Listening to Patients With Lupus: Why Not Proactively Integrate the Internet as a Resource to Drive Improved Care?

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. It is characterized by a broad spectrum of manifestations, depending on the affected organs and the severity of the inflammation at the time of presentation. Despite improvements in management, treatments are required on a chronic, cyclical basis; have high potential for unpleasant side effects; and deliver variable efficacy. Patients require care from multiple specialists, which can be delivered simultaneously and sporadically. Our fragmented health care system further exacerbates the disconnect between intermittent medical care and the lived experiences of patients with SLE. The goals of this research are to (1) assess the current standard of care for patients with SLE through the review of medical literature, including clinical consensus guidelines and systematic reviews; (2) assess the lived experiences of patients with lupus through the review of peer-reviewed literature on social listening, structured interviews, and data available from the open-access digital health platform PatientsLikeMe; and (3) present the perspective that the medical community has an opportunity to acknowledge and review the use of digital health interventions (DHIs) with their patients. The results of this research indicate that patients are incorporating DHIs, such as the internet and social media platforms, as critical components of their care for even the most basic of support. Although patients with SLE are depending on this support to shape their care, it is not considered a primary source of care by clinicians. Integrating the voices of patients brings valuable dimension to understanding the lived experiences of patients with SLE and the impacts of mutually dependent patient needs as patients navigate the disease in daily life. The medical community has a meaningful opportunity to leverage and recommend existing DHIs, such as web-based community platforms and web-based patient registries, at every stage of the patient journey to help patients better manage their condition. This has the potential to proactively build patient trust and well-being, reduce the underreporting of symptoms, increase shared decision-making, inform and shape clinical guidelines and future research, and improve patient outcomes.

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study.

BACKGROUND: Human voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population. OBJECTIVE: This study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI. METHODS: This study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated. RESULTS: The study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755. CONCLUSIONS: Multiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring.

Benefits and challenges of conversational agents in older adults : A scoping review.

BACKGROUND: Commercial conversational agents (CAs) bear the promise of low threshold accessibility for individuals with limited digital competencies. This applies not only for healthy aging older adults but also for specific subgroups such as those with life-long intellectual disabilities (ID). OBJECTIVE: This scoping review aims to synthesize the current evidence on benefits and challenges of CAs for older adults with and without ID. In doing so, we hope to inform future research as well as practical decision-making in the context of CAs as potential quality of life enhancers for older adults with various competence levels. MATERIAL AND METHODS: A literature search was conducted in form of a scoping review. A total of 841 publications were screened for benefits and challenges of CAs, resulting in an extraction of 18 articles targeting healthy aging older adults (60 years+) and 5 articles targeting older adults with ID (50 years+) for synthesis. RESULTS: The existing evidence suggests that CAs come with more benefits than challenges, e.g., general ease of use, easier information access, and feelings of companionship. Higher perceived agency due to using a CA seems to be a specific issue for older adults with ID. Challenges concern mostly learning how to use a CA and privacy concerns. CONCLUSION: The results indicate that CAs can serve as quality of life enhancers both in healthy aging adults and in older adults with ID; nevertheless, thoughtful preparation is necessary, especially in relation to learning needs, capabilities present and privacy concerns.

Noise Source Visualization Using a Digital Voice Recorder and Low-Cost Sensors.

Accurate sound visualization of noise sources is required for optimal noise control. Typically, noise measurement systems require microphones, an analog-digital converter, cables, a data acquisition system, etc., which may not be affordable for potential users. Also, many such systems are not highly portable and may not be convenient for travel. Handheld personal electronic devices such as smartphones and digital voice recorders with relatively lower costs and higher performance have become widely available recently. Even though such devices are highly portable, directly implementing them for noise measurement may lead to erroneous results since such equipment was originally designed for voice recording. In this study, external microphones were connected to a digital voice recorder to conduct measurements and the input received was processed for noise visualization. In this way, a low cost, compact sound visualization system was designed and introduced to visualize two actual noise sources for verification with different characteristics: an enclosed loud speaker and a small air compressor. Reasonable accuracy of noise visualization for these two sources was shown over a relatively wide frequency range. This very affordable and compact sound visualization system can be used for many actual noise visualization applications in addition to educational purposes.

