Healthcare provider experiences of deploying a continuous remote patient monitoring pilot program during the COVID-19 pandemic: a structured qualitative analysis.

Objective: To describe the healthcare provider (HCP) experience of launching a COVID-19 remote patient monitoring (CRPM) program during the global COVID-19 pandemic. Methods: We conducted qualitative, semi-structured interviews with eight HCPs involved in deploying the CRPM pilot program in the Military Health System (MHS) from June to December 2020. Interviews were audio recorded, transcribed, and analyzed thematically using an inductive approach. We then deductively mapped themes from interviews to the updated Consolidated Framework for Implementation Research (CFIR). Results: We identified the following main themes mapped to CFIR domains listed in parentheses: external and internal environments (outer and inner settings), processes around implementation (implementation process domain), the right people (individuals domain), and program characteristics (innovation domain). Participants believed that buy-in from leadership and HCPs was critical for successful program implementation. HCP participants showed qualities of clinical champions and believed in the CRPM program. Conclusion: The MHS deployed a successful remote patient monitoring pilot program during the global COVID-19 pandemic. HCPs found the CRPM program and the technology enabling the program to be acceptable, feasible, and usable. HCP participants exhibited characteristics of clinical champions. Leadership engagement was the most often-cited key factor for successful program implementation.

Remote patient monitoring to facilitate same-day discharge after laparoscopic sleeve gastrectomy: a pilot evaluation.

BACKGROUND: Limited hospital inpatient capacity, exacerbated by SARS-CoV-2 (COVID-19) and associated staffing shortages, has driven interest in converting surgeries historically done as inpatient procedures to same-day surgeries (SDS). Remote patient monitoring (RPM) has the potential to increase safety and confidence in SDS but has had mixed success in a bariatric population. OBJECTIVES: Assess the feasibility of and adherence to a protocol offering patients same-day laparoscopic sleeve gastrectomy (SG) supported by RPM with an updated wearable device. Secondary outcomes were readmissions, costs, adherence, and clinical alarm rates. SETTING: Academic, military tertiary referral center (United States). METHODS: A single-center, retrospective case control study of patients undergoing SG, comparing SDS with RPM to patients admitted to the hospital for SG during this time. Patients for SDS were selected by set inclusion/exclusion criteria and patient/surgeon preference, and perioperative management was standardized. RESULTS: Twenty patients were enrolled in the SDS group, then compared with 53 inpatients. Inpatients were older (46 versus 39, P = .006), but with no significant differences in sex, preoperative body mass index, or co-morbidities. RPM wearable and blood pressure adherence was found to be 97% and 80%, respectively. Readmission rates were similar (10% versus 7.5%, P > .05). RPM alarm rates were .5 (0-1.3) per patient for each 24-hour home monitoring period. SDS patients also demonstrated the potential for cost savings over inpatient SG, depending on the number of patients monitored per day as well as the healthcare setting. CONCLUSIONS: SG as SDS with RPM was a feasible approach. It should be evaluated in other surgical procedures and higher-risk patient populations.

Case report: The gait deviation index may predict neurotherapeutic effects of FES-assisted gait training in children with cerebral palsy.

Background: Children with cerebral palsy (CP) show progressive loss of ambulatory function characterized by kinematic deviations at the hip, knee, and ankle. Functional electrical stimulation (FES) can lead to more typical lower limb kinematics during walking by eliciting appropriately timed muscle contractions. FES-assisted walking interventions have shown mixed to positive results in improving lower limb kinematics through immediate correction of gait during the application of FES, or long-term, persisting effects of non-FES-assisted gait improvements following multi-week FES-assisted gait training, at the absence of stimulation, i.e., neurotherapeutic effects. It is unknown, however, if children with CP will demonstrate a neurotherapeutic response following FES-assisted gait training because of the CP population's heterogeneity in gait deviations and responses to FES. Identifying the neurotherapeutic responders is, therefore, important to optimize the training interventions to those that have higher probability of benefiting from the intervention. Objective: The purpose of this case study was to investigate the relationship between immediate and neurotherapeutic effects of FES-assisted walking to identify responders to a FES-assisted gait training protocol. Methods: The primary outcome was Gait Deviation Index (GDI) and secondary outcome was root mean squared error (RMSE) of the lower extremity joint angles in the sagittal plane between participants with CP and a typically developing (TD) dataset. Potential indicators were defined as immediate improvements from baseline during FES-assisted walking followed by neurotherapeutic improvements at the end of training. Case description: Gait analysis of two adolescent female participants with spastic diplegia (Gross Motor Function Classification System level II and III) was conducted at the start and end of a 12-week FES-assisted treadmill training protocol. Participant 1 had scissoring crouch gait, while participant 2 had jump gait. Outcomes: The GDI showed both immediate (presence of FES) and neurotherapeutic (absence of FES after training period) improvements from baseline in our two participants. Joint angle RMSE showed mixed trends between immediate and neurotherapeutic changes from baseline. The GDI warrants investigation in a larger sample to determine if it can be used to identify responders to FES-assisted gait training.

