Monitoring Activity and Gait in Children (MAGIC) using digital health technologies.

BACKGROUND: Digital health technologies (DHTs) can collect gait and physical activity in adults, but limited studies have validated these in children. This study compared gait and physical activity metrics collected using DHTs to those collected by reference comparators during in-clinic sessions, to collect a normative accelerometry dataset, and to evaluate participants' comfort and their compliance in wearing the DHTs at-home. METHODS: The MAGIC (Monitoring Activity and Gait in Children) study was an analytical validation study which enrolled 40, generally healthy participants aged 3-17 years. Gait and physical activity were collected using DHTs in a clinical setting and continuously at-home. RESULTS: Overall good to excellent agreement was observed between gait metrics extracted with a gait algorithm from a lumbar-worn DHT compared to ground truth reference systems. Majority of participants either "agreed" or "strongly agreed" that wrist and lumbar DHTs were comfortable to wear at home, respectively, with 86% (wrist-worn DHT) and 68% (lumbar-worn DHT) wear-time compliance. Significant differences across age groups were observed in multiple gait and activity metrics obtained at home. CONCLUSIONS: Our findings suggest that gait and physical activity data can be collected from DHTs in pediatric populations with high reliability and wear compliance, in-clinic and in home environments. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04823650 IMPACT: Digital health technologies (DHTs) have been used to collect gait and physical activity in adult populations, but limited studies have validated these metrics in children. The MAGIC study comprehensively validates the performance and feasibility of DHT-measured gait and physical activity in the pediatric population. Our findings suggest that reliable gait and physical activity data can be collected from DHTs in pediatric populations, with both high accuracy and wear compliance both in-clinic and in home environments. The identified across-age-group differences in gait and activity measurements highlighted their potential clinical value.

BET inhibitor nanotherapy halts kidney damage and reduces chronic kidney disease progression after ischemia-reperfusion injury.

Targeting epigenetic mechanisms has emerged as a potential therapeutic approach for the treatment of kidney diseases. Specifically, inhibiting the bromodomain and extra-terminal (BET) domain proteins using the small molecule inhibitor JQ1 has shown promise in preclinical models of acute kidney injury (AKI) and chronic kidney disease (CKD). However, its clinical translation faces challenges due to issues with poor pharmacokinetics and side effects. Here, we developed engineered liposomes loaded with JQ1 with the aim of enhancing kidney drug delivery and reducing the required minimum effective dose by leveraging cargo protection. These liposomes efficiently encapsulated JQ1 in both the membrane and core, demonstrating superior therapeutic efficacy compared to freely delivered JQ1 in a mouse model of kidney ischemia-reperfusion injury. JQ1-loaded liposomes (JQ1-NPs) effectively targeted the kidneys and only one administration, one-hour after injury, was enough to decrease the immune cell (neutrophils and monocytes) infiltration to the kidney-an early and pivotal step to prevent damage progression. By inhibiting BRD4, JQ1-NPs suppress the transcription of pro-inflammatory genes, such as cytokines (il-6) and chemokines (ccl2, ccl5). This success not only improved early the kidney function, as evidenced by decreased serum levels of BUN and creatinine in JQ1-NPs-treated mice, along with reduced tissue expression of the damage marker, NGAL, but also halted the production of extracellular matrix proteins (Fsp-1, Fn-1, α-SMA and Col1a1) and the fibrosis development. In summary, this work presents a promising nanotherapeutic strategy for AKI treatment and its progression and provides new insights into renal drug delivery.

Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study.

INTRODUCTION: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. METHODS: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1-2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. RESULTS: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. CONCLUSIONS: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study.

SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults.

Introduction: Accelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for extracting intuitive, continuous measures of participants' health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep. Methods: In this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation, we include some non-typical features that have shown promise in providing additional context to activity patterns, and provide comparisons to existing algorithms, namely GGIR and the GENEActiv macros. Following this reference comparison, we investigate the association between age and derived physical activity metrics in a healthy adult cohort collected in the Pfizer Innovation Research Lab, comprising 7-14 days of at-home data collected from younger (18-40 years) and older (65-85 years) healthy volunteers. Results: Results showed that activity metrics derived with SKDH had moderate to excellent ICC values (0.550 to 1.0 compared to GGIR, 0.469 to 0.697 compared to the GENEActiv macros), with high correlations for almost all compared metrics (>0.733 except vs GGIR sedentary time, 0.547). Several features show age-group differences, with Cohen's d effect sizes >1.0 and p-values < 0.001. These features included non-threshold methods such as intensity gradient, and activity fragmentation features such as between-states transition probabilities. Discussion: These results demonstrate the validity of the implemented SKDH physical activity algorithm, and the potential of the implemented PA metrics in assessing activity changes in free-living conditions.

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Patient Willingness to Use Digital Health Technologies: A Quantitative and Qualitative Survey in Patients with Cancer Cachexia.

