Physical activity intensity can be accurately monitored by smartphone global positioning system 'app'

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights

BACKGROUND

The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored.

OBJECTIVE

This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies.

METHODS

Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights.

RESULTS

The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events.

CONCLUSIONS

This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures

OBJECTIVE

Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continuous manner. The smartphone global positioning system (GPS) can be used to provide continuous, quantitative, and objective information on patient mobility. The aim of this study was to use daily mobility features derived from the smartphone GPS to characterize the perioperative period of patients undergoing spine surgery and to compare these objective measurements to PROMs, the current gold standard.

METHODS

Eight daily mobility features were derived from smartphone GPS data in a population of 39 patients undergoing spine surgery for a period of 2 months starting 3weeks before surgery. In parallel, three different PROMs for pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]) and functional status (Patient-Reported Outcomes Measurement Information System [PROMIS]) were serially measured. Segmented linear regression analysis was used to assess trends before and after surgery. The Student paired t-test was used to compare pre- and postoperative PROM scores. Pearson's correlation was calculated between the daily average of each GPS-based mobility feature and the daily average of each PROM score during the recovery period.

RESULTS

Smartphone GPS features provided data documenting a reduction in mobility during the immediate postoperative period, followed by a progressive and steady increase with a return to baseline mobility values 1 month after surgery. PROMs measuring pain, physical performance, and disability were significantly different 1 month after surgery compared to the 2 immediate preoperative weeks. The GPS-based features presented moderate to strong linear correlation with pain VAS and PROMIS physical score during the recovery period (Pearson r > 0.7), whereas the ODI and PROMIS mental scores presented a weak correlation (Pearson r approximately 0.4).

CONCLUSIONS

Smartphone-derived GPS features were shown to accurately characterize perioperative mobility trends in patients undergoing surgery for spine-related diseases. Features related to time (rather than distance) were better at describing patient physical and performance status. Smartphone GPS has the potential to be used for the development of accurate, noninvasive and personalized tools for patient mobility monitoring after surgery.

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study

BACKGROUND

There is strong evidence suggesting that higher levels of cardiorespiratory fitness (CRF) are associated with a healthier metabolic profile, and that CRF can serve as a powerful predictor of morbidity and mortality. In this context, a smartphone app based on the 2-km walk test (UKK test) would provide the possibility to assess CRF remotely in individuals geographically distributed around a country or continent, and even between continents, with minimal equipment and low costs.

OBJECTIVE

The overall aim of this study was to evaluate the validity and reliability of 2kmFIT-App developed for Android and iOS mobile operating systems to estimate maximum oxygen consumption (VO2max) as an indicator of CRF. The specific aims of the study were to determine the validity of 2kmFIT-App to track distance and calculate heart rate (HR).

METHODS

Twenty participants were included for field-testing validation and reliability analysis. The participants completed the UKK test twice using 2kmFIT-App. Distance and HR were measured with the app as well as with accurate methods, and VO2max was estimated using the UKK test equation.

RESULTS

The validity results showed the following mean differences (app minus criterion): distance (-70.40, SD 51.47 meters), time (-0.59, SD 0.45 minutes), HR (-16.75, SD 9.96 beats/minute), and VO2max (3.59, SD 2.01 ml/kg/min). There was moderate validity found for HR (intraclass correlation coefficient [ICC] 0.731, 95% CI -0.211 to 0.942) and good validity found for VO2max (ICC 0.878, 95% CI -0.125 to 0.972). The reliability results showed the following mean differences (retest minus test): app distance (25.99, SD 43.21 meters), app time (-0.15, SD 0.94 seconds), pace (-0.18, SD 0.33 min/km), app HR (-4.5, 13.44 beats/minute), and app VO2max (0.92, SD 3.04 ml/kg/min). There was good reliability for app HR (ICC 0.897, 95% CI 0.742-0.959) and excellent validity for app VO2max (ICC 0.932, 95% CI 0.830-0.973). All of these findings were observed when using the app with an Android operating system, whereas validity was poor when the app was used with iOS.

