Abstract
Purpose:
This study tested the hypothesis that ecological momentary assessment (EMA) of pelvic pain (PP) and urinary urgency (UU) would reveal unique Urologic Chronic Pelvic Pain Syndrome (UCPPS) phenotypes that would be associated with disease specific quality of life (QOL) and illness impact metrics (IIM).
Materials and Methods:
A previously validated smart phone app (M-app) was provided to willing Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) participants. M-app notifications were sent 4-times daily for 14 days inquiring about PP and UU severity. A clustering algorithm that accounted for variance placed participants into PP and UU variability? clusters. Associations between clusters and QOL and IIM were then determined.
Results:
A total of 204 participants enrolled in the M-app study (64% female). M-app compliance was high (median 63% of surveys). Cluster analysis revealed k=3 (high, low, none) PP clusters and k=2 (high, low) UU clusters. When adjusting for baseline pain severity, high PP variability, but not UU variability, was strongly associated with QOL and IIM; specifically worse mood, worse sleep and higher anxiety. UU and PP clusters were associated with each other (p<0.0001), but a large percentage (33%) of patients with high PP variability had low UU variability.
Conclusions:
PP variability is an independent predictor of worse QOL and more severe IIM in UCPPS participants after controlling for baseline pain severity and UU. These findings suggest alternative pain indices, such as pain variability and unpredictability, may be useful adjuncts to traditional measures of worst and average pain when assessing UCPPS treatment responses.
Keywords: Ecological Momentary Assessment, Phone Application, Urologic Chronic Pelvic Pain Syndrome, Variability, Quality of Life, Illness Impact
Introduction
The Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) network observed a large cohort of participants with Urologic Chronic Pelvic Pain Syndrome (UCPPS) with the primary goal of understanding unique UCPPS phenotypes, with unique pathophysiology, to better inform future clinical trials and ultimately, improvements in patient care. The standard MAPP protocol assessed pain every-other-week and longitudinal analyses of the cohort revealed that bi-weekly recalled pain variation did not affect QOL or care-seeking behavior1. However, when this same cohort was asked to report their pelvic pain flares weekly, the group with higher flare frequency had significantly worse QOL and Illness Impact measures (IIM), suggesting both that flares are not adequately addressed with standard pain reporting methodology, and that more frequent assessments may further uncover unique pain phenotypes2.
Smart phone applications (“app”) have become a proven means to collect real-time data of subjective clinical symptoms, such as pain and urinary urgency, while minimizing recall biases3,4. In a MAPP pilot study, we showed that an app designed to collect ecological momentary assessments (EMA) of pain and urinary urgency for the MAPP network had high patient usability scores and was able to identify symptom variability amongst participants5. The purpose of the current study was to evaluate the app’s use in the MAPP II Symptom Pattern Study (SPS), with a focus on both pelvic pain and urinary urgency variability as measured by the app.
The study tested three hypotheses: first, we hypothesized that the MAPP phone application (M-app) would have sufficient compliance to identify both inter- and intra-day variability of pelvic pain and urinary urgency amongst participants; second, we hypothesized that cluster analyses will identify unique pelvic pain and urinary urgency clusters and that these clusters will be associated with each other (i.e. urgency and pain variability are related); finally, we hypothesized that patient demographics and patient-reported psychosocial and functional measures, such as sleep and anxiety, will vary significantly across these variability clusters. Specifically, we hypothesize that high variability in pelvic pain and urinary urgency will predict worse psychosocial health, independent of baseline pain severity.
Materials/Methods
Study population and design
The MAPP SPS was a multi-site, three-year longitudinal study of UCPPS participants (with interstitial cystitis/ bladder pain syndrome [IC/BPS] and/or chronic prostatitis/ chronic pelvic pain syndrome [CP/CPPS]) designed to document the natural history of clinical UCPPS6. The study protocol, which has been described in more detail elsewhere, included a screening visit (week 0), three run-in visits (weeks 1-3; online) and then a baseline study visit at week four6. After the baseline/enrollment visit, quarterly online assessments were performed as well as in-person visits at 6, 18, and 36 months. The minimum pelvic pain symptom severity for study inclusion was a diagnosis of UCPPS with pain during any three of the past six months and a “non-zero” score for pelvic pain in the past two weeks7.
