Challenges and Implications for Menopausal Health and Help-Seeking Behaviors in Midlife Women From the United States and China in Light of the COVID-19 Pandemic: Web-Based Panel Surveys.

BACKGROUND: The global population of women of menopausal age is quickly increasing. The COVID-19 pandemic has led to an accelerated increase in the use of telehealth services, especially technological solutions targeting women's health. Understanding the factors behind midlife women's help-seeking behaviors amidst the pandemic will assist in the development of person-centered holistic telehealth solutions targeting menopausal and postreproductive health. OBJECTIVE: This study aimed to compare the factors underlying help-seeking for menopausal distress among midlife women in the United States and China. METHODS: We conducted 2 web-based panel surveys in the United States using Amazon Mechanical Turk and in China using Credamo between July and October 2022. A total of 1002 American and 860 Chinese women aged between 40 and 65 years took part in the survey. The survey was designed based on the Health Belief Model with questions related to their menopausal knowledge, perceived severity of menopausal symptoms, perceived susceptibility to menopausal distress, perceived benefits of help-seeking, perceived COVID-19- and non-COVID-19-related barriers against help-seeking, self-efficacy, and motivation to seek help. Structural equations models were fitted for the data using full information maximum likelihood to manage missing data. RESULTS: Knowledge was not directly related to help-seeking motivation in both samples. Among the Chinese sample, knowledge was negatively related to perceived severity but positively related to COVID-19-related barriers; in turn, higher perceived severity, benefits, COVID-19-related barriers, and self-efficacy and lower non-COVID-19-related barriers were related to more motivation to seek help. In the US sample, knowledge was negatively related to perceived severity, susceptibility, benefits, barriers (COVID-19- and non-COVID-19-related), and self-efficacy; in turn, higher self-efficacy, COVID-19-related barriers, and benefits were associated with more help-seeking motivation. The factors explained 53% and 45.3% of the variance of help-seeking motivation among the American and Chinese participants, respectively. CONCLUSIONS: This study revealed disparate pathways between knowledge, health beliefs, and the motivation for help-seeking among American and Chinese midlife women with respect to menopausal distress. Our findings show that knowledge may not directly influence help-seeking motivation. Instead, perceived benefits and self-efficacy consistently predicted help-seeking motivation. Interestingly, concern over COVID-19 infection was related to higher help-seeking motivation in both samples. Hence, our findings recommend the further development of telehealth services to (1) develop content beyond health education and symptom management that serves to enhance the perceived benefits of addressing women's multidimensional menopausal health needs, (2) facilitate patient-care provider communication with a focus on self-efficacy and a propensity to engage in help-seeking behaviors, and (3) target women who have greater midlife health concerns in the postpandemic era.

Empathic Engagement With the COVID-19 Vaccine Hesitant in Private Facebook Groups: A Randomized Trial.

BACKGROUND: Vaccine misinformation has been widely spread on social media, but attempts to combat it have not taken advantage of the attributes of social media platforms for health education. METHODS: The objective was to test the efficacy of moderated social media discussions about COVID-19 vaccines in private Facebook groups. Unvaccinated U.S. adults were recruited using Amazon's Mechanical Turk and randomized. In the intervention group, moderators posted two informational posts per day for 4 weeks and engaged in relationship-building interactions with group members. In the control group, participants received a referral to Facebook's COVID-19 Information Center. Follow-up surveys with participants (N = 478) were conducted 6 weeks post-enrollment. RESULTS: At 6 weeks follow-up, no differences were found in vaccination rates. Intervention participants were more likely to show improvements in their COVID-19 vaccination intentions (vs. stay same or decline) compared with control (p = .03). They also improved more in their intentions to encourage others to vaccinate for COVID-19. There were no differences in COVID-19 vaccine confidence or intentions between groups. General vaccine and responsibility to vaccinate were higher in the intervention compared with control. Most participants in the intervention group reported high levels of satisfaction. Participants engaged with content (e.g., commented, reacted) 11.8 times on average over the course of 4 weeks. CONCLUSIONS: Engaging with vaccine-hesitant individuals in private Facebook groups improved some COVID-19 vaccine-related beliefs and represents a promising strategy.

