key: cord-0275396-afp7nv8p
authors: Valentine, S.; Klasmer, B.; Dabbah, M.; Balabanovic, M.; Plans, D.
title: Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study
date: 2021-12-05
journal: nan
DOI: 10.1101/2021.12.03.21267247
sha: 2edaf2d2473e1ff91fe00b6f23d0f92fa92a575c
doc_id: 275396
cord_uid: afp7nv8p

Background: Mobile health offers potential benefits to patients and healthcare systems alike. Existing remote technologies to measure respiratory rate (RR) have limitations, such as cost, accessibility and reliability. Using smartphone sensors to measure RR may offer a potential solution. Objective: The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure RR using movement sensors. Methods: In Study 1, 15 participants simultaneously measured their RR with the app, and an FDA cleared reference device. A novel reference method to allow the app to be evaluated "in the wild" was also developed. In Study 2, 165 participants measured their RR using the app, and these measures were compared to the novel reference. Usability of the app was also assessed in both studies. Results: The app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (SD=1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) =-3.27-4.89) and 0.08 (-3.68-3.51). Pearson correlation coefficients were 0.700 and 0.885. 93% of participants successfully operated the app on their first use. Conclusions: The accuracy and usability of the app demonstrated here show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor RR. Further research should validate the benefits that this technology may offer patients and healthcare systems.

Extensive growth in the development and adoption of remote healthcare tools has been seen in recent years in response to increasing demand for traditional offerings. Notably, the COVID-19 global health pandemic has made salient how these mobile health (mHealth) tools may support healthcare systems to manage their patients when resources are pushed to breaking point. [1] [2] [3] As more widely accessible tools can be used by more people -and therefore offer greater impact -many mHealth smartphone applications (apps) have been developed, due to the high global penetration of smartphones. These systems offer a wide variety of services from telemedicine to remote monitoring and self-care, and evidence suggests they may produce improved economic 4 and health outcomes. 5 Respiratory rate (RR) is a fundamental indicator of health status for many health conditions, both general and specific to the respiratory system. [6] [7] [8] [9] [10] [11] As such, mHealth solutions for monitoring of RR may offer significant value to patients and healthcare professionals (HCPs) alike. Although several such solutions exist, many fall short on various factors. Hardwarebased solutions, including piezoelectric sensors 12 pulse oximeters, 13 and multi-sensor devices, [14] [15] are typically expensive, vulnerable to limited means of manufacture and distribution, 16 and may lack interoperability with other health records, which is cited as a critical risk to decentralisation of national healthcare systems. 17 Software-based solutions address limitations of cost, manufacture and distribution; however, they typically employ less-stable mechanisms of action. These mHealth apps often use smartphone cameras or microphones, [18] [19] [20] the latter of which have been evidenced to be vulnerable to environmental noise at the cost of accuracy and usability. [21] [22] . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint Movement sensors may present a promising alternative software-based solution for mHealth RR monitoring. Research indicates that multi-axial accelerometers and gyroscopes -as found ubiquitously in modern smartphones -can accurately capture RR based on chest movements. [23] [24] [25] [26] [27] [28] [29] [30] Additionally, due to their mechanism of action, these sensors are significantly less affected by environmental noise. Overall, smartphone-based measurement of RR provides a potential low cost, and widely available method for RR measurement, both in a remote monitoring environment, or in locations where specialised hardware and software are not available.

This article presents an observational assessment of a novel user-centric mHealth smartphone app that measures RR using smartphone movement sensors. We first conducted a preliminary evaluation of the device and study methods via a small lab-based study, then jointly assessed accuracy and usability on a greater scale and ecological valid environment via a remote study. Ethical approval was provided by the University of Exeter's Research Ethics Board (application ID eUEBS004088) and all research was conducted in compliance with the Declaration of Helsinki.

The preliminary evaluation pursued three aims: (1) to establish the accuracy of the novel mHealth smartphone app relative to a reference device cleared by the US Food and Drug Administration (FDA), (2) to understand the usability of the mHealth app and (3) to evaluate the suitability of a novel reference method that would permit accuracy assessments to be conducted via remote and real-world studies. Through a prospective, non-interventional, non-. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint randomised study conducted on healthy volunteers, RR estimates provided by the FDAcleared reference device were compared to those from the novel mHealth smartphone app and the novel reference.

