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Chronic pain is a multi-dimensional, distressing experience that can occur with or without tissue damage and persists over extended periods of time (at least 3 months) [1]. This disease is a public health problem worldwide [5]. Specifically, it is currently estimated that chronic pain affects between 20 and 30% of the adult population worldwide [3–8] and up to 70% of adults older than 42 years [9]. As a consequence, chronic pain has become the most expensive disease in the world [10] and accounts for up to 2% of the European Gross Domestic Product annually [11]. Additionally, with the age distribution shifting towards the elderly [12], the economic burden of this disease is likely to increase in the coming years.
In this scenario, various efforts have been made to improve chronic pain treatments in recent decades. However, existing reviews only provide modest support for the most popular chronic pain treatments, including medical interventions, physical therapy, psychological treatment, or a combination of these [13–15]. Several factors, such as patient characteristics, unexplored genetic or biomechanical mechanism factors, or the experience of therapists, could help explain the modest effectiveness of existing treatments for chronic pain. However, some authors have suggested that inadequate monitoring of patient progress and response to treatment is likely to be, at least in part, responsible for the limited impact of current therapies for chronic pain [16,17].
Due to its chronic nature, management of chronic pain often requires prolonged and regular contact with the health care system [18]. In this sense, it is important to note that a move towards self-management will be needed, rather than relying on the care of health professionals [19]. However, in doing so monitoring could still remain a challenge because limited resources and existing waiting lists in public health settings limit the quality of patient monitoring in chronic pain settings [20,21]. For example, pain treatment follow-up is still predominantly discreet during on-site appointments. This is problematic because pain-related variables, such as pain intensity, mood, and fatigue can vary across and within days [22,23], even in patients with chronic pain such as osteoarthritis [24], rheumatic diseases [25], multiple sclerosis [23], and fibromyalgia [26].
The aforementioned variability of symptoms in patients with chronic pain means that a single measure may not be representative of the entire experience. Furthermore, retrospective pain assessment leads to recall bias and reduces accuracy [27]. This could be minimized with paper diaries. However, research has shown that the use of paper diaries is problematic due to participant non-compliance (missing data and back-filling) and errors associated with manual data entry [28,29]. Additionally, neither episodic on-site assessment nor paper diaries permit timely communication and response to undesired events experienced by the patient during the course of treatment [20].
Another problem related to the current model of care in chronic pain refers to decision-making in the face of unwanted events. Specifically, the current approach to care requires patients to judge when an undesired event is problematic and what is the preferred action to take in the face of that event [30–32]. This approach is problematic, as some patients may tolerate serious or even urgent problems (e.g., tachycardia, severe drowsiness, persistent vomiting, diarrhea, or urine retention) for too long, while others may seek care for symptoms that are less urgent or not problematic (e.g., very mild or short-term). For patients with chronic pain, and added problem is that patients combine appointments with their general practitioner, emergency services, and specialized pain clinics for the treatment of their pain and related symptoms [33]. This practice is likely to be problematic, as the alternation of different specialized and non-specialized services could lead to unpredictable treatment plans in response to unwanted events.
Telemonitoring with episodic phone calls, which is becoming an increasingly common practice, also only partially solves the aforementioned problems. First, because undesired events (e.g., side medication effects or decreased treatment effectiveness) can occur at different treatment stages [34], which means that control calls will often occur before or long after unwanted events occur. Additionally, because such phone calls require the active participation of a healthcare professional, which makes this procedure resource-consuming and ineffective [18]. Taking all the previous into account, it has been argued that our societies will not be able to sustain the current model of care for this condition [20,21,35,36], especially due to the ageing of the population and the dramatic increase in the prevalence of this disease in the elderly [9]. Indeed, this appears to be true now more than ever as a result of the COVID-19 crisis, imposed restrictions on circulation, and saturation of health systems [37]. Our team has already achieved some important goals in the design, development, and implementation of a new tool, namely a smartphone app called Pain Monitor, which facilitates regular assessment of patient outcomes using mobile technology and minimal healthcare professional involvement in assessment. The app, which has been developed by a multidisciplinary team including physicians, nurses, psychologists, and engineers following guidelines on pain research and eHealth [38–41], was found to have valid content (i.e., comparable to well-established paper-and-pencil measures) and high patient acceptability (i.e., response rates greater than 70% for daily responses over a period of one month) [42]. While important milestones have been achieved, the utility of the app in terms of increased treatment effectiveness (e.g., further reduction in pain severity and associated symptoms) remains unclear.
In fact, although it has been argued that mobile technology (mHealth), especially the use of smartphone apps, facilitates this paradigm shift towards telemonitoring in chronic pain care, reviews on this topic have evidenced that randomized controlled trials (RCT) evaluating the usefulness of these tools are lacking in the chronic pain literature [39,41,43–45]. Therefore, the current investigation constitutes an important step forward into the literature on this important public health condition. In particular, the goal of the present study is to test whether incorporating the Pain Monitor app into routine medical treatment results in better pain-related outcomes in patients with chronic musculoskeletal pain. As a secondary objective, we want to investigate the opinion of healthcare professionals on the app (the patients’ opinion was already evaluated in the 112 validation study [42]), which is key for future implementation [46]. This RCT will have three conditions, that is usual treatment (TAU) with the usual assessment method (episodic, combined on-site and by phone call), TAU with app-based assessment without clinical alarms, and TAU with app-based assessment with clinical alarms. Eligible patients will be adults with chronic musculoskeletal pain, the most common chronic pain condition, which includes pain in the bones, muscles, nerves, ligaments, or tendons [47]. As recommended in the guidelines, we will focus not only on the effectiveness of mHealth in pain severity levels [38]. In particular, outcomes will also include interference of pain on functioning, fatigue, and mood (depression, anxiety, and anger).
The study goal is to compare the response to one month of usual pain treatment for patients in the three monitoring conditions, namely usual episodic monitoring, monitoring with an app without clinical alarms, and monitoring with an app with clinical alarms. All patients will receive the usual treatment for their pain, so differences in outcomes (pain severity and interference, fatigue, and mood) across outcomes are expected to occur as a consequence of the assigned monitoring condition. Another goal is to investigate the opinion about the app of the health professionals involved in the study (e.g., in charge of disseminating the study, helping download the app, and proposing the treatment).
We expect that the use of the app with alarms that are sent to the healthcare professional in the presence of unwanted clinical events will allow a quick detection of patient suffering (see the alarms in Appendix I), including severe pain levels, side medication effects, high interference of pain on functioning, and psychological distress, as well as a quick reaction to these events. As a consequence of the above, we anticipate that patients in the app+alarm condition (telemonitoring) will report a greater reduction in pain severity, pain interference, fatigue, depressed mood, anxiety, and anger. Additionally, we expect that patients in this condition will also experience unwanted clinical events (e.g., poor treatment response or undesired clinical events associated with treatment onset) for a shorter time compared to treatment as usual or treatment as usual with the app but without alarms. In relation to the professionals’ opinion on the app, we expect that the professionals will experience some burden as they help patients to download the app and respond to alarms. However, we also anticipate that they will perceive the app to be useful and will be willing to use it in the future, preferably the version with alarms.
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