Purpose: The objective of this study was to gain insights into the patients' perspectives on the impact of cancer cachexia on physical activity and their willingness to wear digital health technology (DHT) devices in clinical trials. Patients and Methods: We administered a quantitative 20-minute online survey on aspects of physical activity (on a 0-100 scale) to 50 patients with cancer cachexia recruited through Rare Patient Voice, LLC. A subset of 10 patients took part in qualitative 45-minute web-based interviews with a demonstration of DHT devices. Survey questions related to the impact of weight loss (a key characteristic in Fearon's cachexia definition) on physical activity, patients' expectations regarding desired improvements and their level of meaningful activities, as well as preferences for DHT. Results: Seventy-eight percent of patients reported that their physical activity was impacted by cachexia, and for 77% of them, such impact was consistent over time. Patients perceived most impact of weight loss on walking distance, time and speed, and on level of activity during the day. Sleep, activity level, walking quality and distance were identified as the most meaningful activities to improve. Patients would like to see a moderate improvement of activity levels and consider it meaningful to perform physical activity of moderate intensity (eg, walk at normal pace) on a regular basis. The wrist was the preferred location for wearing a DHT device, followed by arm, ankle, and waist. Conclusion: Most patients reported physical activity limitations since the occurrence of weight loss compatible with cancer-associated cachexia. Walking distance, sleep and quality of walk were the most meaningful activities to moderately improve, and patients consider moderate physical activity as meaningful. Finally, this study population found the proposed wear of DHT devices on the wrist and around the waist acceptable for the duration of clinical studies.

Wearable-Based Stair Climb Power Estimation and Activity Classification.

Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.

Mithramycin delivery systems to develop effective therapies in sarcomas.

BACKGROUND: Sarcomas comprise a group of aggressive malignancies with very little treatment options beyond standard chemotherapy. Reposition of approved drugs represents an attractive approach to identify effective therapeutic compounds. One example is mithramycin (MTM), a natural antibiotic which has demonstrated a strong antitumour activity in several tumour types, including sarcomas. However, its widespread use in the clinic was limited by its poor toxicity profile. RESULTS: In order to improve the therapeutic index of MTM, we have loaded MTM into newly developed nanocarrier formulations. First, polylactide (PLA) polymeric nanoparticles (NPs) were generated by nanoprecipitation. Also, liposomes (LIP) were prepared by ethanol injection and evaporation solvent method. Finally, MTM-loaded hydrogels (HG) were obtained by passive loading using a urea derivative non-peptidic hydrogelator. MTM-loaded NPs and LIP display optimal hydrodynamic radii between 80 and 105 nm with a very low polydispersity index (PdI) and encapsulation efficiencies (EE) of 92 and 30%, respectively. All formulations show a high stability and different release rates ranging from a fast release in HG (100% after 30 min) to more sustained release from NPs (100% after 24 h) and LIP (40% after 48 h). In vitro assays confirmed that all assayed MTM formulations retain the cytotoxic, anti-invasive and anti-stemness potential of free MTM in models of myxoid liposarcoma, undifferentiated pleomorphic sarcoma and chondrosarcoma. In addition, whole genome transcriptomic analysis evidenced the ability of MTM, both free and encapsulated, to act as a multi-repressor of several tumour-promoting pathways at once. Importantly, the treatment of mice bearing sarcoma xenografts showed that encapsulated MTM exhibited enhanced therapeutic effects and was better tolerated than free MTM. CONCLUSIONS: Overall, these novel formulations may represent an efficient and safer MTM-delivering alternative for sarcoma treatment.

Linguistic markers predict onset of Alzheimer's disease.

BACKGROUND: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis. METHODS: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task. We compared the predictive performance of linguistic variables with clinical and neuropsychological variables. The study included 703 samples from 270 participants out of which a dataset consisting of a single sample from 80 participants was held out for testing. Half of the participants in the test set developed AD symptoms before 85 years old, while the other half did not. All samples in the test set were collected during the cognitively normal period (before MCI). The mean time to diagnosis of mild AD was 7.59 years. FINDINGS: Significant predictive power was obtained, with AUC of 0.74 and accuracy of 0.70 when using linguistic variables. The linguistic variables most relevant for predicting onset of AD have been identified in the literature as associated with cognitive decline in dementia. INTERPRETATION: The results suggest that language performance in naturalistic probes expose subtle early signs of progression to AD in advance of clinical diagnosis of impairment. FUNDING: Pfizer, Inc. provided funding to obtain data from the Framingham Heart Study Consortium, and to support the involvement of IBM Research in the initial phase of the study. The data used in this study was supported by Framingham Heart Study's National Heart, Lung, and Blood Institute contract (N01-HC-25195), and by grants from the National Institute on Aging grants (R01-AG016495, R01-AG008122) and the National Institute of Neurological Disorders and Stroke (R01-NS017950).

Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back.

The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson's disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: 0.990 vs. 0.868 in healthy adults) and a previously published algorithm (precision: 0.988 vs. 0.643 in persons with Parkinson's disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features.

Age and environment-related differences in gait in healthy adults using wearables.

Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18-40 years) and older (n = 32, 65-85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.