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IntroductionThe objective of this study is to evaluate the reliability and validity of the ReVeReTM word list recall test (RWLRT), which uses speech recognition, when administered remotely and unsupervised.MethodsProspective cohort study. Participants included 249 cognitively intact community dwelling older adults. Measures included clinician administered neuropsychological assessments at baseline and unsupervised remotely administered tests of cognition from six time-points over six months.ResultsThe RWLRT showed acceptable validity. Reliability coefficients varied across time points, with poor reliability between times 1 and 2 and fair-to-good reliability across the remaining five testing sessions. Practice effects were observed with repeated administration as expected.DiscussionUnsupervised computerized tests of cognition, particularly word list learning and memory tests that use speech recognition, have significant potential for large scale early detection and long-term tracking of cognitive decline due to AD.

Remote inflammation monitoring with digital health technologies (DHTs) would provide valuable information for both clinical research and care. Controlled perturbations of the immune system may reveal physiological signatures which could be used to develop a digital biomarker of inflammatory state. In this study, molecular and physiological profiling was performed following an in vivo lipopolysaccharide (LPS) challenge to develop a digital biomarker of inflammation. Ten healthy volunteers received an intravenous LPS challenge and were monitored for 24 h using the VitalConnect VitalPatch (VitalPatch). VitalPatch measurements included heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin temperature (TEMP). Conventional episodic inpatient vital signs and serum proteins were measured pre‐ and post‐LPS challenge. The VitalPatch provided vital signs that were comparable to conventional methods for assessing HR, RR, and TEMP. A pronounced increase was observed in HR, RR, and TEMP as well as a decrease in HRV 1–4 h post‐LPS challenge. The ordering of participants by magnitude of inflammatory cytokine response 2 h post‐LPS challenge was consistent with ordering of participants by change from baseline in vital signs when measured by VitalPatch (r = 0.73) but not when measured by conventional methods (r = −0.04). A machine learning model trained on VitalPatch data predicted change from baseline in inflammatory protein response (R2 = 0.67). DHTs, such as VitalPatch, can improve upon existing episodic measurements of vital signs by enabling continuous sensing and have the potential for future use as tools to remotely monitor inflammation. [ABSTRACT FROM AUTHOR]

Background: Remote, self‐administered assessment of cognitive impairment offers immense value to all those impacted by Alzheimer's disease: patients, carers, healthcare professionals, researchers and pharmaceutical companies alike. The high cost, slow pace and inaccessibility of traditional methods for evaluating novel assessment tools maintains a chasm between innovation and real‐world impact. Focusing on user‐centered design development and technical feature optimisation, we present the next generation of a digitisation of the examiner‐administered Rey Auditory Verbal Learning Test (RAVLT) (Morrison et al., 2018; Mackin et al., 2017) as a smartphone‐based self‐administered test of verbal episodic memory. Method: A smartphone‐based version of RAVLT was developed, integrating artificial intelligence (AI), psychometric assessment principles and user‐centric design. Key risks to user acceptability were identified with moderated and unmoderated testing of the user interface (UI) in 11 users (aged 50+). AI speech‐recognition solutions implemented to replace traditional human components of RAVLT were tested for accuracy, via comparison to a human listener, in a real‐world environment (N = 13). Result: All testers were able to complete the assessment without additional assistance, indicating high UI acceptability. Qualitative outcomes highlighted potential patient anxiety, insights which were used to iterate the UI design. AI solutions, when implemented alongside data‐driven adjustment features, achieved speech‐recognition accuracy of over 92%. Conclusion: We present extrapolatable principles for developers of remote assessment tools. Thorough inclusion of patients in the development process is essential to ensure that technical and clinical accuracy can be mirrored by optimal adherence and patient experience. Novel technical solutions required to adapt clinic‐based tests for remote use must be validated in ecologically appropriate environments. A fully remote validation study is underway to assess user acceptability and speech‐recognition accuracy at scale in people with subjective memory complaints (SMC). These methods and results hold promise for the continued development of effective at‐home digital tools for remote assessment and reduction of time‐to‐market for innovations in Alzheimer's disease and beyond. References: Morrison et al., 2018, Alzheimer's Dement (Amst), 10, 647‐656. doi.org/10.1016/j.dadm.2018.08.010 Mackin et al., 2017, Alzheimer's & Dementia, 13, 598‐599. doi.org/10.1016/j.jalz.2017.07.239. [ABSTRACT FROM AUTHOR]

