An Examination of Day-to-Day and Intraindividual Pain Variability in Low Back Pain

Wesolowicz, Danielle M; Bishop, Mark D.

doi:10.1093/pm/pnab119

Cited by 14 publications

(2 citation statements)

References 54 publications

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“…This study compared Timed Walk against a conventional 6MWT on the same day. People with persistent pain can have variability in day-to-day pain [ 66 , 67 ], which may result in variations in walking speed. Conducting the tests 15 minutes apart removed this source of variability.…”

Section: Discussionmentioning

confidence: 99%

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study

Simmich,

Andrews,

Claus

et al. 2024

JMIR Form Res

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Background The 6-minute walk test (6MWT) is a commonly used method to assess the exercise capacity of people with many health conditions, including persistent pain. However, it is conventionally performed with in-person supervision in a hospital or clinic, therefore requiring staff resources. It may also be difficult when in-person supervision is unavailable, such as during the COVID-19 pandemic, or when the person is geographically remote. A potential solution to these issues could be to use GPS to measure walking distance. Objective The primary aim of this study was to assess the validity of a GPS-based smartphone app to measure walking distance as an alternative to the conventional 6MWT in a population with persistent pain. The secondary aim of this study was to estimate the difference between the pain evoked by the 2 test methods. Methods People with persistent pain (N=36) were recruited to complete a conventional 6MWT on a 30-m shuttle track and a 6MWT assessed by a smartphone app using GPS, performed on outdoor walking circuits. Tests were performed in random order, separated by a 15-minute rest. The 95% limits of agreement were calculated using the Bland-Altman method, with a specified maximum allowable difference of 100 m. Pain was assessed using an 11-point numerical rating scale before and after each walk test. Results The mean 6-minute walk distance measured by the GPS-based smartphone app was 13.2 (SD 46; 95% CI −2.7 to 29.1) m higher than that assessed in the conventional manner. The 95% limits of agreement were 103.9 (95% CI 87.4-134.1) m and −77.6 (95% CI −107.7 to −61) m, which exceeded the maximum allowable difference. Pain increased in the conventional walk test by 1.1 (SD 1.0) points, whereas pain increased in the app test by 0.8 (SD 1.4) points. Conclusions In individuals with persistent pain, the 2 methods of assessing the 6MWT may not be interchangeable due to limited validity. Potential reasons for the differences between the 2 methods might be attributed to the variation in track layout (shuttle track vs continuous circuit); poor GPS accuracy; deviations from the 30-m shuttle track; human variability in walking speed; and the potential impact of a first test on the second test due to fatigue, pain provocation, or a learning effect. Future research is needed to improve the accuracy of the GPS-based approach. Despite its limitations, the GPS-based 6MWT may still have value as a tool for remote monitoring that could allow individuals with persistent pain to self-administer frequent assessments of their functional capacity in their home environment.

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Section: Discussionmentioning

confidence: 99%

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study

Simmich,

Andrews,

Claus

et al. 2024

JMIR Form Res

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“…Chronic pain (ie, pain lasting ≥3 months) is a common symptom of many long-term health conditions [ 1 , 2 ] and is associated with poor quality of life, poor health outcomes, and low participation in work and social activities [ 3 , 4 ]. There is substantial day-to-day variability in the severity of pain experienced [ 5 , 6 ], and people with chronic pain report that this variability leads to feelings of frustration and uncertainty about future pain [ 7 , 8 ]. Studies have identified associations between pain variability and response to treatment [ 9 ] as well as lower quality of life [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Little,

Schultz,

House

et al. 2024

JMIR Mhealth Uhealth

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Background People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity. Objective This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model. Methods Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters. Results Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster. Conclusions The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

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Neural and Psychosocial Mechanisms Underlying Alcohol Use and Pain Interactions: Overview of Current Evidence and Future Directions

Boissoneault,

Stennett-Blackmon,

Gilmour

et al. 2023

Curr Addict Rep

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An Examination of Day-to-Day and Intraindividual Pain Variability in Low Back Pain

Cited by 14 publications

References 54 publications

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Neural and Psychosocial Mechanisms Underlying Alcohol Use and Pain Interactions: Overview of Current Evidence and Future Directions

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