASI) and the 6-minute walk test (6MWT) are 2 methods that have demonstrated the ability to evaluate functional capacity and predict perioperative outcomes. Smartphones offer a novel method to facilitate functional capacity assessment as they can easily administer a survey and accelerometers can track patient activity during a 6MWT. We developed a smartphone application to administer a 6MWT and DASI survey and performed a pilot study to evaluate the accuracy of a smartphone-based functional capacity tool in our Anesthesia and Perioperative Medicine Clinic.

METHODS

Using the Apple ResearchKit software platform, we developed an application that administers a DASI survey and 6MWT on an iOS smartphone. The DASI was presented to the patient 1 question on the screen at a time and the application calculated the DASI score and estimated peak oxygen uptake (VO2). The 6MWT used the CMPedometer class from Apple's core motion facility to retrieve accelerometer data collected from the device's motion coprocessor to estimate steps walked. Smartphone estimated steps were compared to a research-grade pedometer using the intraclass correlation coefficient (ICC). Distance walked was directly measured during the 6MWT and we performed a multivariable linear regression with biometric variables to create a distance estimation algorithm to estimate distance walked from the number of steps recorded by the application.

RESULTS

Seventy-eight patients were enrolled in the study and completed the protocol. Steps measured by the smartphone application as compared to the pedometer demonstrated moderate agreement with an ICC (95% CI) of 0.87 (0.79-0.92; P = .0001). The variables in the distance estimation algorithm included (β coefficient [slope], 95% CI) steps walked (0.43, 0.29-0.57; P < .001), stride length (0.38, 0.22-0.53; P < .001), age in years (-1.90, -3.06 to -0.75; P = .002), and body mass index (-2.59, -5.13 to -0.06; P = .045). The overall model fit was R = 0.72, which indicates a moderate level of goodness of fit and explains 72% of the variation of distance walked during a 6MWT.

CONCLUSIONS

Our pilot study demonstrated that a smartphone-based functional capacity assessment is feasible using the DASI and 6MWT. The DASI was easily completed by patients and the application clearly presented the results of the DASI to providers. Our application measured steps walked during a 6MWT moderately well in a preoperative patient population; however, future studies are needed to improve the smartphone application's step-counting accuracy and distance estimation algorithm.

Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial

OBJECTIVE

To examine whether the self-monitoring interventions of a mobile health app reduce sedentary behavior in the short and long terms.

METHOD

We designed a double-blind randomized control trial. Participants were selected from among the staff of a medical institution and registrants of an online research firm. Forty-nine participants were randomly assigned to either a control group (n = 25) or an intervention group (n = 24). The control group was given only the latest information about sedentary behavior, and the intervention was provided real-time feedback for self-monitoring in addition to the information. These interventions provided for 5 weeks (to measure the short-term effect) and 13 weeks (to measure the long-term effect) via the smartphone app. Measurements were as follows: subjective total sedentary time (SST), objective total sedentary time (OST), mean sedentary bout duration (MSB), and the number of sedentary breaks (SB). Only SST was measured by self-report based on the standardized International Physical Activity Questionnaire and others were measured with the smartphone.

RESULTS

No significant results were observed in the short term. In the long term, while no significant results were also observed in objective sedentary behavior (OST, MSB, SB), the significant differences were observed in subjective sedentary behavior (SST, βint  - βctrl between baseline and 9/13 weeks; 1.73 and 1.50 h/d, respectively).

CONCLUSIONS

Real-time feedback for self-monitoring with smartphone did not significantly affect objective sedentary behavior. However, providing only information about sedentary behavior to users with smartphones may make misperception on the amount of their subjective sedentary behavior.

Identifying characteristics of indicators of sedentary behavior using objective measurements

Objective

Recent attention has been focused on sedentary behavior (SB) affecting health outcomes, but the characteristics of indicators reflecting SB remain to be identified. This cross‐sectional study aims to identify the characteristics of indicators of SB, focusing on the examination of correlations, reliability, and validity of sedentary variables assessed by the smartphone app.