SIG Tel@ rBLH 21 de Novembro de 2011: Perspectivas da Telessaúde no Estado do Amazonas

Palestra com o tema, Perspectivas da Telessaúde no Estado do Amazonas, proferida pelo Dr. Jefferson Guilherme em Congresso Brasileiro de Telessaúde e Telemedicina Manaus/AM.

Social Determinants of Health and Patients' Technology Acceptance of Telehealth During the COVID-19 Pandemic: Pilot Survey.

BACKGROUND: Telehealth has been widely adopted by patients during the COVID-19 pandemic. Many social determinants of health influence the adoption. OBJECTIVE: This pilot study aimed to understand the social determinants of patients' adoption of telehealth in the context of the pandemic. METHODS: A survey methodology was used to capture data from 215 participants using Amazon Mechanical Turk. The study was guided by the technology acceptance model and the social determinants of health framework. The questionnaire included technology acceptance model variables (eg, perceived usefulness [PU] and perceived ease of use [PEOU]), social determinants (eg, access to health care, socioeconomic status, education, and health literacy), and demographic information (eg, age, sex, race, and ethnicity). A series of ordinary least squares regressions were conducted to analyze the data using SPSS Statistics (IBM Corp). RESULTS: The results showed that social determinant factors-safe neighborhood and built environment (P=.01) and economic stability (P=.05)-are predictors of the PEOU of telehealth adoption at a statistically significant or marginally statistically significant level. Furthermore, a moderated mediation model (PROCESS model 85) was used to analyze the effects of COVID-19 on the neighborhood, built environment, and economic stability. PEOU and PU significantly positively affected users' intention to use technology for both variables. CONCLUSIONS: This study draws attention to 2 research frameworks that address unequal access to health technologies. It also adds empirical evidence to telehealth research on the adoption of patient technology. Finally, regarding practical implications, this study will provide government agencies, health care organizations, and health care companies with a better perspective of patients' digital health use. This will further guide them in designing better technology by considering factors such as social determinants of health.

Gamification to Improve Medication Adherence: A Mixed-method Usability Study for MedScrab.

Medication non-adherence is a prevalent healthcare problem with poor health outcomes and added healthcare costs. MedScrab, a gamification-based mHealth app, is the first attempt to deliver crucial life-saving medication information to patients and increase their medication adherence. The paper presents the development of MedScrab and a two-phase mixed-method usability evaluation of MedScrab. Phase I qualitatively evaluated MedScrab using a think-aloud protocol for its usability. With 51 participants, qualitative data analysis of Phase I revealed two themes: positive functionality of the app and four areas of improvement. The improvement recommendations were incorporated into MedScrab's design. Phase I also validated a widely used mHealth App Usability Questionnaire (MAUQ). Quantitative data analysis of Phase I reduced the original 18-item MAUQ scale to a 15-item scale with two factors: ease of use (4 items) and usefulness and satisfaction (11 items). Phase II surveyed 83 participants from Amazon's Mechanical Turk using a modified MAUQ. The modified MAUQ scale showed strong internal consistency (Cronbach alpha = 0.959) and high factor loadings (between 0.623 and 0.987). The study design of the usability evaluation can serve as a methodological guide for designing, evaluating, and improving mHealth apps.The usability study showed that MedScrab was perceived as ease of use (6.24 out of 7) with high usefulness and satisfaction (5.72 out of 7). The quantitative data analysis results support the use of the modified MAUQ as a valid instrument to measure the usability of the MedScrab. However, the instrument should be used with adaptation based on the app's characteristics.

