JMIR Formative Research

ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19. DesignProspective cohort study. SettingPredi-COVID data between May 2020 and May 2021. ParticipantsA total of 1772 voice recordings was used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphones operating system (Android/iOS). The recordings were collected from 296 participants tracked for two weeks following SARS-CoV-2 infection. primary and secondary outcome measuresFour machine learning algorithms (Logistic regression, k-nearest neighbors, support vector machine, and soft voting classifier) were used to train and derive the fatigue vocal biomarker. A t-test was used to evaluate the distribution of the vocal biomarker between the two classes (Fatigue and No fatigue). ResultsThe final study population included 56% of women and had a mean ({+/-}SD) age of 40 ({+/-}13) years. Women were more likely to report fatigue (P<.001). We developed four models for Android female, Android male, iOS female, and iOS male users with a weighted AUC of 79%, 85%, 86%, 82%, and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue (t-test P<.001). ConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.

ObjectiveChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. Materials and MethodsWe surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with Fishers Exact. Furthermore, we used qualitative methods to analyze and code discussions. ResultsWe received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. DiscussionThere was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

There is a growing interest to investigate the feasibility of using voice user interfaces as a platform for digital therapeutics in chronic disease management. While mostly deployed as smartphone applications, some demographics struggle when using touch screens and often cannot complete tasks independently. This research aimed to evaluate how heart failure patients interacted with a voice app version of an already existing digital therapeutic, Medly, using a mixed-methods concurrent triangulation approach. The objective was to determine the acceptability and feasibility of the voice app by better understanding who this platform is be best suited for. Quantitative data included engagement levels and accuracy rates. Participants (n=20) used the voice app over a four week period and completed questionnaires and semi-structured interviews relating to acceptability, ease of use, and workload. The average engagement level was 73%, with a 14% decline between week one and four. The difference in engagement levels between the oldest and youngest demographic was the most significant, 84% and 43% respectively. The Medly voice app had an overall accuracy rate of 97.8% and was successful in sending data to the clinic. Users were accepting of the technology (ranking it in the 80th percentile) and felt it did not require a lot of work (2.1 on a 7-point Likert scale). However, 13% of users were less inclined to use the voice app at the end of the study. The following themes and subthemes emerged: (1) feasibility of clinical integration: user adaptation to voice apps conversational style, device unreliability, and (2) voice app acceptability: good device integration within household, users blamed themselves for voice app problems, and voice app missing desirable user features. The voice app proved to be most beneficial to those who: are older, have flexible schedules, are confident with using technology, and are experiencing other medical conditions.

BackgroundDigital health technologies are increasingly being used to monitor, assess, and treat depressive symptoms in the community. However, many such technologies rely on screening tools which were originally designed for use in primary care clinics, such as the Patient Health Questionnaire (PHQ-9). These scales are symptom-focused and do not capture the wider experiences of the patient. We developed a new screen for assessing depressive symptoms in a digital setting. Named the Mindstep Mood and Cause Examination (MMCE), it was designed to replicate the predictive capabilities of the PHQ-9, while improving user experience and capturing broader determinants of mental health. MethodThis was a cross-sectional study, conducted fully remotely on Prolific. Participants (n=367) completed both the PHQ-9 and the MMCE, in a randomised order. Responses on the MMCE were examined for a range of psychometric properties, including: internal consistency, item selectivity, and convergence with PHQ-9 scores. User experience was assessed with a theory-led acceptability scale and compared across both mental health measures. Thematic analysis was used to analyse participants free text responses, describing their experience of completing the scales. ResultsThe MMCE displayed good internal consistency and strong convergence with the PHQ-9 (r = 0.70), accounting for 49% of the variance in PHQ-9 scores. The MMCE also demonstrated robust predictive capability for the PHQ-9 using a moderate depression symptom cut-off of 10, with an Area Under Curve (AUC) of 0.84. In direct comparisons between the scales, 259 of 367 users (70.1%) preferred the MMCE and the MMCE outperformed the PHQ-9 in 8 out of 12 user experience categories. ConclusionsThe MMCE has demonstrated validity in predicting PHQ-9 scores and offers an improved user experience, while additionally encouraging the user to examine the underlying causes of their depressive symptoms. However, additional research is necessary to evaluate the MMCE in terms of repeated assessments for effective depression monitoring.