The mHealth app contained a purpose-built user interface ( Figure 1 ). The user is instructed to hold their smartphone to their upper-middle chest with the screen facing outwards while sitting still and breathing normally for the duration of the 30-second sensor recording. Data is captured from the smartphone's tri-axial gyroscope and interpolated to achieve an even 100Hz sample frequency. A low-pass Butterworth filter with 0.4 Hz cut-off is applied to remove high-frequency noise while retaining activity associated with breathing, typically in the 0.16-0.33Hz range (10-20 breaths per minute (BPM)). RR is calculated by performing an autocorrelation before normalising the resulting signal. A peak-finding routine then identifies prominent peaks corresponding to the cyclical property of breathing movements. The mean inter-peak interval (IPI) is then calculated and converted to a 'per minute' RR estimation by division by 60 (seconds) (Figure 2 ).

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The MightySat Rx 13 , developed by Masimo Corporation, was selected as a reference due to its FDA-cleared status, continuous measurement and ease of use. The fingertip pulse oximeter derives RR using photoplethysmography (an optical measure of volumetric changes in peripheral blood flow). Continuous estimates of RR produced by this reference were converted to single weighted averages to facilitate comparison with data derived from the mHealth app.

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The novel reference method involves identification of repeated cyclical peak-trough complexes within smartphone movement sensor signals( Figure 3 ). Signals of insufficient quality to derive RR are considered to fail the reference method. This method is conceptually similar to reference methods described in peer-reviewed literature reporting accuracy assessments of multiple RR devices, including successful FDA market clearance applications. 13; 31-33 This method would permit accuracy assessments to be conducted via remote and real-world studies without a need for additional hardware, offering significant value in terms of research scale, cost and ecological validity via avoidance of observation bias. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Participants were recruited via convenience sampling. All were employees of the mHealth app manufacturer. Inclusion criteria included being aged 18 or over and being willing and able to follow the study protocol and complete an informed consent form.

The study took place at the offices of the mHealth app manufacturer. Participants were provided with complete information concerning the study procedures and gave written informed consent to participate. The FDA-cleared reference device was applied to the forefinger of the participant's left hand. Participants were provided with an iPhone XR with the mHealth app installed and received verbal instructions on operating the device: namely, to hold the smartphone to their upper middle chest with the screen facing outwards while sitting still and breathing normally during the 30-second recording. Participants were instructed to capture six recordings, disregarding whether each recording passed or failed the signal check.

Audiovisual footage was captured during the study and used for offline synchronisation of data captured via the mHealth app and FDA-cleared reference. Specifically, this included sounds produced by the mHealth app indicating the start and end of the app's recording period and depicting RR estimates displayed on the FDA-cleared reference's monitor.

Participation took around 10 minutes per participant.

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The error of the mHealth app and novel reference relative to the FDA-cleared reference was assessed through measures of mean absolute error (MAE), relative MAE and using the Bland-Altman method. 34 Due to the non-normal distribution of absolute error data, confidence intervals for MAE and relative MAE were derived via bootstrapping with replacement employing 1000-iterations and a sample size of 100%. The proportion of clinically significant errors, defined as an absolute error greater than three breaths per minute, [35] [36] was also calculated. Direct relationships between RR estimates generated through the mHealth app, novel reference and FDA-cleared reference were assessed via Pearson Product Moment Correlation (PPMC). The usability of the mHealth app was assessed using the proportion and position of recordings that failed the signal check. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint had an absolute error greater than 3 BPM. A Bland-Altman plot indicated error values as a function of RR averaged between the reference and mHealth app ( Figure 4 ). PPMC produced a coefficient of r(63) = 0.700, p < .000, indicating a high or strong association between the reference RR estimates and mHealth app RR estimate 37 (Figure 5 ). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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14 of a total of 15 participants (93.3%) were able to use the system successfully on their first try (Table 1; Figure 8 ). Specifically, this indicates that they could capture one or more recordings that passed the signal check within the first three attempts. All participants were able to use the system successfully by the end of their second try.

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The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint (three consecutive recordings), and the grey dashed line indicates the proportion of individuals who were able to do so by the end of their second use of the system.