Prospecção das condições de trabalho do Samu-192 fluvial em cenários de crise a partir da resiliência no enfrentamento à Covid-19 no Alto Solimões / Investigation of the working conditions of the river-based SAMU-192 in crisis scenarios based on resilience in the face of COVID-19 in Alto Solimões

RESUMO Esta pesquisa teve como objetivo prospectar as condições de trabalho das equipes de socorro do Serviço de Atendimento Móvel de Urgência (Samu-192) fluvial das áreas ribeirinhas e costeiras da região do Alto-Solimões, a partir da análise sistêmica das atividades no serviço de embarcações popularmente chamadas de 'ambulanchas' durante os picos da Covid-19 no estado do Amazonas, quando o sistema de saúde funcionou sob o estresse provocado pela pandemia. Os dados foram obtidos a partir de um desenho transversal exploratório, baseado em dados qualitativos coletados por meio de entrevistas e observação do funcionamento normal do sistema antes da pandemia. A partir daí modelos de dois cenários foram elaborados mostrando o funcionamento do serviço de ambulanchas ao lidar com a pandemia de Covid-19 e o impacto nas condições de trabalho das equipes de socorro interprofissionais de socorro. Entrevistas remotas com trabalhadores das ambulanchas após a pandemia indicaram que a prospecção das condições de trabalho a partir das instâncias dos modelos corresponderam ao funcionamento real do sistema durante a pandemia de Covid-19.

ABSTRACT This research aimed to prospect the working conditions of the rescue teams of the Mobile Emergency Care Service (SAMU-192) at the fluvial areas of the Alto Solimoes region, trough the systemic analysis of the activities of the water ambulances service popularly called 'ambulanchas' during the peaks of COVID-19 in the state of Amazonas, when the health system functioned under stress caused by the pandemic. Data were obtained from an exploratory cross-sectional design, based on qualitative data collected through interviews and observation of the normal functioning of the system before the pandemic. From there, models of two scenarios were developed showing the functioning of the water ambulances service when coping with the COVID-19 pandemic and the impact on the working conditions of the interprofessional rescue teams. Remote interviews with workers after the pandemic indicated that the prospection of working conditions from the instances of the models corresponded to the real functioning of the system during the COVID-19 pandemic.

Access to medication for opioid use disorder supported by telemedicine and healthcare coverage: A web-based survey during the COVID-19 pandemic.

Background and Aims: Medications for opioid use disorder (MOUD) are highly effective in improving treatment outcomes and reducing overdose. Concerns about interrupted access to critical MOUD services led to expansion of telemedicine services during the COVID-19 pandemic in the US. The current study tested the hypothesis that telemedicine usage and healthcare coverage would be significantly associated with access to MOUD in the early phase of the COVID-19 pandemic. Design: A cross-sectional online survey was administered to a non-probability sample from June 18-July 19, 2020 using the Amazon Mechanical Turk platform. Setting: Northeastern United States during the early phase of the COVID-19 pandemic. At the time of the survey, federal regulators had waived the longstanding requirement for in-office visits for MOUD prescription receipt and provided guidance on increasing third-party payer reimbursement rates for telehealth visits in order to mitigate barriers to care associated with COVID-19 safety guidelines. Participants: Individuals 18 years or older residing in Connecticut, Massachusetts, New Jersey, New York, or Rhode Island were eligible to complete the survey. The analytic sample was participants who reported using opioids not as prescribed by a physician in the past seven days. Measurements: Demographics, telemedicine usage, and healthcare coverage were assessed as explanatory variables. The primary outcome was whether participants reported ability to access MOUD in the past four weeks. Findings: In this sample of individuals who used illicit opioids in the past week (N = 191), one in two individuals who utilized telehealth or had healthcare coverage were able to access MOUD, whereas only one in five of their respective counterparts who did not have telehealth access or healthcare coverage were able to access these medications. Conclusions: Telemedicine and healthcare coverage were associated with greater MOUD access early in the COVID-19 pandemic, when barriers to care were high. Such findings speak to the importance of not only extending but also formalizing temporary policy changes instituted during the pandemic to allow MOUD prescribing via telemedicine.

How much are patients willing to pay for spine surgery? An evaluation of attitudes toward out-of-pocket expenses and cost-reducing measures.

