Remote sampling of biomarkers of inflammation with linked patient generated health data in patients with rheumatic and musculoskeletal diseases: an Ecological Momentary Assessment feasibility study

Katie L Druce, David Gibson, Kevin McEleney, Belay B Yimer, Stephanie Meleck, Ben James, Bruce Hellman, William G Dixon, John McBeth

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Abstract

Background: People with rheumatic diseases experience troublesome fluctuations in fatigue. Debated causes include pain, mood and inflammation. To determine the relationships between these potential causes, serial assessments are required but are methodologically challenging. This mobile health (mHealth) study explored the viability of using a smartphone app to collect patient-reported symptoms with contemporaneous Dried Blood Spot Sampling (DBSS) for inflammation. Methods: Over 30 days, thirty-eight participants (12 RA, 13 OA, and 13 FM) used uMotif, a smartphone app, to report fatigue, pain and mood, on 5-point ordinal scales, twice daily. Daily DBSS, from which C-reactive Protein (CRP) values were extracted, were completed on days 1–7, 14 and 30. Participant engagement was determined based on frequency of data entry and ability to calculate within- and between-day symptom changes. DBSS feasibility and engagement was determined based on the proportion of samples returned and usable for extraction, and the number of days between which between-day changes in CRP which could be calculated (days 1–7). Results: Fatigue was reported at least once on 1085/1140 days (95.2%). Approximately 65% of within- and between-day fatigue changes could be calculated. Rates were similar for pain and mood. A total of 287/342 (83.9%) DBSS, were returned, and all samples were viable for CRP extraction. Fatigue, pain and mood varied considerably, but clinically meaningful (≥ 5 mg/L) CRP changes were uncommon. Conclusions: Embedding DBSS in mHealth studies will enable researchers to obtain serial symptom assessments with matched biological samples. This provides exciting opportunities to address hitherto unanswerable questions, such as elucidating the mechanisms of fatigue fluctuations.

Original languageEnglish
Article number770
Pages (from-to)770
Number of pages9
JournalBMC Musculoskeletal Disorders
Volume23
Issue number1
Early online date13 Aug 2022
DOIs
Publication statusPublished (in print/issue) - 13 Aug 2022

Bibliographical note

Funding Information:
This work was supported by a grant from the Inflammation and Repair domain of the Manchester Academic Health Science Centre (MAHSC) and by infrastructure support from the Centre for Epidemiology Versus Arthritis (grant reference 20380). The organisations had no further input into how the study was executed, or in the analysis or interpretation of data.

Funding Information:
The authors would like to thank all of the participants who provided data. They would also like to thank the National Rheumatoid Arthritis Society for their support with recruitment and members of the uMotif team who supported data collection. The data presented in this paper was collected in accordance with the requirements of the Human Tissue Authority licence and with the knowledge of the University of Manchester’s relevant Designated Individual. All individuals involved in the analysis and storage of the samples have had adequate training and that all activity is compliant with the conditions of the University’s licence. An agreement of basic Material Transfer Agreement provisions was obtained between the University of Manchester and Ulster University, to cover the return (to Ulster), analysis, and transfer (Ulster to Manchester) of the samples collected in the study. The research was also conducted in accordance with the Declaration of Helsinki.

Publisher Copyright:
© 2022, The Author(s).

© 2022. The Author(s).

Keywords

  • Rheumatoid Arthritis
  • Fibromyalgia
  • inflammation
  • Fatigue
  • Remote Monitoring
  • C-reactive protein
  • Ecological momentary assessment
  • Rheumatoid arthritis
  • mHealth
  • Inflammation
  • Osteoarthritis
  • Humans
  • Ecological Momentary Assessment
  • Rheumatic Diseases/complications
  • Feasibility Studies
  • Pain/etiology
  • Biomarkers
  • Inflammation/complications
  • Fatigue/diagnosis
  • Patient Generated Health Data

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