Chronic pain is defined as pain that persists despite the resolution of injury or pathology [1] and attributed to changes in the central and peripheral nervous system resulting in amplified or uninhibited pain signals [2,3]. Persistent pain over long periods of time can affect people both physically and psychologically. Chronic pain is also closely linked with distress, affect behavior, and loss of productivity. Some studies have shown that 86% of people with chronic pain report difficulties with sleep [4], 70% have trouble concentrating [4], 44% to 51% report anxiety [5], and 88% express anger due to not seeing improvements [5]. Moreover, productivity losses can also be significant, with 4.6 hours per week rising to 5.5 hours per week lost due to chronic back pain [6]. These adverse effects can also result in other comorbidities such as depression [5,7-9]. In terms of global prevalence, the World Health Organization recognizes chronic pain as a public health problem worldwide [10] with a prevalence of 32% in low-income countries and 30% in high-income countries [11,12]. In the United States alone, 120 million adults are reported to be suffering from chronic pain [13] with related costs exceeding even that of diabetes, cancer, and heart disease [14]. Chronic pain is also related to substance abuse, with 12 million people (aged 12 years and older) reporting nonmedical use of pain medication, and overdose-related deaths are rising annually [15].

A particularly common form of chronic pain is of a musculoskeletal nature, which affects 1 in 10 adults globally. This form of chronic pain is also a leading cause of disability, with 28% reporting limitations in movement due to the condition [16]. That said, it is well understood that successful management of musculoskeletal chronic pain is achievable with regular and sustained physical activity [10]. This is due to activity having the effect of protecting against muscle weakening and inhibiting the neurophysiological mechanisms underlying the spread of pain [4]; in addition, physical activity does not have the issue of side effects that come with the consumption of pharmaceuticals [10,17]. Indeed, several recent reviews and guidelines from the US Centers for Disease Control and Prevention and UK National Institute for Health and Care Excellence have strongly encouraged clinicians to prescribe nonpharmacologic approaches that include movement-based therapies [10,18-20].

Despite the benefits of physical activity, adherence to regular and sustained physical activity is low [12,21]. This is principally due to perceived pain exacerbation from activity, which over time results in negative psychological associations (eg, fear) between movement and pain. Some of the psychological associations, typically fear and anxiety [22,23], manifest at an affective level [5] and are closely linked to more cognitive associations such as negative beliefs [5] and catastrophization [24], where subjective interpretation of pain severity may make the pain seem worse than it is [25,26]. These negative associations can lead to a reluctance to continue and even avoidance of activity and therapies [23,27] that in turn can lead to lowered self-efficacy to engage in preventative measures [24]. Lack of engagement over time can further result in weakening, disability, or even impairment in motor control where there is fproprioceptive dysfunction [28].

Low adherence to physical activity is further compounded by the need to self-manage. Typically, in day-to-day life settings, there is no care provider present to offer encouragement and guidance [12,29]. A variety of mobile phone apps have been proposed in recent years for chronic pain self-management [12,30-32]; however, the majority of mobile health (mHealth) apps to date for chronic pain do not leverage the available array of onboard sensors [33,34]. In this paper, we argue that sensor data can offer a new method that makes self-management of physical activity easier for chronic pain. Self-management becomes easier because suggestions are generated and adapted automatically from sensor data, so participants do not need to manually manage or track the suggestions they want to follow. We further reduce self-management burden by prioritizing features that make the suggestions more actionable within the psychological barriers of chronic pain [35,36]. Thus, participants do not need to use trial and error to figure out which suggestions are more or less actionable for chronic pain.

While the utility of physical exercise for chronic pain is well known, it has also been found that introduction of new exercise tasks is more successful when small changes are made to current daily activities [12]. Other studies advocate a shift of attention strategy [37] as well as focusing on pleasurable activities [38]. These studies suggest the need to base any recommendation system on personal preferences and routine behavior. Given these lessons, we aimed to use mobile phone sensors as a natural way to acquire data on habitual behavior with respect to activity preferences. We also aimed to use this data as a basis from which new activity suggestions can be issued that are based on the contextual information taken from the tracked data. Such suggestions will likely be perceived as familiar. Also, given that the users know they have done similar activities before, this may lead to more positive beliefs and a better sense of self-efficacy. Thus, the suggestions themselves could be perceived as less effortful compared to fixed text message–based approaches, where messages are constructed before a study and the message contents do not adapt to routines or preferences of study participants [39].

