Next Article in Journal
Assisted Suicide and Euthanasia in Mental Disorders: Ethical Positions in the Debate between Proportionality, Dignity, and the Right to Die
Next Article in Special Issue
Digital Health for Patients Undergoing Cardiac Surgery: A Systematic Review
Previous Article in Journal
Characteristics of Frailty in Perimenopausal Women with Long COVID-19
Previous Article in Special Issue
Pre-Attentional Effects on Global Precedence Processing in Children with Autism Spectrum Disorder and Those with Typical Development on a Tablet-Based Modified Navon’s Paradigm Task
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health

The Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
Department of Medicinal Chemistry, L.S. Skaggs College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
Author to whom correspondence should be addressed.
Healthcare 2023, 11(10), 1469;
Submission received: 15 January 2023 / Revised: 25 April 2023 / Accepted: 16 May 2023 / Published: 18 May 2023
(This article belongs to the Special Issue Digital Therapeutics in Healthcare)


Digital therapeutics (DTx, software as a medical device) provide personalized treatments for chronic diseases and expand precision medicine beyond pharmacogenomics-based pharmacotherapies. In this perspective article, we describe how DTx for chronic low back pain (CLBP) can be integrated with pharmaceutical drugs (e.g., NSAIDs, opioids), physical therapy (PT), cognitive behavioral therapy (CBT), and patient empowerment. An example of an FDA-authorized DTx for CLBP is RelieVRx, a prescription virtual reality (VR) app that reduces pain severity as an adjunct treatment for moderate to severe low back pain. RelieVRx is an immersive VR system that delivers at-home pain management modalities, including relaxation, self-awareness, pain distraction, guided breathing, and patient education. The mechanism of action of DTx is aligned with recommendations from the American College of Physicians to use non-pharmacological modalities as the first-line therapy for CLBP. Herein, we discuss how DTx can provide multimodal therapy options integrating conventional treatments with exposome-responsive, just-in-time adaptive interventions (JITAI). Given the flexibility of software-based therapies to accommodate diverse digital content, we also suggest that music-induced analgesia can increase the clinical effectiveness of digital interventions for chronic pain. DTx offers opportunities to simultaneously address the chronic pain crisis and opioid epidemic while supporting patients and healthcare providers to improve therapy outcomes.

1. Introduction

Increasing the prevalence of chronic pain and opioid epidemic are examples of public health challenges that negatively impact individual lives and healthcare systems. Worldwide, 577 million people experienced low back pain in 2017 [1]. According to the Center for Disease Control and Prevention (CDC), 39% of adults in the US had low back pain in 2019 [2]. Chronic low back pain (CLBP) is a painful neurological disorder that affects the lower segment of the spine [3]. CLBP is characterized as pain that occurs consistently for 12 weeks or longer and is one of top causes of disability around the world [4], including the leading cause of years lived with disability [1]. CLBP patients experience reduced health-related quality of life (HRQoL) [5] and suffer from comorbidities such as sleep disorders, anxiety, and depression [6,7,8,9,10,11,12]. People living with CLBP report a negative impact of the disorder on their personal relationships, social life, and work [13].
A majority of CLBP causes are nonspecific and idiopathic, or mechanical (spinal stenosis, radiculopathy, traumatic injury and/or overuse of the spine). Other root causes of CLBP include biomechanical factors such as carrying heavy loads at work and personal medical history, such as higher body mass index [14]. Figure 1 shows examples of modifiable and non-modifiable risk factors that lead to CLBP [14,15,16,17], and also can cause transition from acute low back pain to CLBP [14,17,18,19,20]. Modifiable risk factors include sedentary lifestyle, smoking, falls, social environment, and self-perceived health. Non-modifiable risk factors include genetics, age, family history, and dementia, among others. It is noteworthy that adverse childhood experiences can also increase a risk for CLBP [21,22]. A complex combination of causes and psychosocial, cognitive, and biomechanical factors impacts both the intensity of CLBP and treatment options.
Digital health technologies comprise digital therapeutics (mobile medical apps), mobile health (mHealth) apps, and telemedicine to improve patient care. This relatively new area of health care has shown promise for improving therapy outcomes in diverse chronic diseases ranging from diabetes [23] and depression [24] to insomnia [25] and pain [26]. VR and augmented reality technologies are gaining recognition as a means to improve pain relief [27], mental health [28], neurorehabilitation [29], and physical therapy [30]. Mobile medical apps (digital therapeutics) intended to treat specific medical conditions are regulated by the Food and Drug Administration as medical devices (software as a medical device, SaMD) [31,32,33,34]. There are several VR and mobile medical apps that received market authorization from the FDA as either non-prescription or prescription digital therapeutics (PDT) to treat chronic diseases such as addiction, ADHD, insomnia, diabetes, and CLBP [32,35]. In contrast to non-prescription DTx (also known as over-the-counter or OTC DTx), PDTs are available to patients only by prescription from healthcare professionals [36,37].
The main objectives of this perspective article are: (1) to provide an overview of the current therapies used to treat CLBP in the context of emerging digital therapeutics (DTx), (2) to describe opportunities of DTx to integrate multimodal treatment options for CLBP, (3) to discuss advances of DTx towards exposome-responsive, just-in-time digital interventions, and (4) to encourage all health care stakeholders to advocate for research, development and implementation of DTx to improve therapy outcomes and public health. We hope that this work will contribute towards a broader and deeper understanding of opportunities for DTx to expand multimodal treatment options for neurological disorders.

2. Current Non-Pharmacological and Pharmacological Treatments for CLBP

Given the complexity of the causes of CLBP, there are many diverse treatments addressing both etiology and symptoms [38,39,40,41,42,43,44,45,46]. As illustrated in Figure 2, non-pharmacological interventions include physical and psychological therapies, as well as digital therapies that can deliver both. Depending on the spinal injury, surgical solutions, such as spinal fusion or disc replacement, may offer clinical benefits [47]. Another example of non-pharmacological treatment of CLBP is transcutaneous electrical nerve stimulation (TENS) [48]. Spinal cord stimulation [49,50,51] and nerve ablation [52,53] are also evaluated for their effectiveness in treating CLBP. In addition to diverse therapies and surgeries, self-management and patient education have been recognized as viable means to reduce pain intensity in CLBP patients [54,55,56,57].
Physical therapy is effective in improving pain and reducing disability in CLBP patients [58,59]. Recent network meta-analyses reported that among diverse physical exercise options, Pilates, core strengthening, and McKenzie and functional restoration methods, appeared to be the most effective in improving pain intensity [60,61], while yoga, acupuncture, and spinal manipulation can also reduce CLBP [61,62,63,64,65]. Psychological therapies such as cognitive behavioral therapy (CBT), mindfulness meditation and mindfulness-based stress reduction can improve pain, physical functions, and health-related quality of life of CLBP patients [66,67,68,69,70].
Pharmacological treatments of CLBP include analgesics, muscle relaxants, antidepressants, and anticonvulsant drugs [71,72,73,74,75,76,77,78,79]. Acetaminophen and NSAIDs (ibuprofen and naproxen) are commonly used as either over-the-counter (OTC) or prescription analgesics for CLBP [42]. While opioid-based analgesics provide a short-term reduction in CLPB, their long-term use is controversial due to adverse effects [80,81]. Over-prescription of opioid analgesics for pain resulted in the opioid epidemic crisis in North America [82,83,84,85]. A recent network meta-analysis suggested that only NSAIDs and opioids are effective in improving both pain and functions in CLPB patients [41]. While pharmacological interventions can offer pain relief, limitations of medications for CLBP are drug–drug interactions [86], gastrointestinal and cardiovascular toxicities [87], and the development of opioid use disorder [88].
In 2017, the American College of Physicians (ACP) published evidence-based clinical guidelines for the treatment of CLBP [39]. As shown in Figure 3, ACP recommends that CLBP patients should initially start with non-pharmacological treatments [39]. Patients that have an inadequate response to the non-drug therapies should use NSAIDs as a second line of treatment. The next lines of recommended pharmacotherapies are duloxetine (antidepressant) and then tramadol (opioid). While opioids should be considered as the last option for treatment, they are widely used by CLBP patients [75], in particular those with severe pain [13]. It is noteworthy that many non-pharmacological treatments for CLBP recommended in the ACP guidelines are not covered by insurance as essential health benefits [89].
To address both the complexity of etiology and the diversity of risk factors for CLBP, the multimodal approach offers apparent advantages over monotherapies. Combining physical and psychological therapies for CLBP offer additional benefits; for example, PT and CBT or ACT can further improve pain relief and disability as compared to PT alone [90,91,92]. A multidisciplinary 8-week restoration program for CLBP patients was created by combining physical and psychological interventions (functional strength, calisthenics, aerobics, stretching, education, socioeconomic counseling, and CBT) [93]. Based on RCT, the program decreased the use of medical treatment; 42% of the patients refrained from taking analgesics, and 63% of the patients returned to active/productive work after the program [93].
The multimodal approach to treat CLBP has been recognized by The Back Pain Research Consortium, the National Institute of Health (NIH) initiative to mitigate the opioid crisis, so-called “Helping to End Addiction Long-term” or HEAL [38]. This group has proposed to develop a “precision medicine algorithm” based on combining three non-pharmacological (exercise and manual therapies, ACT and self-management) and one pharmacological (duloxetine) treatment [38]. Commonly used multimodal combinations for CLBP include physical therapy and analgesics or psychotherapies and analgesics.

