Digital Voice Research Articles

Digital voice analysis as a biomarker of acromegaly.

There is a considerable diagnostic delay in acromegaly contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly. Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning. 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. 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%). 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.

Feedback in Linear Programming: Students’ Perceptions at AYO Secondary School of Nkeyema District in the Western Part of Zambia: A Hermeneutic Tactic

Abstract: The foundation of teaching strategies in school environments is feedback. Nonetheless, it needs to be communicated clearly because it is frequently misinterpreted. Due to the negative comments, they gained through their actual internships, students lacked confidence and felt unable to practice their talents. They also felt that receiving comments made it difficult for them to finish their useful exercises. In the western Zambian region of Nkeyema, AYO Secondary School was the site of this investigation. An interpretivist approach was used in an exploratory qualitative design. Eleven (11) secondary school pupils in total and each student was questioned separately. With the participants' consent, all interviews were audio recorded using a digital voice recorder and then verbatim transcriptions were made. After that, qualitative content analysis was used to analyse the data thematically.

Exploring the Feasibility of Digital Voice Assistants for Delivery of a Home-Based Exercise Intervention in Older Adults With Obesity and Type 2 Diabetes Mellitus: Randomized Controlled Trial.

Current clinical guidelines for the management of type 2 diabetes mellitus (T2DM) in older adults recommend the use of antihyperglycemic medications, monitoring of blood glucose levels, regular exercise, and a healthy diet to improve glycemic control and reduce associated comorbidities. However, adherence to traditional exercise programs is poor (<35%). Common barriers to adherence include fear of hypoglycemia and the need for blood glucose level monitoring before exercise. Digital health strategies offer great promise for managing T2DM as they facilitate patient-practitioner communication, support self-management, and improve access to health care services for underserved populations. We have developed a novel web-based software program allowing practitioners to create tailored interventions and deliver them to patients via digital voice assistants (DVAs) in their own homes. We aim to evaluate the feasibility of a 12-week, home-based, personalized lifestyle intervention delivered and monitored by DVAs for older adults with obesity and T2DM. In total, 50 older adults with obesity aged 50-75 years with oral hypoglycemic agent-treated T2DM were randomized to the intervention (DVA, n=25) or a control group (n=25). Participants allocated to the DVA group were prescribed a home-based muscle strengthening exercise program (~20- to 30-min sessions) and healthy eating intervention, delivered via DVAs (Alexa Echo Show 8; Amazon) using newly developed software ("Buddy Link"; Great Australian Pty Ltd). Control group participants received generalized physical activity information via email. Outcomes were feasibility, DVA usability (System Usability Scale), and objectively assessed physical activity and sedentary time (wrist-worn accelerometers). In total, 45 (90%) out of 50 participants completed this study. Mean adherence to prescribed exercise was 85% (SD 43%) with no intervention-related adverse events. System usability was rated above average (70.4, SD 16.9 out of 100). Compared with controls, the DVA group significantly decreased sedentary time (mean difference -67, SD 23; 95% CI -113 to -21 min/d), which was represented by a medium to large effect size (d=-0.6). A home-based lifestyle intervention delivered and monitored by health professionals using DVAs was feasible for reducing sedentary behavior and increasing moderate-intensity activity in older adults with obesity and T2DM. Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000307808; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381364&isReview=true.

A TCCM-Based Review of Consumer Behaviour Towards Digital Voice Assistants and Future Research Agenda

This Systematic Literature Review explores the evolving landscape of consumer behaviour towards DVAs using the TCCM (Theory-Context-Characteristics-Methodology) framework. Through a comprehensive analysis of 64 research papers sourced from the Scopus database, indexed in the ABDC (Australian Business Deans Council) category list, and published until December 2023, we uncover pivotal themes and significant knowledge gaps in the current body of research. The findings show that most studies on consumer-DVA interactions are empirical in nature, predominantly focused on the shopping and hospitality sectors, and grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks. Applying the TCCM framework, this paper uncovers fertile ground for future research, highlighting opportunities for further exploration and development. Building on the identified research gaps and our insights, we present integrated frameworks on consumer behaviour towards DVAs. Additionally, we provide actionable recommendations for DVA researchers, developers, and marketers, highlighting critical areas for future research, such as integrating sentiment analysis and emotional recognition capabilities. We also propose tailored strategies, including customizable DVA personalities and engaging voice experiences, to enhance consumer engagement and brand loyalty. The paper concludes with a summary of limitations and key findings.

