N-iPhone Owned iPhone 11 (17) 20 (31) 34 (52) 10 (15) 24 (37) 31 (48) 10 (13) 47 (63) 18 (24) 45 (60) 54 (72) 37 (49) 11 (13) 58 (68) 16 (19) 64 (75) 65 (77) 37 (44) 0.04 0.31 0.46 0.76 12 (16) 27 (36) 36 (48) 13 (15) 34 (40) 38 (45) 0.09 10 (13) 32 (43) 33 (44) 19 (22) 37 (44) 29 (34) 0.87 75 (65) 67 (89) 10 (13) 10 (13) 10 (13) 21 (28) 50 (67) 56 (9.0) 57 (76) Control 85 (65) 71 (84) 17 (20) 10 (12) 19 (22) 30 (35) 62 (73) 55 (9.8) 62 (73) p-value 0.47 0.29 0.26 0.76 0.14 0.41 0.24 0.45 0.39 0.We also did not observe any

N-iPhone Owned iPhone 11 (17) 20 (31) 34 (52) 10 (15) 24 (37) 31 (48) 10 (13) 47 (63) 18 (24) 45 (60) 54 (72) 37 (49) 11 (13) 58 (68) 16 (19) 64 (75) 65 (77) 37 (44) 0.04 0.31 0.46 0.76 12 (16) 27 (36) 36 (48) 13 (15) 34 (40) 38 (45) 0.09 10 (13) 32 (43) 33 (44) 19 (22) 37 (44) 29 (34) 0.87 75 (65) 67 (89) 10 (13) 10 (13) 10 (13) 21 (28) 50 (67) 56 (9.0) 57 (76) Control 85 (65) 71 (84) 17 (20) 10 (12) 19 (22) 30 (35) 62 (73) 55 (9.8) 62 (73) p-value 0.47 0.29 0.26 0.76 0.14 0.41 0.24 0.45 0.39 0.We also did not observe any differences between the groups with respect to office visits (p = 0.46), ONO-4059 web inpatient stays (p = 0.82), emergency room visits (p = 0.06), or pharmacy claims (p = 0.60). The total health insurance claims amount during enrollment also did not differ by condition (p = 0.50), and we similarly observed no differences in claims specific to each condition or XAV-939 biological activity multiple conditions (Table S6). Alternatively, we examined the differences in health care utilization using an equivalence testing approach. Using a magnitude of region of similarity equal to half a standard deviation for each outcome, in general we discovered that health care utilization was roughly equivalent between groups (Table 2). We discovered that monitoring and control groups were roughly equal with respect to total health insurance claims dollars (p = 0.027), pharmacy claims (p = 0.037), office visits (p = 0.038), inpatient stays (p = 0.042), and total hospital visits (p = 0.014). This suggests that there is unlikely to beBloss et al. (2016), PeerJ, DOI 10.7717/peerj.1554 8/Table 2 Health care utilization outcomes. Top: mean (standard deviation); bottom: median (IQR). PDiff, p-value testing difference between control and monitoring group; PEquiv, p-value testing equivalence between groups; *, Median and IQR all zero. Baseline Control N = 85 Total Claims ( ) Condition Claims ( ) Pharmacy Claims ( ) Total Visits (#) Office Visits (#) ER Visits (#)* Inpatient Stays (#)*Follow-up Monitoring N = 75 7,159 (25,251) 990 (2,340) 2,434 (14,296) 117 (387) 1,859 (5,315) 345 (1,164) 4.92 (6.51) 3 (4) 4.05 (4.09) 3 (4) 0.03 (0.17) 0.85 (4.27) Control N = 65 5,596 (22,187) 807 (2,734) 6,165 (37,153) 111 (379) 1,667 (2,780) 611 (1,603) 4.17 (4.21) 2 (7) 3.95 (3.92) 2 (5) 0.05 (0.37) 0.17 (0.89) Monitoring N = 65 6,026 (21,426) 845 (2,273) 630 (21,43) 179 (516) 2,188 (6,340) 340 (1,458) 4.77 (5.35) 3 (5) 4.32 (4.48) 3 (4) 0.06 (0.30) 0.38 (1.88)Mean Difference Control N = 65 1,331 (21,042) 0 (2,372) 4,653 (35,795) 0 (208) 147 (1,057) 11 (531) -0.32 (3.75) 0 (2) -0.15 (3.30) 0 (2) -0.12 (0.72) -0.05 (1.16) Monitoring N = 65 -1,133 (31,465) 0 (1,780) -1,805 (14,406) 0 (283) 329 (1,860) 0 (321) -0.15 (6.35) 0 (3) 0.28 (3.60) 0 (2) 0.03 (0.35) -0.46 (4.30) 0.06 0.82 0.137 0.042 0.46 0.038 0.57 0.014 0.60 0.037 0.50 0.105 PDiff 0.62 PEquiv 0.4,265 (10,190) 961 (3,166) 1,512 (6,868) 163 (375) 1,519 (2,687) 325 (1,590) 4.49 (5.01) 3 (6) 4.11 (4.41) 3 (5) 0.17 (0.60) 0.22 (0.94)substantial short-term changes in health care utilization as a result of the monitoring intervention. We also examined health insurance utilization in a subset of the monitoring group who we were able to assess as being compliant with the study protocol in at