At the 6-month in-person visit, participants were approached about their willingness to participate in the IRB-approved supplementary 14-day phone app study and were provided additional compensation ($50) if they enrolled. Participants enrolling in M-app were assisted with installing the app onto their phones at the time of the in-person visit and then queried about their “usual” wake and sleep times, such that the first and last M-app notifications were sent to their phones one hour after waking and one hour before bedtime, respectively. The second and third notifications were sent four and eight hours after the wake notification.
The M-app technology, security concerns, and notification methodology have been described elsewhere8. The specific battery and timing of the five questions that were delivered four times daily can be found in the Appendix. For this analysis, we focused on only two of the questions: Q1 – “Are you experiencing pain in any body area now?” and Q2 – “How would you rate your level of urinary urgency over the last hour?”. If participants answered “yes” to Q1, they were prompted with a body map that allowed them to click on the location of pain, and the severity of pain with a visual analog scale ranging from 0 (lowest pain) to 10 (highest pain) (Supplementary Figure 1). Answers from the urgency question ranged from 0 (no urgency) to 10 (unbearable urgency). Participants had one hour from the notification to answer the questions before the survey expired, so as to prevent symptom assessment overlap.
Assessment of QOL and Illness impact
The relationship between pelvic pain and urinary urgency variability with QOL and illness impact was assessed by comparing clusters to several validated questionnaires obtained at the 6-month in person MAPP SPS study visit. These questionnaires included the Perceived Stress Scale9, the PROMIS Sleep Score10, the PROMIS Fatigue Score11 and the HADS Anxiety and Depression Scale12. Clusters were also compared to baseline pelvic pain and urgency scores obtained with the M-app at day two and to validated summary scales of Pelvic Pain Severity (PSS) and Urinary Symptom Severity (USS) obtained at the 6-month MAPP visit to ensure that variability was independent of severity. Testing was performed with and without adjustment for baseline pelvic pain scores.
Statistical analysis
Cluster Analysis
Cluster analysis was performed using a methodology employed previously in this cohort and described elsewhere.13 Specific to this study, we used a functional mixed effects algorithm to identify distinct variability clusters for both pain and urgency that accounted for age, sex and baseline pain. Each subgroup was modeled by a functional mixed effects model, in which both the group-average profiles and the participant-specific functional random deviations are modeled by flexible smooth functions. An iterative classification algorithm was used to classify each participant into one of the sub-groups based on the posterior probability of a participant belonging to a specific cluster. The classification iteration stops when no participant switches groups. We then used a cross-validation predictive classification likelihood criterion to select the optimal number of clusters.
Means and standard deviations were reported for continuous variables and percentages for categorical variables. Student's t-tests and χ2 tests (or Fisher's exact tests, if applicable) were used. Regression models adjusted for baseline pain within clusters.
Results
Study Cohort
A total of 204 (35% of total MAPP-II SPS cohort of 578) participants enrolled in the M-app study, of which 137 (64%) were female and 76 (36%) were male. All six of the participating MAPP sites enrolled participants, ranging from 19 to 54 enrollees (median = 35).
Survey Compliance
Of the 56 surveys sent during the 14-day study period, the median number of surveys in which the pain and urinary urgency questions were completed by participants was 35 (IQR 22 – 47). Survey compliance was not affected by age, baseline pelvic pain, urgency, pain/urgency variability or sex.