Digital voice analysis as a biomarker of acromegaly.

CONTEXT: There is a considerable diagnostic delay in acromegaly contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly. OBJECTIVE: Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning. METHODS: Voice recordings from patients with acromegaly and matched controls were collected using a mobile phone at Swedish university hospitals. Anthropometric and clinical data and the Voice Handicap Index (VHI) were assessed. Digital voice analysis of a sustained and stable vowel [a] resulted in 3274 parameters, which were used for training of machine learning models classifying the speaker as "acromegaly" or "control". The machine learning model was trained with 76% of the data and the remaining 24% was used to assess its performance. For comparison, voice recordings of 50 pairs of participants were assessed by 12 experienced endocrinologists. RESULTS: We included 151 Swedish patients with acromegaly (13% biochemically active and 10% newly diagnosed) and 139 matched controls. The machine learning model identified patients with acromegaly more accurately [area under the receiver operating curve (ROC AUC) 0.84] than experienced endocrinologists (ROC AUC 0.69). Self-reported voice problems were more pronounced in patients with acromegaly than matched controls (median VHI 6 vs 2, P < .01) with higher prevalence of clinically significant voice handicap (VHI ≥20: 22.5% vs 3.6%). CONCLUSION: Digital voice analysis can identify patients with acromegaly from short voice recordings with high accuracy. Patients with acromegaly experience more voice disorders than matched controls.

Detection of Mild Cognitive Impairment From Non-Semantic, Acoustic Voice Features: The Framingham Heart Study.

BACKGROUND: With the aging global population and the rising burden of Alzheimer disease and related dementias (ADRDs), there is a growing focus on identifying mild cognitive impairment (MCI) to enable timely interventions that could potentially slow down the onset of clinical dementia. The production of speech by an individual is a cognitively complex task that engages various cognitive domains. The ease of audio data collection highlights the potential cost-effectiveness and noninvasive nature of using human speech as a tool for cognitive assessment. OBJECTIVE: This study aimed to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification to identify a set of acoustic features derived from voice recordings that exhibit strong MCI detection capability. METHODS: The study included 100 MCI cases and 100 cognitively normal controls matched for age, sex, and education from the Framingham Heart Study. Participants' spoken responses on neuropsychological tests were recorded, and the recorded audio was processed to identify segments of each participant's voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using OpenSMILE and Praat software. Subsequently, a random forest model was constructed to classify cognitive status using the features that exhibited significant differences between the MCI and cognitively normal groups. The MCI detection performance of various audio lengths was further examined. RESULTS: An optimal subset of 29 features was identified that resulted in an area under the receiver operating characteristic curve of 0.87, with a 95% CI of 0.81-0.94. The most important acoustic feature for MCI classification was the number of filled pauses (importance score=0.09, P=3.10E-08). There was no substantial difference in the performance of the model trained on the acoustic features derived from different lengths of voice recordings. CONCLUSIONS: This study showcases the potential of monitoring changes to nonsemantic and acoustic features of speech as a way of early ADRD detection and motivates future opportunities for using human speech as a measure of brain health.

Understanding Employee Voice Behavior Through the Use of Digital Voice Channel in Long-Term Care: Protocol for an Embedded Multiple-Case Study.

