ACR Meeting Abstracts

ACR Meeting Abstracts

  • Home
  • Meetings Archive
    • ACR Convergence 2022
    • ACR Convergence 2021
    • ACR Convergence 2020
    • 2020 ACR/ARP PRSYM
    • 2019 ACR/ARP Annual Meeting
    • 2018 ACR/ARHP Annual Meeting
    • 2017-2009 Meetings
    • Download Abstracts
  • Keyword Index
  • Advanced Search
  • Your Favorites
    • Favorites
    • Login
    • View and print all favorites
    • Clear all your favorites
  • Meeting Resource Center

Abstract Number: 2915

Can Passively-Collected Phone Behavior Determine Rheumatic Disease Activity?

Kaleb Michaud, Sofia Pedro and Rebecca Schumacher, FORWARD, The National Databank for Rheumatic Diseases, Wichita, KS

Meeting: 2018 ACR/ARHP Annual Meeting

Keywords: rheumatic disease and rheumatoid arthritis (RA)

  • Tweet
  • Email
  • Print
Session Information

Date: Wednesday, October 24, 2018

Session Title: 6W007 ACR Abstract: Patient Outcomes, Preferences, & Attitudes II: PROs (2910–2915)

Session Type: ACR Concurrent Abstract Session

Session Time: 9:00AM-10:30AM

Background/Purpose: Advances in reality mining combined with the pervasive use of smart phones have shown measurable changes in phone behavior due to changes in health. We sought to determine if passively collected digital measures could predict patient-reported outcome (PRO) measures in patients with rheumatic diseases.

Methods: Participants were a subset of adult smartphone users enrolled in Forward, The National Databank for Rheumatic Diseases during 2013-2015. They used a custom smartphone app that collected daily passive digital measures including GPS-tracked mobility (single path) and mobility radius (circle that included all daily mobility). Users with Android OS phones also provided number and duration of calls and texts. PROs included daily pain and global assessment and weekly HAQ-II and PAS-II. Disease flares were regularly self-reported. Statistics included linear mixed effect models (MRM) with random slope for days for PROs and logistic GEE (for flares). Best models were selected using stepwise. Moving averages (MA) of the prior week were applied to the log-transformed passive data. Possible confounders accounted for included patient demographics, clinic measures, and seasonal factors.

Results: Of the 446 participants, 292 (66%) had rheumatoid arthritis (RA). They were mostly female (91%), of middle age (54±12 yrs) with moderate disability (HAQ-II 0.8±0.6) and comorbid illness (RDCI 2.3±1.7); 44% were treated with biologics. The MA of text length was most strongly and inversely associated with PROs including weekly pain (β= -0.15, 95%CI -0.26, -0.05). MA of mobility measures were significantly associated with global, HAQ-II, and PAS-II, but not pain. Table 1 shows estimates in RA from the MRM model for passive phone data keeping other variables at mean values. Flare incidence was significantly associated with the MA of mobility radius (OR = 0.92, 95%CI 0.86, 0.98).

Conclusion: Digital measures collected via smartphone may be a less intrusive means to identify worsening in patients with RA and other rheumatic diseases. Additional studies should confirm and expand these findings.

 

Table 1 – Predictions of weekly PRO for ranges of passive data in RA patients

Passive variable

Estimated*: Pain

Global

HAQ-II

PAS-II

SMS length

 

 

 

 

   20 characters

3.46 (2.86 – 4.07)

3.50 (2.98 – 4.02)

0.98 (0.82 – 1.13)

2.61 (2.18 – 3.04)

   50

3.32 (2.73 – 3.91)

3.38 (2.88 – 3.88)

0.96 (0.80 – 1.11)

2.51 (2.09 – 2.93)

   100

3.21 (2.62 – 3.81)

3.29 (2.78 – 3.79)

0.94 (0.79 – 1.10)

2.43 (2.01 – 2.85)

   150

3.14 (2.54 – 3.74)

3.23 (2.71 – 3.74)

0.93 (0.78 – 1.09)

2.38 (1.95 – 2.81)

   500

2.96 (2.33 – 3.60)

3.08 (2.52 – 3.63)

0.91 (0.75 – 1.07)

2.26 (1.81 – 2.70)

Interaction diversity

 

 

 

 

   0

1.88 (0.40 – 3.37)

2.40 (0.92 – 3.88)

0.69 (0.39 – 0.99)

1.46 (0.51 – 2.41)

   1 person

3.14 (2.54 – 3.75)

3.25 (2.74 – 3.77)

0.93 (0.77 – 1.08)

2.38 (1.95 – 2.81)

   3

3.26 (2.67 – 3.85)

3.33 (2.83 – 3.84)

0.95 (0.79 – 1.10)

2.46 (2.04 – 2.89)

   5

3.31 (2.72 – 3.91)

3.37 (2.87 – 3.87)

0.96 (0.80 – 1.11)

2.50 (2.08 – 2.93)

   15

3.43 (2.82 – 4.04)

3.45 (2.92 – 3.97)

0.98 (0.82 – 1.14)

2.59 (2.16 – 3.02)

   30

3.50 (2.87 – 4.13)

3.49 (2.95 – 4.95)

0.99 (0.83 – 1.15)

2.64 (2.20 – 3.08)

Reaction time

 

 

 

 

   0.5 hour

3.27 (2.67 – 3.86)

3.34 (2.84 – 3.84)

 

2.47 (2.05 – 2.89)

   2.5

3.30 (2.71 – 3.89)

3.36 (2.86 – 3.86)

 

2.49 (2.07 – 2.92)

   7

3.32 (2.73 – 3.92)

3.37 (2.87 – 3.88)

 

2.51 (2.09 – 2.93)

   12

3.33 (2.74 – 3.93)

3.38 (2.88 – 3.88)

 

2.52 (2.10 – 2.94)

Mobility

 

 

 

 

   0.5 mile

 

3.31 (2.81 – 3.81)

0.95 (0.79 – 1.10)

2.47 (2.04 – 2.89)

   5

 

3.47 (2.96 – 3.97)

0.97 (0.82 – 1.13)

2.53 (2.11 – 2.95)

   10

 

3.51 (3.00 – 4.02)

0.98 (0.83 – 1.14)

2.55 (2.12 – 2.97)

   20

 

3.56 (3.04 – 4.08)

0.99 (0.84 – 1.15)

2.57 (2.14 – 3.00)

Mobility radius

 

 

 

 

   1 mile

 

3.31 (2.80 – 3.81)

0.96 (0.81 – 1.12)

 

   5

 

3.34 (2.84 – 3.84)

0.96 (0.80 – 1.11)

 

   10

 

3.36 (2.85 – 3.86)

0.95 (0.80 – 1.11)

 

   50

 

3.39 (2.88 – 3.89)

0.95 (0.79 – 1.10)

 

   250

 

3.42 (2.91 – 3.93)

0.94 (0.78 – 1.09)

 

*Adjusted for sex, ethnicity, total income, baseline pain, education, number of persons in household, rheumatic disease comorbidity index (RDCI), sleep scale, self-reported joint count, prior biologic and DMARD exposures, Medicare status, calendar year, season, region and season/region interaction.

 

 


Disclosure: K. Michaud, University of Nebraska Medical Center and FORWARD, The National Databank for Rheumatic Diseases, 3,Rheumatology Research Foundation and Pfizer, 2; S. Pedro, None; R. Schumacher, FORWARD, The National Databank for Rheumatic Diseases, 3.

To cite this abstract in AMA style:

Michaud K, Pedro S, Schumacher R. Can Passively-Collected Phone Behavior Determine Rheumatic Disease Activity? [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 9). https://acrabstracts.org/abstract/can-passively-collected-phone-behavior-determine-rheumatic-disease-activity/. Accessed February 6, 2023.
  • Tweet
  • Email
  • Print

« Back to 2018 ACR/ARHP Annual Meeting

ACR Meeting Abstracts - https://acrabstracts.org/abstract/can-passively-collected-phone-behavior-determine-rheumatic-disease-activity/

Advanced Search

Your Favorites

You can save and print a list of your favorite abstracts during your browser session by clicking the “Favorite” button at the bottom of any abstract. View your favorites »

ACR Pediatric Rheumatology Symposium 2020

© COPYRIGHT 2023 AMERICAN COLLEGE OF RHEUMATOLOGY

Wiley

  • Home
  • Meetings Archive
  • Advanced Search
  • Meeting Resource Center
  • Online Journal
  • Privacy Policy
  • Permissions Policies
  • Cookie Preferences