BackgroundSmartphones and their increasing capabilities have helped humans to communicate and perform many tasks and it leads to a form of dependency, and it may have negative effects on everyone, especially students. ObjectivesTo assess smartphone addiction and its relationship to academic performance among medical students at King Saud University, Kingdom of Saudi Arabia. MethodsAn observational cross-sectional study was conducted from July to September 2022 including students of the College of Medicine at King Saud University, Kingdom of Saudi Arabia. The data collection tool was structured and utilized an electronic survey. ResultsA total of 330 participants answered the study questionnaire. The most common age range of study participants was 18-28 years with 64.2% of the study sample. Male participants represented 63%. The study income is less than 5000 riyals 54.5% per month. Majority of ftudents (65%) believe that using smartphones them to study more efficiently. Analysis of the study results shows that there is a statistically significant correlation between phone addiction and a decrease in the academic performance of college students. ConclusionOur study found that there is a significant correlation between phone addiction and a drop in academic performance. Despite its attractiveness, smartphone addiction is a time waster for students that might disrupts their sleep and causes stress. It is, therefore, necessary to create a comprehensive plan that directs the students towards balanced use.

Breast reconstruction following mastectomy can restore body image and confer psychosocial benefits but can also result in lasting functional deficits in shoulder range of motion (SROM). Early identification of these deficits is crucial for guiding rehabilitation and improving long-term outcomes, but traditional assessment tools are often costly or impractical for routine clinical use. This longitudinal observational study examined the feasibility and validity of an app-based shoulder mobility assessment (MotionDetect) for detecting post-operative changes in SROM compared to traditional inertial measurement units (IMU)-derived measurements among breast cancer patients undergoing reconstruction. Twenty female participants undergoing bilateral mastectomy with immediate breast reconstruction performed at-home SROM assessments before surgery and once at 6-12 weeks post-operatively using both wearable IMUs and the MotionDetect iPhone application. Maximum shoulder abduction and flexion angles were recorded at each time point. Structured interviews gathered patient feedback on app usability. Statistical analyses assessed changes over time, correlation between measurement modalities, and repeatability. Significant reductions in abduction ROM after surgery were observed using both IMU and iPhone app assessments (both p < 0.031), with strong correlations between modalities (r > 0.80). Both approaches demonstrated excellent intra-class coefficients (ICC) repeatability (ICC > 0.89). Patient interviews indicated high feasibility and acceptability, with minor logistical challenges. Overall, this study indicates MotionDetect is a valid and feasible tool for remotely identifying post-operative SROM limitations in breast cancer patients, enabling early referral for rehabilitation to improve long-term quality of life.