We developed MyBehaviorCBP, a mobile phone app that operationalizes various strategies to address psychological barriers of chronic pain. MyBehaviorCBP uses machine learning on sensor data and self-reported physical activity logs and automatically generates physical activity recommendations based on an individual’s past behavior. This strategy of persuasion has been shown to be effective in MyBehavior, our predicating system designed for general populations [35,36]. This earlier app was designed to promote more energetic exercising and lower calorie dietary intake based on the user’s past actions. MyBehavior was shown to have affected a sustained positive behavior change within a general population in a 14-week pilot study. In this study, we repurposed this system and developed MyBehaviorCBP specifically for individuals with chronic pain. In MyBehaviorCBP, the user’s mobility state (episodes of walking or stationary state inferred from the accelerometer signals) and geolocation are passively tracked without the need for any user input. Activities that cannot be captured with sensors are entered manually. The next stage is to find recurring patterns within the physical activity data of each participant. Once recurring patterns are established, a set of new recommendations is issued based on these recurring patterns with small changes applied. We focus on these recurring patterns to generate new suggestions because participants have likely done similar actions before. Furthermore, the app uses the tracked movement data as a way to monitor which recommendations were actually preferred and executed. Subsequent recommendations are adapted based on the most acted upon previous recommendations. To the best of our knowledge, MyBehaviorCBP is the first app to promote physical activity with an automated data-driven approach in the chronic pain context.

In this paper, we report on a formative study on the use of MyBehaviorCBP and present results from a 5-week pilot study among individuals with chronic back pain (N=10). Since the MyBehaviorCBP automated suggestion generation approach is being tested for the first time in the chronic pain context, we investigate the feasibility and acceptability of the approach before an expensive randomized controlled trial. Prior works have recommended small pilot trials (N>4) for novel mHealth apps to investigate early evidence of acceptance and use demonstrating the intervention is affecting the intended outcomes and document lessons learned, if any, for future improvements [40,41]. To this end, we (1) determined whether the MyBehaviorCBP recommendations were perceived as easy and actionable compared to randomly generated recommendations, (2) examined preliminary evidence to see whether the intentions led to an actual increase in physical activity behavior, and (3) solicited participant feedback on using the app to fine-tune future versions of the app.

We conducted a 5-week within-subject study on 10 individuals with chronic back pain. The first week of the study served as a baseline period where participants familiarized with the app. No physical activity recommendation was given in the first week. The next 2 weeks were a control phase where 7 suggestions were randomly chosen every day from a pool of suggestions. This pool of suggestions was created by a fitness expert according to the US National Institutes of Health guidelines for healthy living [42,43] and these suggestions were pilot-tested in past studies [35,36]. These suggestions, however, were generic and unrelated to participants’ past behaviors. In weeks 4 and 5, the experimental phase was conducted where participants received MyBehaviorCBP-generated recommendations based on their own behaviors. The study was single-blinded in that only the experimenters were aware of when the different types of suggestions during the control and experimental phases were activated.

During each day of the control and experimental phases, participants filled out a short in-phone survey in the evening. The survey asked about the ease of following recommendations, how many recommendations they followed, and their emotional state. In addition to the daily surveys, participants completed a Web-based exit survey after the study. The exit survey asked about the helpfulness of the recommendations, what future changes they would want to see, and whether they would recommend this app to other people with chronic back pain.

Given the prevalence of chronic pain, invitation of the study was sent via the Wellness Center and retiree mailing lists from Cornell University. Recruitment was restricted to participants with a history of chronic back pain (≥6 months in duration) and willingness to use MyBehaviorCBP on an Android mobile phone, either their own or one provided by the study. Further inclusion criteria were having some reasonable level of outdoor movement (eg, traveling to and from work), not being significantly housebound, having a basic level of mobile phone proficiency, being between ages 18 and 65 years, and being fluent in English. Exclusion criteria, determined during an initial interview, were the need of mobility aids; having had joint replacement, arthrodesis, or limb amputation; having a learning disability; or being pregnant, but no subject fell into these categories. Eligible participants were invited for a face-to-face session where informed consent was acquired and instructions for using the app were provided (Figure 1 shows the participant flow diagram); 10 participants (3 male, 7 female, aged 31 to 60 years) were recruited, and all participants completed the study. Participants reported a range of causes for their chronic back pain, including herniated disc, rotated vertebrae, scoliosis, sciatica, and tendinitis [44] and had histories of 5 to 33 years of chronic back pain. It should be noted here that independent clinical diagnoses past that of the subjects’ own declarations about their respective causes of chronic pain were not applied. Since we are only interested in the chronic pain aspect of their condition rather the specific causes, we followed the approach of self-reporting of pain. As pain is subjective, self-report of the presence, level, and persistence of pain remains the standard assessment approach. After initial contact, some participants were further excluded as our app was limited to the Android operating system and there was a limited number of available replacement phones. Also, some potential participants were excluded because they would not be in their normal daily routines (eg, going on vacation) during the time of the user study. From the initial 30 contacted, 16 were excluded and 10 enrolled with 4 on standby.