Financial and Clinical Impact of Virtual Care During the COVID-19 Pandemic: Difference-in-Differences Analysis.

BACKGROUND: Virtual care (VC) and remote patient monitoring programs were deployed widely during the COVID-19 pandemic. Deployments were heterogeneous and evolved as the pandemic progressed, complicating subsequent attempts to quantify their impact. The unique arrangement of the US Military Health System (MHS) enabled direct comparison between facilities that did and did not implement a standardized VC program. The VC program enrolled patients symptomatic for COVID-19 or at risk for severe disease. Patients' vital signs were continuously monitored at home with a wearable device (Current Health). A central team monitored vital signs and conducted daily or twice-daily reviews (the nurse-to-patient ratio was 1:30). OBJECTIVE: Our goal was to describe the operational model of a VC program for COVID-19, evaluate its financial impact, and detail its clinical outcomes. METHODS: This was a retrospective difference-in-differences (DiD) evaluation that compared 8 military treatment facilities (MTFs) with and 39 MTFs without a VC program. Tricare Prime beneficiaries diagnosed with COVID-19 (Medicare Severity Diagnosis Related Group 177 or International Classification of Diseases-10 codes U07.1/07.2) who were eligible for care within the MHS and aged 21 years and or older between December 2020 and December 2021 were included. Primary outcomes were length of stay and associated cost savings; secondary outcomes were escalation to physical care from home, 30-day readmissions after VC discharge, adherence to the wearable, and alarms per patient-day. RESULTS: A total of 1838 patients with COVID-19 were admitted to an MTF with a VC program of 3988 admitted to the MHS. Of these patients, 237 (13%) were enrolled in the VC program. The DiD analysis indicated that centers with the program had a 12% lower length of stay averaged across all COVID-19 patients, saving US $2047 per patient. The total cost of equipping, establishing, and staffing the VC program was estimated at US $3816 per day. Total net savings were estimated at US $2.3 million in the first year of the program across the MHS. The wearables were activated by 231 patients (97.5%) and were monitored through the Current Health platform for a total of 3474 (median 7.9, range 3.2-16.5) days. Wearable adherence was 85% (IQR 63%-94%). Patients triggered a median of 1.6 (IQR 0.7-5.2) vital sign alarms per patient per day; 203 (85.7%) were monitored at home and then directly discharged from VC; 27 (11.4%) were escalated to a physical hospital bed as part of their initial admission. There were no increases in 30-day readmissions or emergency department visits. CONCLUSIONS: Monitored patients were adherent to the wearable device and triggered a manageable number of alarms/day for the monitoring-team-to-patient ratio. Despite only enrolling 13% of COVID-19 patients at centers where it was available, the program offered substantial savings averaged across all patients in those centers without adversely affecting clinical outcomes.

Evaluation of physical health status beyond daily step count using a wearable activity sensor.

Physical health status defines an individual's ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

Remote care and triage of obstetric patients with COVID-19 in the community: operational considerations.

BACKGROUND: During the SARS-CoV-2 (COVID-19) pandemic, routine antenatal care was disrupted, and pregnant women positive for COVID-19 were at increased risk of caesarean section, intensive care admission or neonatal unit admission for their baby. Virtual care and telehealth can reduce barriers to care and improve maternity outcomes, and adoption has been encouraged by health authorities in the United Kingdom. METHODS: Norfolk and Norwich University Hospitals Trust deployed a flexible maternity virtual ward (MVW) service using the Current Health platform to care for pregnant women during the pandemic. Patients were monitored either intermittently with finger pulse oximetry or continuously with a wearable device. We outline the MVW technology, intervention and staffing model, triage criteria and patient feedback, as an example of an operational model for other institutions. RESULTS: Between October 2021 and February 2022, 429 patients were referred, of which 228 were admitted to the MVW. Total bed-days was 1,182, mean length of stay was 6 days (SD 2.3, range 1-14 days). Fifteen (6.6%) required hospital admission and one (0.4%) critical care. There were no deaths. Feedback alluded to feelings of increased safety, comfort, and ease with the technology. CONCLUSIONS: The MVW offered a safety net to pregnant women positive for COVID-19. It provided reassurance for staff, while relieving pressures on infrastructure. When setting up similar services in future, attention should be given to identifying clinical champions, triage criteria, technology and alarm selection, and establishing flexible escalation pathways that can adapt to changing patterns of disease.