BackgroundNomophobia, characterised by the dread or anxiety of being without access to a mobile phone, has become an increasing behavioural issue among young adults, especially university students. Excessive reliance on smartphones has been linked to psychological suffering, impaired everyday functioning, and negative academic results. Nonetheless, information about the prevalence and determinants of nomophobia among university students in Bangladesh is scarce. This study aimed to evaluate the prevalence of nomophobia and investigate its associated sociodemographic traits and smartphone-related behaviors among medical and university students in Bangladesh. Materials and MethodsA cross-sectional study was performed with 476 undergraduate medical and non-medical students from eight districts of Bangladesh from September 2023 to July 2025, using an online structured questionnaire. Nomophobia was evaluated via the validated Nomophobia Questionnaire (NMP-Q). The questionnaire was pilot-tested (n=60) and showed good reliability (Cronbachs =0.82). Information on socio-demographics, mobile phone usage trends, and application preferences was collected. Data was processed using STATA version 16. Descriptive statistics, t-tests, ANOVA, Pearson correlation, and multiple linear regression analyses were used to ascertain factors related to nomophobia. ResultsThe average age of participants was 20.70 {+/-} 1.52 years. 46.79% of students exhibited moderate nomophobia, whereas 25.69% had severe nomophobia. Elevated nomophobia scores were strongly correlated with female gender, moderate household income, usage of social media and communication applications, prolonged daily mobile phone usage (>7 hours), frequent phone checking, and instant phone checking upon awakening. Multiple linear regression indicated that extended phone usage, engagement with social communication applications, middle-income position, and early-morning phone checking are independent predictors of elevated nomophobia scores. ConclusionNomophobia is significantly common among university students in Bangladesh. Behavioural patterns such as prolonged daily smartphone use, frequent phone checking, immediate phone use upon waking, and extensive engagement with social communication and media applications are associated with excessive smartphone usage and development of nomophobia. These findings underscore the necessity for awareness initiatives, early detection, and focused interventions to alleviate the adverse psychological and behavioural effects of nomophobia in students.

BackgroundThe COVID-19 pandemic severely disrupted surgical care worldwide, with particularly acute effects in low- and middle-income countries (LMICs). Telemedicine (TM) was rapidly adopted to mitigate these disruptions, but evidence on its role in surgical care in LMIC settings remains limited. ObjectiveTo review the implementation, impact, and challenges of telemedicine in surgical care across LMICs during the COVID-19 pandemic. MethodsFollowing PRISMA 2020 guidelines, we searched PubMed, Embase, Web of Science, and Ovid for peer-reviewed studies from December 2019 to July 2022. Studies assessing TM interventions in any surgical specialty within LMICs were included. Data extraction focused on TM platform types, patient and provider outcomes, feasibility, and policy implications. Quality was appraised using the Joanna Briggs Institute tool for observational studies. ResultsThirteen studies across six surgical specialties and 4,155 patients were included. TM was used for follow-up (46%), consultation (38%), and remote treatment (23%), with high patient satisfaction (mean [&ge;] 85%), improved access, and cost savings reported. Four studies noted positive clinical outcomes (e.g., reduced complications, optimized medication). Barriers included connectivity issues, regulatory gaps, lack of physical examination capability, and infrastructure inequities. ConclusionsTM provided feasible, safe, and effective surgical support during the pandemic in LMICs, particularly in rural settings. However, long-term sustainability requires investment in digital infrastructure, standardized protocols, and data privacy regulation. TM should be integrated into national surgical planning beyond COVID-19. Taxonomy in TelemedicineSeveral terms have been used to indicate remote health services, therefore it is essential to differentiate them, understand their scope and their relation to each other [1]. O_LIE-health: The term includes all types of secure use of information and communication technology (ICT) related to health, for example, applications and websites for health promotion, education, screening, research, assessment, and virtual video-chat sessions [2]. C_LIO_LITelehealth: Compared to e-health, telehealth is limited to healthcare over a distance. It is more extensive than telemedicine and incorporates educational activities related to patients and providers, public health intervention, and health administration [3]. C_LIO_LITelemedicine (TM): A subgroup of telehealth that focuses only on the curative aspect. It can be divided into medical specialties such as dermatology (tele-dermatology), psychiatry (telepsychiatry), and radiology (teleradiology). C_LIO_LITelecare: A subgroup of telehealth that focuses on the preventive aspect. It provides automated monitoring of behavior changes over time. C_LIO_LImHealth: Mobile technologies that can have a spectrum of purposes in the delivery of care. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/25328225v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@31db04org.highwire.dtl.DTLVardef@1220fd1org.highwire.dtl.DTLVardef@13c952forg.highwire.dtl.DTLVardef@666a53_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO the scope and relationship between the different telehealth related terms. C_FIG