3. Emergence of Digital Therapeutics for CLBP

Digital health technologies for CLBP provide diverse treatment and self-management modalities [35,55,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108]. Examples of mobile apps for CLBP include “BackRx” delivering yoga and Pilates exercises for patients with discogenic CLPB [97,109], as well as PainNavigator delivering CLBP management, including education, physical exercises, yoga, and mindfulness [110]. A 12-week study of the use of a mobile app delivering the digital care program Hinge Health for low back pain reported improvement in pain and disability outcomes, as well as depression and anxiety symptoms [111,112]. The “Active ingredients” of this digital care program included exercise therapy, CBT, education, behavioral coaching, and tracking activity and symptoms [111]. Cost-effectiveness analysis of digital interventions for CLBP was favorable for the VR-based therapy, as compared to clinic-based McKenzie method [113].
Another example of digital therapy for back pain is the Kaia Health app that delivers multimodal self-management content [114,115,116]. The Kaia Back Pain app guides users through physical exercises, patient education and relaxation techniques (breathing exercises and progressive muscle relaxation), using a computer vision motion analysis that enables patient’s spatial awareness, ensures proper form during exercise, and quantifies movement as a digital biomarker. The Kaia Back Pain app is intended for adults with non-specific back pain that persists for 4 weeks or longer and is marketed in the US as a low-risk medical device under the FDA enforcement discretion. In Europe, it has the status of class IIa medical device.
RelieVRx (earlier known as EaseVRx) is a prescription digital therapeutic (PDT), an adjunct treatment for CLBP, which received market authorization from the FDA in November 2021 [35]. This digital intervention is available only by prescription from healthcare providers. The SaMD regulatory status of RelieVRx was granted via the 510 k, De Novo premarket pathway, while earlier RelieVRx received the Breakthrough Device designation from the FDA. RelieVRx is an immersive VR system delivering multimodal therapeutic content to treat moderate to severe CLBP in adult patients.
As illustrated in Figure 4 and Figure 5, the multimodal content of RelieVRx includes diverse behavioral and CBT-based “active ingredients” intended to reduce pain, namely patient education, deep relaxation, interoceptive awareness, attention-shifting, distraction, immersive enjoyment, and acceptance of CLBP. Combined together, these aforementioned elements are used to train the patient’s brain (e.g., executive, emotional, and multisensory pathways) to think differently about how it experiences pain. The digital therapy consists of 56 VR sessions, each lasting 2–16 min and are used as part of the daily eight-week program. Each session incorporates the different principles mentioned above to achieve pain relief and reduction in pain interference in daily activities. RelieVRx system consists of a virtual reality controller, headset, and a breathing amplifier that is attached to the headset, which helps in directing a patient’s breath during deep breathing exercises. The system with instructions is shipped to a patient’s home and then returned after the 8-week treatment.
RelieVRx was evaluated in CLBP patients in a 21-day pilot RCT [117], as well as in a 56-day, double-blind, placebo-controlled RCT [98,117,118,119,120]. The pivotal efficacy and safety RCT of RelieVRx (registered as NCT00415177 in included 179 CLBP patients assigned to either the eight-week treatment group using RelieVRx 3D immersive pain relief program, or to the control group (sham VR program displayed 2D nature content and did not provide skills training) [118]. Pain relief and other outcomes were measured using a Defense and Veterans Pain Rating Scale which measured pain intensity, and interference around a patient’s activities, sleep, mood, and stress. Additional measures were collected using the NIH Physical Function and Sleep Disturbance form, Pain Catastrophizing Scale, Pain Self-Efficacy Questionnaire, and Chronic Pain Acceptance. To evaluate the quality of life, the self-reported Patient’s Global Impression of Change Scale was used. Follow-up data from the pivotal RCT were collected and reported after 3, 6, and 18 months [98,119,120].
At the end of the eight weeks, the RelieVRx participants experienced a significant pain intensity reduction of an average of 42.8%, as compared to 25.1% for the control group (p < 0.001), while 46% of patients in the treatment group reached 50% or more pain reduction, as compared to 26% patients in the control group [118]. The treatment group also reported a significant reduction in sleep disturbance, pain interference (mood, stress, and activity), and physical function, as compared to the control VR program. Based on the safety evaluation of RelieVRx, no participants contacted the staff to report discomfort with the virtual reality headset or experienced any motion sickness and nausea. During the 3-month follow-up, the RelieVRx participants reported 30.3% mean pain intensity improvement, while for the control participants this value was 15.8% [98]. Participants in the treatment VR program maintained significant improvement in clinical outcomes after six months, as summarized by the investigators: “Therapeutic VR maintained significant and clinically meaningful effects 6 months posttreatment and remained superior to sham VR for reducing pain intensity and pain-related interference with activity, stress, and sleep (ds = 0.44–0.54; p < 0.003)” [119]. Positive outcomes were also observed after 18 months posttreatment [120]. An additional RCT is intended to evaluate therapy outcomes among CLBP patients such as physical functions, sleep, anxiety, depression, and opioid-based pain management [98,117,118,119,120,121].

4. DTx Expand Multimodal Therapies for CLBP

Figure 6 exemplifies how DTx can expand a repertoire of combination therapies for CLBP. Because software is versatile with respect to diverse digital content, e.g., videos with physical exercise (e.g., PT), interactive education, behavioral therapies (e.g., CBT), medication management (reminders and monitoring), activity tracking, motivational interviewing, mindfulness meditation, etc., it is perfectly positioned to integrate multiple therapeutic modalities. Patient education is an “active ingredient” that shows promise in treating CLBP [57,122,123,124,125]. As exemplified by RelieVRx and the Kaia Back Pain app, DTx are promising tools to integrate pharmacotherapies with patient education and PT (Figure 6).
Opportunities for DTx to be integrated with pharmacotherapies as drug + digital combination therapies have been previously described for chronic pain and epilepsy [33,126,127]. Since digital health technologies can support medication management [128], digital therapeutics can help CLBP patients to support medication dosing, adherence and analgesic tapering while simultaneously providing other therapeutic modalities (e.g., CBT, mindfulness meditation, relaxation) and PT. After the duration of a DTx-based therapy, CLBP patients can continue PT and other types of physical activities (yoga, Pilates, etc.). Such “seamless” transitions during a multimodal therapy are shown in Figure 7. Eventually, patient education and physical exercises delivered via DTx and PT may result in developing self-management skills and habits that can reduce the risk of recurrence of CLBP.

5. Developing Exposome-Responsive DTx by Expanding Treatment Modalities

The exposome encompasses health-related environmental and lifestyle factors, and can be defined as “an integrated function of exposure on our body including what we eat and do, our experiences, and where we live and work” [129]. “The exposome concept strives to capture the diversity and range of exposures, including synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses.” [129]. Given the diversity of environmental, lifestyle and socioeconomic factors, and gene-environment interactions that influence both the cause and treatment of CLBP [14,18,130,131], it is apparent that DTx may address challenges to create personalized multimodal therapies tailored to the exposome of an individual patient.
Development of exposome-responsive DTx for CLBP is illustrated in Figure 8. Since both stress and adequate sleep are associated with CLBP [132,133,134,135], development and validation of wearables that can measure stress and sleep [136] can also advance the development of biofeedback-based digital interventions for CLBP. We recently described how DTx can accommodate multiple modalities to yield personalized drug + digital combination therapies (“precision metapharmacology”) for people living with chronic diseases, such as chronic pain, epilepsy, depression, and cancer [126]. This approach enables integration of pharmacotherapies with just-in-time adaptive interventions (JITAI), a behavior-change therapy that can adjust content based on a patient’s real-time needs and circumstances [137,138]. For CLPB patients, JITAI can address sedentary behaviors [139,140,141,142] and comorbid depression [143], while seamlessly integrating medication management and the precision delivery of non-pharmacological “active ingredients” (PT, CBT, mindfulness meditation, breathing exercises, education, etc.). Given that adverse childhood experiences (ACE) and trauma-associated mental disorders impact both chronic pain and pain management [144,145], it is noteworthy that DTx have an ability to adjust therapeutic content based on ACE scores and emotions (Figure 1B in [126]).
One of the benefits of digital health technologies intended to treat chronic medical conditions is their ability to integrate diverse “active ingredients”, and both, RelieVRx and the Kaia Back Pain app are absolute examples of multimodal digital therapies. Herein, we suggest additional opportunities to create even more personalized interventions for CLBP by harnessing music-induced analgesia [146,147,148,149,150,151] and creating a home environment fostering chronic pain self-management [152]. Music-based interventions for acute and chronic pain show promising clinical outcomes [147,149,151,153,154,155,156], whereas using a mobile app to deliver music for opioid-based analgesia was also reported [148,157]. Music can support pain management via emotional and cognitive regulation [158,159,160,161,162], anti-inflammatory mechanisms [163,164], and potentially via the music-dopamine-reward axis as well [165,166,167]. A prototype of adjunct DTx delivering music-based intervention and self-management to reduce seizures in epilepsy patients [168] illustrates similar opportunities for chronic pain patients.

6. DTx Bridge Precision Medicine and Public Health

DTx can not only improve precision medicine for CLPB but can contribute to public health by: (1) decreasing the use of opioid-based analgesia and thus reducing opioid use disorders, (2) improving health literacy via patient education, and (3) improving therapy outcomes and preventing recurrence of CLBP, hence decreasing the burden of CLBP on healthcare and economy. Digital interventions to support opioid tapering are under development [169,170,171]. To improve the treatment of opioid use disorder, it is noteworthy that an adjunct prescription DTx, reSET-O (developed by Pear Therapeutics), delivers CBT for patients who also receive buprenorphine [172,173,174]. reSET-O was shown to improve therapy outcomes [174], decrease treatment costs [175], as well as reduce hospital readmissions and healthcare resource utilization [176].
Another public health benefit of digital health interventions is their ability to prevent the development and recurrence of chronic pain [177,178,179]. For example, patients who experienced musculoskeletal pain (including back pain) for less than 12 weeks had significantly higher odds of preventing chronic pain when using Hinge Health’s digital intervention (app-guided physical exercises, education, and virtual consultations), as compared to nonparticipants [177]. Preventive interventions can be more effective when using artificial intelligence-based technologies that can identify patients at risk for developing CLBP [180]. Since a combination of health education and physical exercises can be effective in preventing non-specific back pain [181], DTx delivering these modalities can yield both therapeutic and preventive effects. Mobile apps are recognized as means to improve health literacy and preventive behaviors [182,183,184]. Promotion and implementation of lifestyle modifications via DTx may lead to the development of sustainable self-care practices that decrease the risk for recurrence of CLBP.

7. Challenges and Limitations

Despite rapid advances in the development of DTx, there are many challenges in the implementation of digital technologies into healthcare. Reimbursement, interoperability, cybersecurity, patient engagement, attrition rates, evolving regulatory requirements, emerging evidence for clinical effectiveness, and cost-effectiveness of digital interventions are only a few examples that impact the adoption rates and usage of DTx. Commercialization challenges for DTx are illustrated by a recent bankruptcy filing of Pear Therapeutics (April 2023), which was one of the pioneers in developing DTx in the U.S. Adoption rates and reimbursement for digital interventions vary from country to country and are more advanced in Europe (e.g., Germany) compared to the U.S. [185,186,187].
Legal, regulatory, and business barriers to the implementation of DTx into healthcare systems result from a stark contrast between a rapid progress in digital technologies and a slow adoption of innovative medical solutions into clinical practice [188,189]. It is noteworthy that in March 2023, the RelieVRx was categorized as durable medical equipment by the Centers of Medicare and Medicaid Services, facilitating reimbursement codes for a VR program for CLBP patients in the U.S. Advancing digital health educational programs in medical, pharmacy, and nursing schools will also accelerate usage rates of DTx in the future [190,191,192].

8. Conclusions

In this perspective article, we describe unique opportunities for DTx to provide personalized, multimodal treatments for CLBP. DTx intended to treat CLBP, such as RelieVRx and the Kaia Back Pain app, expand non-pharmacological treatment options beyond those currently recommended in the clinical practice guidelines described by the American College of Physicians. It is important to raise awareness among healthcare professionals and patients about DTx and their abilities to integrate non-pharmacological interventions with pharmacotherapies. For R&D communities and funding agencies, it is important to advance the development of personalized digital interventions and health education. For policymakers and healthcare systems, it is important to accelerate adoption rates for DTX for CLBP and other chronic diseases.

