Objective: To evaluate an online intervention for adults with ADHD that aimed to improve organizational skills and attention with the help of smartphone applications. Method: Participants (n = 57) were recruited and assessed through questionnaires and telephone interviews. Diagnoses of ADHD were confirmed for 83% of the participants, 5% most probably had the diagnoses, and 12% did not fulfill all diagnostic criteria despite high levels of symptoms. Participants were randomized between the intervention (n = 29) and a wait-list control group (n = 28). The 6-week intervention involved support from a coach in finding a routine for organizing everyday life with the help of smartphone applications. The primary outcome measure was ASRS Inattention. Secondary outcomes were ASRS sub-scale Hyperactivity and measures of depression, anxiety, stress, quality of life and general level of functioning. Blind evaluators also assessed improvement in organization and inattention at post treatment.Result: The participants receiving the Living Smart course reduced their average scores on ASRS-Inattention from 28.1 (SD = 4.5) to 22.9 (SD = 4.3) which was a significantly larger reduction than found in the control group. 33% of participants were considered clinically significantly improved according to the blind evaluator, compared to 0% in the control group. The same results were found when only participants with a confirmed diagnose were included in the analyses. Conclusion: Adults with ADHD seem to be able to use smartphone applications to organize their everyday life and can be taught how to do this via online interventions.

Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.

The performance of Automatic Speech Recognition (ASR) systems has constantly increased in state-of-the-art development. However, performance tends to decrease considerably in more challenging conditions (e.g., background noise, multiple speaker social conversations) and with more atypical speakers (e.g., children, non-native speakers or people with speech disorders), which signifies that general improvements do not necessarily transfer to applications that rely on ASR, e.g., educational software for younger students or language learners. In this study, we focus on the gap in performance between recognition results for native and non-native, read and spontaneous, Swedish utterances transcribed by different ASR services. We compare the recognition results using Word Error Rate and analyze the linguistic factors that may generate the observed transcription errors.

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