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Tag: assessment
01/31/2023

PTSD Study Uses Precision Medicine Tech from AiCure

Article Excerpt: AiCure’s AI-powered digital biomarker solution enables remote detection of subtle changes in a patient’s health status and response to treatment by capturing audio and visual data between clinic visits. Accessed through AiCure’s Patient Connect application, patients use their smartphone’s front-facing camera to complete brief assessments. AiCure’s algorithm then analyzes behavior, such as emotional expressivity, physical movement and speech patterns. By frequently aggregating these sensitive, objective insights, AiCure empowers pharmaceutical companies to improve their understanding of the disease and treatment side effects, elevating the integrity of their trial data, and optimizing patient outcomes.

Full Article: https://tinyurl.com/yystxpws

Article Source: Applied Clinical Trials

10/03/2022

Mobile App–Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review

Chan E, Sun Y, Aitchison K, Sivapalan S. Mobile App–Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review. JMIR Form Res 2021;5(1):e13770 DOI: 10.2196/13770

to determine the state of evidence for feasibility and validity of mobile app-based self-report questionnaires as tools for monitoring of bipolar symptoms. All papers published in English that assessed adherence to and validity of mobile app-based self-report surveys for monitoring patients with bipolar disorder were included. A total of 13 articles were identified. Four studies assessed the concurrent validity of mobile self-report tools and all 4 found a statistically significant association between mood ratings collected via mobile app self-report and clinical assessment using the Hamilton Depression Rating Scale or Montgomery-Asberg Depression Rating Scale. . Two studies observed statistically significant associations between data collected via mobile app self-report tools and instruments assessing clinically- related factors. Satisfactory adherence rates (at least 70%) were observed in all but 1 study that used a once-daily assessment. There was a wide range of adherence rates in studies using twice-daily assessments (42-95%). Overall, the review demonstrated that mobile app-based self-report instruments are valid relative to established assessment methods for measuring symptoms of mania and depression in patients with bipolar disorder. Future research is needed to evaluate feasibility of mobile self-report methods for identifying acute episodes and to inform insights into differences between patients with bipolar disorder and those without lived experience of this condition.

07/25/2022

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Megerian JT, Dey S, Melmed RD, Coury DL, Lerner M, Nicholls CJ, Sohl K, Rouhbakhsh R, Narasimhan A, Romain J, Golla S, Shareef S, Ostrovsky A, Shannon J, Kraft C, Liu-Mayo S, Abbas H, Gal-Szabo DE, Wall DP, & Taraman S (2022). Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. NPJ Digital Medicine, 5(1), 57–57. https://doi.org/10.1038/s41746-022-00598-6

Researchers conducted a double-blinded, multi-site, active comparator cohort study to test the accuracy of artificial intelligence software for diagnosing autism spectrum disorder (ASD). The software device collects data about child behavioral features from 3 sources (caregiver questionnaire, analysis of two short 1 minute home videos recorded and uploaded by the child’s caregiver, provider questionnaire). Data are processed using a machine learning algorithm to indicate whether a person is ASD positive, ASD negative, or inconclusive (i.e., inputted data are not sufficient for a predictive output). Researchers evaluated the software in a study with 425 children aged 18-72 months for whom a caregiver or provider had a concern about developmental delay. Researchers compared the software outputs to the clinical standard (diagnosis made by a provider based on DSM-5 criteria). Results demonstrated that data collection with the software device took less time to administer and require less specialty training relative to clinical standard process. For about 33% of the sample, the algorithm output supported accurate diagnoses compared with clinical evaluation. Of the children for whom the software algorithm made a definite evaluation, 98.4% with clinically diagnosed ASD received an ASD positive result and 78.9% without a clinical diagnosis of ASD received an ASD negative result. All children who received a false-positive result (n=15) had a non-ASD developmental condition. Only one child received a false negative result in this study. Overall, this machine learning tool demonstrated high sensitivity and good specificity for diagnosing ASD. The tool can potentially expand the ability to effectively diagnose children with ASD in primary care to facilitate early intervention and more efficient use of specialist resources.