Digital self-guided mobile health (mHealth) applications are cost-effective, accessible, and well-suited to improve mental health at scale. This randomized controlled trial (RCT) evaluated the effectiveness of a recently developed mHealth programme based on cognitive-behavioral therapy (CBT) principles in improving worry. We also examined psychological mindedness (PM) as a mediator by which app engagement is thought to improve outcomes. The intervention group completed a 2-week "Anxiety and Worry" programme with daily CBT-informed activities, while the active waitlist-control completed a matched 2-week mHealth programme on procrastination. Participants filled out the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), and Psychological Mindedness Scale (PMS) at baseline, post-intervention and 2-week follow-up. App engagement was measured at post-intervention only. Both groups showed significant improvements on anxiety and depression scores from baseline to post-intervention, but no group differences were observed. From post-intervention to follow-up, only the intervention group showed further improvements for anxiety levels. Higher engagement with the mHealth app reported lower anxiety at post-intervention, and this relationship was fully mediated by psychological mindedness. This study provides evidence that (a) engaging in a CBT mHealth App can effectively reduce anxiety and worry, and (b) Psychological mindedness is a potential pathway by which engaging with a mHealthapp improves worry. While overall effect sizes were small, at the population level, these can make significant contributions to public mental health. Author SummaryIncreasing burden of anxiety amongst young adults has made widely accessible mobile health applications a promising tool in improving anxiety levels at scale. We conducted a randomized controlled trial (N=309) to examine the effectiveness of a brief, publicly available mobile health application (Intellects "Anxiety and Worry" programme) in reducing anxiety and worry levels among young adults. Participants who received the intervention showed significant reduction in anxiety and depression levels, however, effects did not significantly differ from active control. At post-intervention, only the intervention group continued to experience improvements in anxiety level. We also found that higher app engagement with the mHealth app predicted better anxiety and depression outcomes, and this relationship was fully mediated by psychological mindedness. Future work would benefit from inclusion of waitlist control, a larger sample size, and identification of alternative mediators.

AimTo assess the awareness, attitude and perception of Trinidad and Tobago population towards telemedicine MethodA cross-sectional study was conducted using a structured questionnaire on patients that visited health centres in the North Central region. A systematic random sampling method was used to select participants. Descriptive statistics including mean and standard deviation were used to calculate categorical and continuous variables. Comparison between the categorical groups of the demographic variables for each of the three main outcome variables were analysed through one-way analysis of variance (ANOVA). Statistical significance was defined as P<0.05. ResultsA total of 528 participated in the study. Most (60%) of them were female, and aged 21 to 40 years (62.1%). Awareness of telemedicine was 34.4%, but the majority (82.5%) had never used telemedicine before. About half (51.3%) acknowledged the necessity of telemedicine but few (36.4%) were satisfied with the services. Most (64%) were willing to try mobile-based healthcare apps. Concerns over lack of familiarity with telemedicine platforms (44.5%) and result accuracy (15.5%) were the major barriers to using telemedicine. Awareness of telemedicine was significantly associated with being female (P < 0.001), a medical profession (P = 0.004), familiarity in use of computers (P = 0.004) and frequent interaction with doctors online (P < 0.001). Positive attitude towards telemedicine was associated with having a diploma, being a medical professional, being computer literate and frequent interaction with doctor online. Positive perception towards telemedicine was associated with marital status (Single or Previously Married) (P = 0.011), ones ability to use the computer (P = 0.009), their level of competency in computer usage (P = 0.002), and frequency of interacting with doctors online (P < 0.002). ConclusionThe study revealed that although the level of telemedicine awareness is low, the majority of respondents demonstrated positive attitude and perception towards telemedicine. The findings suggest the need to educate the public on the benefits of telemedicine and create awareness of its use in T&T.

