Smartphone-Facilitated N-of-1 Trials for Improving Safety of Analgesic Regimens 1David Odineal, 2Rima Cabrera, 2Maria Marois, 2Sarah Benedetti, 2Peach.

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Smartphone-Facilitated N-of-1 Trials for Improving Safety of Analgesic Regimens 1David Odineal, 2Rima Cabrera, 2Maria Marois, 2Sarah Benedetti, 2Peach Dounias, 2,3Richard Kravitz 1School of Medicine, University of California, Davis; 2Center for Healthcare Policy and Research, University of California, Davis; 3Department of Internal Medicine, University of California, Davis INTRODUCTION Table 2. Proportion of patients with difference in opioid prescriptions between index visit and 6 months The alleviation of pain is a primary goal of medicine, yet a purely pharmacological approach is far from perfect. All drugs have side effects, especially in combination and with pre-existing comorbidities. Opioids have significant addiction potential, even when prescribed responsibly. While greater patient engagement with care might facilitate overuse of opioids, it can also lead to improved outcomes. Psychological effect: N-of-1 trials engage patients in care by encouraging discussions with the clinician. Individual prescribing evidence: N-of-1 trials may demonstrate to patient and clinician that non-opioid regimens and non-pharmacological treatments are as effective as opioids for a given patient. Greater cognizance: By encouraging patients to self-monitor their pain, N-of-1 trials may increase patient recognition of beneficial/aggravating factors, and thus decrease need for pharmacologic analgesia. Hypothesis: Participation in N-of-1 trials will result in the prescription of fewer opioids at the end of 6 months, compared to prescriptions prior to enrollment in the study. Measure: Average amount of prescribed opioids - in morphine equivalents – at index and 6 month follow up visits Table 1. Demographic characteristics of patients with chart abstraction data Opioid difference OVERALL INTERVENTION CONTROL p-value Taking less opioids at 6 months 37 (19.5%) 19 (19.6%) 18 (19.4%)   Same MME at both index and 6-months (no change) 129 (67.9%) 66 (68.0%) 63 (67.7%) Taking more opioids at 6 months 24 (12.6%) 12 (12.4) 12 (12.9%) 0.9   Overall (N=190) n (%) Intervention. (N=97) Control (N=93) P-value Site UCD VA 103 (54.2%) 87 (45.8%) 52 (53.6%) 45 (46.4%) 51 (54.8%) 42 (45.2%) 0.9 Age 35 years or less 36-45 46-55 56-65 66+ 15 (7.9%) 16 (8.4%) 58 (30.5%) 67 (35.3%) 34 (17.9%) 8 (8.3%) 4 (4.1%) 35 (36.1%) 37 (38.1%) 13 (13.4%) 7 (7.5%) 12 (12.9%) 23 (24.7%) 30 (32.3%) 21 (22.6%) 0.06 Male 100 (52.6%) 48 (51.6%) 0.8 Marital Status Married Widowed Divorced Separated Never Married Living with Partner 121 (63.7%) 9 (4.7%) 30 (15.8%) 7 (3.7%) 62 (63.9%) 17 (17.5%) 2 (2.1%) 7 (7.2%) 5 (5.2%) 59 (64.4%) 5 (5.4%) 13 (14.0%) 9 (9.7%) 2 (2.2%) 0.6 Latino/Hispanic 21 (11.1%) 13 (13.5%) 8 (8.6%) 0.3 Race White Black/African American Asian Other 136 (71.6%) 25 (13.2%) 12 (6.3%) 17 (9.0%) 64 (66.0%) 12 (12.4%) 72 (77.4%) 4 (4.3%) 0.09 Employment Full time Part time Retired/Unable/Not currently working 80 (42.1%) 95 (50.0%) 41 (47.3%) 49 (50.5%) 39 (41.9%) 46 (49.5%) Education HS grad Some college AA/Vocational training Bachelor’s degree Post baccalaureate degree 44 (23.2%) 50 (26.3%) 47 (24.7%) 4 (26.7%) 23 (52.3%) 27 (54.0%) 27 (57.5%) 16 (57.5%) 11 (11.8%) 20 (21.5%) 18 (19.4%) Experience w/mobile device Less than 6 months Between 6 months-1 year More than 1 year 10 (5.3%) 11 (5.8%) 169 (89.0%) 85 (87.6%) 6 (6.5%) 3 (3.2%) 84 (90.3%) Downloaded apps on device 168 (88.4%) 85 (50.6%) 83 (89.3%) 0.7 Used WiFi for internet 173 (91.1%) 87 (89.7%) 86 (92.5%) 0.5 STUDY LIMITATIONS Medication data for this analysis was obtained through patient medical records, and the validity of the study relies on the assumption that the medication list was kept up to date. We suspect that highly controlled substances such as opioids are usually kept up to