Co-designing a digital family-led intervention for delirium prevention and management in adult critically ill patients: An application of the double diamond design process

BackgroundCo-designing healthcare interventions is gaining recognition as a novel and collaborative method. Co-design involves end-users from the start, ensuring that an intervention best meets their needs. Despite its potential benefits, this approach is not yet widely used in developing clinical interventions within intensive care units where the perspectives of patients, family members, and clinicians are crucial. ObjectiveTo describe the application, benefits and challenges of the Double Diamond model to co-design a digital family-led voice reorientation intervention for delirium prevention and management in critically ill adult patients. MethodsThe co-design process was guided by the Double Diamond model over a period of 12 months. Development involved patients, family members, and nursing and medical staff as co-designers and decision-makers in the iterative development of the intervention. Data from field notes and group meetings were audio recorded, transcribed verbatim, and content analysed at each phase, which were then presented to the co-designers for verification and refinement. FindingsCo-designers included people with lived experience of the ICU as patients (n = 5) and family members (n = 1) and clinical experts (nursing staff n = 3; medical staff n = 3). Co-designers were highly engaged and reported positive experiences and collaboration in the co-design process. Sharing the diversity of their own personal ICU experiences was found to be beneficial as it not only validated individual feelings but also strengthened intervention development. Differences in interpretations and meanings of the voice messages proposed as part of the intervention were challenging. Maintaining sufficient focus on each phase of the Double Diamond was difficult due to the complexity of the context in which the intervention was being co-designed and the resulting challenges of maintaining the engagement of the co-designers throughout the process. ConclusionsThere were benefits and challenges of engaging people with lived experience in an intensive care unit as co-designers through the Double Diamond design process to develop a digital family-led intervention for delirium prevention and management. Overall, applying the Double Diamond to co-design a clinical intervention is recommended, whereby the collaboration process benefits patients, family members, and clinical staff. Registration numberACTRN12622001568707; ANZCTR — Registration.

Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender Stereotypes

Due to the wide adoption of digital voice assistants (DVAs), interactions with technology have also changed our perceptions, highlighting and reinforcing (mostly) negative gender stereotypes. Regarding the ongoing advancements in the field of human–machine interaction, a developed and improved understanding of and awareness of the reciprocity of gender and DVA technology use is thus crucial. Our work in this field expands prior research by including a nonbinary voice option as a means to eschew gender stereotypes. We used a between-subject quasi-experimental questionnaire study (female voice vs. male voice vs. nonbinary voice), in which n=318 participants provided feedback on gender stereotypes connected to voice perceptions and personality traits. Our findings show that the overall gender perception of our nonbinary voice leaned towards male on the gender spectrum, whereas the female-gendered and male-gendered voices were clearly identified as such. Furthermore, we found that feminine attributes were clearly tied to our female-gendered voice, whereas the connection of masculine attributes to the male voice was less pronounced. Most notably, however, we did not find gender-stereotypical trait attributions with our nonbinary voice. Results also show that the likability of our female-gendered and nonbinary voices was lower than it was with our male-gendered voice, and that, particularly with the nonbinary voice, this likability was affected by people’s personality traits. Thus, overall, our findings contribute (1) additional theoretical grounding for gender-studies in human–machine interaction, and (2) insights concerning peoples’ perceptions of nonbinary voices, providing additional guidance for researchers, technology designers, and DVA providers.