The MyBehaviorCBP app comprised 2 modules: routine behavior recognition module and recommendation generation module.

The MyBehaviorCBP system intends to encourage more physical activity over a sustained period of time. However, given the early stage of the technology, a 5-week pilot study was conducted. The goal of the pilot was to investigate the feasibility of MyBehaviorCBP, which was measured by 3 factors: use, acceptability, and early efficacy. In addition, we report lessons learned for future improvements [40,41,51]. These factors are measured with a combination of phone logs, daily evening surveys during the study, and an exit survey (see Table 1 for measured outcomes). Phone logs contain passively collected records of whether MyBehaviorCBP was opened and the type of physical activity (ie, walking, stationary) for every minute during the study. The daily evening surveys were done in the phone each day of the 5-week study; in these daily evening surveys, we asked about relative ease of the recommendations received, how many recommendations they followed, and their emotional state during the day. After the 5-week study, an exit survey was conducted on the Web that asked about the helpfulness of the recommendations, what future changes they would want to see, and whether they would recommend this app to other people with chronic back pain.

Use was measured by how frequently study participants opened the app, recorded from the phone log. Acceptability, a more complex quantity, was measured by traingulating a variety of self-reports that focused on intention and behavior toward the recommendations. We specifically measured perceived easiness, which indicates the actionability of the recommendations [39,49], intention, behavior, and helpfulness. Early efficacy is another complex quantity we measured with several proximal measures that can lead to reduction of pain in the long term [52]. Finally, open-ended questions were used to pin down which features participants found useful and what features they saw as missing; this data will be used to map out future refinements of MyBehaviorCBP.

Number of times the app is accessed was analyzed using the simple descriptive statistics of mean and standard deviation. The acceptability and early efficacy outcomes are less straight forward to analyze because data points from the same subject being likely correlated and different subjects having different baseline conditions at the start of the study (eg, different levels of physical activity and type of chronic back pain) [53,54]. Therefore, we apply linear mixed-effect models to adjust for repeated measures and intersubject variability [53].

The type of intervention is considered as a fixed effect, and we coded the intervention type as 0 and 1 for control (ie, the static suggestions generated by experts) and experimental phases (ie, MyBehaviorCBP suggestions), respectively. Coded this way, the intervention coefficient would represent the relative improvement of the outcome measure of MyBehaviorCBP over the control. When we included time (as day within the study) as a fixed effect, it was found to be not significant. Also, we tested the study participant identity as a random effect and found it to be significant in likelihood ratio tests (P<.01) for all outcome measures [53], which means significant interpersonal variability exists and a mixed-effect model is necessary. The parameter estimation for all models was generated using maximum likelihood estimation [53]. In addition, we computed effect size measures by dividing the mean difference of an outcome measure between the control and experiment phases with pooled standard deviation [55]. Finally, the open-ended questions in the exit survey are broken down into themes using thematic analysis [56].

Over the 5-week study with 10 participants, the mean number of times the MyBehaviorCBP app was opened is 106.9 during the control and experiment phases (μ=106.9, σ=56.9, q₂₅=76.1, q₅₀=89.6, q₇₅=105.5), which is 3.2 times on average per day. Figure 4 shows the average number of times a participant opened the MyBehaviorCBP app over time. For both control (ie, static suggestions) and experiment phases (ie, MyBehaviorCBP-generated suggestions), there is an initial period of high use but over time the use decreased.

In the exit survey, the participants reacted positively about MyBehaviorCBP recommendations, with 2 of 10 participants finding MyBehaviorCBP recommendations very helpful and 8 of 10 finding MyBehaviorCBP recommendations somewhat helpful. No participant reported the recommendations unhelpful. All participants (10/10) reported that they would likely recommend the app to other people with chronic back pain.