Overall Pelvic Pain and Urinary Urgency
Average daily pelvic pain in the cohort worsened over the course of the day, increasing by an average of 0.2 points on the VAS (1.7 to 1.9; p = 0.012). Notably, in over 50% of the completed surveys, pelvic pain was not reported. Average daily urinary urgency improved significantly over the day, decreasing by an average of 0.2 points on the 0 to 10 scale. In 25% of the completed surveys, urinary urgency was reported to be 0 (absent). Of the pain variance noted within the cohort, 60% was between participants (i.e. pain differences between individuals), 33% was noted within day (i.e. variability within patients reported throughout the day), and only 8% was noted between days (i.e. variability throughout the week – meaning daily patterns of pain were similar in most participants). Similarly, 54% of the urgency variance was between participants, 37% was within the day, and only 9% was throughout the week.
Pelvic Pain Clustering
Cluster analysis revealed three (K=3) variability clusters to have the highest maximum likelihood criteria for choice combined with the greatest clinical interpretability (Figure 1a): 1) no variability (n=20, 10%); 2) low variability (n = 74, 36%) and 3) high variability (n = 110, 54%). The demographics and baseline pain characteristics are shown in Table 1. High pain variability was associated with younger age and female sex.
Figure 1:
(A) Daily pain variability relative to baseline pain score on Day 2 of the M-app for K=3 clusters. (B) Daily urinary urgency variability relative to baseline urgency score on Day 2 of the M-app for K=2 clusters.
Table 1:
Demographics and Baseline Pain and Urgency Scores by Pain (K=3) and Urgency (K=2) Clusters
| Pain Cluster* | Urgency Cluster** | ||||||
|---|---|---|---|---|---|---|---|
| Variable | Cluster 1 (n=20) |
Cluster 2 (n=74) |
Cluster 3 (n=110) |
p-value | Cluster 1 (n=95) |
Cluster 2 (n = 109) | p-value |
| Demographics | |||||||
| Age | 52.56 | 46.70 | 41.85 | 0.007 | 45.59 | 43.85 | 0.40 |
| Sex (Proportion Female) | 0.55 | 0.53 | 0.74 | 0.0084 | 0.58 | 0.70 | 0.080 |
| Race (Proportion non-White) | 0.10 | 0.068 | 0.10 | 0.69 | 0.08 | 0.09 | 0.85 |
| Pelvic Pain | |||||||
| Pelvic Pain Severity (PPS) @ 6 mo | 7.65 | 11.73 | 14.79 | <0.0001 | 12.09 | 13.73 | 0.05 |
| Pelvic Pain Severity (M-app) | 0.00 | 1.40 | 2.16 | <0.0001 | 1.56 | 1.77 | 0.50 |
| Urinary Urgency | |||||||
| Urinary Symptom Severity (USS) @ 6 mo | 8.05 | 10.07 | 11.73 | 0.0059 | 9.65 | 11.72 | 0.017 |
| Urinary Urgency (M-app) | 1.4 | 2.29 | 3.07 | 0.0005 | 2.08 | 3.09 | 0.0011 |
Pain Clusters: 1) no variability; 2) low variability; 3) high variability
Urgency Clusters: 1) low variability; 2) high variability
Urinary Urgency Clustering
Cluster analysis revealed two (K=2) clusters to have the maximum likelihood criteria for choice combined with the greatest clinical interpretability. (Figure 1b): 1) low variability (n = 95, 47%) and 2) high variability (n = 109, 53%). The demographics and baseline pain and urgency characteristics are shown in Table 1.
Clustering and the Relationship to Quality of Life and Illness Impact Measures
Pelvic pain clusters were associated with both quality of life (QOL) and illness impact measures (IIM) as shown in Table 2. Specifically, higher variability clusters were associated with worse scores in all QOL and IIM measures, with only the PSS not being statistically significant. When adjusting for baseline pain, higher variability clusters were associated with worse mood, worse sleep and higher anxiety.