Deployment of an End-to-End Remote, Digitalized Clinical Study Protocol in COVID-19: Process Evaluation.

BACKGROUND: The SARS-CoV-2 (COVID-19) pandemic may accelerate the adoption of digital, decentralized clinical trials. Conceptual recommendations for digitalized and remote clinical studies and technology are available to enable digitalization. Fully remote studies may break down some of the participation barriers in traditional trials. However, they add logistical complexity and offer fewer opportunities to intervene following a technical failure or adverse event. OBJECTIVE: Our group designed an end-to-end digitalized clinical study protocol, using the Food and Drug Administration (FDA)-cleared Current Health (CH) remote monitoring platform to collect symptoms and continuous physiological data of individuals recently infected with COVID-19 in the community. The purpose of this work is to provide a detailed example of an end-to-end digitalized protocol implementation based on conceptual recommendations by describing the study setup in detail, evaluating its performance, and identifying points of success and failure. METHODS: Primary recruitment was via social media and word of mouth. Informed consent was obtained during a virtual appointment, and the CH-monitoring kit was shipped directly to the participants. The wearable continuously recorded pulse rate (PR), respiratory rate (RR), oxygen saturation (SpO2), skin temperature, and step count, while a tablet administered symptom surveys. Data were transmitted in real time to the CH cloud-based platform and displayed in the web-based dashboard, with alerts to the study team if the wearable was not charged or worn. The study duration was up to 30 days. The time to recruit, screen, consent, set up equipment, and collect data was quantified, and advertising engagement was tracked with a web analytics service. RESULTS: Of 13 different study advertisements, 5 (38.5%) were live on social media at any one time. In total, 38 eligibility forms were completed, and 19 (50%) respondents met the eligibility criteria. Of these, 9 (47.4%) were contactable and 8 (88.9%) provided informed consent. Deployment times ranged from 22 to 110 hours, and participants set up the equipment and started transmitting vital signs within 7.6 (IQR 6.3-10) hours of delivery. The mean wearable adherence was 70% (SD 19%), and the mean daily survey adherence was 88% (SD 21%) for the 8 participants. Vital signs were in normal ranges during study participation, and symptoms decreased over time. CONCLUSIONS: Evaluation of clinical study implementation is important to capture what works and what might need to be modified. A well-calibrated approach to online advertising and enrollment can remove barriers to recruitment and lower costs but remains the most challenging part of research. Equipment was effectively and promptly shipped to participants and removed the risk of illness transmission associated with in-person encounters during a pandemic. Wearable technology incorporating continuous, clinical-grade monitoring offered an unprecedented level of detail and ecological validity. However, study planning, relationship building, and troubleshooting are more complex in the remote setting. The relevance of a study to potential participants remains key to its success.

Participant Experiences of a COVID-19 Virtual Clinical Study Using the Current Health Remote Monitoring Platform: Case Study and Qualitative Analysis.