BACKGROUND CONTEXT: With rising healthcare expenditures in the United States, patients and providers are searching to maintain quality while reducing costs. PURPOSE: The aim of this study was to investigate patient willingness to pay for anterior cervical discectomy and fusion (ACDF), degenerative lumbar spinal fusions (LF), and adult spine deformity (ASD) surgery. STUDY DESIGN/SETTING: A survey was developed and distributed to anonymous respondents through Amazon Mechanical Turk (MTurk). METHODS: The survey introduced 3 procedures: ACDF, LF, and ASD surgery. Respondents were asked sequentially if they would pay at each increasing price option. Respondents were then presented with various cost-saving methods and asked to select the options that made them most uncomfortable, even if those would save them out-of-pocket costs. RESULTS: In total, 979 of 1,172 total responses (84%) were retained for analysis. The average age was 36.2 years and 44% of participants reported a household income of $50,000 to 100,000. A total of 63% used Medicare and 13% used Medicaid. A total of 40% stated they had high levels of financial stress. A total of 30.1% of participants were willing to undergo an ACDF, 30.3% were willing to undergo a LF, and 29.6% were willing to undergo ASD surgery for the cost of $3,000 (p=.98). Regression demonstrated that for ACDF surgery, a $100 increase in price resulted in a 2.1% decrease in willingness to pay. This is comparable to degenerative LF surgery (1.8% decrease), and ASD surgery (2%). When asked which cost-saving measures participants were least comfortable with for ACDF surgery, 60% stated "Use of the older generation implants/devices" (LF: 51%, ASD: 60%,), 61% stated "Having the surgery performed at a community hospital instead of at a major academic center" (LF: 49%, ASD: 56%), and 55% stated "Administration of anesthesia by a nurse anesthetist" (LF: 48.01%, ASD: 55%). Conversely, 36% of ACDF patients were uncomfortable with a "Video/telephone postoperative visit" to cut costs (LF: 51%, ASD: 39%). CONCLUSIONS: Patients are unwilling to contribute larger copays for adult spinal deformity correction than for ACDF and degenerative lumbar spine surgery, despite significantly higher procedural costs and case complexity/invasiveness. Patients were most uncomfortable forfeiting newer generation implants, receiving the operation at a community rather than an academic center, and receiving care by physician extenders. Conversely, patients were more willing to convert postoperative visits to telehealth and forgo neuromonitoring, indicating a potentially poor understanding of which cost-saving measures may be implemented without increasing the risk of complications.

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology.

BACKGROUND: The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making. OBJECTIVE: The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care. METHODS: The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory's community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard's interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data. RESULTS: The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security. CONCLUSIONS: Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/46810.

A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study.

BACKGROUND: As trachoma is eliminated, skilled field graders become less adept at correctly identifying active disease (trachomatous inflammation-follicular [TF]). Deciding if trachoma has been eliminated from a district or if treatment strategies need to be continued or reinstated is of critical public health importance. Telemedicine solutions require both connectivity, which can be poor in the resource-limited regions of the world in which trachoma occurs, and accurate grading of the images. OBJECTIVE: Our purpose was to develop and validate a cloud-based "virtual reading center" (VRC) model using crowdsourcing for image interpretation. METHODS: The Amazon Mechanical Turk (AMT) platform was used to recruit lay graders to interpret 2299 gradable images from a prior field trial of a smartphone-based camera system. Each image received 7 grades for US $0.05 per grade in this VRC. The resultant data set was divided into training and test sets to internally validate the VRC. In the training set, crowdsourcing scores were summed, and the optimal raw score cutoff was chosen to optimize kappa agreement and the resulting prevalence of TF. The best method was then applied to the test set, and the sensitivity, specificity, kappa, and TF prevalence were calculated. RESULTS: In this trial, over 16,000 grades were rendered in just over 60 minutes for US $1098 including AMT fees. After choosing an AMT raw score cut point to optimize kappa near the World Health Organization (WHO)-endorsed level of 0.7 (with a simulated 40% prevalence TF), crowdsourcing was 95% sensitive and 87% specific for TF in the training set with a kappa of 0.797. All 196 crowdsourced-positive images received a skilled overread to mimic a tiered reading center and specificity improved to 99%, while sensitivity remained above 78%. Kappa for the entire sample improved from 0.162 to 0.685 with overreads, and the skilled grader burden was reduced by over 80%. This tiered VRC model was then applied to the test set and produced a sensitivity of 99% and a specificity of 76% with a kappa of 0.775 in the entire set. The prevalence estimated by the VRC was 2.70% (95% CI 1.84%-3.80%) compared to the ground truth prevalence of 2.87% (95% CI 1.98%-4.01%). CONCLUSIONS: A VRC model using crowdsourcing as a first pass with skilled grading of positive images was able to identify TF rapidly and accurately in a low prevalence setting. The findings from this study support further validation of a VRC and crowdsourcing for image grading and estimation of trachoma prevalence from field-acquired images, although further prospective field testing is required to determine if diagnostic characteristics are acceptable in real-world surveys with a low prevalence of the disease.

Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions.

BACKGROUND: Preventive care helps patients identify and address medical issues early when they are easy to treat. The internet offers vast information about preventive measures, but the sheer volume of data can be overwhelming for individuals to process. To help individuals navigate this information, recommender systems filter and recommend relevant information to specific users. Despite their popularity in other fields, such as e-commerce, recommender systems have yet to be extensively studied as tools to support the implementation of prevention strategies in health care. This underexplored area presents an opportunity for recommender systems to serve as a complementary tool for medical professionals to enhance patient-centered decision-making and for patients to access health information. Thus, these systems can potentially improve the delivery of preventive care. OBJECTIVE: This study proposes practical, evidence-based propositions. It aims to identify the key factors influencing patients' use of recommender systems and outlines a study design, methods for creating a survey, and techniques for conducting an analysis. METHODS: This study proposes a 6-stage approach to examine user perceptions of the factors that may influence the use of recommender systems for preventive care. First, we formulate 6 research propositions that can be developed later into hypotheses for empirical testing. Second, we will create a survey instrument by collecting items from extant literature and then verify their relevance using expert analysis. This stage will continue with content and face validity testing to ensure the robustness of the selected items. Using Qualtrics (Qualtrics), the survey can be customized and prepared for deployment on Amazon Mechanical Turk. Third, we will obtain institutional review board approval because this is a human subject study. In the fourth stage, we propose using the survey to collect data from approximately 600 participants on Amazon Mechanical Turk and then using R to analyze the research model. This platform will serve as a recruitment tool and the method of obtaining informed consent. In our fifth stage, we will perform principal component analysis, Harman Single Factor test, exploratory factor analysis, and correlational analysis; examine the reliability and convergent validity of individual items; test if multicollinearity exists; and complete a confirmatory factor analysis. RESULTS: Data collection and analysis will begin after institutional review board approval is obtained. CONCLUSIONS: In pursuit of better health outcomes, low costs, and improved patient and provider experiences, the integration of recommender systems with health care services can extend the reach and scale of preventive care. Examining recommender systems for preventive care can be vital in achieving the quadruple aims by advancing the steps toward precision medicine and applying best practices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43316.

Exploratory Descriptive Analysis of Smart Speaker Utilization in the Emergency Department During the COVID-19 Pandemic.

BACKGROUND: In March 2020, the U.S. Department of Health and Human Services Office for Civil Rights stated that they would use discretion when enforcing the Health Insurance Portability and Accountability Act regarding remote communication technologies that promoted telehealth delivery during the COVID-19 pandemic. This was in an effort to protect patients, clinicians, and staff. More recently, smart speakers-voice-activated, hands-free devices-are being proposed as productivity tools within hospitals. OBJECTIVE: We aimed to characterize the novel use of smart speakers in the emergency department (ED). METHODS: A retrospective observational study of Amazon Echo Show® utilization from May 2020 to October 2020 in a large academic Northeast health system ED. Voice commands and queries were classified as either patient care-related or non-patient care-related, and then further subcategorized to explore the content of given commands. RESULTS: Of 1232 commands analyzed, 200 (16.23%) were determined to be patient care-related. Of these commands, 155 (77.5%) were clinical in nature (i.e., "drop in on triage") and 23 (11.5%) were environment-enhancing commands (i.e., "play calming sounds"). Among non-patient care-related commands, 644 (62.4%) were for entertainment. Among all commands, 804 (65.3%) were during night-shift hours, which was statistically significant (p < 0.001). CONCLUSIONS: Smart speakers showed notable engagement, primarily being used for patient communication and entertainment. Future studies should examine content of patient care conversations using these devices, effects on frontline staff wellbeing, productivity, patient satisfaction, and even explore opportunities for "smart" hospital rooms.