BACKGROUND: Specific challenges in the health care sector, such as hierarchical structures, shortages of nursing staff, and high turnover of nursing staff, can be addressed by a change process of organizational culture into shared governance. Data from business organizations show that the use of digital voice channels provides employee voice. This approach makes concrete the opportunity for employees to raise their voices by answering surveys and making comments in an anonymous forum, which subsequently positively influences staff turnover and sick leave. Since there is no clear understanding of how a digital voice channel can be used in long-term care to address employee voice, a research gap has been identified. OBJECTIVE: The purpose of ADVICE (Understanding Employee Voice Behavior; the acronym for this study) is to understand how the use of a digital voice channel performs in long-term care (residential long-term care and home care facilities). The aim of this study is to understand how the digital voice channel can support staff in making their voices heard and to see what managers need to use the voice channel to change the work environment. METHODS: An embedded multiple-case study will be used to explore the experiences of 2 health care providers who have already implemented a digital voice channel. ADVICE is organized into two main phases: (1) a scoping review and (2) an embedded multiple-case study. For this purpose, focus group interviews with employees, discursive-dialogical interviews with managers, meeting protocols, and data from the digital voice channel will be analyzed. First, all units of analysis from every embedded unit will be separately analyzed and then comprehensively analyzed to obtain a case vignette from every embedded unit (within-analysis). In the second stage, the analyzed data from the embedded units will be compared with each other in a comparative analysis (cross-analysis). RESULTS: The results will provide insight into how digital voice channels can be used in long-term care to address employee voice. We expect to find how the digital voice channel can empower nurses to speak up and, consequently, create a better work environment. Data collection began in August 2023, and from a current perspective, the first results are expected in summer 2024. CONCLUSIONS: In summary, the results may help to better understand the use of a digital voice channel in the health care sector and its transformative potential for leadership. At the organizational level, research can help to improve the attractiveness of the workplace by understanding how to give employees a voice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48601.

Association Between Acoustic Features and Brain Volumes: the Framingham Heart Study.

Introduction: Although brain magnetic resonance imaging (MRI) is a valuable tool for investigating structural changes in the brain associated with neurodegeneration, the development of non-invasive and cost-effective alternative methods for detecting early cognitive impairment is crucial. The human voice has been increasingly used as an indicator for effectively detecting cognitive disorders, but it remains unclear whether acoustic features are associated with structural neuroimaging. Methods: This study aims to investigate the association between acoustic features and brain volume and compare the predictive power of each for mild cognitive impairment (MCI) in a large community-based population. The study included participants from the Framingham Heart Study (FHS) who had at least one voice recording and an MRI scan. Sixty-five acoustic features were extracted with the OpenSMILE software (v2.1.3) from each voice recording. Nine MRI measures were derived according to the FHS MRI protocol. We examined the associations between acoustic features and MRI measures using linear regression models adjusted for age, sex, and education. Acoustic composite scores were generated by combining acoustic features significantly associated with MRI measures. The MCI prediction ability of acoustic composite scores and MRI measures were compared by building random forest models and calculating the mean area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation. Results: The study included 4,293 participants (age 57 ± 13 years, 53.9% women). During 9.3±3.7 years follow-up, 106 participants were diagnosed with MCI. Seven MRI measures were significantly associated with more than 20 acoustic features after adjusting for multiple testing. The acoustic composite scores can improve the AUC for MCI prediction to 0.794, compared to 0.759 achieved by MRI measures. Discussion: We found multiple acoustic features were associated with MRI measures, suggesting the potential for using acoustic features as easily accessible digital biomarkers for the early diagnosis of MCI.

Understanding the Potential of Home-Based Digital Voice Assistants for People with Disabilities.

As the numbers of people with disabilities actively using technology to support their day-to-day activities increases the benefits afforded by these technologies are ever more evident. Much of the technology used by people with disabilities is often characterised as Assistive Technology (AT) which is designed and developed to address the specific needs of people with disabilities. In contrast to AT which is focused on serving the needs of people with disabilities, consumer digital technology refers to those technologies that are developed for use by the general public. The aim of this study was to explore the assistive potential of a range of exemplar consumer digital technology, namely, digital voice assistants and internet of things. A qualitative study was conducted in the context of a field-trial of a range of digital consumer technologies which included a Digital Voice Assistant alongside voice-operated Internet of Things technologies.

