ArticleSensors (Basel, Switzerland)2022
Smartphone-Based Ecological Momentary Assessment for Collecting Pain and Function Data for Those with Low Back Pain.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- In-Field Ecological Momentary Assessment From Wearable Motion Sensors and Self-Report in a Chronic Low Back Pain Cohort.JOR spine · 2026Article
- Using Android Smartphones to Collect Precise Measures of Reaction Times to Multisensory Stimuli.Sensors (Basel, Switzerland) · 2025Article
- Understanding Appropriation of Digital Self-Monitoring Tools in Mental Health Care: Qualitative Analysis.JMIR human factors · 2025Article
- The Effect of Everyday-Life Social Contact on Pain.Journal of medical Internet research · 2024Review
- Advances in E-Health and Mobile Health Monitoring.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to (i) determine the feasibility of collecting pain and function data using smartphone-based EMA, (ii) examine pain data collected using EMA compared to traditional methods, (iii) characterize individuals' progress in relation to pain and function, and (iv) investigate the appropriation of the method. Our results showed that an individual's 'pain intensity index' provided a measure of the burden of their low back pain, which differed from but complemented traditional 'change in pain intensity' measures. We found significant variations in the pain and function over the course of an individual's back pain that was not captured by the cohort's mean scores, the approach currently used as the gold standard in clinical trials. The EMA method was highly acceptable to the participants, and the Model of Technology Appropriation provided information on technology adoption. This study highlights the potential of the smartphone-based EMA method for enhancing the collection of outcome data and providing a personalized approach to the management of low back pain.
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Registered trials
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