date. In determining morphine equivalents, we calculated the maximal dose prescribed by the clinician. Patients may take less than the maximal prescribed dose, making the calculated dose greater than the dose that the patient is actually taking. Thus our analysis explores the safety of prescribed regimen, and not the regimen that the patient is taking. Figure 2. Flowchart of study recruitment and enrollment RESULTS METHODS CONCLUSIONS The control group was prescribed an average of 27.68 mg of morphine equivalents per day (MME) before the index visit, and 39.60 MME at 6 months. The intervention group was prescribed an average of 31.01 MME before the index visit, and 34.55 MME at 6 months. The comparison between intervention and control groups on MME at index visit was not statistically significant, p-value = 0.67. Likewise, MME comparison at 6 months was not significant, p-value = 0.61. While the results did not show significant improvement in the safety of analgesic prescribing, they also do not appear to reduce the safety of analgesic prescribing. This suggests that increasing patient engagement will not encourage drug-seeking behavior. Thus, n-of-1 trials are still a promising option for increasing patient engagement. The primary aim of the PREEMPT study examines whether intervention patients show greater improvement in pain interference compared to the control (usual care) group. If n-of-1 trials achieve a higher level of analgesia than standard treatment, but with similar safety, n-of-1 trials may be preferred for some patients. It must be noted that the Trialist© app is not designed to enhance the safety of prescribing. Improving the safety of analgesic prescribing was also not a stated goal of the clinicians in the study. Further research is warranted to determine whether n-of-1 trials may be more effective than standard care in improving the safety of analgesic prescribing, when improving safety is an explicit goal. The PREEMPT Study enrolled patients with chronic pain seen in primary care settings. Patients in the intervention group worked with their clinicians and set up a personalized trial (n-of-1) to compare two treatments for pain. These treatments could consist of analgesic regimens or complementary therapies or a combination. Patients were recruited from Family Medicine and Internal Medicine within the UC Davis Primary Care Network and VA Northern California Health Care System at Mather and McClellan. 26 UC Davis-affiliated and 15 VA-affiliated clinicians enrolled at least 1 patient in the study. Out of 1092 patients with chronic pain assessed for eligibility, 215 were enrolled and randomized into the study. 190 completed 6 months in the study and were included in the chart abstraction component. Study patients completed surveys at the index visit, 3-, 6- and 12-months obtaining demographic characteristics and study outcomes, including pain interference, pain intensity, participatory decision making and patient satisfaction. Patients completing 6 months in the study and with signed HIPAA Authorization were included in the medical record abstraction. Records were abstracted for the following information: pain type and location, coexisting diagnoses, concomitant medications, tobacco/drug/alcohol/Rx/marijuana abuse, actively prescribed non-opioid and opioid medications just prior to index visit and at 6 months. Medication list in the chart notes for the index visit and 6 month visit was used to determine the prescribed analgesic regimens. Opiate prescriptions were converted to morphine equivalents and averages were compared from index to 6 month follow-up visits. Protocol Paper Barr C, Marois M, Sim I, Schmid C, Wilsey B, Ward D, Duan N, Hays R, Selsky J, Servadio J, et al. 2015. The PREEMPT study – evaluating smartphone-assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial. Trials. 16:67 FUNDING Figure 3. Bar chart of prescribed opioids, in morphine equivalents Grant Number: 1R01NR013938