The impact of self-conscious emotions on the continuance intention of digital voice assistants in private and public contexts

Digital voice assistants (DVAs) are application programs that can understand and interpret natural language voice commands. They are embedded in various products like smartphones and smart speakers and have thus become integral in everyday life, interpersonal communications, and social relationships. Despite users tend to humanize DVAs, emotions that arise in interpersonal interactions such as self-conscious emotions (i.e., pride, shame, guilt, and vicarious embarrassment) and their influence on human-machine interaction remain unstudied. Grounding on regulatory focus theory, we argue that these emotions are fundamental for promoting or preventing future DVA use. Additionally, drawing on social influence theory, we contend that the influence of self-conscious emotions on continuous DVA use varies across specific usage situations. Thus, we extend the expectation-confirmation model with self-conscious emotions and empirically compare user reactions between different social scenarios (alone and with friends in private vs. public places). Analyzing 860 DVA user responses through structural equation modeling and multigroup analysis, our findings reveal that pride consistently positively influences continuance intention across all social contexts. Furthermore, shame acts as an important inhibitor of continuance intentions in public, while guilt inhibits continuance intentions in private places. Vicarious embarrassment, however, does not exhibit significant effects in any scenario. These results carry valuable implications for both research and management in understanding and optimizing DVA user experiences.

Lexical associations in patients with neurodegenerative disorders. Two case studies: A patient with spinocerebellar ataxia type 1 (SCA-1) and a patient with Parkinson’s disease (PD

This article presents case studies of the relative ability of two subjects (OB1 and OB2) to make lexical associations; they have spinocerebellar ataxia type 1 and Parkinson’s disease, respectively. Test method: subjects gave associations (orally, with no time limit) to 63 verbal stimuli presented on a computer screen. Verbal stimuli were selected from Gatkowska’s experimental study (Gatkowska 2017). Responses were recorded on an Olympus 650 digital voice recorder, then analysed linguistically and psychologically. Results: psychological analysis – subjects within intellectual norm (Raven’s Matrices test), dysarthric disorder, difficulty in implementing instructions. Linguistic analysis: OB1 – predominance of paradigmatic relations (hyponymy, meronymy, synonymy) over syntagmatic relations (diffi culty building context), lack of causality. OB2 – richer paradigmatic relations (hyponymy, meronymy, synonymy, antonymy, complementarity), syntagmatic relations diverse. Conclusions: possible complex background of language disorders (executive, emotional), higher level of language functioning of OB2 compared to OB1. The study illustrates the need for an interdisciplinary and holistic approach in diagnostic and therapeutic management.

Pediatric nurses' fatigue descriptions in occupational injury reports: A descriptive qualitative study1.

Despite the high risks associated with occupational fatigue in healthcare, few organizations require nurses to screen and report fatigue symptoms. As a result, little is known about if and how nurses would report fatigue while on the job. To determine if hospital-based pediatric nurses reported fatigue as part of an active injury reporting method. This secondary analysis of qualitative data used a descriptive design with content analysis. Data from the parent study were collected at a U.S. pediatric hospital where nurses verbally reported on-shift injuries or near misses and pre- and post-shift health status via a digital voice recorder. Researchers used content analysis to independently code data for nurses' references to fatigue. Codes were then analyzed for patterns and themes. Approximately 30% (n = 104) of participants reported fatigue-related content. Emergent themes were Work Stressors, Individual Risk Factors, Fatigue Descriptors, Adverse Outcomes from Fatigue, Fatigue Buffers, Descriptors for Buffered Fatigue, and Favorable Outcomes from Buffered Fatigue. Fatigue descriptions align with prior literature, demonstrating the accuracy of the voice recorder data collection method. In addition, nurses expressed uncertainty about the appropriateness of reporting fatigue symptoms. Findings from this study support pediatric nurses will report fatigue, when provided an opportunity. Additional efforts are needed to better understand effective ways to improve fatigue reporting among nurses, including harnessing current technology for real-time reporting and how to change the culture around fatigue reporting.

A qualitative exploration of patients' perception regarding the comprehensive dental services availed at a primary health center.