A single-blind, parallel-group randomised trial of a Technology-assisted and remotely delivered Cognitive Behavioural Therapy intervention (Tech-CBT) versus usual care to reduce anxiety in people with mild cognitive impairment and dementia: study protocol for a randomised trial.

BACKGROUND: Anxiety is commonly experienced by people living with mild cognitive impairment (MCI) and dementia. Whilst there is strong evidence for late-life anxiety treatment using cognitive behavioural therapy (CBT) and delivery via telehealth, there is little evidence for the remote delivery of psychological treatment for anxiety in people living with MCI and dementia. This paper reports the protocol for the Tech-CBT study which aims to investigate the efficacy, cost-effectiveness, usability and acceptability of a technology-assisted and remotely delivered CBT intervention to enhance delivery of anxiety treatment for people living with MCI and dementia of any aetiology. METHODS: A hybrid II single-blind, parallel-group randomised trial of a Tech-CBT intervention (n = 35) versus usual care (n = 35), with in-built mixed methods process and economic evaluations to inform future scale-up and implementation into clinical practice. The intervention (i) consists of six weekly sessions delivered by postgraduate psychology trainees via telehealth video-conferencing, (ii) incorporates voice assistant app technology for home-based practice, and (iii) utilises a purpose-built digital platform, My Anxiety Care. The primary outcome is change in anxiety as measured by the Rating Anxiety in Dementia scale. Secondary outcomes include change in quality of life and depression, and outcomes for carers. The process evaluation will be guided by evaluation frameworks. Qualitative interviews will be conducted with a purposive sample of participants (n = 10) and carers (n = 10), to evaluate acceptability and feasibility, as well as factors influencing participation and adherence. Interviews will also be conducted with therapists (n = 18) and wider stakeholders (n = 18), to explore contextual factors and barriers/facilitators to future implementation and scalability. A cost-utility analysis will be undertaken to determine the cost-effectiveness of Tech-CBT compared to usual care. DISCUSSION: This is the first trial to evaluate a novel technology-assisted CBT intervention to reduce anxiety in people living with MCI and dementia. Other potential benefits include improved quality of life for people with cognitive impairment and their care partners, improved access to psychological treatment regardless of geographical location, and upskilling of the psychological workforce in anxiety treatment for people living with MCI and dementia. TRIAL REGISTRATION: This trial has been prospectively registered with ClinicalTrials.gov: NCT05528302 [September 2, 2022].

Patient Empowerment Through Digital Voice Pens: A Feasibility Study.

Reading is an important ability, especially for patients during their medical treatment. It is needed, for instance, to complete administrative forms and patient-reported outcome questionnaires in clinical routine. Unfortunately, not every patient is able to read caused by illiteracy, low vision or simply speaking another language. Thus, a minder is required to support the mentioned reading tasks. Providing patients with the possibility to read and understand texts without additional help is an important factor to improve their self-empowerment. Digital voice pens can be programmed to play prerecorded audio files if tipped onto predefined areas of interactive paper. They can be a tool for impaired patients to read texts aloud in multiple languages. In this work, we wanted to evaluate the possibilities of these digital voice pens. A feasibility study was conducted by using the commercially available tiptoi digital voice pen by Ravensburger AG and the tttool application by Joachim Breitner for the programming of the pen. Focusing on the use case of questionnaires, a schematic questionnaire was implemented which enforced the usage of a digital voice pen. To simulate foreign languages or illiteracy, questions and answers of the document were represented by placeholders and the digital voice pen was required to read aloud the question texts. The correctness of the given answers was documented and the usability of the digital voice pen was measured by the System Usability Scale. The evaluation was performed by 15 volunteers (8 male/7 female) between 24 and 35 years old. The usability and acceptance of digital voice pens were rated as "Good" in our constructed setting.

Identification of digital voice biomarkers for cognitive health.

AIM: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. METHODS: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. RESULTS: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P < 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P < 0.05/48 = 1 × 10-3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10-7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. CONCLUSIONS: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.