BackgroundClinical diagnostic assessments and outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating presence and monitoring of outcome of depression. ObjectiveThis paper explores the potential of using behavioral data collected with mobile phones to detect and monitor depression symptoms in patients diagnosed with depression. MethodsIn a prospective cohort study, we collected smartphone behavioral data for up to one year. The study consists of observations from 99 subjects, including healthy controls (n=25) and patients diagnosed with various depressive disorders: major depressive disorder (MDD) (n=46), major depressive disorder with comorbid borderline personality disorder (MDD|BPD) (n=16), and bipolar disorder with major depressive episodes (MDE|BD) (n=12). Data were labeled based on depression severity, using the 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and employed supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. ResultsWe identified 32 behavioral markers associated with the changes in depressive state. Our analysis classified depressed subjects with an accuracy of 82% and depression state transitions with an accuracy of 75%. ConclusionsThe use of mobile phone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and relapse of clinical depression and monitoring its outcome, particularly if combined with intermittent use of self-report of symptoms.

IntroductionThe susceptibility to suicidal behaviour has been linked to cognitive functioning deficits. Gamified assessments have emerged as a practical and engaging approach to assess these deficits, though their acceptability amongst young adults with suicidal thoughts is currently understudied. MethodsThirteen young Australian adults aged 18 to 25 years who experienced suicidal thoughts in the past year were recruited to evaluate the smartphone based CogGame app. Inductive thematic analysis was utilised to identify the themes obtained from the interviews. The relationships between cognitive functioning deficits and the severity of suicidal thoughts were explored by correlational analyses. ResultsAll participants found the GogGame app easy to learn to use and navigate. Positive experiences and high user satisfaction were reported with the use of CogGame app. Major areas for improvement include having clearer instructions and app information, adjusting the difficulty of the exercises, and addressing a few technical issues such as decreasing loading time. Higher levels of suicidal thoughts were found to be significantly associated with poorer visual learning performance on the CogGame app (p = .01). ConclusionPositive participant experiences with CogGame revealed the promising potential of gamified assessments to measure cognitive functioning in young adults with suicidal thoughts.

IntroductionThe capacity to deliver essential health services has been negatively impacted by the COVID-19 pandemic particularly due to lockdown restrictions. Telemedicine provides a safe, efficient, and effective solution that addresses the needs of patients and the health system. However, there remain implementation challenges and barriers to patient adoption in resource-limited settings as in the Philippines. This study thus aimed to describe patient perspectives and experiences with telemedicine services, and explore the factors that influence telemedicine use and satisfaction. MethodsThis study used a mixed-methods design through online surveys and in-depth interviews. An online survey using Consumer Assessment of Healthcare Providers and Systems (CAHPS) Clinician & Group Adult Visit Survey 4.0 (beta) and Telehealth Usability Questionnaire (TUQ) was accomplished by 200 participants aged 18 to 65 years. A subsample of 16 participants was interviewed to provide insights to the quantitative data. We used descriptive statistics to analyze survey data and grounded theory to analyze data from interviews. ResultsParticipants were generally satisfied with telemedicine services, with most reporting that this was an efficient and convenient alternative to face-to-face consultations. However, only 2 in 5 perceived telemedicine as affordable. Our quantitative findings suggest that participants preferred telemedicine services rather than in-person consultations, especially in cases where they feel that their condition is not urgent and does not need extensive physical examination. Safety against COVID-19, and the availability of multiple communication platforms contributed to patient satisfaction with telemedicine. Negative perceptions of patients on their telemedicine provider, perceived higher costs, poor connectivity and other technological issues were found to be barriers to patient satisfaction. DiscussionTelemedicine is viewed as a safe and efficient alternative to receiving care. Continued adoption of telemedicine will require improvements in technology and better patient communication related to their telemedicine provider and the associated costs.

BackgroundApproximately 19% of adults in Europe are affected by chronic pain, which reduces quality of life. Pain management apps (mHealth) offer a promising solution for self-management, but users engagement and adherence can be a limitation to their clinical impact. User experience design and studies play an important role in optimizing usability and long-term adoption of digital health interventions. ObjectiveThis study aims to evaluate the user experience of Dolodoc, a mobile application for chronic pain self-management, using a mixed-methods approach that evaluates acceptability through a content quality survey and examines usability by analyzing overall usage patterns. MethodsA cross-sectional acceptability study was conducted among chronic pain patients recruited from the Geneva University Hospitals pain center and through snowball sampling. Participants rated 84 evidence-based self-management strategies using a five-point Likert scale based on 5 acceptability criterias: understandability, motivation, feasibility, relevance, and alignment with the related quality-of-life dimension. Usability was assessed through usage metrics that were collected over six months using Piwik PRO analytics to observe the usage behaviors of real-world Dolodoc users. ResultsIn the acceptability study, a total of 33 participants rated the self-management strategies positively across all dimensions. On a scale from -2 to 2, the strategies were well understood (mean = 1,47), motivational (1.12), feasible(1.01), relevant (0.99), and aligned with the dimensions (1.33). The usability study demonstrated that 60% of patients used Dolodoc only once, indicating that long-term adherence remains a challenge. Within Dolodoc, pain tracking, useful links and medication logging were the most actively used features. DiscussionThis study highlights the gap between acceptability and long-term adherence to mHealth solutions. Improving personalization and accessibility could increase user engagement and long-term adherence. Future iterations of the app should incorporate tailored interventions and real-time feedback mechanisms. In addition, taking advantage of a digital navigation follow-up could facilitate user adoption and sustained engagement.

BackgroundDigital phenotyping is an emerging field that aims to contribute to the clinical care of patients with mental disorders by offering objective, passive behavioural monitoring. This monitoring could be used for applications such as predicting the onset of episodes of mental illness. However, behaviours are often not unique to clinical disorders, and other factors in a persons life may contribute to their digital phenotyping behavioural pattern. ObjectiveWe aimed to investigate non-clinical factors that may be relevant for personalisation in digital phenotyping, such as the area in which participants live and their regular phone habits, and discussed their implications in a depression relapse case study. MethodsIn the MENTALPRECISION study we collected passive smartphone data (phone usage and location behaviours) in a predominantly healthy cohort (n=73) using the Behapp application. We administered a novel questionnaire, the "Smartphone Usage and Lifestyle Questionnaire" (SULQ), to the participants to gather information on their phone usage and lifestyle habits that could impact their digital phenotyping data. We trained a hidden Markov model (HMM) on the smartphone data and developed two types of digital phenotyping measures from the identified hidden behavioural states of the HMM. The "total dwell time" gave the percentage of time each participant spent in each state. The "individual transition probability" was extracted from the HMM itself for each participant, giving their personalised probabilities of transitioning between each of the hidden states. We compared the HMM-generated hidden state sequences and reported events such as holidays and illness. We carried out logistic regression between the digital phenotyping measures and various SULQ measures. We then provide a proof-of-concept for predicting depression relapse in recurrent depression using a HMM and consider the implication of non-clinical factors for this clinical application. ResultsVisible differences in behaviour surrounding holidays and illness were observed in the generated hidden state sequences from the MENTALPRECISION study, as well as surrounding the depression relapse in the proof-of-concept. Participants who use another phone in addition to their personal smartphone spent significantly less time in the "socially inactive home time" state (FDR-corrected P=0.03, odds ratio 0.9196, 95% CI 0.8583-0.9808). iOS users spent significantly less time in the "socially active home time" state (FDR-corrected P=0.04, odds ratio 0.9330, 95% CI 0.8804-0.9857) than Android users, and participants reporting a smartphone addiction spent significantly more time in this state (FDR-corrected P=0.009, odds ratio 1.0787, 95% CI 1.0301-1.1272) when compared to participants reporting no smartphone addiction. Relationships between the mean transition probabilities and SULQ measures did not survive multiple comparison correction. We observed decreases in the monthly likelihood surrounding the depression relapse period, providing a possible metric for relapse prediction. ConclusionsWhen searching for clinically relevant behavioural signals, digital phenotyping researchers should consider additional non-clinical factors that may be contributing to the measured digital signal. Including this additional information could be used to improve personalisation of digital phenotyping models, leading to improved modelling abilities for goals such as depression relapse prediction.