Author Contributions

Conceptualization, G.B.; literature review and writing—original draft preparation, A.R. and G.B.; review, editing and revisions A.R. and G.B. All authors have read and agreed to the published version of the manuscript.


This research was funded by a grant from the ALSAM Foundation.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.


G.B. acknowledges a funding award from the ALSAM Foundation to support this project. The authors would like to thank Gregory Misa, Ben Forman and Todd Maddox from AppliedVR, Inc. (Van Nuys, CA, USA), for providing images used in Figure 5, and Adam White from the University of Utah Print and Mail Services for graphic design assistance with preparation of figures. Names of companies and their commercial products described in this article are protected by registered trademarks and copyright.

Conflicts of Interest

G.B. is a founder and owner of OMNI Self-care, LLC, a health promotion and consulting company supporting diverse self-care modalities. GB is a co-inventor of two issued US patents 9,569,562 and 9,747,423 “Disease Therapy Game Technology” and patent-pending application “Multimodal Platform for Treating Epilepsy”. These patents are related to digital health technologies and are owned by the University of Utah. AKR declares the absence of a potential conflict of interest.


  1. Wu, A.; March, L.; Zheng, X.; Huang, J.; Wang, X.; Zhao, J.; Blyth, F.M.; Smith, E.; Buchbinder, R.; Hoy, D. Global low back pain prevalence and years lived with disability from 1990 to 2017: Estimates from the global burden of disease study 2017. Ann. Transl. Med. 2020, 8, 299. [Google Scholar] [CrossRef] [PubMed]
  2. Lucas, J.W.; Connor, E.M.; Bose, J. Back, lower limb, and upper limb pain among U.S. Adults, 2019. NCHS Data Brief 2021, 415, 1–8. [Google Scholar] [CrossRef]
  3. Hartvigsen, J.; Hancock, M.J.; Kongsted, A.; Louw, Q.; Ferreira, M.L.; Genevay, S.; Hoy, D.; Karppinen, J.; Pransky, G.; Sieper, J.; et al. What low back pain is and why we need to pay attention. Lancet 2018, 391, 2356–2367. [Google Scholar] [CrossRef] [PubMed]
  4. Vos, T.; Lim, S.S.; Abbafati, C.; Abbas, K.M.; Abbasi, M.; Abbasifard, M.; Abbasi-Kangevari, M.; Abbastabar, H.; Abd-Allah, F.; Abdelalim, A.; et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. Lancet 2020, 396, 1204–1222. [Google Scholar] [CrossRef]
  5. Agnus Tom, A.; Rajkumar, E.; John, R.; George, A.J. Determinants of quality of life in individuals with chronic low back pain: A systematic review. Health Psychol. Behav. Med. 2022, 10, 124–144. [Google Scholar] [CrossRef]
  6. Artner, J.; Cakir, B.; Spiekermann, J.A.; Kurz, S.; Leucht, F.; Reichel, H.; Lattig, F. Prevalence of sleep deprivation in patients with chronic neck and back pain: A retrospective evaluation of 1016 patients. J. Pain Res. 2013, 6, 1–6. [Google Scholar] [CrossRef]
  7. Bahouq, H.; Allali, F.; Rkain, H.; Hmamouchi, I.; Hajjaj-Hassouni, N. Prevalence and severity of insomnia in chronic low back pain patients. Rheumatol. Int. 2013, 33, 1277–1281. [Google Scholar] [CrossRef]
  8. Von Korff, M.; Crane, P.; Lane, M.; Miglioretti, D.L.; Simon, G.; Saunders, K.; Stang, P.; Brandenburg, N.; Kessler, R. Chronic spinal pain and physical-mental comorbidity in the united states: Results from the national comorbidity survey replication. Pain 2005, 113, 331–339. [Google Scholar] [CrossRef]
  9. Dersh, J.; Gatchel, R.J.; Mayer, T.; Polatin, P.; Temple, O.R. Prevalence of psychiatric disorders in patients with chronic disabling occupational spinal disorders. Spine 2006, 31, 1156–1162. [Google Scholar] [CrossRef]
  10. Ciaramella, A.; Poli, P. Chronic low back pain: Perception and coping with pain in the presence of psychiatric comorbidity. J. Nerv. Ment. Dis. 2015, 203, 632–640. [Google Scholar] [CrossRef]
  11. Fernandez, M.; Colodro-Conde, L.; Hartvigsen, J.; Ferreira, M.L.; Refshauge, K.M.; Pinheiro, M.B.; Ordoñana, J.R.; Ferreira, P.H. Chronic low back pain and the risk of depression or anxiety symptoms: Insights from a longitudinal twin study. Spine J. 2017, 17, 905–912. [Google Scholar] [CrossRef] [PubMed]
  12. LaRowe, L.R.; Powers, J.M.; Garey, L.; Rogers, A.H.; Zvolensky, M.J.; Ditre, J.W. Pain-related anxiety, sex, and co-use of alcohol and prescription opioids among adults with chronic low back pain. Drug Alcohol. Depend. 2020, 214, 108171. [Google Scholar] [CrossRef] [PubMed]
  13. Fullen, B.; Morlion, B.; Linton, S.J.; Roomes, D.; van Griensven, J.; Abraham, L.; Beck, C.; Wilhelm, S.; Constantinescu, C.; Perrot, S. Management of chronic low back pain and the impact on patients’ personal and professional lives: Results from an international patient survey. Pain Pract. 2022, 22, 463–477. [Google Scholar] [CrossRef] [PubMed]
  14. Nieminen, L.K.; Pyysalo, L.M.; Kankaanpää, M.J. Prognostic factors for pain chronicity in low back pain: A systematic review. Pain Rep. 2021, 6, e919. [Google Scholar] [CrossRef] [PubMed]
  15. Wong, A.Y.L.; Karppinen, J.; Samartzis, D. Low back pain in older adults: Risk factors, management options and future directions. Scoliosis Spinal Disord. 2017, 12, 14. [Google Scholar] [CrossRef] [PubMed]
  16. Wong, C.K.; Mak, R.Y.; Kwok, T.S.; Tsang, J.S.; Leung, M.Y.; Funabashi, M.; Macedo, L.G.; Dennett, L.; Wong, A.Y. Prevalence, incidence, and factors associated with non-specific chronic low back pain in community-dwelling older adults aged 60 years and older: A systematic review and meta-analysis. J. Pain 2022, 23, 509–534. [Google Scholar] [CrossRef]
  17. Ramond-Roquin, A.; Bouton, C.; Bègue, C.; Petit, A.; Roquelaure, Y.; Huez, J.F. Psychosocial risk factors, interventions, and comorbidity in patients with non-specific low back pain in primary care: Need for comprehensive and patient-centered care. Front. Med. 2015, 2, 73. [Google Scholar] [CrossRef]
  18. Stevans, J.M.; Delitto, A.; Khoja, S.S.; Patterson, C.G.; Smith, C.N.; Schneider, M.J.; Freburger, J.K.; Greco, C.M.; Freel, J.A.; Sowa, G.A.; et al. Risk factors associated with transition from acute to chronic low back pain in us patients seeking primary care. JAMA Netw. Open 2021, 4, e2037371. [Google Scholar] [CrossRef]
  19. Itz, C.J.; Geurts, J.W.; van Kleef, M.; Nelemans, P. Clinical course of non-specific low back pain: A systematic review of prospective cohort studies set in primary care. Eur. J. Pain 2013, 17, 5–15. [Google Scholar] [CrossRef]
  20. Chou, R.; Shekelle, P. Will this patient develop persistent disabling low back pain? JAMA 2010, 303, 1295–1302. [Google Scholar] [CrossRef]
  21. You, D.S.; Albu, S.; Lisenbardt, H.; Meagher, M.W. Cumulative childhood adversity as a risk factor for common chronic pain conditions in young adults. Pain Med. 2019, 20, 486–494. [Google Scholar] [CrossRef] [PubMed]
  22. Kopec, J.A.; Sayre, E.C. Stressful experiences in childhood and chronic back pain in the general population. Clin. J. Pain 2005, 21, 478–483. [Google Scholar] [CrossRef] [PubMed]
  23. Eberle, C.; Löhnert, M.; Stichling, S. Effectiveness of disease-specific mhealth apps in patients with diabetes mellitus: Scoping review. JMIR Mhealth Uhealth 2021, 9, e23477. [Google Scholar] [CrossRef] [PubMed]
  24. Firth, J.; Torous, J.; Nicholas, J.; Carney, R.; Pratap, A.; Rosenbaum, S.; Sarris, J. The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry 2017, 16, 287–298. [Google Scholar] [CrossRef] [PubMed]
  25. Horsch, C.H.; Lancee, J.; Griffioen-Both, F.; Spruit, S.; Fitrianie, S.; Neerincx, M.A.; Beun, R.J.; Brinkman, W.P. Mobile phone-delivered cognitive behavioral therapy for insomnia: A randomized waitlist controlled trial. J. Med. Internet Res. 2017, 19, e70. [Google Scholar] [CrossRef] [PubMed]
  26. Shetty, A.; Delanerolle, G.; Zeng, Y.; Shi, J.Q.; Ebrahim, R.; Pang, J.; Hapangama, D.; Sillem, M.; Shetty, S.; Shetty, B.; et al. A systematic review and meta-analysis of digital application use in clinical research in pain medicine. Front. Digit. Health 2022, 4, 850601. [Google Scholar] [CrossRef]
  27. Goudman, L.; Jansen, J.; Billot, M.; Vets, N.; De Smedt, A.; Roulaud, M.; Rigoard, P.; Moens, M. Virtual reality applications in chronic pain management: Systematic review and meta-analysis. JMIR Serious Games 2022, 10, e34402. [Google Scholar] [CrossRef]
  28. Baghaei, N.; Chitale, V.; Hlasnik, A.; Stemmet, L.; Liang, H.N.; Porter, R. Virtual reality for supporting the treatment of depression and anxiety: Scoping review. JMIR Ment. Health 2021, 8, e29681. [Google Scholar] [CrossRef]
  29. Hao, J.; Xie, H.; Harp, K.; Chen, Z.; Siu, K.C. Effects of virtual reality intervention on neural plasticity in stroke rehabilitation: A systematic review. Arch. Phys. Med. Rehabil. 2022, 103, 523–541. [Google Scholar] [CrossRef]
  30. Vinolo Gil, M.J.; Gonzalez-Medina, G.; Lucena-Anton, D.; Perez-Cabezas, V.; Ruiz-Molinero, M.D.C.; Martín-Valero, R. Augmented reality in physical therapy: Systematic review and meta-analysis. JMIR Serious Games 2021, 9, e30985. [Google Scholar] [CrossRef]
  31. Shuren, J.; Patel, B.; Gottlieb, S. Fda regulation of mobile medical apps. JAMA 2018, 320, 337–338. [Google Scholar] [CrossRef] [PubMed]
