The Integration of Technology into Treatment Programs to Aid in the Reduction of Chronic Pain

Eckard, Chad; Asbury, Caitlyn; Bolduc, Brandon; Camerlengo, Chelsea; Gotthardt, Julia; Healy, L.; Waialae, Laura; Zeigler, Ceirra; Childers, Jennifer; Horzempa, Joseph

doi:10.35248/2684-1320.16.2.118

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…Pain management apps such as e-Ouch or Habit Changer are designed in the form of a pain diary, where patients can log in, make journal entries thrice a day, and get feedback along with coaching. The app also links to social media websites such as Twitter [ 11 ]. ICBT has also aided in pain reduction, in improving stiffness, and in reducing distress and symptoms of depression in adults older than 50 years with knee osteoarthritis [ 40 ].…”

Section: Reviewmentioning

confidence: 99%

“…These programs are used to treat mild to moderate depression and for pain management. Applications like Twitter can be utilized for patient coaching and can link with the ICBT app easily for sharing posts [ 10 - 11 ]. There are also web pages like 'Living Life To The Full,' which deliver cognitive behavioral therapy (CBT) for maladaptive thoughts, sleeping disorders, anxiety control, relaxation techniques, exercise, and diet.…”

Section: Introductionmentioning

confidence: 99%

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The Effectiveness of Internet-Based Cognitive Behavioral Therapy in Treatment of Psychiatric Disorders

Kumar¹,

Sattar²,

Bseiso³

et al. 2017

Cureus

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This review article is an overview of the effectiveness of internet-based cognitive behavioral therapy (ICBT) in the treatment of psychiatric disorders. ICBT’s effectiveness has been investigated in treating and managing conditions like depression, generalized anxiety disorder (GAD), panic disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), adjustment disorder, bipolar disorder, chronic pain, and phobias. ICBT’s role in the treatment of medical conditions such as diabetes mellitus with comorbid psychiatric illnesses was also explored. Furthermore, this study elaborates on its cost-effectiveness and its impact in rural areas. We conducted a thorough literature search using PubMed and Google Scholar with no restrictions on the date. ICBT's role in treating and controlling psychiatric illnesses has been established in the literature. From the data compiled, we conclude that ICBT is useful in treating mental health and medical illnesses with psychiatric comorbidities. It has also been found to be cost-effective for patients and society. ICBT is a potential tool emerging with modern day technological advancements and is useful in rural and urban settings, across various languages and cultures, and on a global scale. Larger randomized control trials on its use in clinical practice and in reaching rural populations are bound to shed more light on the effectiveness of this tool along with spreading awareness among physician and patient communities.

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Section: Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Internet-Based Cognitive Behavioral Therapy in Treatment of Psychiatric Disorders

Kumar¹,

Sattar²,

Bseiso³

et al. 2017

Cureus

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“…The inability to properly assess and effectively report specific pain episodes can be caused by various factors ranging from psychological or cognitive impairments, to physical and cultural predispositions. In such cases, the reliance on self-reporting tools such as the Visual Analogue Scale (VAS) (Hawker et al, 2011 ) or the Numerical Rating Scale (NRA) (Eckard et al, 2016 ) would potentially lead to some unsuitable and inadequate pain relief therapy. Meanwhile, suitable information stemming from an autonomous and objective pain assessment system based on measurable behavioral, anatomical and physiological parameters could provide some additional and relevant insight regarding the underlying pain episode, therefore helping to significantly improve both pain assessment and management.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

et al. 2021

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Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.

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“…Computer systems are currently being developed to promote better care for individual patients, even after early discharge (Garrett et al, 2017). Although there is no definitive evidence supporting the benefits of applications (Apps) for pain assessment and management (Lalloo et al, 2017), the use of smartphones to manage health-related issues is constantly expanding (Eckard et al, 2016; Lee et al, 2018; McGeary et al, 2012). On the one hand, Apps could support self-management by improving self-monitoring of pain at home among pediatric patients and their parents.…”

Section: Introductionmentioning

confidence: 99%

Using an App to monitor postoperative pain at home in pediatric patients

Tiozzo

Biagioli

Brancaccio

et al. 2021

J Child Health Care

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A prospective comparative study was conducted in 487 pediatric patients (69% male, mean age = 6.4 ± 4.0) to evaluate (a) the incidence, intensity, and characteristics of pain in pediatric patients at home during the first 24 hours and 5 days after surgery and (b) the factors associated with higher pain intensity, including the impact of an application (App) compared to the paper-and-pencil approach. Postoperative pain was assessed by patients or their parents at home using the ‘Bambino Gesù’ Children’s Hospital (Ospedale Pediatrico Bambino Gesù, OPBG) tool for participants aged 4–17 years or the Faces, Legs, Arms, Cry, and Consolability scale for participants less than four years old. Participants were assigned to two groups: those who used the paper-and-pencil version of the pain scale and those who used the App. Overall, 209 of the 472 (44%) participants reported pain during the first 24 hours, and 92 of the 420 (22%) reported pain between one and five days after surgery. Higher pain intensity scores were associated with being in the App group, directly assessing own pain, and using the OPBG tool. The App was effective in facilitating pain assessment. Health professionals could empower pediatric patients and their parents in assessing pain at home through a dedicated App.

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The Integration of Technology into Treatment Programs to Aid in the Reduction of Chronic Pain

Cited by 16 publications

References 24 publications

The Effectiveness of Internet-Based Cognitive Behavioral Therapy in Treatment of Psychiatric Disorders

The Effectiveness of Internet-Based Cognitive Behavioral Therapy in Treatment of Psychiatric Disorders

Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

Using an App to monitor postoperative pain at home in pediatric patients

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