Background: Performance validity and test-retest reliability of ReVeRe.D, an iPad-administered cognitive test battery in major depressive disorder (MDD) were analyzed.Methods: Participants aged 18-59 years had DSM-5 diagnosis of MDD with adequate visual and hearing acuity. All had responded to oral antidepressant treatment for a major depressive episode within the most recent 24-months and were stable with no greater than mild depressive symptoms as evidenced by Montgomery Asberg Depression Rating Scale total score <17. Participants were randomly assigned to 1 of 2 test sequences (AABB or BBAA; A=ReVeRe.D; B=examiner-administered tests) in a crossover design.Results: 244 randomized participants (AABB: n=123; BBAA: n=121) had mean age of 38.3 years; 54.9% had a college, baccalaureate, or higher education. At first administration, Pearson correlation coefficients (PCC) for 6/10 pairs of corresponding ReVeRe.D vs examiner-administered tests exceeded the pre-specified acceptance criterion (PCC=0.53) for the primary analysis; 8 test score pairs had PCC exceeding 0.40. At second administration, PCC for 9/10 test scores pairs exceeded PCC=0.53. Together, the series of PCCs supports the concurrent validity for ReVeRe.D. Test-retest reliability for ReVeRe.D test scores was generally moderate to high.Limitations: The study included stable participants with MDD who had responded to oral antidepressant treatment, with most in at least partial remission. The sample was limited to English-speaking participants, and skewed towards white, college-educated women. Further studies in acutely ill MDD patients who represent a broader demographic, are warranted.Conclusions: iPad-administered ReVeRe.D is a valid and reliable computerized test battery for assessment of cognitive performance in MDD. [ABSTRACT FROM AUTHOR]

Remote inflammation monitoring with digital health technologies (DHTs) would provide valuable information for both clinical research and care. Controlled perturbations of the immune system may reveal physiological signatures which could be used to develop a digital biomarker of inflammatory state. In this study, molecular and physiological profiling was performed following an in vivo lipopolysaccharide (LPS) challenge to develop a digital biomarker of inflammation. Ten healthy volunteers received an intravenous LPS challenge and were monitored for 24 h using the VitalConnect VitalPatch (VitalPatch). VitalPatch measurements included heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin temperature (TEMP). Conventional episodic inpatient vital signs and serum proteins were measured pre- and post-LPS challenge. The VitalPatch provided vital signs that were comparable to conventional methods for assessing HR, RR, and TEMP. A pronounced increase was observed in HR, RR, and TEMP as well as a decrease in HRV 1-4 h post-LPS challenge. The ordering of participants by magnitude of inflammatory cytokine response 2 h post-LPS challenge was consistent with ordering of participants by change from baseline in vital signs when measured by VitalPatch (r = 0.73) but not when measured by conventional methods (r = -0.04). A machine learning model trained on VitalPatch data predicted change from baseline in inflammatory protein response (R 2  = 0.67). DHTs, such as VitalPatch, can improve upon existing episodic measurements of vital signs by enabling continuous sensing and have the potential for future use as tools to remotely monitor inflammation., (© 2024 Janssen Pharmaceutical Research and Development (Johnson & Johnson). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Introduction: The objective of this study is to evaluate the reliability and validity of the ReVeRe TM word list recall test (RWLRT), which uses speech recognition, when administered remotely and unsupervised., Methods: Prospective cohort study. Participants included 249 cognitively intact community dwelling older adults. Measures included clinician administered neuropsychological assessments at baseline and unsupervised remotely administered tests of cognition from six time-points over six months., Results: The RWLRT showed acceptable validity. Reliability coefficients varied across time points, with poor reliability between times 1 and 2 and fair-to-good reliability across the remaining five testing sessions. Practice effects were observed with repeated administration as expected., Discussion: Unsupervised computerized tests of cognition, particularly word list learning and memory tests that use speech recognition, have significant potential for large scale early detection and long-term tracking of cognitive decline due to AD.