  32. Patel, N.A.; Butte, A.J. Characteristics and challenges of the clinical pipeline of digital therapeutics. NPJ Digit. Med. 2020, 3, 159. [Google Scholar] [CrossRef] [PubMed]
  33. Sverdlov, O.; van Dam, J.; Hannesdottir, K.; Thornton-Wells, T. Digital therapeutics: An integral component of digital innovation in drug development. Clin. Pharmacol. Ther. 2018, 104, 72–80. [Google Scholar] [CrossRef] [PubMed]
  34. Sverdlov, O.; van Dam, J. Digital Therapeutics: Strategic, Scientific, Developmental, and Regulatory Aspects; CRC Press: Boca Raton, FL, USA, 2022. [Google Scholar]
  35. Rubin, R. Virtual reality device is authorized to relieve back pain. JAMA 2021, 326, 2354. [Google Scholar] [CrossRef] [PubMed]
  36. Shafai, G.; Aungst, T.D. Prescription digital therapeutics: A new frontier for pharmacists and the future of treatment. J. Am. Pharm. Assoc. 2023; in press. [Google Scholar]
  37. Brezing, C.A.; Brixner, D.I. The rise of prescription digital therapeutics in behavioral health. Adv. Ther. 2022, 39, 5301–5306. [Google Scholar] [CrossRef] [PubMed]
  38. Mauck, M.C.; Aylward, A.F.; Barton, C.E.; Birckhead, B.; Carey, T.; Dalton, D.M.; Fields, A.J.; Fritz, J.; Hassett, A.L.; Hoffmeyer, A.; et al. Evidence-based interventions to treat chronic low back pain: Treatment selection for a personalized medicine approach. Pain Rep. 2022, 7, e1019. [Google Scholar] [CrossRef]
  39. Qaseem, A.; Wilt, T.J.; McLean, R.M.; Forciea, M.A.; Denberg, T.D.; Barry, M.J.; Boyd, C.; Chow, R.D.; Fitterman, N.; Harris, R.P.; et al. Noninvasive treatments for acute, subacute, and chronic low back pain: A clinical practice guideline from the american college of physicians. Ann. Intern. Med. 2017, 166, 514–530. [Google Scholar] [CrossRef]
  40. Krenn, C.; Horvath, K.; Jeitler, K.; Zipp, C.; Siebenhofer-Kroitzsch, A.; Semlitsch, T. Management of non-specific low back pain in primary care—A systematic overview of recommendations from international evidence-based guidelines. Prim. Health Care Res. Dev. 2020, 21, e64. [Google Scholar] [CrossRef]
  41. Jiang, J.; Pan, H.; Chen, H.; Song, L.; Wang, Y.; Qian, B.; Chen, P.; Fan, S.; Lin, X. Comparative efficacy of pharmacological therapies for low back pain: A bayesian network analysis. Front. Pharmacol. 2022, 13, 811962. [Google Scholar] [CrossRef]
  42. Peck, J.; Urits, I.; Peoples, S.; Foster, L.; Malla, A.; Berger, A.A.; Cornett, E.M.; Kassem, H.; Herman, J.; Kaye, A.D.; et al. A comprehensive review of over the counter treatment for chronic low back pain. Pain Ther. 2021, 10, 69–80. [Google Scholar] [CrossRef]
  43. Tagliaferri, S.D.; Miller, C.T.; Owen, P.J.; Mitchell, U.H.; Brisby, H.; Fitzgibbon, B.; Masse-Alarie, H.; Van Oosterwijck, J.; Belavy, D.L. Domains of chronic low back pain and assessing treatment effectiveness: A clinical perspective. Pain Pract. 2020, 20, 211–225. [Google Scholar] [CrossRef] [PubMed]
  44. Chou, R.; Deyo, R.; Friedly, J.; Skelly, A.; Hashimoto, R.; Weimer, M.; Fu, R.; Dana, T.; Kraegel, P.; Griffin, J.; et al. Nonpharmacologic therapies for low back pain: A systematic review for an american college of physicians clinical practice guideline. Ann. Intern. Med. 2017, 166, 493–505. [Google Scholar] [CrossRef] [PubMed]
  45. Kolber, M.R.; Ton, J.; Thomas, B.; Kirkwood, J.; Moe, S.; Dugré, N.; Chan, K.; Lindblad, A.J.; McCormack, J.; Garrison, S.; et al. Peer systematic review of randomized controlled trials: Management of chronic low back pain in primary care. Can. Fam. Physician 2021, 67, e20–e30. [Google Scholar] [CrossRef] [PubMed]
  46. Hochheim, M.; Ramm, P.; Amelung, V. The effectiveness of low-dosed outpatient biopsychosocial interventions compared to active physical interventions on pain and disability in adults with nonspecific chronic low back pain: A systematic review with meta-analysis. Pain Pract. 2023, 23, 409–436. [Google Scholar] [CrossRef] [PubMed]
  47. Barrey, C.Y.; Le Huec, J.C. Chronic low back pain: Relevance of a new classification based on the injury pattern. Orthop. Traumatol. Surg. Res. 2019, 105, 339–346. [Google Scholar] [CrossRef] [PubMed]
  48. Johnson, M.I.; Paley, C.A.; Jones, G.; Mulvey, M.R.; Wittkopf, P.G. Efficacy and safety of transcutaneous electrical nerve stimulation (tens) for acute and chronic pain in adults: A systematic review and meta-analysis of 381 studies (the meta-tens study). BMJ Open 2022, 12, e051073. [Google Scholar] [CrossRef]
  49. Traeger, A.C.; Gilbert, S.E.; Harris, I.A.; Maher, C.G. Spinal cord stimulation for low back pain. Cochrane Database Syst. Rev. 2023, 3, Cd014789. [Google Scholar] [CrossRef]
  50. Knotkova, H.; Hamani, C.; Sivanesan, E.; Le Beuffe, M.F.E.; Moon, J.Y.; Cohen, S.P.; Huntoon, M.A. Neuromodulation for chronic pain. Lancet 2021, 397, 2111–2124. [Google Scholar] [CrossRef]
  51. Guzzi, G.; Della Torre, A.; La Torre, D.; Volpentesta, G.; Stroscio, C.A.; Lavano, A.; Longhini, F. Spinal cord stimulation in chronic low back pain syndrome: Mechanisms of modulation, technical features and clinical application. Healthcare 2022, 10, 1953. [Google Scholar] [CrossRef]
  52. Schnapp, W.; Martiatu, K.; Delcroix, G.J. Basivertebral nerve ablation for the treatment of chronic low back pain: A scoping review of the literature. Pain Physician 2022, 25, E551–E562. [Google Scholar]
  53. Urits, I.; Noor, N.; Johal, A.S.; Leider, J.; Brinkman, J.; Fackler, N.; Vij, N.; An, D.; Cornett, E.M.; Kaye, A.D.; et al. Basivertebral nerve ablation for the treatment of vertebrogenic pain. Pain Ther. 2021, 10, 39–53. [Google Scholar] [CrossRef] [PubMed]
  54. Du, S.; Hu, L.; Dong, J.; Xu, G.; Chen, X.; Jin, S.; Zhang, H.; Yin, H. Self-management program for chronic low back pain: A systematic review and meta-analysis. Patient Educ. Couns. 2017, 100, 37–49. [Google Scholar] [CrossRef] [PubMed]
  55. Du, S.; Liu, W.; Cai, S.; Hu, Y.; Dong, J. The efficacy of e-health in the self-management of chronic low back pain: A meta analysis. Int. J. Nurs. Stud. 2020, 106, 103507. [Google Scholar] [CrossRef] [PubMed]
  56. Furlong, B.; Etchegary, H.; Aubrey-Bassler, K.; Swab, M.; Pike, A.; Hall, A. Patient education materials for non-specific low back pain and sciatica: A systematic review and meta-analysis. PLoS ONE 2022, 17, e0274527. [Google Scholar] [CrossRef]
  57. Darnall, B.D.; Roy, A.; Chen, A.L.; Ziadni, M.S.; Keane, R.T.; You, D.S.; Slater, K.; Poupore-King, H.; Mackey, I.; Kao, M.-C.; et al. Comparison of a single-session pain management skills intervention with a single-session health education intervention and 8 sessions of cognitive behavioral therapy in adults with chronic low back pain: A randomized clinical trial. JAMA Netw. Open 2021, 4, e2113401. [Google Scholar] [CrossRef]
  58. Hayden, J.A.; Ellis, J.; Ogilvie, R.; Malmivaara, A.; van Tulder, M.W. Exercise therapy for chronic low back pain. Cochrane Database Syst. Rev. 2021, 9, Cd009790. [Google Scholar] [CrossRef]
  59. Grooten, W.J.A.; Boström, C.; Dedering, Å.; Halvorsen, M.; Kuster, R.P.; Nilsson-Wikmar, L.; Olsson, C.B.; Rovner, G.; Tseli, E.; Rasmussen-Barr, E. Summarizing the effects of different exercise types in chronic low back pain—A systematic review of systematic reviews. BMC Musculoskelet. Disord. 2022, 23, 801. [Google Scholar] [CrossRef]
  60. Hayden, J.A.; Ellis, J.; Ogilvie, R.; Stewart, S.A.; Bagg, M.K.; Stanojevic, S.; Yamato, T.P.; Saragiotto, B.T. Some types of exercise are more effective than others in people with chronic low back pain: A network meta-analysis. J. Physiother. 2021, 67, 252–262. [Google Scholar] [CrossRef]
  61. Shi, J.; Hu, Z.Y.; Wen, Y.R.; Wang, Y.F.; Lin, Y.Y.; Zhao, H.Z.; Lin, Y.T.; Wang, Y.L. Optimal modes of mind-body exercise for treating chronic non-specific low back pain: Systematic review and network meta-analysis. Front. Neurosci. 2022, 16, 1046518. [Google Scholar] [CrossRef]
  62. Zhu, F.; Zhang, M.; Wang, D.; Hong, Q.; Zeng, C.; Chen, W. Yoga compared to non-exercise or physical therapy exercise on pain, disability, and quality of life for patients with chronic low back pain: A systematic review and meta-analysis of randomized controlled trials. PLoS ONE 2020, 15, e0238544. [Google Scholar] [CrossRef]
  63. Rubinstein, S.M.; de Zoete, A.; van Middelkoop, M.; Assendelft, W.J.J.; de Boer, M.R.; van Tulder, M.W. Benefits and harms of spinal manipulative therapy for the treatment of chronic low back pain: Systematic review and meta-analysis of randomised controlled trials. BMJ 2019, 364, l689. [Google Scholar] [CrossRef] [PubMed]
  64. Baroncini, A.; Maffulli, N.; Eschweiler, J.; Molsberger, F.; Klimuch, A.; Migliorini, F. Acupuncture in chronic aspecific low back pain: A bayesian network meta-analysis. J. Orthop. Surg. Res. 2022, 17, 319. [Google Scholar] [CrossRef] [PubMed]
  65. Asano, H.; Plonka, D.; Weeger, J. Effectiveness of acupuncture for nonspecific chronic low back pain: A systematic review and meta-analysis. Med. Acupunct. 2022, 34, 96–106. [Google Scholar] [CrossRef] [PubMed]