BackgroundIn recent years, Instagram has become the most popular tool among professional doctors in Azerbaijan for educating their patients. The use of the Instagram application aims to raise patients awareness of the importance of taking care of their health and to increase their knowledge about their health conditions using modern services. In this article, the authors examine the quality of Instagram content for health education among the population. MethodsWe conducted a survey to collect anonymous data from more than 205 respondents and summarized the following points. Results65% of the respondents were already obtaining health information from Instagram before to participating in the study. 15.1 % of them frequently visit Instagram for health information while 5% had found the health information accessed there harmful. 71% of respondents think accessing health information in this way is beneficial but that the quality and usefulness of the content is average. 95% of respondents reported that the health information they obtained from the identical platform was not causing them any harm ConclusionThe medical information shared on Instagram is generally considered useful and beneficial by the population, but it is desirable to improve the quality of the content.

Physician-Assisted Suicide (PAS) is considered by some patients who learn they are at risk for a cognitive decline owing to Alzheimers Disease. At the same time, the prospect of PAS may raise patients fear of imminent death. Can PAS be offered in a way that is preference-sensitive on one hand, and mitigates patients fear of imminent death on the other? A thought experiment of a Probabilistic Approach to PAS (Probabilistic PAS) is proposed here as a possible solution in jurisdictions where PAS is legal. Consider the following scenario: A patient diagnosed with Alzheimers Disease who is considering PAS might request that when he or she can no longer recognize their loved ones, their doctor will give them a lethal pill that has a 1/100 daily probability of being activated. As a result, after taking the pill, every day the patient will have a 1/100 probability of dying. The likelihood of the patient dying within a year from taking the pill is 97.4%, and within two years, it is 99.9%. As such, a Probabilistic PAS with which on any given day the probability of dying is low, can help patients avoid the fear of imminent death - which traditional PAS entails - while respecting their preference to end their lives. To examine the potential reception of a Probabilistic PAS, a survey was administered to a nationally-representative sample of US residents, using Prolific, a research participant recruitment platform. 499 participants were presented with a short description of a patient who was diagnosed with Alzheimers Disease, is writing an advance directive and is considering ways to end their life painlessly when they can no longer recognize their loved ones. Participants were asked about their own preferences in case they were to face a similar situation, and whether helping administer Probabilistic PAS would be ethical for the patients provider. 498 participants responded to the question about their own preference. 73.5% indicated that they would choose one of the two PAS options. Among those, 9.8% preferred a Probabilistic PAS over traditional PAS. Men were more likely than women to favor Probabilistic PAS for themselves. 482 participants indicated which option would be most ethical for the patients provider to administer. 48.1% indicated one of the two PAS options as most ethical. Among those, 10.3% considered Probabilistic PAS to be more ethical than traditional PAS. Men were more likely than women to consider the provider administering Probabilistic PAS to be most ethical. A version of the Probabilistic PAS proposed here should be considered as a preference-sensitive option presented by healthcare providers to patients considering advance care planning in places where PAS is available.

ObjectivesA majority of patients suffering from acute COVID-19 are expected to recover symptomatically and functionally. However there are reports that some people continue to experience symptoms even beyond the stage of acute infection. This phenomenon has been called longcovid. Study designThis study attempted to analyse symptoms reported by users on twitter self-identifying as longcovid. MethodsThe search was carried out using the twitter public streaming application programming interface using a relevant search term. ResultsWe could identify 89 users with usable data in the tweets posted by them. A majority of users described multiple symptoms the most common of which were fatigue, shortness of breath, pain and brainfog/concentration difficulties. The most common course of symptoms was episodic. ConclusionsGiven the public health importance of this issue, the study suggests that there is a need to better study post acute-COVID symptoms.

IntroductionTraditional diagnostic methods of hearing assessment, such as pure tone audiometry, may not be equally accessible to everyone due to geographical or mobility limitations. Utilizing a mobile application (app) for self-assessment of hearing is a promising alternative. However, the effectiveness of apps, as well as their usability across different age groups, remains largely unexplored. The objective of the present study was to assess, across different age groups, the usability of the "Hearing Test" app which allows self-testing of hearing on a mobile phone. Materials and methodsThe study was conducted on 77 participants from three age groups (16-39 years, 40-59 years, 60 years and older) who self-tested their hearing thresholds using the mobile app and who later underwent pure tone audiometry with an audiologist. The usability of the app was evaluated using a questionnaire based on the Mobile App Rating Scale (MARS), which was complemented by participant observation and interview. ResultsThe app generally yielded results comparable to pure tone audiometry. However, older age groups tended to report higher levels of difficulty across several usability dimensions. Specifically, the oldest group rated the app lower in terms of functionality (M = 2.30; SD = 1.27) and engagement-customization (M = 2.11; SD = 1.28). For the oldest participants, the greatest difficulties related to installation (48%), and interpretation of results (26%). None of the participants aged 60 or older were able to complete the test independently, in contrast to 67% of the youngest participants and 28% of the middle-aged who did not require assistance. All age groups expressed a preference for a conventional hearing test over an app-based assessment, although the youngest group showed the greatest openness to using mobile apps. ConclusionsThe "Hearing Test" app has demonstrated its potential as a tool for initial hearing assessment, particularly among younger users. However, older individuals often encounter difficulties with installation, interpretation of results, and overall usability. Adapting the interface to meet the specific needs of older users, including user-friendly tutorials and clear presentation of results, is crucial for enhancing its usability.

BackgroundSmartphone addiction is a growing social problem especially in young mobile users. This study investigated indicators of smartphone use, smartphone addiction, and their associations with demographic and behavior-related variables in young people. Methods460 participants were secondary school students (Mage = 17,10, SDage = 0.92, 51.1% males, 52.4% high school students), took part in an anonymous questionnaire consisting of the following elements: the Mobile Phone Addiction Assessment Questionnaire (KBUTK), original questions regarding problematic smartphones usage, along with a subjective assessment of the use of such devices. Logistic regression model using forward stepwise method was used to characterize a typical smartphone user. Smartphone addiction was measured using KBUTK. Multiple logistic regression analysis was performed to determine factors associated with smartphone addiction. ResultsA total of 460 participants admitted to using a smartphone. Gender, age, type of school, place of living influenced the ways respondents used their smartphones. Being female (OR = 5.80; p < 0.0001), sixteen-year-old (OR = 0,41; p = 0.0456), and student of technical school (OR = 2.66; p = 0.0025) turned out to be the characteristics of a typical smartphone user. 21.7% of adolescents considered themselves addicted to smartphones, 22.2% admitted that they had problems with face-to-face relationships and girls significantly more often than boys (61.8% vs. 51.5%) neglected home or school duties as a result of using a smartphone. The overall rate of smartphone addiction was significantly higher (p < 0.0001) among girls (2.31 pts) than boys (2.03 pts), and correlated positively with the perception of being a smartphone addict (rho = 0.223; p < 0.0001). Addiction to smartphones was also significantly more common among students of technical schools, and respondents living in blocks of flats. ConclusionsThe way adolescents used smartphones differed depending on gender, age and type of school. Interventions for reducing the negative effects of smartphone use should take into account these context, as well as education both adolescents and their parents.