Method

Objectively measured data of SB of eligible 46 Japanese workers obtained from smartphones were used. We assessed the characteristics of current indicators being used with a 10‐minute or 30‐minute thresholds, in addition to the conventional indicators of total sedentary time, mean sedentary bout duration, and total number of sedentary bouts. They were evaluated from three perspectives: (a) association among the indicators, (b) reliability of the indicators, and (c) criterion validity.

Results

Total sedentary time under 10 minutes (U10) and U30 had negative associations with Total sedentary time (r = −.47 and −.21 respectively). The correlation between Mean sedentary bout duration and Total number of sedentary bouts was −.84, whereas between Mean sedentary bout duration 10, 30 and Total number of sedentary bouts were −.54 and −.21, respectively. The intraclass correlation coefficients of almost all indicators were around .80. Mean sedentary bout duration, Mean sedentary bout duration 10, Total number of sedentary bouts, Total sedentary time 30, U30 and U10 have significant differences between three BMI groups.

Conclusion

This study comprehensively revealed the rationale of advantage in the current indicator being used with a 10‐minute or 30‐minute threshold, rather than the conventional total amount of SB.

Assessing a novel way to measure three common rehabilitation outcome measures using a custom mobile phone application

BACKGROUND

Clinicians often use thirty-second-sit (chair)-to-stand (30CST), timed-up-and-go (TUG), and the five-times-sit-to-stand (5xSTS) since these outcome measures (OMs) are sensitive for strength, balance and mobility.

RESEARCH QUESTION

The purpose of this study was to validate a custom smart phone application (App) that can remotely assess the 30CST, TUG, and 5xSTS.

METHODS

Thirty-one healthy adults (range: 22-55 y; 54.6-106.8 kg; 160-185 cm; 19 females) participated in this cross-sectional study. Each participant performed the 30CST, TUG, and 5xSTS at a slow and normal speed. They performed each OMs twice while the App collected their performance data using both an iOS and Android phone. The gold standard of each test was the average of the silent count of two investigators for the 30CST and the time recorded by two investigators using stopwatches for the TUG and 5xSTS. Investigators analyzed the data using Intraclass Correlation coefficients (ICC), Pearson R coefficients, Signed Rank Tests, and Wilcoxon Rank-Sum Tests.

RESULTS AND SIGNIFICANCE

A significant correlation was observed between the performances recorded by the phones and the direct observation gold standard for all three OMs (r > 0.97). For 30CST, no significant mean count differences were found for the following comparisons: between phones, within phone types, or within phone-by-speed levels. (P-values range 0.06-1.00). While a statistically significant difference was found in all of the time comparisons when performing TUG and 5xSTS (p < 0.0001) except for the between phone comparison with TUG (p = 0.27). For TUG and 5xSTS, the time difference was less than a second when compared to the gold standard and ICCs showed moderate to strong agreement when comparing the phone application to the gold standard (ICCs range 0.60-0.99). These data suggested that the App could validly measure performance of these OMs.

Mobile Apps for Rheumatoid Arthritis: Opportunities and Challenges

Mobile applications have the potential to improve health outcomes in patients with rheumatoid arthritis (RA). Whereas other chronic diseases such as diabetes and heart failure have a well-established presence in the mobile application realm, apps focused on RA are still in their infancy. This article presents an overview of the types of mobile apps that can be used for RA and discusses the opportunities and challenges associated with them.

Soft-Material-Based Smart Insoles for a Gait Monitoring System

Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R² ≥ 0.90, p < 0.05). Bilateral gait coordination analysis using phase coordination index (PCI) was performed to test clinical feasibility. PCI values of the SMSI resulted in 1.75 ± 0.80% (C1), 1.72 ± 0.81% (C2), 1.72 ± 0.79% (C3), and 1.73 ± 0.80% (C4), and those of the F-scan resulted in 1.66 ± 0.66%, 1.70 ± 0.66%, 1.67 ± 0.62%, and 1.70 ± 0.62%, respectively, showing the presence of a high correlation (R² ≥ 0.94, p < 0.05). The insole developed in this study was found to have an equivalent performance to commercial sensors, and thus, can be used not only for future sensor-based monitoring device development studies but also in clinical setting for patient gait evaluations.