The acceptability of MyBehaviorCBP was also measured using (1) self-reported rating of easiness of the recommendations, (2) how many recommendations the participants wanted to follow, and (3) how many recommendations the participants actually followed. The results of the statistical analysis are reported in Table 2, and Figure 5 shows the mean and standard deviations. In the interest of convenience, only important statistics are shown in Table 2. The intervention coefficients (β_int ) are reported along with P values (P_int ) and 95% confidence intervals. We observe there were significant changes of β_int for number of recommendations followed (β_int =0.46, P<.001). In real terms, participants within the experimental phase adopted 1 extra recommendation every 2 days. On the other hand, participants wanted to follow the control phase recommendations more than the experimental phase (β_int =–0.2, P=.02), which means control group recommendations were effective to increase intention but not for actualizing a recommendation. Regarding easiness, participants perceived the MyBehaviorCBP recommendations to be easier than the control, which means they were perceived as low-burden (β_int =0.42, P<.001). This is important in the chronic pain population, which is often reluctant to be physically active.

The number of self-reported recommendations followed and wanted to follow, however, had important differences for different emotional states in the day. Figure 6 shows the means for several outcomes under different emotional states captured through the PAM [57]. Generally, participants adopted or wanted to adopt more recommendations during positive emotional states. However, during high positive emotional states, generic recommendations were perceived as easy and participants wanted to follow them more, but the MyBehaviorCBP recommendations were adopted more. During negative emotional states, the number of recommendations participants wanted to follow and actually followed are both higher than the control group recommendations.

From Table 2 and Figure 7, the intervention coefficient for daily walked minutes (β_int =4.9, P=.02) indicates that participants walked for a further 4.9 minutes per day compared to the control phase. β_int for nonwalking exercise (β_int =9.5, P=.31) indicated that participants in the experimental phase exercised for a further 9.5 minutes, but the difference was not significant. Regarding pain level, there was a small nonsignificant 0.19 unit of reduction of pain (β_int =–0.19, P=.24).

Participants provided qualitative feedback in the exit survey, which provided further insights about the quantitative results and gave directions for future changes. For instance, when we asked participants to compare control group recommendations with MyBehaviorCBP, participants reported that they liked the personalization of MyBehaviorCBP. They also mentioned MyBehaviorCBP recommendations were more actionable and easier, and they were more likely to succeed if they tried the recommendations.

In addition, some participants liked the specificity of the recommendations and how they could be carried out in a smaller piecewise manner.

Regarding the control phase suggestions, some participants struggled with their nonpersonalized nature and how they needed to plan ahead to execute them.

However, one participant did not like MyBehaviorCBP recommendations and wanted more variety.

Participants also asked for the following features: (1) a reminder system to plan in the morning and notifications in the moment, (2) adapt suggestions based on weather or weekend/weekday, and (3) better insight to relate high pain days and activity level, etc.

Finally, one participant wanted to use the app even after the study, and mentioned the following:

To the best of our knowledge, MyBehaviorCBP is the first mobile app to provide automatically generated data driven physical activity recommendations in the chronic pain context. We conducted a pilot study to examine the feasibility of the approach. In the study, we found participants used the MyBehaviorCBP app 1 or more times a day. Furthermore, we observed early indication of acceptance and efficacy in both the qualitative and quantitative data. For instance, in the daily surveys, participants perceived the tracked data-based recommendations to be easier to follow. In the qualitative feedback on the exit survey, participants were positive to successfully complete MyBehaviorCBP recommendations. This means participants likely had a greater sense of self-efficacy toward MyBehaviorCBP-generated suggestions. According to protection motivation theory, higher self-efficacy may cause the recommendations to be carried out despite the presence of fear in chronic pain [58]. Indeed, empirically the participants also acted on the MyBehaviorCBP suggestions according to the automated walking inference. This increase gives early evidence that the perceived easiness may have transferred to actual behavior. However, the change in nonwalking exercise was not significant. This may have happened because it is hard to ascertain regularity in exercising within a 2-week period [35]. Furthermore, there was a small reduction of reported pain, which was not significant. This can be a type II error, and the small-scale study may not be sufficient to reject the system’s efficacy [54].

From Figure 4, we observed that the number of times participants opened the app decreased over time. This may mean engagement decreased over time. Fortunately, participants also gave us valuable feedback to improve future versions of the app, which may increase engagement. For instance, several of our participants asked for notifications when the context was appropriate for a recommendation. Indeed, Fogg [39] argues that a trigger or notification may be necessary even when the suggested action can be executed with less effort. However, providing just-in-time notifications has particular technical barriers since it requires constant monitoring to detect the right context without draining the phone battery. In addition, the notifications can interrupt the participants’ daily workflow, and it is unclear when the right time to provide an intervention would be. Therefore, future research may focus on just-in-time interventions that are acceptable to participants and more battery efficient.