  66. Petrucci, G.; Papalia, G.F.; Russo, F.; Vadalà, G.; Piredda, M.; De Marinis, M.G.; Papalia, R.; Denaro, V. Psychological approaches for the integrative care of chronic low back pain: A systematic review and metanalysis. Int. J. Environ. Res. Public Health 2021, 19, 60. [Google Scholar] [CrossRef] [PubMed]
  67. Smith, S.L.; Langen, W.H. A systematic review of mindfulness practices for improving outcomes in chronic low back pain. Int. J. Yoga 2020, 13, 177–182. [Google Scholar] [CrossRef]
  68. Soundararajan, K.; Prem, V.; Kishen, T.J. The effectiveness of mindfulness-based stress reduction intervention on physical function in individuals with chronic low back pain: Systematic review and meta-analysis of randomized controlled trials. Complement. Ther. Clin. Pract. 2022, 49, 101623. [Google Scholar] [CrossRef]
  69. Yang, J.; Lo, W.L.A.; Zheng, F.; Cheng, X.; Yu, Q.; Wang, C. Evaluation of cognitive behavioral therapy on improving pain, fear avoidance, and self-efficacy in patients with chronic low back pain: A systematic review and meta-analysis. Pain Res. Manag. 2022, 2022, 4276175. [Google Scholar] [CrossRef]
  70. Williams, A.C.C.; Fisher, E.; Hearn, L.; Eccleston, C. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst. Rev. 2020, 8, Cd007407. [Google Scholar] [CrossRef]
  71. Migliorini, F.; Maffulli, N.; Eschweiler, J.; Betsch, M.; Catalano, G.; Driessen, A.; Tingart, M.; Baroncini, A. The pharmacological management of chronic lower back pain. Expert Opin. Pharmacother. 2021, 22, 109–119. [Google Scholar] [CrossRef]
  72. Price, M.R.; Cupler, Z.A.; Hawk, C.; Bednarz, E.M.; Walters, S.A.; Daniels, C.J. Systematic review of guideline-recommended medications prescribed for treatment of low back pain. Chiropr. Man. Therap. 2022, 30, 26. [Google Scholar] [CrossRef]
  73. Chou, R.; Deyo, R.; Friedly, J.; Skelly, A.; Weimer, M.; Fu, R.; Dana, T.; Kraegel, P.; Griffin, J.; Grusing, S. Systemic pharmacologic therapies for low back pain: A systematic review for an american college of physicians clinical practice guideline. Ann. Intern. Med. 2017, 166, 480–492. [Google Scholar] [CrossRef] [PubMed]
  74. Enke, O.; New, H.A.; New, C.H.; Mathieson, S.; McLachlan, A.J.; Latimer, J.; Maher, C.G.; Lin, C.C. Anticonvulsants in the treatment of low back pain and lumbar radicular pain: A systematic review and meta-analysis. CMAJ 2018, 190, E786–E793. [Google Scholar] [CrossRef] [PubMed]
  75. Shmagel, A.; Ngo, L.; Ensrud, K.; Foley, R. Prescription medication use among community-based u.S. Adults with chronic low back pain: A cross-sectional population based study. J. Pain 2018, 19, 1104–1112. [Google Scholar] [CrossRef]
  76. Gore, M.; Sadosky, A.; Stacey, B.R.; Tai, K.S.; Leslie, D. The burden of chronic low back pain: Clinical comorbidities, treatment patterns, and health care costs in usual care settings. Spine 2012, 37, E668–E677. [Google Scholar] [CrossRef] [PubMed]
  77. Mathieson, S.; Kasch, R.; Maher, C.G.; Pinto, R.Z.; McLachlan, A.J.; Koes, B.W.; Lin, C.W.C. Combination drug therapy for low back pain. Cochrane Database Syst. Rev. 2019, 2019. [Google Scholar] [CrossRef]
  78. Hirase, T.; Hirase, J.; Ling, J.; Kuo, P.H.; Hernandez, G.A.; Giwa, K.; Marco, R. Duloxetine for the treatment of chronic low back pain: A systematic review of randomized placebo-controlled trials. Cureus 2021, 13, e15169. [Google Scholar] [CrossRef]
  79. Weng, C.; Xu, J.; Wang, Q.; Lu, W.; Liu, Z. Efficacy and safety of duloxetine in osteoarthritis or chronic low back pain: A systematic review and meta-analysis. Osteoarthr. Cartil. 2020, 28, 721–734. [Google Scholar] [CrossRef]
  80. Deyo, R.A.; Von Korff, M.; Duhrkoop, D. Opioids for low back pain. BMJ 2015, 350, g6380. [Google Scholar] [CrossRef]
  81. Tucker, H.R.; Scaff, K.; McCloud, T.; Carlomagno, K.; Daly, K.; Garcia, A.; Cook, C.E. Harms and benefits of opioids for management of non-surgical acute and chronic low back pain: A systematic review. Br. J. Sport. Med. 2020, 54, 664. [Google Scholar] [CrossRef]
  82. King, N.B.; Fraser, V.; Boikos, C.; Richardson, R.; Harper, S. Determinants of increased opioid-related mortality in the united states and canada, 1990–2013: A systematic review. Am. J. Public Health 2014, 104, e32–e42. [Google Scholar] [CrossRef]
  83. Dunn, K.M.; Saunders, K.W.; Rutter, C.M.; Banta-Green, C.J.; Merrill, J.O.; Sullivan, M.D.; Weisner, C.M.; Silverberg, M.J.; Campbell, C.I.; Psaty, B.M.; et al. Opioid prescriptions for chronic pain and overdose: A cohort study. Ann. Intern. Med. 2010, 152, 85–92. [Google Scholar] [CrossRef] [PubMed]
  84. Jones, M.R.; Viswanath, O.; Peck, J.; Kaye, A.D.; Gill, J.S.; Simopoulos, T.T. A brief history of the opioid epidemic and strategies for pain medicine. Pain Ther. 2018, 7, 13–21. [Google Scholar] [CrossRef]
  85. Stoicea, N.; Costa, A.; Periel, L.; Uribe, A.; Weaver, T.; Bergese, S.D. Current perspectives on the opioid crisis in the us healthcare system: A comprehensive literature review. Medicine 2019, 98, e15425. [Google Scholar] [CrossRef] [PubMed]
  86. Fendrick, A.M.; Pan, D.E.; Johnson, G.E. Otc analgesics and drug interactions: Clinical implications. Osteopath Med. Prim. Care 2008, 2, 2. [Google Scholar] [CrossRef]
  87. Salvo, F.; Fourrier-Réglat, A.; Bazin, F.; Robinson, P.; Riera-Guardia, N.; Haag, M.; Caputi, A.P.; Moore, N.; Sturkenboom, M.C.; Pariente, A. Cardiovascular and gastrointestinal safety of nsaids: A systematic review of meta-analyses of randomized clinical trials. Clin. Pharmacol. Ther. 2011, 89, 855–866. [Google Scholar] [CrossRef]
  88. Martell, B.A.; O’Connor, P.G.; Kerns, R.D.; Becker, W.C.; Morales, K.H.; Kosten, T.R.; Fiellin, D.A. Systematic review: Opioid treatment for chronic back pain: Prevalence, efficacy, and association with addiction. Ann. Intern. Med. 2007, 146, 116–127. [Google Scholar] [CrossRef]
  89. Bonakdar, R.; Palanker, D.; Sweeney, M.M. Analysis of state insurance coverage for nonpharmacologic treatment of low back pain as recommended by the american college of physicians guidelines. Glob. Adv. Health Med. 2019, 8, 2164956119855629. [Google Scholar] [CrossRef]
  90. Hajihasani, A.; Rouhani, M.; Salavati, M.; Hedayati, R.; Kahlaee, A.H. The influence of cognitive behavioral therapy on pain, quality of life, and depression in patients receiving physical therapy for chronic low back pain: A systematic review. PM&R 2019, 11, 167–176. [Google Scholar] [CrossRef]
  91. Godfrey, E.; Wileman, V.; Holmes, M.G.; McCracken, L.M.; Norton, S.; Moss-Morris, R.; Noonan, S.; Barcellona, M.; Critchley, D. Physical therapy informed by acceptance and commitment therapy (pact) versus usual care physical therapy for adults with chronic low back pain: A randomized controlled trial. J. Pain 2020, 21, 71–81. [Google Scholar] [CrossRef]
  92. Galea Holmes, M.N.; Wileman, V.; Hassan, S.; Denning, J.; Critchley, D.; Norton, S.; McCracken, L.M.; Godfrey, E. Physiotherapy informed by acceptance and commitment therapy for chronic low back pain: A mixed-methods treatment fidelity evaluation. Br. J. Health Psychol. 2022, 27, 935–955. [Google Scholar] [CrossRef]
  93. Hildebrandt, J.; Pfingsten, M.; Franz, C.; Saur, P.; Seeger, D. Multidisciplinary treatment program for chronic low back pain, part 1. Overview. Schmerz 1996, 10, 190–203. [Google Scholar] [CrossRef] [PubMed]
  94. Nagpal, A.S.; Raghunandan, A.; Tata, F.; Kibler, D.; McGeary, D. Virtual reality in the management of chronic low back pain: A scoping review. Front. Pain Res. 2022, 3, 856935. [Google Scholar] [CrossRef] [PubMed]
  95. Ahern, M.M.; Dean, L.V.; Stoddard, C.C.; Agrawal, A.; Kim, K.; Cook, C.E.; Garcia, A.N. The effectiveness of virtual reality in patients with spinal pain: A systematic review and meta-analysis. Pain Pract. 2020, 20, 656–675. [Google Scholar] [CrossRef] [PubMed]
  96. Escriche-Escuder, A.; De-Torres, I.; Roldán-Jiménez, C.; Martín-Martín, J.; Muro-Culebras, A.; González-Sánchez, M.; Ruiz-Muñoz, M.; Mayoral-Cleries, F.; Biró, A.; Tang, W.; et al. Assessment of the quality of mobile applications (apps) for management of low back pain using the mobile app rating scale (mars). Int. J. Environ. Res. Public Health 2020, 17, 9209. [Google Scholar] [CrossRef]
  97. Vad, V.B.; Madrazo-Ibarra, A.; Estrin, D.; Pollak, J.P.; Carroll, K.M.; Vojta, D.; Vad, A.; Trapness, C. Back rx, a personalized mobile phone application for discogenic chronic low back pain: A prospective pilot study. BMC Musculoskelet. Disord. 2022, 23, 923. [Google Scholar] [CrossRef]
  98. Garcia, L.M.; Birckhead, B.J.; Krishnamurthy, P.; Mackey, I.; Sackman, J.; Salmasi, V.; Louis, R.; Maddox, T.; Darnall, B.D. Three-month follow-up results of a double-blind, randomized placebo-controlled trial of 8-week self-administered at-home behavioral skills-based virtual reality (vr) for chronic low back pain. J. Pain 2022, 23, 822–840. [Google Scholar] [CrossRef]
  99. Piette, J.D.; Newman, S.; Krein, S.L.; Marinec, N.; Chen, J.; Williams, D.A.; Edmond, S.N.; Driscoll, M.; LaChappelle, K.M.; Kerns, R.D.; et al. Patient-centered pain care using artificial intelligence and mobile health tools: A randomized comparative effectiveness trial. JAMA Intern. Med. 2022, 182, 975–983. [Google Scholar] [CrossRef]