least one-third of the weeks of the study. Again, we did not observe any differences with respect to the total amount of health insurance claims (p = 0.17), office visits (p = 0.34), or inpatient stays (p = 0.34). Though there was slight trend towards an incre.N-iPhone Owned iPhone 11 (17) 20 (31) 34 (52) 10 (15) 24 (37) 31 (48) 10 (13) 47 (63) 18 (24) 45 (60) 54 (72) 37 (49) 11 (13) 58 (68) 16 (19) 64 (75) 65 (77) 37 (44) 0.04 0.31 0.46 0.76 12 (16) 27 (36) 36 (48) 13 (15) 34 (40) 38 (45) 0.09 10 (13) 32 (43) 33 (44) 19 (22) 37 (44) 29 (34) 0.87 75 (65) 67 (89) 10 (13) 10 (13) 10 (13) 21 (28) 50 (67) 56 (9.0) 57 (76) Control 85 (65) 71 (84) 17 (20) 10 (12) 19 (22) 30 (35) 62 (73) 55 (9.8) 62 (73) p-value 0.47 0.29 0.26 0.76 0.14 0.41 0.24 0.45 0.39 0.We also did not observe any differences between the groups with respect to office visits (p = 0.46), inpatient stays (p = 0.82), emergency room visits (p = 0.06), or pharmacy claims (p = 0.60). The total health insurance claims amount during enrollment also did not differ by condition (p = 0.50), and we similarly observed no differences in claims specific to each condition or multiple conditions (Table S6). Alternatively, we examined the differences in health care utilization using an equivalence testing approach. Using a magnitude of region of similarity equal to half a standard deviation for each outcome, in general we discovered that health care utilization was roughly equivalent between groups (Table 2). We discovered that monitoring and control groups were roughly equal with respect to total health insurance claims dollars (p = 0.027), pharmacy claims (p = 0.037), office visits (p = 0.038), inpatient stays (p = 0.042), and total hospital visits (p = 0.014). This suggests that there is unlikely to beBloss et al. (2016), PeerJ, DOI 10.7717/peerj.1554 8/Table 2 Health care utilization outcomes. Top: mean (standard deviation); bottom: median (IQR). PDiff, p-value testing difference between control and monitoring group; PEquiv, p-value testing equivalence between groups; *, Median and IQR all zero. Baseline Control N = 85 Total Claims ( ) Condition Claims ( ) Pharmacy Claims ( ) Total Visits (#) Office Visits (#) ER Visits (#)* Inpatient Stays (#)*Follow-up Monitoring N = 75 7,159 (25,251) 990 (2,340) 2,434 (14,296) 117 (387) 1,859 (5,315) 345 (1,164) 4.92 (6.51) 3 (4) 4.05 (4.09) 3 (4) 0.03 (0.17) 0.85 (4.27) Control N = 65 5,596 (22,187) 807 (2,734) 6,165 (37,153) 111 (379) 1,667 (2,780) 611 (1,603) 4.17 (4.21) 2 (7) 3.95 (3.92) 2 (5) 0.05 (0.37) 0.17 (0.89) Monitoring N = 65 6,026 (21,426) 845 (2,273) 630 (21,43) 179 (516) 2,188 (6,340) 340 (1,458) 4.77 (5.35) 3 (5) 4.32 (4.48) 3 (4) 0.06 (0.30) 0.38 (1.88)Mean Difference Control N = 65 1,331 (21,042) 0 (2,372) 4,653 (35,795) 0 (208) 147 (1,057) 11 (531) -0.32 (3.75) 0 (2) -0.15 (3.30) 0 (2) -0.12 (0.72) -0.05 (1.16) Monitoring N = 65 -1,133 (31,465) 0 (1,780) -1,805 (14,406) 0 (283) 329 (1,860) 0 (321) -0.15 (6.35) 0 (3) 0.28 (3.60) 0 (2) 0.03 (0.35) -0.46 (4.30) 0.06 0.82 0.137 0.042 0.46 0.038 0.57 0.014 0.60 0.037 0.50 0.105 PDiff 0.62 PEquiv 0.4,265 (10,190) 961 (3,166) 1,512 (6,868) 163 (375) 1,519 (2,687) 325 (1,590) 4.49 (5.01) 3 (6) 4.11 (4.41) 3 (5) 0.17 (0.60) 0.22 (0.94)substantial short-term changes in health care utilization as a result of the monitoring intervention. We also examined health insurance utilization in a subset of the monitoring group who we were able to assess as being compliant with the study protocol in at least one-third of the weeks of the study. Again, we did not observe any differences with respect to the total amount of health insurance claims (p = 0.17), office visits (p = 0.34), or inpatient stays (p = 0.34). Though there was slight trend towards an incre.

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