Background Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. Objective The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both. Methods A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information. Results The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially. Conclusions A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.
1574 Background: Most treatment guidelines recommend chemotherapy at maximum tolerated doses, which does not always lead to optimal efficacy, but implicitly results in toxicity. To overcome this challenge, we developed CURATE.AI, a small data, AI-derived platform that harnesses only a patient’s own prospectively/longitudinally acquired data to dynamically identify their own optimal and personalized doses. We subsequently harnessed CURATE.AI to dynamically modulate individualized chemotherapy doses for patients in a prospective clinical trial. Methods: We conducted an open-label, multi-center, single-arm, prospective feasibility trial in patients diagnosed with advanced solid tumors and treated with single-agent capecitabine, XELOX or XELIRI (+/- biologics) (NCT04522284). The standard-of-care (SOC) capecitabine dose was 1000 mg/m2, unless adjusted by clinician to account for patient’s comorbidities and organ dysfunction. Using an AI-discovered second-order correlation between patient-specific variation of capecitabine doses and corresponding tumor marker (CEA, CA19-9 or CA-125) readouts for each cycle, CURATE.AI generated individualized patient digital avatars and recommended bespoke dose for the subsequent cycle. The clinicians were permitted to accept CURATE.AI dose recommendations, or reject the recommendations and dose based on clinical judgement. Results: Since August 2020 we recruited ten patients: single-agent capecitabine (n = 1), XELOX (n = 6), and XELIRI (n = 3). As of 20 Jan 2022, one patient remains on the trial. The prescribed dose was on average reduced by 20 % (± 13.8 %) as compared to the projected SOC dose. The nine reported patients completed 3.9 cycles (± 2.2 cycles), with the longest participation lasting 8 cycles. CURATE.AI recommendations were considered in 27 out of 40 total dosing decisions and accepted for prescription in 26 of those decisions. The reasons for not considering CURATE.AI included insufficient time from patient recruitment to the first dose administration and complex medical circumstances at the time of the dosing decisions. Conclusions: CURATE.AI has been successfully incorporated into the clinical workflow of dynamic dose selection in the treatment of solid tumors under a clinical trial. Prospective validation of CURATE.AI led to a reduction of an average prescribed capecitabine dose, which alongside additional preliminary findings may eventually play an important role in improving patient response rates and durations compared to SOC. Results from the PRECISE CURATE.AI trial support the initiation of a randomized clinical trial and potential expansion towards other oncologic indications. Clinical trial information: NCT04522284.
Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations.Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology.Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals.Trial registration number: NCT04522284
Background Digital therapeutics (DTx), a class of software-based clinical interventions, are promising new technologies that can potentially prevent, manage, or treat a spectrum of medical disorders and diseases as well as deliver unprecedented portability for patients and scalability for health care providers. Their adoption and implementation were accelerated by the need for remote care during the COVID-19 pandemic, and awareness about their utility has rapidly grown among providers, payers, and regulators. Despite this, relatively little is known about the capacity of DTx to provide economic value in care. Objective This study aimed to systematically review and summarize the published evidence regarding the cost-effectiveness of clinical-grade mobile app–based DTx and explore the factors affecting such evaluations. Methods A systematic review of economic evaluations of clinical-grade mobile app–based DTx was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Major electronic databases, including PubMed, Cochrane Library, and Web of Science, were searched for eligible studies published from inception to October 28, 2022. Two independent reviewers evaluated the eligibility of all the retrieved articles for inclusion in the review. Methodological quality and risk of bias were assessed for each included study. Results A total of 18 studies were included in this review. Of the 18 studies, 7 (39%) were nonrandomized study–based economic evaluations, 6 (33%) were model-based evaluations, and 5 (28%) were randomized clinical trial–based evaluations. The DTx intervention subject to assessment was found to be cost-effective in 12 (67%) studies, cost saving in 5 (28%) studies, and cost-effective in 1 (6%) study in only 1 of the 3 countries where it was being deployed in the final study. Qualitative deficiencies in methodology and substantial potential for bias, including risks of performance bias and selection bias in participant recruitment, were identified in several included studies. Conclusions This systematic review supports the thesis that DTx interventions offer potential economic benefits. However, DTx economic analyses conducted to date exhibit important methodological shortcomings that must be addressed in future evaluations to reduce the uncertainty surrounding the widespread adoption of DTx interventions. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42022358616; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022358616
Background: Faced with a trade-off between efficacy and toxicity, oncologists have conventionally administered the maximum tolerated doses in chemotherapy on an assumption that higher doses increase efficacy. However, multiple studies have shown that this method of toxicity-guided dosing may result in more frequent toxicities and potentially suboptimal efficacy. With the advent of artificial intelligence (AI), personalized dosing in chemotherapy may be considered to optimize patient care. CURATE.AI is an efficacy-driven, indication-agnostic and mechanism-independent personalized dosing platform that may offer an optimal solution. In contrast to traditional AI approaches based on massive volumes of population data, CURATE.AI requires only the individual patient's medical profile for dose recommendations. Based on the observation that the relationship between a drug dose and a phenotypic response in a human system can be modelled by a dynamic quadratic surface, CURATE.AI continually guides dosing throughout the treatment duration to optimize efficacy. While CURATE.AI has been used in various clinical settings, there are no prior randomized controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumors. Therefore, we are conducting a pilot study to assess the technical and logistical feasibility of an RCT for CURATE.AI-guided solid tumor chemotherapy dosing. We aim to collect exploratory data on efficacy and toxicity, and on the use of longitudinal blood tumor marker measurements, including ctDNA, to inform dose guidance decision. Methods: PRECISE is an open-label, single-arm, multi-centre, prospective pilot clinical trial on using CURATE.AI to achieve personalized, efficacy-driven and dynamically optimized chemotherapy dosing for solid tumors (NCT04522284). Adults with metastatic solid tumors and raised baseline tumor marker levels who are planned for palliative-intent, capecitabine-based chemotherapy will be recruited. CURATE.AI will guide drug dosing for each participant based only on their own tumor marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. As an exploratory outcome, we will analyze the utility of tumor markers including CEA, CA19-9 and ctDNA in high frequency serial measurements. We aim to initially enroll 10 participants from 2 hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or a RCT based on initial pilot data. Recruitment of patients began in August 2020. As of December 2020, 2 participants have been enrolled with recruitment planned for 1 year. Citation Format: Chong Boon Teo, Benjamin Kye Jyn Tan, Xavier Tadeo, Siyu Peng, Hazel Pei Lin Soh, Sherry De Xuan Du, Vilianty Wen Ya Luo, Aishwarya Bandla, Raghav Sundar, Dean Ho, Theodore Kee, Agata Blasiak. Personalized, rational, efficacy-driven chemotherapy dosing via an artificial intelligence system (PRECISE): A protocol for the PRECISE CURATE.AI pilot clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT211.
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