  100. Lewkowicz, D.; Wohlbrandt, A.M.; Bottinger, E. Digital therapeutic care apps with decision-support interventions for people with low back pain in germany: Cost-effectiveness analysis. JMIR Mhealth Uhealth 2022, 10, e35042. [Google Scholar] [CrossRef]
  101. Rintala, A.; Rantalainen, R.; Kaksonen, A.; Luomajoki, H.; Kauranen, K. Mhealth apps for low back pain self-management: Scoping review. JMIR Mhealth Uhealth 2022, 10, e39682. [Google Scholar] [CrossRef]
  102. MacIntyre, E.; Sigerseth, M.; Larsen, T.F.; Fersum, K.V.; Meulders, M.; Meulders, A.; Michiels, B.; Braithwaite, F.A.; Stanton, T.R. Get your head in the game: A replicated single-case-experimental-design evaluating the effect of a novel virtual reality intervention in people with chronic low back pain. J. Pain 2023. [Google Scholar] [CrossRef]
  103. Groenveld, T.D.; Smits, M.L.M.; Knoop, J.; Kallewaard, J.W.; Staal, J.B.; de Vries, M.; van Goor, H. Effect of a behavioural therapy-based virtual reality application on quality of life in chronic low back pain. Clin. J. Pain 2023. [Google Scholar] [CrossRef] [PubMed]
  104. Lazaridou, A.; Paschali, M.; Vilsmark, E.S.; Sadora, J.; Burton, D.; Bashara, A.; Edwards, R.R. Biofeedback emg alternative therapy for chronic low back pain (the beat-pain study). Digit. Health 2023, 9, 20552076231154386. [Google Scholar] [CrossRef] [PubMed]
  105. de Vries, F.S.; van Dongen, R.T.M.; Bertens, D. Pain education and pain management skills in virtual reality in the treatment of chronic low back pain: A multiple baseline single-case experimental design. Behav. Res. Ther. 2023, 162, 104257. [Google Scholar] [CrossRef] [PubMed]
  106. Rothbaum, A.O.; Tannenbaum, L.R.; Zimand, E.; Rothbaum, B.O. A pilot randomized controlled trial of virtual reality delivered relaxation for chronic low back pain. Virtual Real. 2023. [Google Scholar] [CrossRef]
  107. Rughani, G.; Nilsen, T.I.L.; Wood, K.; Mair, F.S.; Hartvigsen, J.; Mork, P.J.; Nicholl, B.I. The selfback artificial intelligence-based smartphone app can improve low back pain outcome even in patients with high levels of depression or stress. Eur. J. Pain 2023, 27, 568–579. [Google Scholar] [CrossRef] [PubMed]
  108. Sandal, L.F.; Bach, K.; Øverås, C.K.; Svendsen, M.J.; Dalager, T.; Jensen, J.S.D.; Kongsvold, A.; Nordstoga, A.L.; Bardal, E.M.; Ashikhmin, I.; et al. Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: A selfback randomized clinical trial. JAMA Intern. Med. 2021, 181, 1288–1296. [Google Scholar] [CrossRef]
  109. Vad, V.B.; Bhat, A.L.; Tarabichi, Y. The role of the back rx exercise program in diskogenic low back pain: A prospective randomized trial. Arch. Phys. Med. Rehabil. 2007, 88, 577–582. [Google Scholar] [CrossRef]
  110. Browne, J.D.; Vaninetti, M.; Giard, D.; Kostas, K.; Dave, A. An evaluation of a mobile app for chronic low back pain management: Prospective pilot study. JMIR Form. Res. 2022, 6, e40869. [Google Scholar] [CrossRef]
  111. Shebib, R.; Bailey, J.F.; Smittenaar, P.; Perez, D.A.; Mecklenburg, G.; Hunter, S. Randomized controlled trial of a 12-week digital care program in improving low back pain. NPJ Digit. Med. 2019, 2, 1. [Google Scholar] [CrossRef]
  112. Bailey, J.F.; Agarwal, V.; Zheng, P.; Smuck, M.; Fredericson, M.; Kennedy, D.J.; Krauss, J. Digital care for chronic musculoskeletal pain: 10,000 participant longitudinal cohort study. J. Med. Internet Res. 2020, 22, e18250. [Google Scholar] [CrossRef]
  113. Fatoye, F.; Gebrye, T.; Mbada, C.E.; Fatoye, C.T.; Makinde, M.O.; Ayomide, S.; Ige, B. Cost effectiveness of virtual reality game compared to clinic based mckenzie extension therapy for chronic non-specific low back pain. Br. J. Pain 2022, 16, 601–609. [Google Scholar] [CrossRef]
  114. Huber, S.; Priebe, J.A.; Baumann, K.M.; Plidschun, A.; Schiessl, C.; Tolle, T.R. Treatment of low back pain with a digital multidisciplinary pain treatment app: Short-term results. JMIR Rehabil. Assist. Technol. 2017, 4, e11. [Google Scholar] [CrossRef] [PubMed]
  115. Toelle, T.R.; Utpadel-Fischler, D.A.; Haas, K.K.; Priebe, J.A. App-based multidisciplinary back pain treatment versus combined physiotherapy plus online education: A randomized controlled trial. NPJ Digit. Med. 2019, 2, 34. [Google Scholar] [CrossRef]
  116. Priebe, J.A.; Haas, K.K.; Sanchez, L.F.M.; Schoefmann, K.; Utpadel-Fischler, D.A.; Stockert, P.; Thoma, R.; Schiessl, C.; Kerkemeyer, L.; Amelung, V.; et al. Digital treatment of back pain versus standard of care: The cluster-randomized controlled trial, rise-up. J. Pain Res. 2020, 13, 1823–1838. [Google Scholar] [CrossRef] [PubMed]
  117. Darnall, B.D.; Krishnamurthy, P.; Tsuei, J.; Minor, J.D. Self-administered skills-based virtual reality intervention for chronic pain: Randomized controlled pilot study. JMIR Form. Res. 2020, 4, e17293. [Google Scholar] [CrossRef]
  118. Garcia, L.M.; Birckhead, B.J.; Krishnamurthy, P.; Sackman, J.; Mackey, I.G.; Louis, R.G.; Salmasi, V.; Maddox, T.; Darnall, B.D. An 8-week self-administered at-home behavioral skills-based virtual reality program for chronic low back pain: Double-blind, randomized, placebo-controlled trial conducted during COVID-19. J. Med. Internet Res. 2021, 23, e26292. [Google Scholar] [CrossRef] [PubMed]
  119. Garcia, L.; Birckhead, B.; Krishnamurthy, P.; Mackey, I.; Sackman, J.; Salmasi, V.; Louis, R.; Castro, C.; Maddox, R.; Maddox, T.; et al. Durability of the treatment effects of an 8-week self-administered home-based virtual reality program for chronic low back pain: 6-month follow-up study of a randomized clinical trial. J. Med. Internet Res. 2022, 24, e37480. [Google Scholar] [CrossRef]
  120. Maddox, T.; Garcia, H.; Ffrench, K.; Maddox, R.; Garcia, L.; Krishnamurthy, P.; Okhotin, D.; Sparks, C.; Oldstone, L.; Birckhead, B.; et al. In-home virtual reality program for chronic low back pain: Durability of a randomized, placebo-controlled clinical trial to 18 months post-treatment. Reg. Anesth. Pain Med. 2022. [Google Scholar] [CrossRef]
  121. Birckhead, B.; Eberlein, S.; Alvarez, G.; Gale, R.; Dupuy, T.; Makaroff, K.; Fuller, G.; Liu, X.; Yu, K.S.; Black, J.T.; et al. Home-based virtual reality for chronic pain: Protocol for an nih-supported randomised-controlled trial. BMJ Open 2021, 11, e050545. [Google Scholar] [CrossRef]
  122. Heymans, M.W.; van Tulder, M.W.; Esmail, R.; Bombardier, C.; Koes, B.W. Back schools for nonspecific low back pain: A systematic review within the framework of the cochrane collaboration back review group. Spine 2005, 30, 2153–2163. [Google Scholar] [CrossRef]
  123. Gül, H.; Erel, S.; Toraman, N.F. Physiotherapy combined with therapeutic neuroscience education versus physiotherapy alone for patients with chronic low back pain: A pilot, randomized-controlled trial. Turk. J. Phys. Med. Rehabil. 2021, 67, 283–290. [Google Scholar] [CrossRef] [PubMed]
  124. Alcon, C.A.; Wang-Price, S. Non-invasive brain stimulation and pain neuroscience education in the cognitive-affective treatment of chronic low back pain: Evidence and future directions. Front. Pain Res. 2022, 3, 959609. [Google Scholar] [CrossRef] [PubMed]
  125. Martins, C.; Sayegh, S.; Faundez, A.; Fourchet, F.; Bothorel, H. Effectiveness of a group-based rehabilitation program combining education with multimodal exercises in the treatment of patients with nonspecific chronic low back pain: A retrospective uncontrolled study. Biology 2022, 11, 1508. [Google Scholar] [CrossRef] [PubMed]
  126. Bulaj, G.; Clark, J.; Ebrahimi, M.; Bald, E. From precision metapharmacology to patient empowerment: Delivery of self-care practices for epilepsy, pain, depression and cancer using digital health technologies. Front. Pharmacol. 2021, 12, 612602. [Google Scholar] [CrossRef] [PubMed]
  127. Bulaj, G. Combining non-pharmacological treatments with pharmacotherapies for neurological disorders: A unique interface of the brain, drug-device, and intellectual property. Front. Neurol. 2014, 5, 126. [Google Scholar] [CrossRef]
  128. Tabi, K.; Randhawa, A.S.; Choi, F.; Mithani, Z.; Albers, F.; Schnieder, M.; Nikoo, M.; Vigo, D.; Jang, K.; Demlova, R.; et al. Mobile apps for medication management: Review and analysis. JMIR Mhealth Uhealth 2019, 7, e13608. [Google Scholar] [CrossRef]
  129. Vermeulen, R.; Schymanski, E.L.; Barabási, A.L.; Miller, G.W. The exposome and health: Where chemistry meets biology. Science 2020, 367, 392–396. [Google Scholar] [CrossRef]
  130. Junqueira, D.R.; Ferreira, M.L.; Refshauge, K.; Maher, C.G.; Hopper, J.L.; Hancock, M.; Carvalho, M.G.; Ferreira, P.H. Heritability and lifestyle factors in chronic low back pain: Results of the australian twin low back pain study (the autback study). Eur. J. Pain 2014, 18, 1410–1418. [Google Scholar] [CrossRef]
  131. Ferreira, P.H.; Beckenkamp, P.; Maher, C.G.; Hopper, J.L.; Ferreira, M.L. Nature or nurture in low back pain? Results of a systematic review of studies based on twin samples. Eur. J. Pain 2013, 17, 957–971. [Google Scholar] [CrossRef]