Table 2:
Quality of Life and Illness Impact Measures by Pain (K=3) and Urgency (K=2) Clusters
| Pain Cluster | Urgency Cluster | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| QoL and Illness Impact | Cluster 1 | Cluster 2 | Cluster 3 | p-value | Adjusted p- value* |
Cluster 1 | Cluster 2 | p-value | Adjusted p- value** |
| SYM-Q7 Mood (0-10 @ 6 mos. | 2.05 | 3.82 | 4.40 | 0.0002 | 0.0044 | 3.68 | 4.20 | 0.13 | 0.29 |
| Perceived Stress Score (PSS) @ 6 mos | 13.24 | 16.38 | 17.26 | 0.0581 | 0.3478 | 16.16 | 16.86 | 0.54 | 0.97 |
| PROMIS Sleep @ 6 mos. | 49.72 | 53.24 | 56.44 | 0.0009 | 0.0436 | 53.70 | 55.37 | 0.22 | 0.91 |
| PROMIS Fatigue @ 6 mos. | 49.22 | 54.84 | 56.82 | 0.0027 | 0.0926 | 54.96 | 55.64 | 0.63 | 0.45 |
| HADS Depression (0-21) @ 6 mos. | 3.43 | 5.76 | 6.08 | 0.0415 | 0.4587 | 5.54 | 5.83 | 0.65 | 0.67 |
| HADS Anxiety (0-21) @ 6 mos. | 5.30 | 6.84 | 8.37 | 0.0021 | 0.0222 | 6.76 | 8.13 | 0.042 | 0.12 |
| CSQ Sum @ 6 mos. | 5.66 | 9.50 | 11.47 | 0.0031 | 0.0605 | 9.55 | 10.70 | 0.33 | 0.54 |
Adjustment for baseline pain (PPS, 6 months)
Adjustment for baseline urinary urgency (USS, 6 months)
The associations between urinary urgency clusters and QOL and IIM are also shown in Table 2. Notably, only HAD Anxiety was associated with urgency variability, which when adjusting for baseline urgency scores, was no longer significant.
Relationship Between Pelvic Pain and Urinary Urgency Clusters:
The relationship between pelvic pain and urgency clusters is shown in the Table 3 contingency table. Inclusion in the high pain variability cohort was associated with the high urgency cluster (Chi-squared 18.46; p < 0.0001), but high variability in one domain did not uniformly predict variability in the other, with nearly 1/3rd of high pain variability patient reporting low urgency variability – and over 1/3rd of the no pain variability reporting high urgency variability.
Table 3:
Relationship Between Urgency (k=2) and Pain (k=3) Clusters
| Pain Cluster | ||||
|---|---|---|---|---|
| Urgency Cluster | Cluster 1 (no variability) |
Cluster 2 (low variability |
Cluster 3 (high variability) |
Total |
| Cluster 1 (Low variable) | 12 (60%) |
47 (64%) |
36 (33%) |
95 |
| Cluster 2 (High variable) | 8 (40%) |
27 (36%) |
74 (67%) |
109 |
| Total | 20 | 74 | 110 | 204 |
p<0.0001
Discussion
The purpose of this study was to utilize our previously validated phone app to identify inter- and intra-day variability in pelvic pain and urinary urgency in UCPPS participants. The results revealed the M-app to have sufficiently high compliance to allow for identification of three distinct pelvic pain clusters (minimal/none, low and high variability) and two distinct urinary urgency clusters (low and high). Overall, pelvic pain worsened throughout the day, while urinary urgency improved. Most of the variation was seen between participants, and within-day, with little variation seen between days, suggesting that individual patient daily patterns of pain are consistent (though, importantly, this variation may not always be appreciated by patients). While urinary urgency and pelvic pain are both classic features of UCPPS, this study showed that variability clusters did not always overlap, confirming these two UCPPS features likely have distinct underlying mechanisms that need to be measured and clinically addressed separately, as has been discussed in previous MAPP publications14.
Assessing pain variability, along with more traditional measures such as pain intensity and average pain, has become more common as researchers begin to appreciate how pain variability, and pain predictability, independent of pain intensity, may affect both response to treatment and psychosocial measures associated with pain. In a study of 300 patients with osteoarthritis, daily pain variability was commonly reported and participants with the most variability had higher levels of depression and frustration, and lower self-efficacy15. A separate study of 25 individuals with patellofemoral pain (PFP) showed that pain variability was a greater predictor of worse subjective symptoms and functional limitations than overall pain score with the researchers postulating that their participants had an “inability to predict pain could make coping with pain difficult, resulting in decreased quality of life…” 16 In the fibromyalgia population, where EMA has been used most extensively, the alternative pain indices of “pain variability” and “time in high pain” were found to be independently associated with QOL and IIM.17
Importantly, there also appears to be a disconnect between clinicians, researchers, and patients about what aspects of pain are most important to measure. In a study by Stone et. al that evaluated seven pain intensity variables derived from EMA analysis, the results showed that although both participants and providers ranked “worst pain” as the most important pain measure to track clinically, participants were significantly more likely than providers to rate “pain variability” and “pain unpredictability” as clinically significant features needing to be monitored18. Qualitative data also collected from the participants suggested that both fluctuation in pain and the inability to predict when the pain will be severe significantly affects stress and depression, and likely more than the “average pain” that is commonly collected clinically and reported in pain studies.
We had previously shown in the development phase of the M-app that compliance for the phone application was high – and this finding was confirmed here with a median survey compliance of 63% - a rate in line with other EMA studies8,19,20. Confirming our first hypothesis, this was a sufficient number of surveys to identify inter- and intra-day variability both within and among the study cohort.
Our second hypothesis stated that analysis of the EMA data would allow for the development of clinically significant pelvic pain and urinary urgency clusters. Ultimately, three pain clusters and two urinary urgency clusters were developed that were both statistically and clinically meaningful. Pelvic Pain clusters revealed “no”, “low” and “high” variability clusters, which is in line with previous UCPPS studies that have attempted to cluster pain phenotypes21,22. Notably, many of the UCPPS participants enrolled in the study were pelvic-pain free at the time of their 6-month visit; this counterintuitive finding is consistent with other studies of chronic pain conditions that show significant pain-free intervals and often an inability to elicit pain on physical exam17,18,23.
Our third hypothesis stated that variability clusters would be associated with QOL and IIM. Supporting this hypothesis for pelvic pain, we found that participants reporting higher daily pain variability had significantly worse QOL and IIM. Specifically, participants with more pain variability reported worse mood, higher anxiety and worse sleep. Importantly, these associations persisted even when controlling for their baseline pain, age and sex (in cluster development), implying that pain variability may be an independent driver of overall disease morbidity.
Variability of urinary urgency clusters (low/high) was not associated with any QOL or IIMs. Furthermore, membership in high and low pelvic pain and urgency variability clusters did not readily overlap. Moreover, the trajectory of pelvic pain throughout the day was positive (worse pain), whereas in contrast the urgency trajectory was negative (improved urgency). The discordance between urinary and pelvic pain variability in this cohort was not surprising, as prior work in this cohort has shown that pelvic pain and urinary urgency are frequently found together, but are independently associated with QOL and IIMs,29.
There are limitations that must be addressed. First, this study focused on the relationship between pelvic pain, urinary urgency and QOL/IIM; other EMA data collected on non-pelvic pain data were not included here. Second, although smartphone utilization is now nearly ubiquitous in the US population, the requirement of participants to have their own smartphone may mean the results are not fully representative of the UCPPS population. Third, engagement with the M-app was robust for most participants, but it is possible that patient fatigue with the notifications may have led some to report “zero pain” when pain was indeed present. Finally, variability of pain was used to place patients into clusters that more readily discount baseline and highest pain, but other alternative pain indices, such as variability measured by simple “standard deviation from mean”, or “time in highest pain,” may prove to be more clinically useful. These, along with other alternative pain indices obtained with EMA data will need to be studied further.
Conclusions:
EMA pain data derived from a smartphone app developed specifically for the MAPP study revealed that pelvic pain variability, though not urinary urgency variability, was an independent predictor of worse quality of life and more severe illness impact in UCPPS participants after controlling for average pain. The implications from this finding suggest that alternative pain indices, such as pain variability and pain unpredictability, may be useful adjuncts to traditional measures of pain when assessing the impact of treatments and interventions for UCPPS.
Supplementary Material
Ethics and Integrity Statement:
Full data will be made available upon request. This is an NIDDK/NIH funded project. Funding for the MAPP Research Network was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH) (DK82370, DK82342, DK82315, DK82344, DK82325, DK82345, DK82333, and DK82316.). Additional funding was obtained for WG and MY through a separate grant mechanism (R01-DK117208). We have no conflicts of interest regarding the findings and content of this manuscript to disclose. IRB approval was obtained at each participating site and all subjects provided consent to participate with signed consent forms available upon request. All materials in the manuscript are original and thus, permission from other sources is not required. This is an observational study and thus, it was not registered as a clinical trial
Appendix 1. M-app Survey Questions delivered by phone notification 4-times daily: 1) 1 hour after “usual wake-up time”; 2) 4 hours after wake-up notification; 3) 8 hours after wake-up notification; 4) 1 hour before “usual bedtime"
| Construct | Item | Response Options | Frequency of Item | |
|---|---|---|---|---|
| Urinary Urgency | How would you rate your level of urinary urgency over the last hour? | Sliding scale 0-10 10 = unbearable urgency |
Four times per day | |
| Stress | What is your stress level right now? | Sliding scale 0-10 10 = unbearable stress |
Four times per day | |
| Negative Emotion | How intense are your negative emotions right now, including things like sadness, anxiety, and anger? | Sliding scale 0-10 | Four times per day | |
| Positive Emotion | How intense are your positive emotions right now, including things like happiness, joy, and relaxation? | Sliding scale 0-10 | Four times per day | |
| Pain | Are you experiencing pain in any body area right now? | Yes No |
Four times per day | |
| If yes… | ||||
| Pain | Looking at the front view, which areas have pain? | Body map front view | If ‘Yes’ on 190, four times per day | |
| Pain | Looking at the back view, which areas have pain? | Body map back view | If ‘Yes’ on 190, four times per day | |
| Pain | How intense is the pain in your Buttocks area right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Back right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Hips/Legs right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Pelvis/Genitalia/Anal area right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Chest/Abdomen right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Shoulders/Arms right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
| Pain | How intense is the pain in your Head/Neck right now? | Sliding scale 0-10 | If marked on body map, four times per day | |
Contributor Information
Bradley A. Erickson, Department of Urology, University of Iowa, Iowa City, IA, United States
James W. Griffith, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
Guo Wensheng, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
You Mengying, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Ted Herman, Department of Computer Sciences, University of Iowa College of Liberal Arts and Sciences, Iowa City, Iowa.
Catherine S. Bradley, Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States
J. Quentin Clemens, Department of Urology, University of Michigan, Ann Arbor, MI, United States.
John T Farrar, Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
Priyanka Gupta, Department of Urology, University of Michigan, Ann Arbor, Michigan, USA.
Karl J Kreder, Department of Urology, University of Iowa, Iowa City, IA, United States.
H. Henry Lai, Division of Urologic Surgery, Department of Surgery, and Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.
Bruce D Naliboff, G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, CA, United States.
Diane K. Newman, Penn Center for Continence and Pelvic Health, Division of Urology, University of Pennsylvania, Philadelphia, Pennsylvania
Larissa V Rodriguez, Department of Urology, Cornell University, New York, NY, United States.
Theresa Spitznagle, Program in Physical Therapy, Washington University School of Medicine, St Louis, Missouri..
Siobhan Sutcliffe, Division of Public Health Sciences, Department of Surgery and the Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
Suzette E Sutherland, Department of Urology, University of Washington, Seattle, WA, USA.
Bayley J. Taple, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
J. Richard Landis, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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