Study 1 results indicated strong relationships between the FDA-cleared reference and both the mHealth app and the novel reference. Notably, these relationships were highly comparable to functional outcomes for alternative FDA-cleared RR monitoring devices. 12, 14, 36, 38 Accordingly, these results supported both continued assessment of the mHealth app and application of the novel reference to accuracy analyses in Study 2, as described below.

Study 2 aimed to establish the accuracy of the mHealth app 'in the wild' via remote data capture, compared to the novel reference validated in the Study 1. The usability of the mHealth app was additionally assessed in a larger sample. Measures and statistics were as described for Study 1.

Participants were recruited via an online research platform, with study enrollment controlled to ensure a proportionate distribution of age, gender and smartphone ownership (iOS versus Android). Inclusion criteria included being aged 18 or over, having access to a smartphone of minimum requirements to download the mHealth app and being willing and able to follow the study protocol and complete an informed consent form. As researchers would not monitor participants during their participation, additional safety criteria excluded individuals who were pregnant, breastfeeding, had a pacemaker, or a chest or spine problem that could affect their breathing.

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Participants were directed to online documentation containing full information about the study procedures before completing an online eConsent procedure. They then completed a baseline questionnaire concerning their demographics, including age, sex, ethnicity, height and weight, before receiving instructions to download and activate the mHealth app.

Participants were requested to follow instructions provided within the mHealth app to capture 10 RR recordings, including recordings that both passed and failed the signal check, before completing a System Usability Scale (SUS) 39 and providing separate qualitative feedback on their experience using the mHealth app. Study-specific procedures took approximately 20 minutes, for which participants were reimbursed £2.50 through the research platform.

165 participants enrolled in the study, of whom 152 completed the baseline questionnaire concerning their demographics ( Table 2) . Medical conditions reported included asthma (respiratory), arthritis and Parkinson's disease (movement). 5 participants were excluded due to significant deviation from the study protocol, resulting in a participant cohort of 160, for whom a mode of 11 mHealth app recordings each was captured. 987 recordings passed the signal check and were included in accuracy analyses. Recordings were submitted from 64 unique smartphone models, 46 (71.9%) of which were Android and the rest were iPhone models.

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The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint between the reference and mHealth app (Figure 9 ). PPMC produced a coefficient of r(986) = 0.855, p < .000, indicating a high or strong association 37 (Figure 10 ).

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The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint Usability 149 (93.1%) of a total of 160 participants who captured mHealth app recordings were able to use the system successfully on their first try (Table 3; Figure 11 ). 155 (96.9%) did so by their second try.

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The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint The mean SUS score was 73.2 (SD = 5.39). Of the sub-scales, each scored between 0 and 4, those most agreed with by participants were: I would imagine that most people would learn to use this system very quickly (3.2) , I thought the system was easy to use (3.1) and I felt very confident using the system (3.0). The lowest scoring, indicating participant disagreement, were: I think that I would need the support of a technical person to be able to use this system (0.5) and I needed to learn a lot of things before I could get going with this system (0.8).

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To the authors' knowledge, this is the first study to assess at scale a user-operated novel mHealth smartphone application designed to capture a user's RR using smartphone movement sensors, considering both accuracy and usability in an ecologically valid study environment. Outcomes for the mHealth app were highly comparable to results published for medical devices available on the market today (Table 4 ). In addition, as changes in breathing rate greater than 3 BPM may indicate clinical deterioration [35] [36] , observations that error values for the mHealth app were typically less than this threshold suggest the device may carry low clinical risk. Study 2 revealed a small cluster of substantial overestimation errors (5) (6) (7) (8) (9) (10) for lower RRs (8) (9) (10) (11) (12) (13) (14) . Although this observation was not found in the Study 1, this may be due to the smaller sample size in that analysis. The nature of these overestimations is unclear based on the present analyses. Overestimation of RR carries clinical risk with regard to both underdiagnosis of bradypnea (low RR) and overdiagnosis of tachypnea (elevated RR) that may lead to clinical decision making based on misinformation, although it should be noted that RR is rarely used in isolation to inform clinical decision making. Future research should seek to identify and mitigate the cause of these errors.

