Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors

Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review

BACKGROUND

Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.

OBJECTIVE

The primary objective of this scoping review was to characterize publications' reporting practices for research that collects PGHD using mobile devices.

METHODS

We comprehensively searched PubMed and screened the results. Qualifying publications were classified according to 6 dimensions-1 covering key bibliographic details (for all articles) and 5 covering reporting criteria considered necessary for reproducible and responsible research (ie, "participant," "data," "device," "study," and "ethics," for original research). For each of the 5 reporting dimensions, we also assessed reporting completeness.

RESULTS

Out of 3602 publications screened, 100 were included in this review. We observed a rapid increase in all publications from 2016 to 2021, with the largest contribution from US authors, with 1 exception, review articles. Few original research publications used crowdsourcing platforms (7%, 3/45). Among the original research publications that reported device ownership, most (75%, 21/28) reported using participant-owned devices for data collection (ie, a Bring-Your-Own-Device [BYOD] strategy). A significant deficiency in reporting completeness was observed for the "data" and "ethics" dimensions (5 reporting factors were missing in over half of the research publications). Reporting completeness for data ownership and participants' access to data after contribution worsened over time.

CONCLUSIONS

Our work depicts the reporting practices in publications about research involving PGHD from mobile devices. We found that very few papers reported crowdsourcing platforms for data collection. BYOD strategies are increasingly popular; this creates an opportunity for improved mechanisms to transfer data from device owners to researchers on crowdsourcing platforms. Given substantial reporting deficiencies, we recommend reaching a consensus on best practices for research collecting PGHD from mobile devices. Drawing from the 5 reporting dimensions in this scoping review, we share our recommendations and justifications for 9 items. These items require improved reporting to enhance data representativeness and quality and empower participants.

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review

Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that offer an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.

Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study

PURPOSE

To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers.

METHODS

Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied.

RESULTS

There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints.

CONCLUSIONS

These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.

Fampridine and real-life walking in multiple sclerosis: Low predictive value of clinical test for habitual short-term changes

BACKGROUND

Fampridine improves walking speed in patients with multiple sclerosis (MS) in performance-based tests. The impact on habitual mobility and its correlation with clinical tests has not been analysed.

OBJECTIVE

To investigate the association between clinical response criteria and habitual mobility in MS patients starting a fampridine treatment.

METHODS

During a four-week baseline-to-treatment study, we assessed in 28 patients (median EDSS 4.75, range 4-6.5) walking tests as the Timed-25-Foot-Walk (T25FW) and mobility questionnaires at day 0, 14 (start of treatment) and 28. Habitual steps and distance per day, total activity and walking speed was measured by accelerometry over four weeks. Beside improvement in real-life mobility, we investigated if such measures differed between non-responders and responders defined by a 20% improvement in clinical tests.

RESULTS

All clinical test, patient reported outcomes and total activity improved significantly (p<0.05). 46% improved (any change >0) in three of four real-life measures. Change of the T25FW predicted only an increase of distance per day. Subjective rating of patients performed better by predicting distance and walking speed changes correctly.

CONCLUSION

Fampridine might improve walking in daily life of MS, but clinical tests are weak predictors. Accelerometry opens a new perspective on mobility measurment, but the current data do not show a consistent effect on non-performance based accelerometry outcomes.

Feasibility and Acceptability of Global Positioning System (GPS) Methods to Study the Spatial Contexts of Substance Use and Sexual Risk Behaviors among Young Men Who Have Sex with Men in New York City: A P18 Cohort Sub-Study

Background

No global positioning system (GPS) technology study has been conducted among a sample of young gay, bisexual, and other men who have sex with men (YMSM). As such, the purpose of this study was to evaluate the feasibility and acceptability of using GPS methods to understand the spatial context of substance use and sexual risk behaviors among a sample of YMSM in New York City, a high-risk population.

Methods

Data came from a subsample of the ongoing P18 Cohort Study (n = 75). GPS feasibility and acceptability among participants was measured with: 1) a pre- and post-survey and 2) adherence to the GPS protocol which included returning the GPS device, self-report of charging and carrying the GPS device as well as objective data analyzed from the GPS devices. Analyses of the feasibility surveys were treated as repeated measures as each participant had a pre- and post-feasibility survey. When comparing the similar GPS survey items asked at baseline and at follow-up, we present percentages and associated p-values based on chi-square statistics.

Results

Participants reported high ratings of pre-GPS acceptability, ease of use, and low levels of wear-related concerns in addition to few concerns related to safety, loss, or appearance, which were maintained after baseline GPS feasibility data collection. The GPS return rate was 100%. Most participants charged and carried the GPS device on most days. Of the total of 75 participants with GPS data, 75 (100%) have at least one hour of GPS data for one day and 63 (84%) had at least one hour on all 7 days.

Conclusions

Results from this pilot study demonstrate that utilizing GPS methods among YMSM is feasible and acceptable. GPS devices may be used in spatial epidemiology research in YMSM populations to understand place-based determinants of health such as substance use and sexual risk behaviors.

Mapping Multi-Day GPS Data: A Cartographic Study in NYC

Multi-day GPS data is increasingly being used in research-including in the field of spatial epidemiology. We present several maps as ways to present multi-day GPS data. Data come from the NYC Low-Income Housing, Neighborhoods and Health Study (n=120). Participants wore a QStarz BT-Q1000XT GPS device for about a week (mean: 7.44, SD= 2.15). Our maps show various ways to visualize multi-day GPS data; these data are presented by overall GPS data, by weekday/weekend and by day of the week. We discuss implications for each of the maps.

Application of global positioning system methods for the study of obesity and hypertension risk among low-income housing residents in New York City: a spatial feasibility study

The purpose of this study was to evaluate the feasibility of using global positioning system (GPS) methods to understand the spatial context of obesity and hypertension risk among a sample of low-income housing residents in New York City (n = 120). GPS feasibility among participants was measured with a pre- and post-survey as well as adherence to a protocol which included returning the GPS device as well as objective data analysed from the GPS devices. We also conducted qualitative interviews with 21 of the participants. Most of the sample was overweight (26.7%) or obese (40.0%). Almost one-third (30.8%) was pre-hypertensive and 39.2% was hypertensive. Participants reported high ratings of GPS acceptability, ease of use and low levels of wear-related concerns in addition to few concerns related to safety, loss or appearance, which were maintained after the baseline GPS feasibility data collection. Results show that GPS feasibility increased over time. The overall GPS return rate was 95.6%. Out of the total of 114 participants with GPS, 112 (98.2%) delivered at least one hour of GPS data for one day and 84 (73.7%) delivered at least one hour on 7 or more days. The qualitative interviews indicated that overall, participants enjoyed wearing the GPS devices, that they were easy to use and charge and that they generally forgot about the GPS device when wearing it daily. Findings demonstrate that GPS devices may be used in spatial epidemiology research in low-income and potentially other key vulnerable populations to understand geospatial determinants of obesity, hypertension and other diseases that these populations disproportionately experience.