  132. Van Looveren, E.; Bilterys, T.; Munneke, W.; Cagnie, B.; Ickmans, K.; Mairesse, O.; Malfliet, A.; De Baets, L.; Nijs, J.; Goubert, D.; et al. The association between sleep and chronic spinal pain: A systematic review from the last decade. J. Clin. Med. 2021, 10, 3836. [Google Scholar] [CrossRef]
  133. Buscemi, V.; Chang, W.-J.; Liston, M.B.; McAuley, J.H.; Schabrun, S.M. The role of perceived stress and life stressors in the development of chronic musculoskeletal pain disorders: A systematic review. J. Pain 2019, 20, 1127–1139. [Google Scholar] [CrossRef] [PubMed]
  134. Tsuboi, Y.; Ueda, Y.; Naruse, F.; Ono, R. The association between perceived stress and low back pain among eldercare workers in japan. J. Occup. Environ. Med. 2017, 59, 765–767. [Google Scholar] [CrossRef] [PubMed]
  135. Choi, S.; Nah, S.; Jang, H.D.; Moon, J.E.; Han, S. Association between chronic low back pain and degree of stress: A nationwide cross-sectional study. Sci. Rep. 2021, 11, 14549. [Google Scholar] [CrossRef]
  136. Peake, J.M.; Kerr, G.; Sullivan, J.P. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Front. Physiol. 2018, 9, 743. [Google Scholar] [CrossRef] [PubMed]
  137. Wang, L.; Miller, L.C. Just-in-the-moment adaptive interventions (jitai): A meta-analytical review. Health Commun. 2019, 35, 1531–1544. [Google Scholar] [CrossRef]
  138. Nahum-Shani, I.; Smith, S.N.; Spring, B.J.; Collins, L.M.; Witkiewitz, K.; Tewari, A.; Murphy, S.A. Just-in-time adaptive interventions (jitais) in mobile health: Key components and design principles for ongoing health behavior support. Ann. Behav. Med. 2018, 52, 446–462. [Google Scholar] [CrossRef]
  139. Thomas, J.G.; Bond, D.S. Behavioral response to a just-in-time adaptive intervention (jitai) to reduce sedentary behavior in obese adults: Implications for jitai optimization. Health Psychol. 2015, 34, 1261–1267. [Google Scholar] [CrossRef] [PubMed]
  140. Müller, A.M.; Blandford, A.; Yardley, L. The conceptualization of a just-in-time adaptive intervention (jitai) for the reduction of sedentary behavior in older adults. Mhealth 2017, 3, 37. [Google Scholar] [CrossRef]
  141. Hardeman, W.; Houghton, J.; Lane, K.; Jones, A.; Naughton, F. A systematic review of just-in-time adaptive interventions (jitais) to promote physical activity. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 31. [Google Scholar] [CrossRef]
  142. Mair, J.L.; Hayes, L.D.; Campbell, A.K.; Buchan, D.S.; Easton, C.; Sculthorpe, N. A personalized smartphone-delivered just-in-time adaptive intervention (jitabug) to increase physical activity in older adults: Mixed methods feasibility study. JMIR Form. Res. 2022, 6, e34662. [Google Scholar] [CrossRef]
  143. Teepe, G.W.; Da Fonseca, A.; Kleim, B.; Jacobson, N.C.; Sanabria, A.S.; Car, L.T.; Fleisch, E.; Kowatsch, T. Just-in-time adaptive mechanisms of popular mobile apps for individuals with depression: Systematic app search and literature review. J. Med. Internet Res. 2021, 23, e29412. [Google Scholar] [CrossRef] [PubMed]
  144. Tidmarsh, L.V.; Harrison, R.; Ravindran, D.; Matthews, S.L.; Finlay, K.A. The influence of adverse childhood experiences in pain management: Mechanisms, processes, and trauma-informed care. Front. Pain Res. 2022, 3, 923866. [Google Scholar] [CrossRef] [PubMed]
  145. Sachs-Ericsson, N.J.; Sheffler, J.L.; Stanley, I.H.; Piazza, J.R.; Preacher, K.J. When emotional pain becomes physical: Adverse childhood experiences, pain, and the role of mood and anxiety disorders. J. Clin. Psychol. 2017, 73, 1403–1428. [Google Scholar] [CrossRef] [PubMed]
  146. Lunde, S.J.; Vuust, P.; Garza-Villarreal, E.A.; Vase, L. Music-induced analgesia: How does music relieve pain? Pain 2018. [Google Scholar] [CrossRef] [PubMed]
  147. Garza-Villarreal, E.A.; Pando, V.; Vuust, P.; Parsons, C. Music-induced analgesia in chronic pain conditions: A systematic review and meta-analysis. Pain Physician 2017, 20, 597–610. [Google Scholar] [CrossRef]
  148. Chai, P.R.; Carreiro, S.; Ranney, M.L.; Karanam, K.; Ahtisaari, M.; Edwards, R.; Schreiber, K.L.; Ben-Ghaly, L.; Erickson, T.B.; Boyer, E.W. Music as an adjunct to opioid-based analgesia. J. Med. Toxicol. Off. J. Am. Coll. Med. Toxicol. 2017, 13, 249–254. [Google Scholar] [CrossRef]
  149. Sihvonen, A.J.; Pitkäniemi, A.; Särkämö, T.; Soinila, S. Isn’t there room for music in chronic pain management? J. Pain 2022, 23, 1143–1150. [Google Scholar] [CrossRef]
  150. Basiński, K.; Zdun-Ryżewska, A.; Greenberg, D.M.; Majkowicz, M. Preferred musical attribute dimensions underlie individual differences in music-induced analgesia. Sci. Rep. 2021, 11, 8622. [Google Scholar] [CrossRef]
  151. Lee, J.H. The effects of music on pain: A meta-analysis. J. Music Ther. 2016, 53, 430–477. [Google Scholar] [CrossRef]
  152. Huntsman, D.D.; Bulaj, G. Healthy dwelling: Design of biophilic interior environments fostering self-care practices for people living with migraines, chronic pain, and depression. Int. J. Environ. Res. Public Health 2022, 19, 2248. [Google Scholar] [CrossRef]
  153. van der Heijden, M.J.; Araghi, S.O.; van Dijk, M.; Jeekel, J.; Hunink, M.G. The effects of perioperative music interventions in pediatric surgery: A systematic review and meta-analysis of randomized controlled trials. PLoS ONE 2015, 10, e0133608. [Google Scholar] [CrossRef] [PubMed]
  154. Garza-Villarreal, E.A.; Jiang, Z.; Vuust, P.; Alcauter, S.; Vase, L.; Pasaye, E.H.; Cavazos-Rodriguez, R.; Brattico, E.; Jensen, T.S.; Barrios, F.A. Music reduces pain and increases resting state fmri bold signal amplitude in the left angular gyrus in fibromyalgia patients. Front. Psychol. 2015, 6, 1051. [Google Scholar] [CrossRef] [PubMed]
  155. Garza-Villarreal, E.A.; Wilson, A.D.; Vase, L.; Brattico, E.; Barrios, F.A.; Jensen, T.S.; Romero-Romo, J.I.; Vuust, P. Music reduces pain and increases functional mobility in fibromyalgia. Front. Psychol. 2014, 5, 90. [Google Scholar] [CrossRef] [PubMed]
  156. Hsu, H.F.; Chen, K.M.; Belcastro, F. The effect of music interventions on chronic pain experienced by older adults: A systematic review. J. Nurs. Sch. 2022, 54, 64–71. [Google Scholar] [CrossRef] [PubMed]
  157. Chai, P.R.; Schreiber, K.L.; Taylor, S.W.; Jambaulikar, G.D.; Kikut, A.; Hasdianda, M.A.; Boyer, E.W. The feasibility and acceptability of a smartphone-based music intervention for acute pain. Proc. Annu. Hawaii Int. Conf. Syst. Sci. 2019, 2019, 3917–3925. [Google Scholar]
  158. Bernatzky, G.; Presch, M.; Anderson, M.; Panksepp, J. Emotional foundations of music as a non-pharmacological pain management tool in modern medicine. Neurosci. Biobehav. Rev. 2011, 35, 1989–1999. [Google Scholar] [CrossRef]
  159. Fernandez-Sotos, A.; Fernandez-Caballero, A.; Latorre, J.M. Influence of tempo and rhythmic unit in musical emotion regulation. Front. Comput. Neurosci. 2016, 10, 80. [Google Scholar] [CrossRef]
  160. Koelsch, S. Music-evoked emotions: Principles, brain correlates, and implications for therapy. Ann. N. Y. Acad. Sci. 2015, 1337, 193–201. [Google Scholar] [CrossRef]
  161. Villarreal, E.A.; Brattico, E.; Vase, L.; Ostergaard, L.; Vuust, P. Superior analgesic effect of an active distraction versus pleasant unfamiliar sounds and music: The influence of emotion and cognitive style. PLoS ONE 2012, 7, e29397. [Google Scholar] [CrossRef]
  162. Howlin, C.; Rooney, B. The cognitive mechanisms in music listening interventions for pain: A scoping review. J. Music Ther. 2020, 57, 127–167. [Google Scholar] [CrossRef]
  163. Chanda, M.L.; Levitin, D.J. The neurochemistry of music. Trends Cogn. Sci. 2013, 17, 179–193. [Google Scholar] [CrossRef] [PubMed]
  164. Fancourt, D.; Ockelford, A.; Belai, A. The psychoneuroimmunological effects of music: A systematic review and a new model. Brain Behav. Immun. 2014, 36, 15–26. [Google Scholar] [CrossRef] [PubMed]
  165. Serafini, R.A.; Pryce, K.D.; Zachariou, V. The mesolimbic dopamine system in chronic pain and associated affective comorbidities. Biol. Psychiatry 2020, 87, 64–73. [Google Scholar] [CrossRef] [PubMed]
  166. Salimpoor, V.N.; Benovoy, M.; Larcher, K.; Dagher, A.; Zatorre, R.J. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nat. Neurosci. 2011, 14, 257–262. [Google Scholar] [CrossRef] [PubMed]
  167. Mitsi, V.; Zachariou, V. Modulation of pain, nociception, and analgesia by the brain reward center. Neuroscience 2016, 338, 81–92. [Google Scholar] [CrossRef] [PubMed]
  168. Afra, P.; Bruggers, C.S.; Sweney, M.; Fagatele, L.; Alavi, F.; Greenwald, M.; Huntsman, M.; Nguyen, K.; Jones, J.K.; Shantz, D.; et al. Mobile software as a medical device (samd) for the treatment of epilepsy: Development of digital therapeutics comprising behavioral and music-based interventions for neurological disorders. Front. Hum. Neurosci. 2018, 12, 171. [Google Scholar] [CrossRef]
  169. Magee, M.; Gholamrezaei, A.; McNeilage, A.G.; Dwyer, L.; Sim, A.; Ferreira, M.; Darnall, B.; Glare, P.; Ashton-James, C. Evaluating acceptability and feasibility of a mobile health intervention to improve self-efficacy in prescription opioid tapering in patients with chronic pain: Protocol for a pilot randomised, single-blind, controlled trial. BMJ Open 2022, 12, e057174. [Google Scholar] [CrossRef]