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The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint were typically in the range of 95% or higher. 40-42 Subjective usability outcomes were also promising, with an overall SUS score well above the industry average of 68. 43 Study 2 revealed a general trend of high signal check pass rates for later sequential recording . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint attempts, suggesting that participants found it easier to capture RR recordings the more they used the mHealth app. Although this learning effect was not observed in the Study 1 results, this may be due to observer bias and a small sample size within that study setting. This observation holds promise for improved usability with long-term use of the mHealth app, although it may indicate greater clinical risk during early use of the system. Future research may seek to steepen the learning curve to minimise clinical risk.

Strengths of the present study include the application of a remote study design that lends ecological validity to the results and selective recruitment to ensure a heterogeneous participant cohort, which suggests good generalisability of the results. In addition, the inclusion of usability assessment allows a holistic perspective on the mHealth app to be generated. In all, these results hold promise for the use of smartphone movement sensors as a viable means of remote RR monitoring. Software-based mHealth may offer cost and scalability benefits compared to hardware-based monitoring. Additionally, movement sensors may better protect RR signal quality than alternative devices that use microphone and camera sensors, as these are vulnerable to noise from environmental light and sound that is difficult to control. These benefits suggest that RR monitoring based on smartphone movement sensors may support healthcare systems to care for their patients when they are outside of the clinic better than currently available alternatives.

As only healthy participants were recruited, it is also unclear how the observed results may extrapolate to healthcare patients who would be likely real-world users of the mHealth appparticularly those with abnormal breathing rates and patterns due to a respiratory condition.

Participants both from Study 1 (employees of the mHealth app manufacturer) and Study 2 (members of an online research community) were likely to be technologically confident and . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint may have therefore been predisposed to successfully operating the mHealth app. Future research should seek to incorporate individuals of low technical literacy and target end-users with relevant medical conditions to better understand these results' generalisability.

Concerning methodology, the FDA-cleared reference used in Study 1 has its own measurement error. 13 Hence, error estimates presented here are, in fact, an unknown combination of errors associated with the FDA-cleared reference and mHealth app versus true RR. The Study 2 reference also underwent only limited validation in Study 1 and should be assessed more rigorously. Future research may wish to apply a wider range of reference methods, including gold and industry-standard references, to reduce the vulnerability of the mHealth app to shortcomings of any single reference.

Additionally, the present research design does not directly address potential benefits the mHealth app may offer if applied in a healthcare setting. Although expectations that moving health assessments outside of a clinical setting via mHealth technologies will improve healthcare economics have been somewhat supported by literature, 4 clinical evidence suggests that mHealth technologies are highly heterogeneous in their ability to improve health outcomes. [44] [45] Suggestions that mHealth may help to overcome social, economic and geographical barriers to healthcare are also yet to be validated. [46] [47] [48] Future research should seek to understand the clinical, economic and social outcomes associated with real-world use of the mHealth app.

Decentralised healthcare technology holds the potential to offer clinical and economic benefits to patients, HCPs and healthcare systems. Breathing is an important indicator of health, and although solutions for remote RR monitoring exist, many entail significant . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 5, 2021. ; https://doi.org/10.1101/2021.12.03.21267247 doi: medRxiv preprint shortcomings that may limit their ability to capitalise on potential benefits of mHealth.

Results from the present study hold promise for the use of smartphone movement sensors as a robust means for remote RR monitoring. However, future research should address residual questions and risks associated with the technology identified in this article and seek to validate the impact of similar technologies as applied in the real world.

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The authors are grateful to Huma's wonderful developers Michele Colombo, Stanislas Heili, Leonardo Festa, Davide Mascitti, Emanuele Distefano, Matteo Puccinelli and Matteo Vigoni for their diligent efforts in preparing the mHealth app for this research. Additional thanks to Emily Binning for her continued strategic support that helped this research come to fruition.

This research was sponsored by Huma Therapeutics Ltd.

Ethical approval was provided by the University of Exeter's Research Ethics Board (application ID eUEBS004088) and all research was conducted in compliance with the Declaration of Helsinki.

DP accepts the responsibility of acting as Guarantor.

All authors are current or previous employees of Huma Therapeutics Ltd, which is the developer of the mHealth smartphone app. No additional conflicts of interest relevant to this study are declared.. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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