Comprehensive oral care is a service centered around the patient, and individuals who need it the most often face limited access. Patient perception acts as a guide for enhancing quality, ensuring patients' future intent to utilize the services and facilitating recommendations to others. The present study aimed to assess the patients' perception of comprehensive dental services availed at a Primary Health Center (PHC). This qualitative study was based on a phenomenological interpretive approach, and judgment sampling method was employed. A validated interview guide, developed from relevant literature was employed in the local language to conduct interviews among adults visiting the PHC, gathering their views regarding the services provided. The interviews were audio recorded on a digital voice recorder, and files were password protected. Content saturation guided the determination of the final number of participants interviewed. After translating and transcribing the interviews, thematic analysis and coding were performed using ATLAS. ti 23 for Windows. A total of 12 participants were included in the study, following data saturation. Among them, there were 8(66.7%) female and 4(33.3%) male participants. Ten overarching main themes were discerned through the assigned codes, including positive views, neutral views, negative views, previous dental clinics visited, previous experience with dental treatment, treatments sought at the center, referrals, source of information about the dental center, subsequent visits and suggestions for improvement. The findings of this study revealed a positive patient perception of the comprehensive dental services offered at the PHC. Through insightful interviews, various strengths, and areas for improvement regarding the center and care provision were identified. These insights provide valuable suggestions that can be applied to elevate the utilization of dental services, ensuring continuous improvement in patient care.

Digital voice analysis as a biomarker for acromegaly

Digital voice analysis as a biomarker for acromegaly

Method for asynchronous analysis of a glottal source based on a two-level autoregressive model of the speech signal

The task of analyzing a glottal source over a short observation interval is considered. The acute problem of insufficient performance of known methods for analyzing a glottal source is pointed out, regardless of the mode of data preparation: synchronous with the main tone of speech sounds or asynchronous. A method for analyzing the glottal source based on a two-level autoregressive model of the speech signal is proposed. Its software implementation based on the high-speed Burg-Levinson computational procedure is described. It does not require synchronization of the sequence of observations used with the main tone of the speech signal and is characterized by a relatively small amount of computational costs. Using the described software implementation, a full-scale experiment was set up and conducted, where the vowel sounds of the control speaker’s speech were used as the object of study. Based on the results of the experiment, the increased performance of the proposed method was confirmed and its requirements for the duration of the speech signal during voice analysis in real time were formulated. It is shown that the optimal duration is in the range from 32 to 128 ms. The results obtained can be used in the development and research of digital speech communication systems, voice control, biometrics, biomedicine and other speech systems where the voice characteristics of the speaker’s speech are of paramount importance.

Caregivers' experiences and practices for malnourished children undergoing tuberculosis treatment in Eswatini.

Eswatini is one of the countries affected by malnutrition and tuberculosis (TB) and some cases remained untreated. These two conditions are major public health problems. This study aimed to explore and describe caregivers' experiences and practices of children's nutrition during treatment. Baylor College of Nursing Children's Foundation - Eswatini (BCMCF-SD). A qualitative study following a narrative design used purposive sampling to identify 12 caregivers of malnourished children and informed consent obtained. In-depth interview used semi-structured interview guide and digital voice recorder. Field notes were taken, transcribed, translated and analysed using NVivo version 11. Two themes emerged as home's nutritional situation and health facility's nutritional support. The study found that most of the caregivers gave children unbalanced diet, while those less than a year were mixed-fed. Some caregivers reported experience of lost breadwinners, unemployment and high number of children than what the family could afford. The caregivers' practices around food by prescription included inadequate supply of the ready-to-use therapeutic food and sharing of prescribed food supplies with other healthy children. During treatment, children's caregivers need short health education and support. The Ministry of Health in Eswatini should consider using some comic books to guide that. Moreover, upscale vocational training promotes entrepreneurship and agricultural activities. Association of malnutrition and TB outcomes has provided evidence-based information for more comprehensive integration between nutrition programmes and tuberculosis programmes. The study's findings contributed to the growing body of knowledge about the association between malnutrition and diagnosed drug-susceptible TB among children aged from 0 - 15 years.

Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network

This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of 97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.

Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction.

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI's inherent conscious or moral status.