  170. Magee, M.R.; McNeilage, A.G.; Avery, N.; Glare, P.; Ashton-James, C.E. Mhealth interventions to support prescription opioid tapering in patients with chronic pain: Qualitative study of patients’ perspectives. JMIR Form. Res. 2021, 5, e25969. [Google Scholar] [CrossRef]
  171. Magee, M.R.; Gholamrezaei, A.; McNeilage, A.G.; Sim, A.; Dwyer, L.; Ferreira, M.L.; Darnall, B.D.; Glare, P.; Ashton-James, C.E. A digital video and text messaging intervention to support people with chronic pain during opioid tapering: Content development using co-design. JMIR Form. Res. 2022, 6, e40507. [Google Scholar] [CrossRef]
  172. Maricich, Y.A.; Xiong, X.; Gerwien, R.; Kuo, A.; Velez, F.; Imbert, B.; Boyer, K.; Luderer, H.F.; Braun, S.; Williams, K. Real-world evidence for a prescription digital therapeutic to treat opioid use disorder. Curr. Med. Res. Opin. 2021, 37, 175–183. [Google Scholar] [CrossRef]
  173. Velez, F.F.; Luderer, H.F.; Gerwien, R.; Parcher, B.; Mezzio, D.; Malone, D.C. Evaluation of the cost-utility of a prescription digital therapeutic for the treatment of opioid use disorder. Postgrad. Med. 2021, 133, 421–427. [Google Scholar] [CrossRef] [PubMed]
  174. Maricich, Y.A.; Gerwien, R.; Kuo, A.; Malone, D.C.; Velez, F.F. Real-world use and clinical outcomes after 24 weeks of treatment with a prescription digital therapeutic for opioid use disorder. Hosp. Pract. 2021, 49, 348–355. [Google Scholar] [CrossRef] [PubMed]
  175. Velez, F.F.; Huang, D.; Mody, L.; Malone, D.C. Five-year budget impact of a prescription digital therapeutic for patients with opioid use disorder. Expert Rev. Pharm. Outcomes Res. 2022, 22, 599–607. [Google Scholar] [CrossRef] [PubMed]
  176. Velez, F.F.; Anastassopoulos, K.P.; Colman, S.; Shah, N.; Kauffman, L.; Murphy, S.M.; Ruetsch, C.; Maricich, Y.A. Reduced healthcare resource utilization in patients with opioid use disorder in the 12 months after initiation of a prescription digital therapeutic. Adv. Ther. 2022, 39, 4131–4145. [Google Scholar] [CrossRef]
  177. Hong, M.; Topete, M.; Yang, M.; Bailey, J.F. Effects of a digital musculoskeletal acute care program on chronic pain prevention: An observational study with nonparticipant comparison group. J. Pain Res. 2022, 15, 3605–3613. [Google Scholar] [CrossRef]
  178. Elbers, S.; Pool, J.; Wittink, H.; Köke, A.; Scheffer, E.; Smeets, R. Mobile health app (agrippa) to prevent relapse after successful interdisciplinary treatment for patients with chronic pain: Protocol for a randomized controlled trial. JMIR Res. Protoc. 2020, 9, e18632. [Google Scholar] [CrossRef]
  179. Fledderus, M.; Schreurs, K.M.; Bohlmeijer, E.T.; Vollenbroek-Hutten, M.M. Development and pilot evaluation of an online relapse-prevention program based on acceptance and commitment therapy for chronic pain patients. JMIR Hum. Factors 2015, 2, e1. [Google Scholar] [CrossRef]
  180. Shim, J.G.; Ryu, K.H.; Cho, E.A.; Ahn, J.H.; Kim, H.K.; Lee, Y.J.; Lee, S.H. Machine learning approaches to predict chronic lower back pain in people aged over 50 years. Medicina 2021, 57, 1230. [Google Scholar] [CrossRef]
  181. Hernandez-Lucas, P.; Leirós-Rodríguez, R.; Lopez-Barreiro, J.; García-Soidán, J.L. Is the combination of exercise therapy and health education more effective than usual medical care in the prevention of non-specific back pain? A systematic review with meta-analysis. Ann. Med. 2022, 54, 3107–3116. [Google Scholar] [CrossRef]
  182. Lee, Y.H.; Huang, L.H.; Chen, S.H.; Shao, J.H.; Lai, C.H.; Yang, N.P. Effects of mobile application program (app)-assisted health education on preventive behaviors and cancer literacy among women with cervical intraepithelial neoplasia. Int. J. Environ. Res. Public Health 2021, 18, 11603. [Google Scholar] [CrossRef]
  183. Westerlinck, P.; Coucke, P. Review of interactive digital solutions improving health literacy of personal cancer risks in the general public. Int. J. Med. Inform. 2021, 154, 104564. [Google Scholar] [CrossRef] [PubMed]
  184. Emerson, M.R.; Buckland, S.; Lawlor, M.A.; Dinkel, D.; Johnson, D.J.; Mickles, M.S.; Fok, L.; Watanabe-Galloway, S. Addressing and evaluating health literacy in mhealth: A scoping review. Mhealth 2022, 8, 33. [Google Scholar] [CrossRef] [PubMed]
  185. Mantovani, A.; Leopaldi, C.; Nighswander, C.M.; Di Bidino, R. Access and reimbursement pathways for digital health solutions and in vitro diagnostic devices: Current scenario and challenges. Front. Med. Technol. 2023, 5, 1101476. [Google Scholar] [CrossRef]
  186. Gerke, S.; Stern, A.D.; Minssen, T. Germany’s digital health reforms in the COVID-19 era: Lessons and opportunities for other countries. NPJ Digit. Med. 2020, 3, 94. [Google Scholar] [CrossRef] [PubMed]
  187. Prodan, A.; Deimel, L.; Ahlqvist, J.; Birov, S.; Thiel, R.; Toivanen, M.; Kolitsi, Z.; Kalra, D. Success factors for scaling up the adoption of digital therapeutics towards the realization of p5 medicine. Front. Med. 2022, 9, 854665. [Google Scholar] [CrossRef] [PubMed]
  188. Morris, Z.S.; Wooding, S.; Grant, J. The answer is 17 years, what is the question: Understanding time lags in translational research. J. R. Soc. Med. 2011, 104, 510–520. [Google Scholar] [CrossRef]
  189. Hanney, S.R.; Castle-Clarke, S.; Grant, J.; Guthrie, S.; Henshall, C.; Mestre-Ferrandiz, J.; Pistollato, M.; Pollitt, A.; Sussex, J.; Wooding, S. How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy, and practice. Health Res. Policy Syst. 2015, 13, 1. [Google Scholar] [CrossRef]
  190. Khurana, M.P.; Raaschou-Pedersen, D.E.; Kurtzhals, J.; Bardram, J.E.; Ostrowski, S.R.; Bundgaard, J.S. Digital health competencies in medical school education: A scoping review and delphi method study. BMC Med. Educ. 2022, 22, 129. [Google Scholar] [CrossRef]
  191. Aungst, T.D.; Patel, R. Integrating digital health into the curriculum-considerations on the current landscape and future developments. J. Med. Educ. Curric. Dev. 2020, 7, 2382120519901275. [Google Scholar] [CrossRef]
  192. Kleib, M.; Arnaert, A.; Nagle, L.M.; Ali, S.; Idrees, S.; Kennedy, M.; da Costa, D. Digital health education and training for undergraduate and graduate nursing students: A scoping review protocol. JBI Evid. Synth. 2022. [Google Scholar] [CrossRef]
Figure 1. Root causes and risk factors for CLBP.
Figure 1. Root causes and risk factors for CLBP.
Healthcare 11 01469 g001
Figure 2. The diversity of non-pharmacological and pharmacological treatments for CLBP.
Figure 2. The diversity of non-pharmacological and pharmacological treatments for CLBP.
Healthcare 11 01469 g002
Figure 3. The clinical practice guidelines from the American College of Physicians recommending treatments for CLBP.
Figure 3. The clinical practice guidelines from the American College of Physicians recommending treatments for CLBP.
Healthcare 11 01469 g003
Figure 4. RelieVRx is a prescription digital therapeutic (PDT) for CLBP, delivering multimodal therapeutic content via VR-based technology.
Figure 4. RelieVRx is a prescription digital therapeutic (PDT) for CLBP, delivering multimodal therapeutic content via VR-based technology.
Healthcare 11 01469 g004
Figure 5. The digital therapy content of RelieVRx. Images show weekly themes and screenshots of what CLBP patients see and experience using the RelieVRx system. Images are courtesy of AppliedVR and are available on their website (
Figure 5. The digital therapy content of RelieVRx. Images show weekly themes and screenshots of what CLBP patients see and experience using the RelieVRx system. Images are courtesy of AppliedVR and are available on their website (
Healthcare 11 01469 g005
Figure 6. Diverse combinations of multimodal therapies enable personalized medicine for CLBP.
Figure 6. Diverse combinations of multimodal therapies enable personalized medicine for CLBP.
Healthcare 11 01469 g006
Figure 7. An example of the multimodal treatment of CLBP comprising analgesics, DTx and physical therapy. The duration of DTx and PT treatments can extend beyond the initial use of analgesics to reduce pain.
Figure 7. An example of the multimodal treatment of CLBP comprising analgesics, DTx and physical therapy. The duration of DTx and PT treatments can extend beyond the initial use of analgesics to reduce pain.
Healthcare 11 01469 g007
Figure 8. Exposome-responsive digital therapeutics delivering just-in-time adaptive interventions (JITAI) adjusted for a patient’s real-time needs and circumstances.
Figure 8. Exposome-responsive digital therapeutics delivering just-in-time adaptive interventions (JITAI) adjusted for a patient’s real-time needs and circumstances.
Healthcare 11 01469 g008
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rohaj, A.; Bulaj, G. Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health. Healthcare 2023, 11, 1469.

AMA Style

Rohaj A, Bulaj G. Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health. Healthcare. 2023; 11(10):1469.

Chicago/Turabian Style

Rohaj, Aarushi, and Grzegorz Bulaj. 2023. "Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health" Healthcare 11, no. 10: 1469.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop