A Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) in Older Adults With Obesity: Protocol for the MORPH Trial

Jason Fanning, Amber K Brooks, Edward Ip, Barbara J Nicklas, W Jack Rejeski, Jason Fanning, Amber K Brooks, Edward Ip, Barbara J Nicklas, W Jack Rejeski

Abstract

Background: Chronic pain is a complex, age-related health issue that affects both physical functioning and quality of life. Because the impact of chronic pain is worsened by obesity and inactivity, nonpharmacological interventions that promote movement, reduce sitting, and aid in weight loss are needed to help manage pain symptoms among older adults with chronic pain.

Objective: The Mobile Intervention to Reduce Pain and Improve Health (MORPH) pilot trial aims to develop and test the feasibility and acceptability of a novel, patient-centered intervention to reduce chronic pain and improve physical functioning in older adults, leveraging the combination of telecoaching and individually adaptive mHealth tools to decrease both body mass and sedentary behavior.

Methods: MORPH comprises 2 phases, including a 1-year iterative development phase, and a 1-year pilot randomized controlled trial (RCT). During the development phase, representative participants will engage in one-on-one structured interviews and a 1-week field test. The resulting feedback will be used to guide the development of the finalized MORPH intervention package. During the second phase, the finalized intervention will be tested in a pilot RCT (N=30) in which older adult participants with chronic pain and obesity will be assigned to receive the 12-week MORPH intervention or to a waitlist control. Primary outcomes include self-reported pain symptoms and physical function.

Results: Phase 1 recruitment is ongoing as of December 2017.

Conclusions: The MORPH intervention brings together a strong body of evidence using group-based behavioral intervention designs with contemporary mHealth principles, allowing for intervention when and where it matters the most. Given the ubiquity of smartphone devices and the popularity of consumer activity and weight monitors, the results of this study may serve to inform the development of scalable, socially driven behavioral pain management interventions.

Trial registration: ClinicalTrials.gov NCT03377634; https://ichgcp.net/clinical-trials-registry/NCT03377634 (Archived by WebCite at http://www.webcitation.org/6yj0J5Pan).

Registered report identifier: RR1-10.2196/9712.

Keywords: chronic pain; mindfulness; sedentary lifestyle; technology; weight loss.

Conflict of interest statement

Conflicts of Interest: None declared.

©Jason Fanning, Amber K Brooks, Edward Ip, Barbara J Nicklas, W Jack Rejeski. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 14.05.2018.

Figures

Figure 1
Figure 1
Pathways between chronic pain, sedentary behavior, and compromised physical function and health-related quality of life (HRQOL).
Figure 2
Figure 2
Key Mobile Intervention to Reduce Pain and Improve Health (MORPH) intervention components.
Figure 3
Figure 3
Pathways between intervention support, sedentary behavior, pain, and physical function. MORPH: Mobile Intervention to Reduce Pain and Improve Health; PF: physical function; SB: sedentary behavior.
Figure 4
Figure 4
Technology influences on behavioral determinants. MORPH: Mobile Intervention to Reduce Pain and Improve Health.
Figure 5
Figure 5
Example of "striated" sitting time feedback. Blue represents sitting; Green represents movement.
Figure 6
Figure 6
"Bronze Stand Award" (left) and "Stadium Stand" (right) medals.
Figure 7
Figure 7
Web-app and text-message ecological momentary assessment options.
Figure 8
Figure 8
Equation for estimating effect sizes. Y represents the primary outcomes (Short Physical Performance Battery [SPPB] score, Patient-Reported Outcomes Measurement Information System [PROMIS] pain scales); subscripts FU and BL denote measurement at week 12-week follow-up and baseline, respectively; Int represents intervention status; beta represents each regression coefficient; and epsilon represents random error.

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