Background Story recall is a simple and sensitive cognitive test that is commonly used to measure changes in episodic memory function in early Alzheimer disease (AD). Recent advances in digital technology and natural language processing methods make this test a candidate for automated administration and scoring. Multiple parallel test stimuli are required for higher-frequency disease monitoring. Objective This study aims to develop and validate a remote and fully automated story recall task, suitable for longitudinal assessment, in a population of older adults with and without mild cognitive impairment (MCI) or mild AD. Methods The “Amyloid Prediction in Early Stage Alzheimer’s disease” (AMYPRED) studies recruited participants in the United Kingdom (AMYPRED-UK: NCT04828122) and the United States (AMYPRED-US: NCT04928976). Participants were asked to complete optional daily self-administered assessments remotely on their smart devices over 7 to 8 days. Assessments included immediate and delayed recall of 3 stories from the Automatic Story Recall Task (ASRT), a test with multiple parallel stimuli (18 short stories and 18 long stories) balanced for key linguistic and discourse metrics. Verbal responses were recorded and securely transferred from participants’ personal devices and automatically transcribed and scored using text similarity metrics between the source text and retelling to derive a generalized match score. Group differences in adherence and task performance were examined using logistic and linear mixed models, respectively. Correlational analysis examined parallel-forms reliability of ASRTs and convergent validity with cognitive tests (Logical Memory Test and Preclinical Alzheimer’s Cognitive Composite with semantic processing). Acceptability and usability data were obtained using a remotely administered questionnaire. Results Of the 200 participants recruited in the AMYPRED studies, 151 (75.5%)—78 cognitively unimpaired (CU) and 73 MCI or mild AD—engaged in optional remote assessments. Adherence to daily assessment was moderate and did not decline over time but was higher in CU participants (ASRTs were completed each day by 73/106, 68.9% participants with MCI or mild AD and 78/94, 83% CU participants). Participants reported favorable task usability: infrequent technical problems, easy use of the app, and a broad interest in the tasks. Task performance improved modestly across the week and was better for immediate recall. The generalized match scores were lower in participants with MCI or mild AD (Cohen d=1.54). Parallel-forms reliability of ASRT stories was moderate to strong for immediate recall (mean rho 0.73, range 0.56-0.88) and delayed recall (mean rho=0.73, range=0.54-0.86). The ASRTs showed moderate convergent validity with established cognitive tests. Conclusions The unsupervised, self-administered ASRT task is sensitive to cognitive impairments in MCI and mild AD. The task showed good usability, high parallel-forms reliability, and high convergent validity with established cognitive tests. Remote, low-cost, low-burden, and automatically scored speech assessments could support diagnostic screening, health care, and treatment monitoring.

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IntroductionNeurodegenerative and psychiatric disorders (NPDs) confer a huge health burden, which is set to increase as populations age. New, remotely delivered diagnostic assessments that can detect early stage NPDs by profiling speech could enable earlier intervention and fewer missed diagnoses. The feasibility of collecting speech data remotely in those with NPDs should be established.Methods and analysisThe present study will assess the feasibility of obtaining speech data, collected remotely using a smartphone app, from individuals across three NPD cohorts: neurodegenerative cognitive diseases (n=50), other neurodegenerative diseases (n=50) and affective disorders (n=50), in addition to matched controls (n=75). Participants will complete audio-recorded speech tasks and both general and cohort-specific symptom scales. The battery of speech tasks will serve several purposes, such as measuring various elements of executive control (eg, attention and short-term memory), as well as measures of voice quality. Participants will then remotely self-administer speech tasks and follow-up symptom scales over a 4-week period. The primary objective is to assess the feasibility of remote collection of continuous narrative speech across a wide range of NPDs using self-administered speech tasks. Additionally, the study evaluates if acoustic and linguistic patterns can predict diagnostic group, as measured by the sensitivity, specificity, Cohen’s kappa and area under the receiver operating characteristic curve of the binary classifiers distinguishing each diagnostic group from each other. Acoustic features analysed include mel-frequency cepstrum coefficients, formant frequencies, intensity and loudness, whereas text-based features such as number of words, noun and pronoun rate and idea density will also be used.Ethics and disseminationThe study received ethical approval from the Health Research Authority and Health and Care Research Wales (REC reference: 21/PR/0070). Results will be disseminated through open access publication in academic journals, relevant conferences and other publicly accessible channels. Results will be made available to participants on request.Trial registration numberNCT04939818.

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Introduction Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD). Methods The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ–) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity. Results Eighty‐six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aβ (AUC = 0.62 ±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results. Discussion Speech‐based testing offers simple and accessible screening for early‐stage AD.