Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study

Anna L Beukenhorst; Matthew Parkes; Louise A. Cooke; Rebecca Barnard; Sabine van der Veer; Max A Little; Kelly Howells; Caroline Sanders; Jamie C Sergeant; Terence O'Neill; John McBeth; William G Dixon

doi:10.2196/10238

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study

Anna L Beukenhorst, Matthew Parkes, Louise A. Cooke, Rebecca Barnard, Sabine van der Veer, Max A Little, Kelly Howells, Caroline Sanders, Jamie C Sergeant, Terence O'Neill, John McBeth, William G Dixon

Research output: Contribution to journal › Article › peer-review

Abstract

BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.

OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.

METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.

RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.

CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.

Original language	English
Article number	e10238
Pages (from-to)	e10238
Journal	JMIR Research Protocols
Volume	8
Issue number	1
Early online date	2 Mar 2018
DOIs	https://doi.org/10.2196/10238
Publication status	Published - 23 Jan 2019

Bibliographical note

©Anna L Beukenhorst, Matthew J Parkes, Louise Cook, Rebecca Barnard, Sabine N van der Veer, Max A Little, Kelly Howells,
Caroline Sanders, Jamie C Sergeant, Terence W O'Neill, John McBeth, William G Dixon. Originally published in JMIR Research
Protocols (http://www.researchprotocols.org), 23.01.2019. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The
complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright
and license information must be included.

Access to Document

10.2196/10238

Collecting Symptoms and Sensor Data With Consumer Smartwatches
©Anna L Beukenhorst, Matthew J Parkes, Louise Cook, Rebecca Barnard, Sabine N van der Veer, Max A Little, Kelly Howells, Caroline Sanders, Jamie C Sergeant, Terence W O'Neill, John McBeth, William G Dixon. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 23.01.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
Final published version, 1.14 MBLicence: CC BY 3.0

Cite this

Beukenhorst, A. L., Parkes, M., Cooke, L. A., Barnard, R., van der Veer, S., Little, M. A., Howells, K., Sanders, C., Sergeant, J. C., O'Neill, T., McBeth, J., & Dixon, W. G. (2019). Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study. JMIR Research Protocols, 8(1), e10238. Article e10238. https://doi.org/10.2196/10238

@article{6efc4db048bb4f378669b21c24e0c15d,

title = "Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study",

abstract = "BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.",

author = "Beukenhorst, {Anna L} and Matthew Parkes and Cooke, {Louise A.} and Rebecca Barnard and {van der Veer}, Sabine and Little, {Max A} and Kelly Howells and Caroline Sanders and Sergeant, {Jamie C} and Terence O'Neill and John McBeth and Dixon, {William G}",

note = "{\textcopyright}Anna L Beukenhorst, Matthew J Parkes, Louise Cook, Rebecca Barnard, Sabine N van der Veer, Max A Little, Kelly Howells, Caroline Sanders, Jamie C Sergeant, Terence W O'Neill, John McBeth, William G Dixon. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 23.01.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included. ",

year = "2019",

month = jan,

day = "23",

doi = "10.2196/10238",

language = "English",

volume = "8",

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Beukenhorst, AL, Parkes, M, Cooke, LA, Barnard, R, van der Veer, S, Little, MA, Howells, K, Sanders, C, Sergeant, JC, O'Neill, T, McBeth, J & Dixon, WG 2019, 'Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study', JMIR Research Protocols, vol. 8, no. 1, e10238, pp. e10238. https://doi.org/10.2196/10238

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study. / Beukenhorst, Anna L; Parkes, Matthew; Cooke, Louise A. et al.
In: JMIR Research Protocols, Vol. 8, No. 1, e10238, 23.01.2019, p. e10238.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study)

T2 - Protocol for a Longitudinal, Observational Feasibility Study

AU - Beukenhorst, Anna L

AU - Parkes, Matthew

AU - Cooke, Louise A.

AU - Barnard, Rebecca

AU - van der Veer, Sabine

AU - Little, Max A

AU - Howells, Kelly

AU - Sanders, Caroline

AU - Sergeant, Jamie C

AU - O'Neill, Terence

AU - McBeth, John

AU - Dixon, William G

N1 - ©Anna L Beukenhorst, Matthew J Parkes, Louise Cook, Rebecca Barnard, Sabine N van der Veer, Max A Little, Kelly Howells, Caroline Sanders, Jamie C Sergeant, Terence W O'Neill, John McBeth, William G Dixon. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 23.01.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

PY - 2019/1/23

Y1 - 2019/1/23

N2 - BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.

AB - BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.

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U2 - 10.2196/10238

DO - 10.2196/10238

M3 - Article

SN - 1929-0748

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JO - JMIR Research Protocols

JF - JMIR Research Protocols

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M1 - e10238

ER -

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study

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