Daily Levels and Dynamic Metrics of Affective-Cognitive Constructs Associate With Suicidal Thoughts and Behaviours in Adults After Psychiatric Hospitalization

The period after psychiatric hospitalization is an extraordinarily high-risk period for suicidal thoughts and behaviours (STBs). Affective-cognitive constructs (ACCs) are salient risk factors for STBs, and intensive longitudinal metrics of these constructs may improve personalized risk detection and intervention. However, limited research has examined how within-person daily levels and between-person dynamic metrics of ACCs relate to STBs after hospital discharge. Adult psychiatric inpatients (N = 95) completed a 65-day ecological momentary assessment protocol after discharge as part of a 6-month follow-up period. Using dynamic structural equation models, we examined both within-person daily levels and between-person dynamic metrics (intensity, variability and inertia) of positive and negative affect, rumination, distress intolerance and emotion dysregulation as risk factors for STBs. Within-person lower daily levels of positive affect and higher daily levels of negative affect, rumination, distress intolerance and emotion dysregulation were risk factors for next-day suicidal ideation (SI). Same-day within-person higher rumination and negative affect were also risk factors for same-day SI. At the between-person level, higher overall positive affect was protective against active SI and suicidal behaviour over the 6-month follow-up, while greater variability of rumination and distress intolerance increased risk for active SI, suicidal behaviour and suicide attempt. The present study provides the most comprehensive examination to date of intensive longitudinal metrics of ACCs as risk factors for STBs. Results support the continued use of intensive longitudinal methods to improve STB risk detection. Interventions focusing on rumination and distress intolerance may specifically help to prevent suicidal crises during critical transitions in care.

Understanding the Impact of COVID-19 on Online Mental Health Forums

Like many of the disasters that have preceded it, the COVID-19 pandemic is likely to have a profound impact on people’s mental health. Understanding its impact can inform strategies for mitigating negative consequences. This work seeks to better understand the impacts of COVID-19 on mental health by examining how discussions on mental health subreddits have changed in the three months following the WHO’s declaration of a global pandemic. First, the rate at which the pandemic is discussed in each community is quantified. Then, volume of activity is measured to determine whether the number of people with mental health concerns has risen, and user interactions are analyzed to determine how they have changed during the pandemic. Finally, the content of the discussions is analyzed. Each of these metrics is considered with respect to a set of control subreddits to better understand if the changes present are specific to mental health subreddits or are representative of Reddit as a whole. There are numerous changes in the three mental health subreddits that we consider, r/Anxiety, r/depression, r/SuicideWatch; there is reduced posting activity in most cases, and there are significant changes in discussion of some topics such as work and anxiety. The results suggest that there is not an overwhelming increase in online mental health support-seeking on Reddit during the pandemic, but that discussion content related to mental health has changed.

Jointly Aligning and Predicting Continuous Emotion Annotations

Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, which is inherent in human evaluations. To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner. The proposed network is a stack of convolutional layers followed by an aligner network that aligns the speech signal and emotion labels. This network is implemented using a new convolutional layer that we introduce, the delayed sinc layer. It is a time-shifted low-pass (sinc) filter that uses a gradient-based algorithm to learn a single delay. Multiple delayed sinc layers can be used to compensate for a non-stationary delay that is a function of the acoustic space. We test the efficacy of this system on two common emotion datasets, RECOLA and SEWA, and show that this approach obtains state-of-the-art speech-only results by learning time-varying delays while predicting dimensional descriptors of emotions.

Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)

Automatic speech emotion recognition provides computers with critical context to enable user understanding. While methods trained and tested within the same dataset have been shown successful, they often fail when applied to unseen datasets. To address this, recent work has focused on adversarial methods to find more generalized representations of emotional speech. However, many of these methods have issues converging, and only involve datasets collected in laboratory conditions. In this paper, we introduce Adversarial Discriminative Domain Generalization (ADDoG), which follows an easier to train "meet in the middle" approach. The model iteratively moves representations learned for each dataset closer to one another, improving cross-dataset generalization. We also introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed method to more than two datasets, simultaneously. Our results show consistent convergence for the introduced methods, with significantly improved results when not using labels from the target dataset. We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered. Even though our experiments focus on cross-corpus speech emotion, these methods could be used to remove unwanted factors of variation in other settings.

Understanding speech and swallowing difficulties in individuals with Huntington disease: Validation of the HDQLIFE Speech Difficulties and Swallowing Difficulties Item Banks

PURPOSE

As Huntington disease (HD) progresses, speech and swallowing difficulties become more profound. These difficulties have an adverse effect on health-related quality of life (HRQOL), thus psychometrically robust measures of speech and swallowing are needed to better understand the impact of these domains across the course of the disease. Therefore, the purpose of this study is to establish the clinical utility of two new patient-reported outcome measures (PROs), HDQLIFE Speech Difficulties and HDQLIFE Swallowing Difficulties.

METHODS

Thirty-one participants with premanifest or manifest HD, and 31 age- and sex-matched healthy control participants were recruited for this study. Participants completed several PROs [HDQLIFE Speech Difficulties, HDQLIFE Swallowing Difficulties, Communication Participation Item Bank (CPIB)], as well as several clinician-rated assessments of speech and functioning. A computational algorithm designed to detect features of spoken discourse was also examined. Analyses were focused on establishing the reliability and validity of these new measures.

RESULTS

Internal consistency was good for Swallowing (Cronbach's alpha = 0.89) and excellent for Speech and the CPIB (both Cronbach's alpha ≥ 0.94), and convergent/discriminant validity was supported. Known groups validity for the PROs was supported by significant group differences among control participants and persons with different stages of HD (all p < 0.0001). All PROs were able to distinguish those with and without clinician-rated dysarthria (likelihood ratios far exceeded the threshold for clinical decision making [all ≥ 3.28]).

CONCLUSIONS

Findings support the clinical utility of the HDQLIFE Speech and Swallowing PROs and the CPIB for use across the HD disease spectrum. These PROs also have the potential to be clinically useful in other populations.

Classification of Manifest Huntington Disease using Vowel Distortion Measures

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorder that causes cognitive disturbances, neuropsychiatric symptoms, and impaired motor abilities (e.g., gait, speech, voice). Due to its progressive nature, HD treatment requires ongoing clinical monitoring of symptoms. Individuals with the Huntingtin gene mutation, which causes HD, may exhibit a range of speech symptoms as they progress from premanifest to manifest HD. Speech-based passive monitoring has the potential to augment clinical information by more continuously tracking manifestation symptoms. Differentiating between premanifest and manifest HD is an important yet under-studied problem, as this distinction marks the need for increased treatment. In this work we present the first demonstration of how changes in speech can be measured to differentiate between premanifest and manifest HD. To do so, we focus on one speech symptom of HD: distorted vowels. We introduce a set of Filtered Vowel Distortion Measures (FVDM) which we extract from read speech. We show that FVDM, coupled with features from existing literature, can differentiate between premanifest and manifest HD with 80% accuracy.

Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks

OBJECTIVE

Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.

METHODS

Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.

RESULTS

Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69-92%.

CONCLUSION

These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.

SIGNIFICANCE

Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

Classification of Huntington Disease using Acoustic and Lexical Features

Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses. Speech analyses are currently conducted using either transcriptions created manually by trained professionals or using global rating scales. Manual transcription is both expensive and time-consuming and global rating scales may lack sufficient sensitivity and fidelity [1]. Ultimately, what is needed is an unobtrusive measure that can cheaply and continuously track disease progression. We present first steps towards the development of such a system, demonstrating the ability to automatically differentiate between healthy controls and individuals with HD using speech cues. The results provide evidence that objective analyses can be used to support clinical diagnoses, moving towards the tracking of symptomatology outside of laboratory and clinical environments.

MOOD STATE PREDICTION FROM SPEECH OF VARYING ACOUSTIC QUALITY FOR INDIVIDUALS WITH BIPOLAR DISORDER

Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment. It is necessary to mitigate variability effects in order to expand the impact of these technologies. This paper explores speech collected from phone recordings for analysis of mood in individuals with bipolar disorder. Two different phones with varying amounts of clipping, loudness, and noise are employed. We describe methodologies for use during preprocessing, feature extraction, and data modeling to correct these differences and make the devices more comparable. The results demonstrate that these pipeline modifications result in statistically significantly higher performance, which highlights the potential of distributed mental health systems.

ECOLOGICALLY VALID LONG-TERM MOOD MONITORING OF INDIVIDUALS WITH BIPOLAR DISORDER USING SPEECH

Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP.

Improved access to women's health services for Alaska natives through community health aide training

This project demonstrates the effect of increasing the skills of Community Health Aides (CHAs) on the use of specific preventive health services by women in remote Alaska villages. Eight CHAs were trained in specimen collection for Pap and sexually transmitted disease testing, and in clinical breast examination. Skill competency was monitored. Computerized medical records of all women between the ages of 18 and 75 in the four villages with trained CHAs and in four comparison villages (n = 1093) were checked for Pap status prior to CHA training and again 12 months later. All eight CHAs achieved competency and provided services in their village clinics with telephone support from an experienced clinician. The post-training year Pap test rate of women who were overdue for a Pap test was 0.44 in the villages with trained CHAs; the rate among the women in the comparison villages was 0.32 (p = .079).

Cervical cancer screening on the Yukon-Kuskokwim Delta, Southwest Alaska. A population-based study

BACKGROUND

Alaska Native women suffer excess morbidity and mortality due to cervical cancer. This population-based study analyzed data from a regional Pap smear registry to describe the 2-year prevalence of cervical cancer screening for the women who live in remotely located villages in the Yukon-Kuskokwim Delta region.

METHODS

All women older than 18 years of age who lived in one of the villages of the Yukon-Kuskokwim Delta were included (n = 6916). A 2-year Pap prevalence rate for each village was estimated by counting the number of women who had at least one Pap smear between September 1, 1992, and August 31, 1994, using the computer-based Pap registry located at the regional medical center in Bethel, Alaska. Population estimates for each village were obtained from a database maintained by the local office of the State of Alaska's Public Health Nurses.

RESULTS

The overall 2-year Pap prevalence rate for the population was 62%. By age group, 2-year prevalence rates were as follows: women 18 to 44 years, 64%; women 45 to 64 years, 58%; and women 65 years and older, 52%.

CONCLUSION

This study demonstrated that there is a significant need to improve access to and use of cervical cancer screening in this region to achieve national and state objectives. Village-based clinic staff are underutilized for cancer prevention service delivery; with additional training and supervision, staff members may serve as a means to improve this preventive health care service.