Barriers and enablers associated with participation in a home-based pragmatic exercise snacking program in older adults delivered and monitored by Amazon Alexa: a qualitative study.

BACKGROUND: 'Exercise snacking', which is characterised by shorter and more frequent exercise bouts compared with traditional exercise guidelines, may be an acceptable strategy for increasing physical activity and reducing sedentary behaviour in older adults. AIM: The aim of this study was to evaluate the enablers and barriers for older adults associated with participation in a home-based exercise snacking program delivered and monitored using an Amazon Echo Show 5 device (Alexa). METHODS: This study used an interpretive description qualitative design to conduct semi-structured interviews following a 12-week pilot study in 15 adults aged 60-89 years with at least one chronic condition. All participants were prescribed a home based, individualised, lower limb focussed 'exercise snacking' program (involving ≤ 10 min of bodyweight exercises 2-4 times per day) delivered and monitored by an Alexa. Qualitative interview data were analysed using thematic analysis. RESULTS: All 15 participants (mean age 70.3 years) attended the semi-structured interview. Themes including time efficiency, flexibility, perceived health benefits, and motivation were enablers for participation in the 'exercise snacking' program. A lack of upper body exercises and omission of exercise equipment in the program, as well as a lack of time and motivation for performing exercise snacks three or more times per day, were barriers to participation. CONCLUSION: While 'exercise snacking' is acceptable for older adults, future trials should provide equipment (e.g. adjustable dumbbells, exercise bands), prescribe whole-body exercise programs, and establish strategies to support participation in more than three exercise snacks per day.

User Experience Regarding Digital Primary Health Care in Santarém, Amazon: Evaluation of Patient Satisfaction and Doctor's Feedback.

BACKGROUND: With the arrival of the pandemic, telemedicine has been widely used to provide medical care and can be used to assist patients in regions far from urban centers that are difficult to access, such as riverside communities in the Brazilian Amazon region. A telemedicine project connecting São Paulo, a mega-metropolis, to Paysandú, a riverside district in the Amazon, was built to serve the local population where access to the nearest medical care is 6 hours away by speedboat. OBJECTIVE: This study aims to assess the feedback from patients and doctors regarding the use of telemedicine in outpatient care at Paysandú, a riverside district in the Amazon. METHODS: This is a single-center study following the guidelines "Evaluating digital health products" from Public Health England, with local adaptations for the project and the Brazilian reality, that was conducted between São Paulo and Santarém in Brazil. A survey was carried out with patients who were treated by a doctor in the city of São Paulo, about 2500 km from the local basic health unit, between September 27 to December 15, 2021. At the end of each teleconsultation, the attending physician answered an administrative survey form, and the patient answered a satisfaction survey. RESULTS: A total of 111 patients completed the satisfaction survey from a total of 220 consultations carried out during the period (95% CI margin error 0.22%). According to the survey, more than 95% of patients were satisfied with the service, 87.4% (n=97) had previous experience with videoconferencing, and 76.6% (n=85) reported that their demand was fully solved. Additionally, according to the hired doctor's feedback, the average duration of the consultations was between 15 and 20 minutes. Of the 220 teleconsultations performed, 90.9% (n=200) of the demands were solved with support from the local health team, and 99.1% (n=218) of the appointments had a problem with audio or video. CONCLUSIONS: This teleconsultation project between São Paulo and Paysandú showed that it is possible to offer medical care from more developed locations to communities far from urban centers, as is the case with Paysandú District. Beyond the feasibility of the infrastructure, acceptance and satisfaction among patients were high. This health care supply model has proven to be functional and should be expanded nationally or perhaps internationally to regions lacking medical assistance. Escalation of the project does not seem too difficult once infrastructure issues are solved.

Factors Shifting Preference Toward Telemedicine in the Delivery of Skin Cancer Reconstruction Care.

