Citations

Background: The incidence of contralateral anterior cruciate ligament (ACL) injury after ACL reconstruction (ACLR) is high. Often, return-to-sport (RTS) tests of strength and functional hopping rely on limb symmetry indices (LSIs) to identify deficits, although the utility of these measures in athletes with bilateral ACL injuries is unknown. Purpose/Hypothesis: The aim of this study was to investigate if LSIs used as RTS criteria in female patients after unilateral ACLR were appropriate for female patients with bilateral ACL involvement. The hypothesis tested was that asymmetries with traditional LSI measures would be present in a population after unilateral ACLR but would not be present in a population with bilateral ACLR due to the lack of a healthy internal control limb. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 45 female subjects were classified into 3 groups: after second (contralateral) ACLR (ACLR-B; n = 15); after primary unilateral ACLR (ACLR-U; n = 15); and uninjured controls (CTRL; n = 15). After being cleared for RTS, each subject completed a single-legged hop for distance (SLHD), triple hop for distance (THD), and triple-crossover hop for distance (CHD) test, in addition to an isometric quadriceps strength test on both limbs. Means and LSI ([ involved limb/ uninvolved limb] × 100) were calculated for each test. Limb symmetry deficits were defined by LSI <90%. Results: Analysis of functional hop testing revealed a side × group interaction for SLHD ( P = .001), THD ( P = .019), and CHD ( P = .04). Side-to-side differences were found in the ACLR-U group for all hop tests ( P = .001-.003) and in the ACLR-B group for SLHD ( P = .002) and THD tests ( P = .024). No side-to side differences were seen in the CTRL group ( P > .05). A side × group interaction was found for isometric quadriceps strength ( P = .006), with lower LSI seen in the ACLR-U group (81% ± 17.6%) compared with the CTRL group (102.2% ± 10.8%) and the ACLR-B group (95.6% ± 24.9%). Although no interaction was seen, side-to-side differences were noted in the THD in the ACLR-U group ( P = .013) and ACLR-B group ( P = .024) and in the CHD in the ACLR-U group ( P = .001). Despite absence of an LSI deficit, bilateral peak quadriceps strength in the ACLR-B group was comparable to the involved limb of the ACLR-U group and less than in the CTRL group ( P = .012). Conclusion: Both the ACLR-U and the ACLR-B groups demonstrated side-to-side deficits during functional hop tests; however, these deficits were not identified according to the clinically accepted LSI values of ≥90%, calling into question the efficacy of current RTS criteria. At the time of RTS, only individuals in the ACLR-U group demonstrated an altered LSI in quadriceps strength. Significantly lower quadriceps strength of both limbs in the ACLR-B group was seen compared with the CTRL group, despite no LSI deficits in quadriceps strength. Current use of LSIs during strength and performance tests may not be an appropriate means of identifying residual deficits in female patients after bilateral ACLR at time of RTS. Furthermore, a better indicator of strength performance in this population may need to include a comparison of strength performance values to the normative values of healthy controls.

Background: The Apple (Cupertino, CA) HealthKit is a new telemonitoring platform that promises to make it easier for patients and healthcare institutions to collect, transmit, and store data from devices that monitor common conditions such as diabetes, hypertension, and asthma. To assess the potential use for this platform in primary care, we need to know how many Apple Healthkit users there are and if they have conditions that could benefit from telemonitoring. Materials and Methods: We examined patients in the Mayo Clinic primary care practice in Rochester, MN, who registered to connect to their Mayo Clinic medical record with Apple HealthKit. We used the primary care registry to identify users with chronic conditions of diabetes, hypertension, asthma, and depression. We also examined users for recent measurements of blood pressure, glucose, hemoglobin A1C, and cholesterol. Results: Of 98,151 patients there were 503 registrants of HealthKit. There were 95 (19%) who had hypertension, 37 (7.4%) who had diabetes, 125 (25%) who had depression, and 56 (11%) who had asthma. Overall, there were 245 (49%) who had readily telemonitorable conditions. Conclusions: Almost half of primary care Apple HealthKit registrants have conditions that could benefit from telemonitoring. This pre-installed telemonitoring platform, available on every new iPhone ® (Apple), can be used to monitor a significant number of primary care patients. However, it also has continued provider and informatics barriers that need to be addressed.