App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review.

BACKGROUND: Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. OBJECTIVE: The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. METHODS: We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. RESULTS: We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. CONCLUSIONS: Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

Smartphone movement sensors for the remote monitoring of respiratory rates: Technical validation.

Background: Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues. Objective: The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors. Methods: In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated 'in the wild' was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies. Results: The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 (SD = 1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = -3.27 to 4.89) and 0.08 (-3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use. Conclusions: The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.

A prospective observational real world feasibility study assessing the role of app-based remote patient monitoring in reducing primary care clinician workload during the COVID pandemic.

BACKGROUND: The novel coronavirus disease in 2019 (COVID-19) has placed unprecedented strain on healthcare providers, in particular, primary care services. General practitioners (GP) have to effectively manage patients remotely preserving social distancing. We aim to assess an app-based remote patient monitoring solution in reducing the workload of a clinician and reflect this as time-saved in an economic context. Primary care COVID patients in West London deemed medium risk were recruited into the virtual ward. Patients were monitored for 14 days by telephone or by both the Huma app and telephone. Information on number of phone calls, duration of phone calls and duration of time spent reviewing the app data was recorded. RESULTS: The amount of time spent reviewing one patient in the telephone only arm of the study was 490 min, compared with 280 min spent reviewing one patient who was monitored via both the Huma app and telephone. Based on employed clinicians monitoring patients, this equates to a 0.04 reduction of full-time equivalent staffing I.e. for every 100 patients, it would require 4 less personnel to remotely monitor them. There was no difference in mortality or adverse events between the two groups. CONCLUSION: App-based remote patient monitoring potentially holds large economic benefit to COVID-19 patients. In wake of further waves or future pandemics, and even in routine care, app-based remote monitoring patients could free up vital resources in terms of clinical team's time, allowing a better reallocation of services.

Implementation of a mHealth solution to remotely monitor patients on a cardiac surgical waiting list: service evaluation.

BACKGROUND: The emergence of COVID-19 resulted in postponement of nonemergent surgical procedures for cardiac patients in London. mHealth represented a potentially viable mechanism for highlighting deteriorating patients on the lengthened cardiac surgical waiting lists. OBJECTIVE: To evaluate the deployment of a digital health solution to support continuous triaging of patients on a cardiac surgical waiting list. METHOD: An NHS trust utilized an app-based mHealth solution (Huma Therapeutics) to help gather vital information on patients awaiting cardiac surgery (valvular and coronary surgery). Patients at a tertiary cardiac center on a waiting list for elective surgery were given the option to be monitored remotely via a mobile app until their date of surgery. Patients were asked to enter their symptoms once a week. The clinical team monitored this information remotely, prompting intervention for those patients who needed it. RESULTS: Five hundred and twenty-five patients were on boarded onto the app. Of the 525 patients using the solution, 51 (9.71%) were identified as at risk of deteriorating based on data captured via the remote patient monitoring platform and subsequently escalated to their respective consultant. 81.7% of patients input at least one symptom after they were on boarded on the platform. DISCUSSION: Although not a generalizable study, this change in practice clearly demonstrates the feasibility and potential benefit digital remote patient monitoring can have in triaging large surgical wait lists, ensuring those that need care urgently receive it. We recommend further study into the potential beneficial outcomes from preoperative cardiac mHealth solutions.

The combination of epigallocatechin gallate and curcumin suppresses ER alpha-breast cancer cell growth in vitro and in vivo.

Both epigallocatechin gallate (EGCG) and curcumin have shown efficacy in various in vivo and in vitro models of cancer. This study was designed to determine the efficacy of these naturally derived polyphenolic compounds in vitro and in vivo, when given in combination. Studies in MDA-MB-231 cells demonstrated that EGCG + curcumin was synergistically cytotoxic and that this correlated with G(2)/M-phase cell cycle arrest. After 12 hr, EGCG (25 microM) + curcumin (3 microM) increased the proportion of cells in G(2)/M-phase to 263 +/- 16% of control and this correlated with a 50 +/- 4% decrease in cell number compared to control. To determine if this in vitro result would translate in vivo, athymic nude female mice were implanted with MDA-MB-231 cells and treated with curcumin (200 mg/kg/day, po), EGCG (25 mg/kg/day, ip), EGCG + curcumin, or vehicle control (5 ml/kg/day, po) for 10 weeks. Tumor volume in the EGCG + curcumin treated mice decreased 49% compared to vehicle control mice (p < 0.05), which correlated with a 78 +/- 6% decrease in levels of VEGFR-1 protein expression in the tumors. Curcumin treatment significantly decreased tumor protein levels of EGFR and Akt, however the expression of these proteins was not further decreased following combination treatment. Therefore, these results demonstrate that the combination of EGCG and curcumin is efficacious in both in vitro and in vivo models of ER alpha-breast cancer and that regulation of VEGFR-1 may play a key role in this effect.

Curcumin modulates drug metabolizing enzymes in the female Swiss Webster mouse.

Curcumin, the yellow pigment found in turmeric, exhibits potent chemopreventative properties in both in vivo and in vitro cancer models. We hypothesized that this effect may occur via curcumin-mediated changes in enzymes involved in both carcinogen bioactivation and estrogen metabolism. Female Swiss Webster mice were treated with either curcumin (200 mg/kg or 400 mg/kg, p.o.) or vehicle control for 1 or 2 weeks. The results demonstrated that curcumin had no effect on the catalytic activities of ovarian aromatase, hepatic catechol-O-methyltransferase or hepatic UDP-glucuronosyltransferase. However, both doses of curcumin caused a 25% decrease in CYP1A catalytic activity, but not polypeptide levels, following 2 weeks of treatment. Additionally, following 2 weeks of curcumin at 400 mg/kg, there was a 20% decrease in the catalytic activity and a 28% decrease in polypeptide levels of CYP3A. While 2 weeks of curcumin treatment (400 mg/kg) caused a 20% increase in glutathione S-transferase activity, there was no parallel increase in hepatic stores of the co-factor glutathione. In conclusion small changes in CYP1A, CYP3A and GST following long term treatment (2 weeks) suggest that the combination of all three metabolic pathways may play a small role in curcumin's chemopreventative action.