The current MyBehaviorCBP system is a variant of a prior system, MyBehavior [36]. MyBehaviorCBP borrows several ideas from MyBehavior, such as clustering routine activities and the MAB algorithm. However, there are a few key differences between MyBehavior and MyBehaviorCBP. MyBehavior was designed for a weight loss population with no chronic pain, and the objective function of the MAB was to maximize calorie expenditure. On the other hand, MyBehaviorCBP is designed for people with chronic pain, and the goal of the MAB is to maximize number of minutes performing low-effort exercises. As a result, between walking and gym exercise, MyBehaviorCBP would prioritize walking since walking is less effortful, but MyBehavior would prioritize gym exercises since gym exercises give more calorie expenditure. In addition, MyBehaviorCBP provides recommendations such that the total number of recommended minutes to exercise does not exceed 60 minutes. MyBehavior did not have any limit on total calories for the recommendations.

Despite the similarities between the two systems, the effect sizes in Table 2 are lower than those in the previous MyBehavior trials [36]. This is expected, since MyBehavior was designed for weight loss and did not deal with the negative psychological challenges in chronic pain. Indeed, out of the 321 reported daily surveys for the MyBehaviorCBP, in 39.9% cases negative emotions were reported in the PAM [57]. In a study with MyBehavior, out of 687 daily survey responses, 32.1% cases negative emotions were reported. Therefore, MyBehaviorCBP likely made participants active despite the prevalence of negative emotions.

Over the years, a variety of mobile apps have been proposed for chronic pain self-management, with some apps aiming at prescribing cognitive behavioral components [15,30,32]. Other apps use diary-based approaches by logging subjective self-reports and provide basic feedback such as reminders for medication [12], and some newer apps such as the WebMD PainCoach [31] enable pain monitoring, setting and tracking activity goals, and generating related messages. However, MyBehaviorCBP differs in that it uses an in-phone machine learning approach directly on tracked physical activity data and automatically generates new person-specific suggestions based entirely on this. This also lends MyBehaviorCBP’s automated approach to be complementary to other approaches (eg, methods based on cognitive behavioral therapy that could be combined with the tracked data-driven suggestions from MyBehaviorCBP).

One limitation is the small number of study participants and relatively short study length. However, MyBehaviorCBP is an early stage technology. It is difficult to acquire resources to conduct efficacy trials with unproven technology on a potentially vulnerable chronic pain population. As a result, the purpose of this pilot study was to inform feasibility and acceptability. In our future work, we will use the lessons learned in this pilot study to conduct longer term studies on a larger population and also to include specific back pain outcomes such as the Oswestry Low Back Pain Disability Questionnaire [59].

Another limitation is that MyBehaviorCBP does not fully address the question of whether even moderate exercise can have adverse consequences. If there is a short-term pain flare, then moderate exercising can temporarily increase pain and MyBehaviorCBP should not recommend exercising during a pain flare. However, it is not clear whether exercising has any long-term adverse effect on pain. Some prior work [60,61] argues exercising may have longer term effects and suggests activity pacing, where a participant engages in an active phase of exercise for a certain amount of days followed by a resting phase with decreased or no exercise. However, we are not aware of any published studies that have examined the question of long-term adverse effects empirically. Some providers recommend rest while others recommend that patients remain active without “overdoing it” based on their clinical experience. Future research is needed to address this important question of longer term effects. Using apps like MyBehaviorCBP could help address this question. For example, the static upper limit of a 60 minutes threshold could be dynamic. This value could be determined either automatically by a model-based approach or by an expert such as a physiotherapist. Should it be deemed that the user needs rest, the limit could be set to zero or a low value.

In this paper, we presented the acceptability and feasibility of MyBehaviorCBP, a data-driven physical activity recommender system for chronic pain. We found preliminary evidence of increased walking activity; a few key areas of improvements have been also identified. In future work, we will incorporate these improvements and run a randomized controlled trial. If efficacy is demonstrated, then a technology like MyBehaviorCBP could have great promise because it is an automated system with no second person involved (eg, a physiotherapist). Also, all the data processing of MyBehaviorCBP is kept inside the phone which allows the app to preserve user privacy. Such automated and privacy-preserving features imply that MyBehaviorCBP has few barriers to scalability.