Agreement of sleep specialists with registered nurses' sleep study orders in supervised clinical practice.

STUDY OBJECTIVES: Incorporating registered nurses (RN-level) into obstructive sleep apnea (OSA) management decisions has the potential to augment the workforce and improve patient access, but the appropriateness of such task-shifting in typical practice is unclear. METHODS: Our medical center piloted a nurse triage program for sleep medicine referrals. Using a sleep specialist-designed decision-making tool, nurses triaged patients referred for initial sleep studies to either home sleep apnea test (HSAT) or in-laboratory polysomnography (PSG). During the first 5 months of the program, specialists reviewed all nurse triages. We compared agreement between specialists and nurses. RESULTS: Of 280 consultations triaged by nurses, nurses deferred management decisions to sleep specialists in 6.1% (n = 17) of cases. Of the remaining 263 cases, there was 88% agreement between nurses and specialists (kappa 0.80, 95% confidence interval 0.74-0.87). In the 8.8% (n = 23) of cases where supervising specialists changed sleep study type, specialists changed from HSAT to PSG in 16 cases and from PSG to HSAT in 7. The most common indication for change in sleep study type was disagreement regarding OSA pretest probability (n = 14 of 23). Specialists changed test instructions in 3.0% (n = 8) of cases, with changes either related to the use of transcutaneous carbon dioxide monitoring (n = 4) or adaptive servo-ventilation (n = 4). CONCLUSIONS: More than 80% of sleep study triages by registered nurses in a supervised setting required no sleep specialist intervention. Future research should focus on how to integrate nurses into the sleep medicine workforce in a manner that maximizes efficiency while preserving or improving patient outcomes.

Effects of Filtering the Center of Pressure Feedback Provided in Visually Guided Mediolateral Weight Shifting.

Thirty healthy adults completed a mediolateral weight-shifting balance task in which they were instructed to shift their weight to visually displayed target regions. A model-based filter and three different moving average filters employing 10, 34, and 58 samples were applied to the center of pressure visual feedback that guided the activity. The effects of filter selection on both the displayed feedback and the shift performance were examined in terms of shift time and non-minimum phase behavior. Shift time relates to feedback delay and shift speed, whereas non-minimum phase behavior relates to the force applied in shift initiation. Results indicated that increasing the number of samples in moving average filters (indicative of stronger filtering) significantly increases shift speed and shift initiation force. These effects indicate that careful selection and documentation of data filtering is warranted in future work and suggest opportunities for strategic filtering of visual feedback in clinical weight-shifting balance activities in order to improve outcomes based on such feedback.

Interpreting lateral dynamic weight shifts using a simple inverted pendulum model.

Seventy-five young, healthy adults completed a lateral weight-shifting activity in which each shifted his/her center of pressure (CoP) to visually displayed target locations with the aid of visual CoP feedback. Each subject's CoP data were modeled using a single-link inverted pendulum system with a spring-damper at the joint. This extends the simple inverted pendulum model of static balance in the sagittal plane to lateral weight-shifting balance. The model controlled pendulum angle using PD control and a ramp setpoint trajectory, and weight-shifting was characterized by both shift speed and a non-minimum phase (NMP) behavior metric. This NMP behavior metric examines the force magnitude at shift initiation and provides weight-shifting balance performance information that parallels the examination of peak ground reaction forces in gait analysis. Control parameters were optimized on a subject-by-subject basis to match balance metrics for modeled results to metric values calculated from experimental data. Overall, the model matches experimental data well (average percent error of 0.35% for shifting speed and 0.05% for NMP behavior). These results suggest that the single-link inverted pendulum model can be used effectively to capture lateral weight-shifting balance, as it has been shown to model static balance.

Relative efficacy of various strategies for visual feedback in standing balance activities.

Seventy-nine young, healthy adults were led through static balance and weight-shifting activities in order to study the effects of visual feedback on balance. Based on their performance, the relative effects of various feedback properties were analyzed: (1) arrangement [direct center of pressure (CoP) vs. lateral weight distribution feedback], (2) numbers (presence vs. absence of numeric feedback), and (3) dimensionality (1D vs. 2D CoP information). In the static balance activity, subjects were instructed to maintain equal weight across both feet; in the dynamic weight-shifting activity, subjects were instructed to shift their weight to each displayed target location. For static balance, lateral symmetry and sway were measured by classical parameters using CoP, center of gravity (CoG), and the difference between the two (CoP-CoG). Weight-shifting balance performance was measured using the time required to shift between target CoP positions. Results indicated that feedback arrangement had a significant effect on static sway and dynamic weight shifting, with direct CoP feedback resulting in better balance performance than lateral weight distribution. Also, numbers had a significant effect on static sway, reducing lateral sway compared to feedback without numbers. Finally, 2D CoP feedback resulted in faster performance than 1D CoP feedback in dynamic weight shifting. These results show that altering different properties of visual feedback can have significant effects on resulting balance performance; therefore, proper selection of visual feedback strategy needs to take these effects into consideration.