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Establishing the reliability and validity of digital tools to collect patient generated data


Project Description

Background
The rise in consumer electronics ownership, such as smartphones and smartwatches, offers unique opportunities to collect granular subject-reported and passively collected research data via remote monitoring. This requires the transfer of established paper-based instruments to a digital format, whilst also creating the opportunity for new data collection tools to be developed. The process of developing a data collection tool is in large part focused on reducing error in the measurement process: reliability (the quality of the data collected) and validity (the extent to which the data collection tool measures what it is meant to measure) are the key indicators of the quality of data collected. However, while evidence regarding the viability of smartphones and smartwatches as data collection tools is accumulating, questions about the reliability and validity of the data have not been addressed. Furthermore, trust in novel data collection tools collected using digital technology requires a robust process to assess their reliability and validity.

Aim of this PhD
To establish the reliability and validity of digital tools designed to collect patient generated data.

Methods

1. Systematic review
Conduct a systematic review of studies of best practice in the validation and reliability of digital data collection tools.

2. Assess the reliability and validity of digital data collection tools
Use the results of the systematic review to assess the validity and reliability of digital data collection tools, for example:

The uMotif application (app): the app, delivered via smartphones, tablets, laptops, and desktops, has a unique 10-segment ’motif’ interface that allows participants to track up to 10 daily symptoms such as pain, fatigue and mood.

KOALAP: a smartwatch digital data collection tool developed to measure pain and physical activity to construct a composite outcome measure.

Digital pain manikin: collects pain location, severity and impact data via a digital interactive collection tool.

Future tools are expected to include patient-reported medication use and adverse event information.

3. Define standards and develop guidelines
For use in future research that aims to assess the validation and reliability of digital data collection tools.

The Centre for Epidemiology (www.cfe.manchester.ac.uk) is leading in the development and application of novel digital data collection tools in epidemiological studies. Training will be provided in:

• Systematic reviews and guideline development
• Digital data collection methods
• Epidemiology (introductory and advanced), including principles of reliability and validity
• Traditional and novel statistical methods to analyse complex datasets

Entry Requirements
Candidates are expected to hold (or be due to obtain) a minimum upper-second (or equivalent) class undergraduate degree in statistics, informatics, psychology, or a relevant subject, and will have strong statistical skills. A Masters degree in a related subject and/or relevant research experience is desirable.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 0/ Standard fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Druce KL, Cordingley L, Short V, Moore S, Hellman B, James B, Lunt M, Kyle SD, Dixon WG, McBeth J. Quality of life, sleep and rheumatoid arthritis (QUASAR): a protocol for a prospective UK mHealth study to investigate the relationship between sleep and quality of life in adults with rheumatoid arthritis. BMJ Open. 2018 Jan 26;8(1):e018752.

Beukenhorst AL, Schultz DM, McBeth J, Lakshminarayana R, Sergeant JC, Dixon WG. Using Smartphones for Research Outside Clinical Settings: How Operating Systems, App Developers, and Users Determine Geolocation Data Quality in mHealth Studies. Stud Health Technol Inform. 2017;245:10-14

Druce KL, McBeth J, van der Veer SN, Selby DA, Vidgen B, Georgatzis K, Hellman B, Lakshminarayana R, Chowdhury A, Schultz DM, Sanders C, Sergeant JC, Dixon WG. Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association between Weather and Pain: Cohort Study. JMIR Mhealth Uhealth. 2017 Nov 1;5(11):e168.

Reade S, Spencer K, Sergeant JC, Sperrin M, Schultz DM, Ainsworth J, Lakshminarayana R, Hellman B, James B, McBeth J, Sanders C, Dixon WG. Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and Physical Activity in Patients with Rheumatoid Arthritis. JMIR Mhealth Uhealth. 2017 Mar 24;5(3):e37.

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