CONCLUSIONS

This study shows that 2kmFIT-App is a new, scientifically valid and reliable tool able to objectively and remotely estimate CRF, HR, and distance with an Android but not iOS mobile operating system. However, certain limitations such as the time required by 2kmFIT-App to calculate HR or the temperature environment should be considered when using the app.

Intra- and Inter-Device Reliability of the Change-of-Direction Angles Using a Smartphone Application for Sailing

The smartphone has recently become a commonly used tool for satellite navigation. The reliability of built-in smartphone Global Navigation Satellite Systems receivers was analyzed in terms of distance, velocity/speed and acceleration, but little is known about the accuracy of angular change-of-direction measurements. This might be important in the assessment of usefulness in sailing navigation. The aim of the study was to assess the reliability of the calculated change-of-direction angles, measured with the built-in smartphone Global Navigation Satellite Systems technology using the SoniSailing application. One individual completed five trials in an urban open space (sports ground), wearing six identical Samsung Galaxy J5 smartphones. The trials simulated an upwind sailing race (127 m), including two consecutive courses at 45° angle to the line of the tacking leg. To assess the reliability of change-of-direction angle measures the intra- and inter-device correlation coefficients were calculated. The analysis showed excellent reliability in change-of-direction angle measures—no less than 0.95 and 0.93 in case of correlation coefficients for inter- and intra-device, respectively. Correlation coefficients for average measures were no less than 0.99 in both cases. The study confirmed high reliability of the calculated change-of-direction angles, measured with the Global Navigation Satellite Systems technology using the SoniSailing application for smartphones.

Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity

The purpose of this study was to systematically review and evaluate the evidence on the accuracy (validity) and consistency (reliability) of mobile apps used to quantify physical activity. Systematic literature searches were conducted in Pubmed, Science Direct, Web of Science, Physiotherapy Evidence Database (PEDro), Academic Search Complete and IEEE Xplore. Studies were included if they reported on the validity and/or reliability of a mobile application aiming primarily at measuring physical activity in humans with or without pathology. The reference lists of included articles were also screened for reports not identified through electronic searches. The methodological quality of included studies was assessed by 2 independent reviewers and data extracted by one reviewer and checked for accuracy by a second reviewer. A total of 25 articles were included in this review, of which 18 refer to validity and 7 to both validity and reliability. Mean percentage difference was used as an indicator of validity and varied between 0.1% and 79.3%. Intraclass Correlation Coefficients varied between 0.02 and 0.99 indicating poor to excellent reliability. There is conflicting and insufficient evidence on the validity and reliability, respectively, of apps for measuring physical activity. Nevertheless, velocity and the place where the smartphone is carried seem to have an impact on validity.

Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies

Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

Older Adults' Outdoor Walking: Inequalities in Neighbourhood Safety, Pedestrian Infrastructure and Aesthetics

Older adults living in high-deprivation areas walk less than those living in low-deprivation areas. Previous research has shown that older adults’ outdoor walking levels are related to the neighbourhood built environment. This study examines inequalities in perceived built environment attributes (i.e., safety, pedestrian infrastructure and aesthetics) and their possible influences on disparities in older adults’ outdoor walking levels in low- and high-deprivation areas of Birmingham, United Kingdom. It applied a mixed-method approach, included 173 participants (65 years and over), used GPS technology to measure outdoor walking levels, used questionnaires (for all participants) and conducted walking interviews (with a sub-sample) to collect data on perceived neighbourhood built environment attributes. The results show inequalities in perceived neighbourhood safety, pedestrian infrastructure and aesthetics in high- versus low-deprivation areas and demonstrate that they may influence disparities in participants’ outdoor walking levels. Improvements of perceived neighbourhood safety, pedestrian infrastructure and aesthetic in high-deprivation areas are encouraged.