Evidence map›Paper›PMID 30368433›Full record

ArticleJournal of medical Internet research2018

Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults.

Mashfiqui Rabbi, Min Sh Aung, Geri Gay, M Cary Reid, Tanzeem Choudhury

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
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  16. Health information-seeking experience in people with head and neck neoplasms undergoing treatment: a qualitative study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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  20. Using artificial intelligence to improve pain assessment and pain management: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mashfiqui Rabbi *Department of Statistics, Harvard University, Cambridge, MA, United States.ORCID 0000-0003-4928-553X
Min Sh Aung *Information Science Department, Cornell University, Ithaca, NY, United States.ORCID 0000-0002-4376-0849
Geri GayInformation Science Department, Cornell University, Ithaca, NY, United States.ORCID 0000-0002-6324-9132
M Cary ReidDepartment of Medicine, Weill Cornell Medical College, New York, NY, United States.ORCID 0000-0001-8117-662X
Tanzeem ChoudhuryInformation Science Department, Cornell University, Ithaca, NY, United States.ORCID 0000-0002-5952-4955

Funding

Pilot CoreP30AG022845 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Manney Carrington Reid · 2003 to 2026
$15.2M
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
TRAINING U54EB020404 · NIBIB · UNIVERSITY OF MEMPHIS · PI KUMAR, SANTOSH · 2014 to 2018
$11.3M
Data-Based Methods for Just-In-Time Adaptive Interventions in Alcohol UseR01AA023187 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MURPHY, SUSAN A · 2015 to 2019
$2.6M
Reid Aging and Pain Research Training ProgramK24AG053462 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI REID, MANNEY CARRINGTON · 2016 to 2025
$1.8M
NIAAA NIH HHS R01 AA023187NIA NIH HHS K24 AG053462NIA NIH HHS P30 AG022845NIBIB NIH HHS U54 EB020404NIDA NIH HHS P50 DA039838
6 · The paper itself

Abstract

backgroundChronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution.

objectiveMyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain.

methodsA 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities.

resultsIn all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (β

conclusionsIn the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time.

Indexed as

AdultCell PhoneChronic PainExerciseFeasibility StudiesFemaleHumansMachine LearningMalePilot Projectschronic back painchronic painmachine learningpersonalizationreinforcement learning

Identifiers

PMID30368433
PMCPMC6229514

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.