BACKGROUND: During the COVID-19 pandemic, individuals with a positive viral test were enrolled in a study, within 48 hours, to remotely monitor their vital signs to characterize disease progression and recovery. A virtual trial design was adopted to reduce risks to participants and the research community in a study titled Risk Stratification and Early Alerting Regarding COVID-19 Hospitalization (RiskSEARCH). The Food and Drug Administration-cleared Current Health platform with a wearable device is a continuous remote patient monitoring technology that supports hospital-at-home care and is used as a data collection tool. Enrolled participants wore the Current Health wearable device continuously for up to 30 days and took a daily symptom survey via a tablet that was provided. A qualitative substudy was conducted in parallel to better understand virtual trial implementation, including barriers and facilitators for participants. OBJECTIVE: This study aimed to understand the barriers and facilitators of the user experience of interacting with a virtual care platform and research team, while participating in a fully virtual study using qualitative and quantitative data. METHODS: Semistructured interviews were conducted to understand participants' experience of participating in a virtual study during a global pandemic. The schedule included their experience of enrollment and their interactions with equipment and study staff. A total of 3 RiskSEARCH participants were interviewed over telephone, and transcriptions were inductively coded and analyzed using thematic analysis. Themes were mapped onto the Theoretical Domains Framework (TDF) to identify and describe the factors that influenced study adherence. Quantitative metrics, including adherence to wearable and scheduled tasks collected as part of the RiskSEARCH main study, were paired with the interviews to present an overall picture of participation. RESULTS: All participants exceeded our definition of a fully adherent participant and reported that participation was feasible and had a low burden. The symptoms progressively resolved during the trial. Inductive thematic analysis identified 13 main themes from the interview data, which were deductively mapped onto 11 of the 14 TDF domains, highlighting barriers and facilitators for each. CONCLUSIONS: Participants in the RiskSEARCH substudy showed high levels of adherence and engagement throughout participation. Although participants experienced some challenges in setting up and maintaining the Current Health kit (eg, charging devices), they reported feeling that the requirements of participation were both reasonable and realistic. We demonstrated that the TDF can be used for inductive thematic analysis. We anticipate expanding this work in future virtual studies and trials to identify barriers and enabling factors for implementation.

Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset.

The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO2). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO2, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms' median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden.

Evaluation of Individualized Functional Electrical Stimulation-Induced Acute Changes during Walking: A Case Series in Children with Cerebral Palsy.

Functional electrical stimulation (FES) walking interventions have demonstrated improvements to gait parameters; however, studies were often confined to stimulation of one or two muscle groups. Increased options such as number of muscle groups targeted, timing of stimulation delivery, and level of stimulation are needed to address subject-specific gait deviations. We aimed to demonstrate the feasibility of using a FES system with increased stimulation options during walking in children with cerebral palsy (CP). Three physical therapists designed individualized stimulation programs for six children with CP to target participant-specific gait deviations. Stimulation settings (pulse duration and current) were tuned to each participant. Participants donned our custom FES system that utilized gait phase detection to control stimulation to lower extremity muscle groups and walked on a treadmill at a self-selected speed. Motion capture data were collected during walking with and without the individualized stimulation program. Eight gait metrics and associated timing were compared between walking conditions. The prescribed participant-specific stimulation programs induced significant change towards typical gait in at least one metric for each participant with one iteration of FES-walking. FES systems with increased stimulation options have the potential to allow the physical therapist to better target the individual's gait deviations than a one size fits all device.

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based 'Gold Standard'.

Video- and sensor-based gait analysis systems are rapidly emerging for use in 'real world' scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to 'gold standard' force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: -9.54 ± 0.66 ms, SK: -33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit.

Importance: Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed. Objective: To evaluate novel ambulation metrics in predicting 30-day readmission rates, discharge location, and length of stay using a real-time location system to continuously monitor the voluntary ambulations of postoperative cardiac surgery patients. Design, Setting, and Participants: This prognostic cohort study of the mobility of 100 patients after cardiac surgery in a progressive care unit at Johns Hopkins Hospital was performed using a real-time location system. Enrollment occurred between August 29, 2016, and April 4, 2018. Data analysis was performed from June 2018 to December 2019. Main Outcomes and Measures: Outcome measures included 30-day readmission, discharge location, and length of stay. Digital records of all voluntary ambulations were created where each ambulation consisted of multiple segments defined by distance and speed. Ambulation profiles consisted of 19 parameters derived from the digital ambulation records. Results: A total of 100 patients (81 men [81%]; mean [SD] age, 63.1 [11.6] years) were evaluated. Distance and speed were recorded for more than 14 000 segments in 840 voluntary ambulations, corresponding to a total of 127.8 km (79.4 miles) using a real-time location system. Patient ambulation profiles were predictive of 30-day readmission (sensitivity, 86.7%; specificity, 88.2%; C statistic, 0.925 [95% CI, 0.836-1.000]), discharge to acute rehabilitation (sensitivity, 84.6%; specificity, 86.4%; C statistic, 0.930 [95% CI, 0.855-1.000]), and length of stay (correlation coefficient, 0.927). Conclusions and Relevance: Remote monitoring provides a high-information content description of mobility, incorporating elements of step count (ambulation distance and related parameters), gait speed (ambulation speed and related parameters), frequency of ambulation, and changes in parameters on successive ambulations. Ambulation profiles incorporating multiple aspects of mobility enables accurate prediction of clinically relevant outcomes.

