Passive Data Collection

Author(s):  
Kyle Goslin ◽  
Markus Hofmann
Keyword(s):  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sujen Man Maharjan ◽  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Prabin Byanjankar ◽  
Ada Thapa ◽  
...  

Abstract Background Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal. Methods Mothers (15–25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a Bluetooth beacon to collect data in four domains: the mother’s location using the Global Positioning System (GPS), physical activity using the phone’s accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with the Bluetooth beacon attached to the infant’s clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. Endline qualitative interviews were conducted to understand mothers’ experiences and perceptions of passive data collection. Results Of the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families’ understanding of passive sensing and families’ awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection. Conclusion Per sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734


2021 ◽  
Author(s):  
Yatharth Ranjan ◽  
Malik Althobiani ◽  
Joseph Jacob ◽  
Michele Orini ◽  
Richard Dobson ◽  
...  

BACKGROUND Chronic Lung disorders like COPD and IPF are characterised by exacerbations which are a significant problem: unpleasant for patients, and sometimes severe enough to cause hospital admission (and therefore NHS pressures) and death. Reducing the impact of exacerbations is very important. Moreover, due to the COVID-19 pandemic, the vulnerable populations with these disorders are at high risk and hence their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution of gaining visibility into the health of people in their daily life. Thus, remote monitoring of patients in their daily lives using mobile and wearable devices could be useful especially in high vulnerability groups. A scenario we consider here is to monitor patients and detect disease exacerbation and progression and investigate the opportunity of detecting exacerbations in real-time with a future goal of real-time intervention. OBJECTIVE The primary objective is to assess the feasibility and acceptability of remote monitoring using wearable and mobile phones in patients with pulmonary diseases. The aims will be evaluated over these areas: Participant acceptability, drop-out rates and interpretation of data, Detection of clinically important events such as exacerbations and disease progression, Quantification of symptoms (physical and mental health), Impact of disease on mood and wellbeing/QoL and The trajectory-tracking of main outcome variables, symptom fluctuations and order. The secondary objective of this study is to provide power calculations for a larger longitudinal follow-up study. METHODS Participants will be recruited from 2 NHS sites in 3 different cohorts - COPD, IPF and Post hospitalised Covid. A total of 60 participants will be recruited, 20 in each cohort. Data collection will be done remotely using the RADAR-Base mHealth platform for different devices - Garmin wearable devices, smart spirometers, mobile app questionnaires, surveys and finger pulse oximeters. Passive data collected includes wearable derived continuous heart rate, SpO2, respiration rate, activity, and sleep. Active data collected includes disease-specific PROMs, mental health questionnaires and symptoms tracking to track disease trajectory in addition to speech sampling, spirometry and finger Pulse Oximetry. Analyses are intended to assess the feasibility of RADAR-Base for lung disorder remote monitoring (include quality of data, a cross-section of passive and active data, data completeness, the usability of the system, acceptability of the system). Where adequate data is collected, we will attempt to explore disease trajectory, patient stratification and identification of acute clinically interesting events such as exacerbations. A key part of this study is understanding the potential of real-time data collection, here we will simulate an intervention using the Exacerbation Rating Scale (ERS) to acquire responses at-time-of-event to assess the performance of a model for exacerbation identification from passive data collected. RESULTS RALPMH study provides a unique opportunity to assess the use of remote monitoring in the study of lung disorders. The study is set to be started in mid-May 2021. The data collection apparatus, questionnaires and wearable integrations have been set up and tested by clinical teams. While waiting for ethics approval, real-time detection models are currently being constructed. CONCLUSIONS RALPMH will provide a reference infrastructure for the use of wearable data for monitoring lung diseases. Specifically information regarding the feasibility and acceptability of remote monitoring and the potential of real-time remote data collection and analysis in the context of chronic lung disorders. Moreover, it provides a unique standpoint to look into the specifics of novel coronavirus without burdensome interventions. It will help plan and inform decisions in any future studies that make use of remote monitoring in the area of Respiratory health. CLINICALTRIAL https://www.isrctn.com/ISRCTN16275601


Author(s):  
Rachel Horwitz ◽  
Sarah Brockhaus ◽  
Felix Henninger ◽  
Pascal J. Kieslich ◽  
Malte Schierholz ◽  
...  

2019 ◽  
Vol 129 ◽  
pp. 242-247 ◽  
Author(s):  
Nicole A. Maher ◽  
Joeky T. Senders ◽  
Alexander F.C. Hulsbergen ◽  
Nayan Lamba ◽  
Michael Parker ◽  
...  

2020 ◽  
pp. 004912412091492
Author(s):  
Florian Keusch ◽  
Sebastian Bähr ◽  
Georg-Christoph Haas ◽  
Frauke Kreuter ◽  
Mark Trappmann

Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targeting the general population. Based on data from the Panel Study Labour Market and Social Security (PASS), an annual probability-based mixed-mode survey on the labor market and poverty in Germany, we find that smartphone ownership and ownership of smartphones with specific OSs are correlated with a number of sociodemographic and substantive variables. The use of weighting techniques based on sociodemographic information available for both owners and nonowners reduces these differences but does not eliminate them.


Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6085
Author(s):  
Bence Ságvári ◽  
Attila Gulyás ◽  
Júlia Koltai

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.


10.2196/16338 ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. e16338
Author(s):  
Molly Adrian ◽  
Jessica Coifman ◽  
Michael D Pullmann ◽  
Jennifer B Blossom ◽  
Casey Chandler ◽  
...  

Background Technology-enabled services (TESs), which integrate human service and digital components, are popular strategies to increase the reach and impact of mental health interventions, but large-scale implementation of TESs has lagged behind their potential. Objective This study applied a mixed qualitative and quantitative approach to gather input from multiple key user groups (students and educators) and to understand the factors that support successful implementation (implementation determinants) and implementation outcomes of a TES for universal screening, ongoing monitoring, and support for suicide risk management in the school setting. Methods A total of 111 students in the 9th to 12th grade completed measures regarding implementation outcomes (acceptability, feasibility, and appropriateness) via an open-ended survey. A total of 9 school personnel (school-based mental health clinicians, nurses, and administrators) completed laboratory-based usability testing of a dashboard tracking the suicide risk of students, quantitative measures, and qualitative interviews to understand key implementation outcomes and determinants. School personnel were presented with a series of scenarios and common tasks focused on the basic features and functions of the dashboard. Directed content analysis based on the Consolidated Framework for Implementation Research was used to extract multilevel determinants (ie, the barriers or facilitators at the levels of the outer setting, inner setting, individuals, intervention, and implementation process) related to positive implementation outcomes of the TES. Results Overarching themes related to implementation determinants and outcomes suggest that both student and school personnel users view TESs for suicide prevention as moderately feasible and acceptable based on the Acceptability of Intervention Measure and Feasibility of Intervention Measure and as needing improvements in usability based on the System Usability Scale. Qualitative results suggest that students and school personnel view passive data collection based on social media data as a relative advantage to the current system; however, the findings indicate that the TES and the school setting need to address issues of privacy, integration into existing workflows and communication patterns, and options for individualization for student-centered care. Conclusions Innovative suicide prevention strategies that rely on passive data collection in the school context are a promising and appealing idea. Usability testing identified key issues for revision to facilitate widespread implementation.


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