The effect of a smartphone-based pain management application on pain intensity and quality of life in adolescents with chronic pain

Shaygan, Maryam; Jaberi, Azita

doi:10.1038/s41598-021-86156-8

Cited by 33 publications

(24 citation statements)

References 49 publications

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“…Nevertheless, in view of our results, the age of the participants and the rate of precariousness were similar to those found in the general French population, with and without chronic pain [ 77 , 99 ]. We also observed that our population included many patients with nociplastic pain (mainly fibromyalgia, 50/80, 62.5%), which was not the case in other foreign studies [ 20 , 24 , 27 , 31 , 72 , 75 ]. Another French study [ 77 ] also found a high rate of fibromyalgia (42%), which seems to show that the population of French pain clinics includes a large proportion of fibromyalgia patients.…”

Section: Discussionmentioning

confidence: 51%

“…Another study [ 20 ], which assessed a remotely delivered pain management program in a web-based format (web platform), showed that 76% of patients adhered [ 20 ] for at least 3 months. A study [ 75 ] with adolescents with chronic pain showed a high level of adherence (78%) and satisfaction, and a study [ 72 ] with patients with multiple sclerosis and migraine that evaluated the feasibility of using a smartphone app for patient follow-up showed an adherence rate of 49% after 90 days. The eDOL tool seems to be accepted in a similar way to these other smartphone-based or web-based apps.…”

Section: Discussionmentioning

confidence: 99%

“…In our study, only an email reminder was sent to our patients if they had not used the app within 2 weeks after their inclusion and only 1 visit (included in their usual care path) was scheduled after at least 3 months. The studies [ 20 , 24 , 27 , 31 , 72 , 75 ] cited above included regular telephone follow-up or frequent visits. Moreover, according to the mean score (5.7, SD 3.1) for the statement “During the time that I have been using eDOL, I feel that my doctor has better monitored my symptoms and that my pain has been better managed,” patients perceived that there was a lack of involvement of physicians in the eDOL tool.…”

Section: Discussionmentioning

confidence: 99%

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eDOL mHealth App and Web Platform for Self-monitoring and Medical Follow-up of Patients With Chronic Pain: Observational Feasibility Study

Kerckhove¹,

Delage²,

Cambier³

et al. 2022

JMIR Form Res

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Background Chronic pain affects approximately 30% of the general population, severely degrades quality of life (especially in older adults) and professional life (inability or reduction in the ability to work and loss of employment), and leads to billions in additional health care costs. Moreover, available painkillers are old, with limited efficacy and can cause significant adverse effects. Thus, there is a need for innovation in the management of chronic pain. Better characterization of patients could help to identify the predictors of successful treatments, and thus, guide physicians in the initial choice of treatment and in the follow-up of their patients. Nevertheless, current assessments of patients with chronic pain provide only fragmentary data on painful daily experiences. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs can address this issue. Objective We hypothesized that regular patient self-monitoring using an mHealth app would lead physicians to obtain deeper understanding and new insight into patients with chronic pain and that, for patients, regular self-monitoring using an mHealth app would play a positive therapeutic role and improve adherence to treatment. We aimed to evaluate the feasibility and acceptability of a new mHealth app called eDOL. Methods We conducted an observational study to assess the feasibility and acceptability of the eDOL tool. Patients completed several questionnaires using the tool over a period of 2 weeks and repeated assessments weekly over a period of 3 months. Physicians saw their patients at a follow-up visit that took place at least 3 months after the inclusion visit. A composite criterion of the acceptability and feasibility of the eDOL tool was calculated after the completion of study using satisfaction surveys from both patients and physicians. Results Data from 105 patients (of 133 who were included) were analyzed. The rate of adherence was 61.9% (65/105) after 3 months. The median acceptability score was 7 (out of 10) for both patients and physicians. There was a high rate of completion of the baseline questionnaires and assessments (mean 89.3%), and a low rate of completion of the follow-up questionnaires and assessments (63.8% (67/105) and 61.9% (65/105) respectively). We were also able to characterize subgroups of patients and determine a profile of those who adhered to eDOL. We obtained 4 clusters that differ from each other in their biopsychosocial characteristics. Cluster 4 corresponds to patients with more disabling chronic pain (daily impact and comorbidities) and vice versa for cluster 1. Conclusions This work demonstrates that eDOL is highly feasible and acceptable for both patients with chronic pain and their physicians. It also shows that such a tool can integrate many parameters to ensure the detailed characterization of patients for future research works and pain management. Trial Registration ClinicalTrial.gov NCT03931694; http://clinicaltrials.gov/ct2/show/NCT03931694

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Section: Discussionmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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eDOL mHealth App and Web Platform for Self-monitoring and Medical Follow-up of Patients With Chronic Pain: Observational Feasibility Study

Kerckhove¹,

Delage²,

Cambier³

et al. 2022

JMIR Form Res

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“…The effects of smartphone usage on cognitive abilities for educational, occupational, and social functioning can be classi ed as negative or positive from their socio-emotional components [10].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

Giraldo-Jiménez

Gaviria-Chavarro

Urrutia-Valdés

et al. 2021

Preprint

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Background: Machine learning techniques allow highly accurate prediction of different tasks by measuring the event probabilities. This research proposes a prediction model for dependency on smartphones based on machine learning techniques. Methods: We performed an analytical observational study with a retrospective case–control approach; the different classification methods used were decision tree, random forest, logistic regression, and support vector machine. The sample demographic included 1228 students from a private university in Cali. The tests were 1) smartphone dependency assessment and 2) the Nordic musculoskeletal symptoms questionnaire. Results: It was found that some of the variables related to smartphone dependency are academic curriculum, school, marital status, socioeconomic status, rules, discussions, and discrimination. Conclusions: The support vector machine model evidences highest prediction precision for smartphone dependency, obtained through the stratified-k-fold cross-validation technique.

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“…Patient education allows patients to have the right knowledge of their pain, which can motivate them to actively participate in effective treatments. Recently, a pain management education program using a smartphone-based application reduced severity of pain and improved social functioning in adolescents with chronic pain [ 15 ]. A recent systematic review reported the beneficial effects of smartphone applications in pain management in an outpatient setting [ 16 ].…”

mentioning

confidence: 99%

Digital therapeutics in pain medicine

Lee

2021

Korean J Pain

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The effect of a smartphone-based pain management application on pain intensity and quality of life in adolescents with chronic pain

Cited by 33 publications

References 49 publications

eDOL mHealth App and Web Platform for Self-monitoring and Medical Follow-up of Patients With Chronic Pain: Observational Feasibility Study

eDOL mHealth App and Web Platform for Self-monitoring and Medical Follow-up of Patients With Chronic Pain: Observational Feasibility Study

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

Digital therapeutics in pain medicine

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