OBJECTIVES: Identify which delivery modality for skin reconstruction care, face-to-face (FTF) in-person versus two telemedicine modalities, store-and-forward (S&F) and live video chat (LVC), is patient preferred and how cost, access, wait time, and demographics influence this preference. STUDY DESIGN: Cross-sectional survey. METHODS: A 16-question survey querying demographics and five scenario-specific preferences questions for the delivery of skin cancer reconstruction care was created and distributed via Amazon Mechanical Turk (MTurk), a crowdsourcing online marketplace, and in-person to Mohs micrographic surgery patients. RESULTS: 1394 MTurk and 55 in-person responses were included. While 82.1% of online respondents prefer FTF clinic visits, this decreases to 58.3% with an in-person visit cost (p < 0.01) and furthermore to a minority 43.5% with both an in-person visit cost and wait time (p < 0.01) despite 77.8% believing that usefulness to the surgeon would improve FTF. Both the MTurk and in-person cohorts demonstrated similar response patterns despite considerable demographic differences. Multivariable analyses revealed that telemedicine was preferred by MTurk respondents with Medicaid (adjusted OR [95% CI]: 1.97 [1.18-3.31]) or Medicare (1.69 [1.10-2.59]) versus private insurance, and prior skin cancer (2.01 [1.18-3.42]) and less preferred by those earning $140,000+ per year (0.49 [0.29-0.82]) compared to those earning <$20,000 per year. CONCLUSIONS: FTF visits are preferred for skin cancer reconstruction care; this shifts toward virtual care with a cost and wait time in spite of the perceived quality of care. Individuals with socioeconomic barriers to access prefer telemedicine. MTurk can be a valuable tool for behavioral research in FPRS. LEVEL OF EVIDENCE: NA Laryngoscope, 133:294-301, 2023.

Assessment of Mobile Health Applications for Management of Knee and/or Hip Osteoarthritis Using the Mobile Application Rating Scale.

BACKGROUND: Mobile health applications (apps) can help individuals with knee and/or hip osteoarthritis (OA) learn about, monitor, and manage their condition. These apps have not been formally evaluated. OBJECTIVE: The aim of this study was to evaluate the publicly available mobile health apps for individuals with knee and/or hip OA using a systematic review. METHODS: We searched the Apple App Store, Android Google Play, and Amazon Appstore using the terms "arthritis," "osteoarthritis," "hip OA," "knee OA," "hip," "knee," "rehabilitation," "rehab," and "physical therapy" in December of 2021. Applications that met the inclusion/exclusion criteria were reviewed using the Mobile Application Rating Scale (MARS; 29 items across 6 sections, each rated at 1-5). RESULTS: Among 1104 identified apps, 94 met the inclusion/exclusion criteria for MARS appraisal. Fourteen apps met the predetermined score thresholds for final summary. Of the 14 apps appraised, the total overall mean app score on the MARS ranged from 3.12 to 4.20 (mean, 3.51 ± 0.37). Although app features varied, common features were symptom tracking, exercise recommendations, education, goal setting, and improving well-being. Many apps allowed for sharing with health care providers and included some measures to protect privacy. Jointfully Osteoarthritis was the top-rated app in both the Apple App Store and Android Google Play. CONCLUSIONS: The majority of the apps we identified for knee and/or hip OA did not meet predetermined score thresholds for final summary. Many failed to provide comprehensive education and deliver management plans and lacked scientific testing. Future research should focus on apps that fit the needs of health care providers and patients including quality information, structured exercise programs tailored to individual needs, secure communication methods, and health information protection.

"Alexa, did the pandemic make you smarter?" A follow up content analysis of a virtual assistant's responses to a prenatal query.

To compare responses to 40 common prenatal questions from Amazon's virtual assistant, Alexa, one year apart during the COVID pandemic. Participants: Two researchers replicated a prenatal query using unique Alexa devices. A conceptual content analysis was conducted where the researchers independently queried Alexa the identical questions from their 2020 study during the same one-week timeframe, between May 20, 2021 and May 27, 2021. Alexa's responses were compared to the 2020 study and the American College of Obstetricians and Gynecologists data and verified by one of the researchers, a Certified Nurse Midwife. Alexa provided accurate responses to 26 (65%) of the questions, an increase by 55 percentage points from 2020. Alexa was able to recite the symptoms of COVID-19 illness but was unable to provide a response to the two other COVID-specific questions. Compared to the 2020 query, Alexa provided more reputable sources for the responses including the CDC, WHO, NIH, and Mayo Clinic. Alexa's ability to provide more accurate, evidence-based responses was remarkably improved in 2021. Mobile health tools, like Amazon Alexa, are highly utilized by the public, particularly with limited healthcare access during the COVID-19 pandemic. Technology-based platforms should provide credible, evidence-based content.

Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access.

Data from digital health technologies (DHT), including wearable sensors like Apple Watch, Whoop, Oura Ring, and Fitbit, are increasingly being used in biomedical research. Research and development of DHT-related devices, platforms, and applications is happening rapidly and with significant private-sector involvement with new biotech companies and large tech companies (e.g. Google, Apple, Amazon, Uber) investing heavily in technologies to improve human health. Many academic institutions are building capabilities related to DHT research, often in cross-sector collaboration with technology companies and other organizations with the goal of generating clinically meaningful evidence to improve patient care, to identify users at an earlier stage of disease presentation, and to support health preservation and disease prevention. Large research consortia, cross-sector partnerships, and individual research labs are all represented in the current corpus of published studies. Some of the large research studies, like NIH's All of Us Research Program, make data sets from wearable sensors available to the research community, while the vast majority of data from wearable sensors and other DHTs are held by private sector organizations and are not readily available to the research community. As data are unlocked from the private sector and made available to the academic research community, there is an opportunity to develop innovative analytics and methods through expanded access. This Session solicited research results leveraging digital health technologies, including wearable sensor data, describing novel analytical methods, and issues related to diversity, equity, inclusion (DEI) of both the underlying research data sets and the community of researchers working in this area. We particularly encouraged submissions describing opportunities for expanding and democratizing academic research using data from wearable sensors and related digital health technologies.

Challenges of design, implementation, acceptability, and potential for, biomedical technologies in the Peruvian Amazon.

BACKGROUND: Biomedical technologies have the potential to be advantageous in remote communities. However, information about barriers faced by users of technology in general and in remote Indigenous communities is scarce. The purpose of this study was to characterize the leading challenges faced by researchers who have used biomedical technologies in the Peruvian Amazon. METHODS: This exploratory, qualitative study with a phenomenological approach depicts the lived experience of participants who were researchers with experience working with biomedical technologies in the Peruvian Amazon in the past five years. Analysis was based on three core themes: design, implementation, and acceptability. Sub-themes included environment, community, and culture. Of the 24 potential participants identified and contacted, 14 agreed to participate, and 13 met inclusion criteria and completed semi-structured interviews. Results were sent to each participant with the opportunity to provide feedback and partake in a 30-minute validation meeting. Five participants consented to a follow-up meeting to validate the results and provide further understanding. RESULTS: Participants recognized significant challenges, including technologies designed out-of-context, difficulty transporting the technologies through the Amazon, the impact of the physical environment (e.g., humidity, flooding), and limited existing infrastructure, such as electricity and appropriately trained health personnel. Participants also identified cultural factors, including the need to address past experiences with technology and health interventions, understand and appropriately communicate community benefits, and understand the effect of demographics (e.g., age, education) on the acceptance and uptake of technology. Complementary challenges, such as corruption in authority and waste disposal, and recommendations for technological and health interventions such as co-design were also identified. CONCLUSIONS: This study proposes that technological and health interventions without efforts to respect local cultures and health priorities, or understand and anticipate contextual challenges, will not meet its goal of improving access to healthcare in remote Amazon communities. Furthermore, the implications of corruption on health services, and improper waste disposal on the environment may lead to more detrimental health inequities.

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment.

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance. All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients' medical records due to cloud computing. The cloud's services are very cost-effective and scalable, and provide various mobile access options for a patient's electronic health records (EHRs). EHR privacy and security are critical concerns despite the many benefits of the cloud. Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards. This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider. The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security. There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them. End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (KCF), and access to cloud services is granted based on a person's identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme. The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness. Using different Amazon EC2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases. The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant. The proposed secure access scheme for cloud-based web applications was compared to the Ethereum blockchain platform, which uses internet of things (IoT) devices in terms of execution time, throughput, and latency.