Sentiment Analysis on Article Blog Post as an Application of NLP Chatbot

Within artificial intelligence, natural language processing is a relatively recent topic. With an estimated eight billion digital voice assistants in use due to their popularity, some of the most well-known instances of natural language processing in action include Apple Siri, Amazon Alexa, and, more recently, Google Duplex. With so much information gathered from these exchanges, further research and development on Natural Language Processing may be done, and it can be used in a number of sectors, such as business, technology, and healthcare. Sentiment analysis may be used, for example, in the healthcare industry to diagnose patients and build diagnostic models for early diagnosis of chronic disease. Sentiment analysis handles these large datasets more rapidly and efficiently with the use of Natural Language Industries. Chatbots, which have uses in customer service, healthcare, education, and workplace assistance, are becoming more important entry points to digital services and information. On the other hand, not much is known about how chatbots affect people individually, in groups, or in society as a whole. Moreover, a number of problems need to be resolved before chatbots can reach their full potential. As a result, in recent years, chatbots have become a prominent area of research. We propose a research agenda that outlines future objectives and challenges for chatbot research in order to further knowledge in this rapidly expanding field of study. This proposal synthesizes years of chatbot research debate at the CONVERSATIONS workshop series. utilizing a collaborative approach for study analysis among. Sentiment analysis is a technique or procedure used to identify and extract certain subjects from spoken and written language, such as beliefs and attitudes. Sentiment analysis, broadly speaking, is the capacity to evaluate the sentiment of a subject and categorize the general polarity of the topic phrase as positive, negative, or neutral (Kang &amp; Park, 2014). Over the past ten years, sentiment analysis has gained significant scholarly attention.

A qualitative exploration of patients’ perception regarding the comprehensive dental services availed at a primary health center

Introduction Comprehensive oral care is a service centered around the patient, and individuals who need it the most often face limited access. Patient perception acts as a guide for enhancing quality, ensuring patients’ future intent to utilize the services and facilitating recommendations to others. The present study aimed to assess the patients’ perception of comprehensive dental services availed at a Primary Health Center (PHC). Methods This qualitative study was based on a phenomenological interpretive approach, and judgment sampling method was employed. A validated interview guide, developed from relevant literature was employed in the local language to conduct interviews among adults visiting the PHC, gathering their views regarding the services provided. The interviews were audio recorded on a digital voice recorder, and files were password protected. Content saturation guided the determination of the final number of participants interviewed. After translating and transcribing the interviews, thematic analysis and coding were performed using ATLAS. ti 23 for Windows. Results A total of 12 participants were included in the study, following data saturation. Among them, there were 8(66.7%) female and 4(33.3%) male participants. Ten overarching main themes were discerned through the assigned codes, including positive views, neutral views, negative views, previous dental clinics visited, previous experience with dental treatment, treatments sought at the center, referrals, source of information about the dental center, subsequent visits and suggestions for improvement. Conclusions The findings of this study revealed a positive patient perception of the comprehensive dental services offered at the PHC. Through insightful interviews, various strengths, and areas for improvement regarding the center and care provision were identified. These insights provide valuable suggestions that can be applied to elevate the utilization of dental services, ensuring continuous improvement in patient care.

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

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. 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. 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). 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. 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. PRR1-10.2196/48601.

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study.

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.

Designing a Communication Model Based on Digital Communications to Foster Customer Loyalty

The objective of this applied-developmental research was to design a communication model to foster customer loyalty based on digital communications. From the perspective of data collection methods, this study is categorized as a survey, employing an exploratory mixed-methods research approach (qualitative-quantitative). The data collection tools included semi-structured interviews in the qualitative phase and a researcher-made questionnaire in the quantitative phase. The qualitative population consisted of loyal customers of Parsian Electronic Commerce Company (organizations), and theoretical and purposive sampling methods were used until data saturation, which was achieved after 18 semi-structured interviews. The quantitative population included all employees of these organizations. The sample size was estimated using Cochran's formula, and ultimately, 393 individuals participated in the study through stratified random sampling. Grounded theory was used to analyze the specialized interviews in the qualitative section, and partial least squares (PLS) method was employed to validate the final model in the quantitative section. After open and axial coding, 6 selective codes, 8 axial codes, and 60 open codes were extracted. The results indicated that customer experience management, digital customer voice, and digital infrastructure are causal factors influencing digital communications, which in turn affects digital marketing strategy. The quality of digital services provides the context, and the intensity of industry competition is an intervening factor that impacts digital marketing strategy. Ultimately, the digital marketing strategy leads to customer loyalty.