Real-Time Detection of Seven Phases of Gait in Children with Cerebral Palsy Using Two Gyroscopes.

A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP.

Evaluation of Gait Phase Detection Delay Compensation Strategies to Control a Gyroscope-Controlled Functional Electrical Stimulation System During Walking.

Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of stimulation. Wearable sensors provide the necessary input to differentiate gait phases during walking and trigger stimulation. However, gait phase detection is associated with inherent system delays. In this study, five stimulator triggers designed to compensate for gait phase detection delays were tested to determine which trigger most accurately delivered stimulation at the desired times of the gait cycle. Motion capture data were collected on seven typically-developing children while walking on an instrumented treadmill. Participants wore one inertial measurement unit on each ankle and gyroscope data were streamed into the gait phase detection algorithm. Five triggers, based on gait phase detection, were used to simulate stimulation to five muscle groups, bilaterally. For each condition, stimulation signals were collected in the motion capture software via analog channels and compared to the desired timing determined by kinematic and kinetic data. Results illustrate that gait phase detection is a viable finite-state control, and appropriate system delay compensations, on average, reduce stimulation delivery delays by 6.7% of the gait cycle.

Use of a Novel Functional Electrical Stimulation Gait Training System in 2 Adolescents With Cerebral Palsy: A Case Series Exploring Neurotherapeutic Changes.

BACKGROUND AND PURPOSE: Cerebral palsy (CP) is characterized by decreased passive joint range-of-motion and impaired walking, resulting in progressive loss of function. Typical gait training interventions for children with CP appear insufficient to mitigate these effects. The purpose of this case report is to describe the use of a new treadmill-based gait training intervention using active correction with functional electrical stimulation (FES) in 2 adolescents with CP. CASE DESCRIPTION: Two participants with CP (13-year-old girls, Gross Motor Function Classification System [GMFCS] level II and III) trained by walking on a treadmill, with FES assistance, for 30 minutes, 3 times per week, for 12 weeks. The intervention used a feedback control system to detect all 7 phases of gait in real time and triggered FES to the appropriate muscle groups (up to 5 bilaterally) based on the detected gait phase. Joint kinematics, step width, stride length, walking endurance, peak oxygen uptake ($\dot{v}^{o}_{2}$), and oxygen (O2) cost of walking were evaluated preintervention and postintervention. OUTCOMES: Both participants showed improved knee and ankle angles and step width relative to children who are typically developing, and both exhibited increased stride length. One participant (GMFCS III) improved peak $\dot{v}^{o}_{2}$and walking endurance but not O2 cost of walking at her original self-selected walking speed. The other participant (GMFCS II) improved O2 cost of walking but not peak $\dot{v}^{o}_{2}$ or walking endurance. These differences are partly explained by differences in gait type, functional abilities, and initial fitness levels. Most improvements persisted at follow-up, indicating short-term neurotherapeutic effects. DISCUSSION: Most improvements persisted at follow-up, suggesting short-term neurotherapeutic effects. This case series demonstrates the promising utility of FES-assisted gait-training interventions, tailored to target individual gait deviations, in improving walking performance.

Distinguishing positions and movements in bed from load cell signals.

OBJECTIVE: To characterize and classify six positions and movements for individuals in a bed using the output signals of four load cell sensors. APPROACH: A bed equipped with four load cell sensors and synchronized video was used to assess the load cell response of 54 healthy individuals in prescribed positions and as they moved between positions. Stationary positions were characterized by the signals from the four load cells and the coordinates of the center of mass (CoM). Movements were characterized by the changes in load cell signals, four parameters associated with the trajectory of the CoM between the initial and final position (Euclidean distance, length of the trajectory, and the x- and y- variances), and the initial position's CoM coordinates. Classification and decision tree models were used to assess the ability of these parameters to identify specific positions or movements. MAIN RESULTS: Six positions were classified with an accuracy of 74.9% and six movements were classified with an accuracy of 79.7%. SIGNIFICANCE: This study demonstrates the feasibility of distinguishing certain positions and movements with load cell sensors. The identification of positions and movements for individuals in bed can be used as a tool in a variety of clinical settings.