07/19/2021

Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation

Byrne ML, Lind MN, Horn SR, Mills KL, Nelson BW, Barnes ML, Slavich GM, Allen NB. (2021). Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation. Digital Health. https://doi.org/10.1177/20552076211037227

Researchers conducted a pilot study to validate a mobile sensing collection tool called Effortless Assessment of Risk States with measures of stress, mental health, sleep duration and inflammation. The study collected affective text language from smartphones among 25 young adult participants at a university. Participants installed a custom keyboard on their phones that collect every third word typed across all apps and the researchers analyzed text sentiment using a software package. The study collected data at two timepoints: once during a relatively less academically demanding period and once during a final exam period when participants are likely to be more stressed. Measures of stress, mental health and sleep are self-reported surveys. Saliva samples were collected to assess inflammation by analyzing the level of sCRP protein. Results indicate that the total number of positive words, total of negative words, and total of emotion expression words were strongly associated with lifetime stress exposure. Total negative words were found to be associated with decreased hours of sleep. Affective language was also shown to be associated with higher levels of stress and lower sCRP protein levels. Findings support the potential of using a mobile sensing tool to identify high stress and stress-related problems. For future directions, it could be helpful to develop a tool that can collect and analyze phrases of text (rather than words) and use alternate mobile sensing tools outside of keyboard usage.

07/26/2019

The past, present and future of opioid withdrawal assessment: A scoping review of scales and technologies

Nuamah JK, Sasangohar F, Erranguntla M, Mehta RK. (2019). The past, present and future of opioid withdrawal assessment: A scoping review of scales and technologies. BMC Medical Informatics and Decision Making. 19(113). doi: 10.1186/s12911-019-0834-8

Researchers conducted a scoping literature review for methods of assessing opioid withdrawal symptoms to describe current approaches to monitoring opioid withdrawal symptoms and identify areas for innovation. Read More

11/30/2018

Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: Development of the Mobile Agnew Relationship Measure (mARM) questionnaire

Berry K, Salter A, Morris R, James S, Bucci S. (2018). Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: Development of the Mobile Agnew Relationship Measure (mARM) questionnaire. Journal of Medical Internet Research. 20(4): e90. doi: 10.2196/jmir.8252

Researchers conducted a three-phase study to guide adaptation of the Agnew Relationship Measure (ARM) for mobile mental health interventions as a part of a larger trial of a mobile application (app) for people experiencing early psychosis (Acticisst). Read More

10/19/2018

Adapting the Consolidated Framework for Implementation Research to create organizational readiness and implementation tools for Project ECHO

Serhal E, Arena A, Sockalingam S, Mohri L, Crawford A. (2018). Adapting the Consolidated Framework for Implementation Research to create organizational readiness and implementation tools for Project ECHO. Journal of Continuing Education in the Health Professions. 38(2): 145-151. doi: 10.1097/ceh.0000000000000195

Project Extension for Community Health Outcomes (ECHO) uses a “hub and spoke” model to promote knowledge sharing between health care specialists and primary care providers. Citing the need for organizations to evaluate capacity to successfully implement and sustain ECHO prior to implementation, researchers developed 2 tools to evaluate readiness for implementation and improve quality of implementation activities. Read More

08/03/2018

Rapid and accurate behavioral health diagnostic screening: Initial validation study of a web-based, self-report tool (the SAGE-SR)

Brodey B, Purcell SE, Rhea K, et al. (2018). Rapid and accurate behavioral health diagnostic screening: Initial validation study of a web-based self-report tool (the SAGE-SR). Journal of Medical Internet Research. 20(3): e108. doi: 10.2196/jmir.9428

Researchers developed the Screening Assessment for Guiding Evaluation-Self Report (SAGE-SR), a computerized self-report assessment of Diagnostic Statistical Manual-5 (DSM-5) diagnoses to improve the time commitment and clinician burden involved in administering the Structured Clinical Interview for DSM-5 (SCID-5). Read More

05/04/2018

“Wish you were here”: Examining characteristics, outcomes, and statistical solutions for missing cases in web-based psychotherapeutic trials.

Karin E, Dear BF, Heller GZ, Crane MF, Titov N. (2018). “Wish you were here”: Examining characteristics, outcomes, and statistical solutions for missing cases in web-based psychotherapeutic trials. JMIR Mental Health. 5(2): e22. doi: 10.2196/mental.8363

Researchers used data from 3 randomized controlled trials of web-based cognitive behavioral therapy (N=820) to evaluate characteristics of participants missing at post-treatment assessment, but who responded to 3-month follow-up assessments (n=55; re-contacted cases) to identify how to best address missing cases in web-based psychotherapy trials. Read More