DIGITAL HEALTH

The ability to cope with concussion symptoms and manage stress is an important determinant of risk for prolonged symptoms.

This open-label mixed-methods pilot study assessed the acceptability and credibility of a mindfulness-based intervention delivered through a digital therapeutic (DTx; therapeutic smartphone app) for pediatric concussion.

Participants aged 12 to 18 years were recruited from an emergency department within 48 hours of a concussion (acute cohort) or from a tertiary care clinic at least 1-month post-concussion (persisting symptoms cohort). Participants completed a novel 4-week mindfulness-based intervention, for 10 to 15 minutes/day, at a minimum of 4 days/week. At 2 weeks, participants completed a credibility and expectancy questionnaire. At 4 weeks, participants completed questionnaires assessing satisfaction, usability and working alliance, as well as a semi-structured phone interview.

Ten participants completed the study outcomes (7 acute; 3 persisting symptoms). The intervention was perceived as credible (median/max possible = 6.50/9.00 [6.83,8.75]) and DTx was usable (median/max possible = 70.00/100.00 [55.00,82.50]). Participants rated their satisfaction with the DTx (median/max possible = 27.00/32.00 [24.50,29.50]) and the working alliance with the digital mindfulness guides (median/max possible = 3.92/5.00 [3.38–4.33]) as high. Four themes were identified from the qualitative data: (a) positive attributes; (b) negative attributes; (c) ideas for modifications; and (d) technical issues.

Results show modifications to the DTx, instructions and mindfulness intervention, and potential ways to increase adherence by leveraging positive attributes. A randomized control trial will assess the effectiveness of the DTx MBI to decrease the risk of persisting symptoms and reduce the symptom burden following pediatric concussion.

ChatGPT has recently emerged as a disruptive technology, potentially impacting various societal dimensions, including pharmacy practices. In Thailand, community pharmacists are navigating transitions as patients increasingly rely on digital tools for healthcare recommendations. This study explores the attitudes of community pharmacists in Hatyai, one of Thailand's most populated cities, towards the integration of ChatGPT in pharmacy services.

ChatGPT-3.5 was used to generate responses to three questions concerning the use of medicine in special populations in the Thai language. These responses were then incorporated into a questionnaire and evaluated using a Likert scale from 1 to 5. Participants who consented were asked to rate the responses and participate in an in-depth interview.

The majority of participants rated the responses favorably, with scores of 4 and 5 accounting for at least 60% of the ratings. Only a small proportion of responses received doubtful ratings (score of 3) or was in disagreement, ranging from 20% to 40%. Moreover, open opinions extracted from the interviews suggested that participants viewed ChatGPT as a capable assistant, as it provided fast yet reasonably accurate information in the Thai language.

The findings indicate that community pharmacists view ChatGPT as a capable assistant, albeit noting the need for further refinements. The study underscores the importance for pharmacists to proactively adapt to technological advancements, particularly those affecting patient safety, to enhance healthcare delivery and optimize treatment outcomes.

Smartphone photography and crowdsourcing feedback could reduce participant burden for dietary self-monitoring.

To assess if untrained individuals can accurately crowdsource diet quality ratings of food photos using the Traffic Light Diet (TLD) approach.

Participants were recruited via Amazon Mechanical Turk and read a one-page description on the TLD. The study examined the participant accuracy score (total number of correctly categorized foods as red, yellow, or green per person), the food accuracy score (accuracy by which each food was categorized), and if the accuracy of ratings increased when more users were included in the crowdsourcing. For each of a range of possible crowd sizes (n = 15, n = 30, etc.), 10,000 bootstrap samples were drawn and a 95% confidence interval (CI) for accuracy constructed using the 2.5th and 97.5th percentiles.

Participants (n = 75; body mass index 28.0 ± 7.5; age 36 ± 11; 59% attempting weight loss) rated 10 foods as red, yellow, or green. Raters demonstrated high red/yellow/green accuracy (>75%) examining all foods. Mean accuracy score per participant was 77.6 ± 14.0%. Individual photos were rated accurately the majority of the time (range = 50%–100%). There was little variation in the 95% CI for each of the five different crowd sizes, indicating that large numbers of individuals may not be needed to accurately crowdsource foods.

Nutrition-novice users can be trained easily to rate foods using the TLD. Since feedback from crowdsourcing relies on the agreement of the majority, this method holds promise as a low-burden approach to providing diet-quality feedback.

To conceptualize new methods for integrating patient-centered measurement into team-based care.

A standalone portal was introduced into a rural clinic to support conceptualization of new methods for integration of patient-centered measurement in team-based care. The portal housed mental health-related online resources, three patient-reported measures and a self-action plan. Six providers and four patients used the portal for four months. Our data collection techniques included clinic discussions, one-on-one interviews, workflow diagrams and data generated through the portal. Analysis was supported through coding interview transcripts, looking across multiple sources of research data and research team discussions.

Our research team conceptualized five team-based patient-centered measurement methods through this study. Patient-centered measurement Team Mapping offfers a technique to provide greater clarity of care-team roles and responsibilities in data collected through patient-centered measurement. Longitudinal Care Alignment can guide the care-team on incorporating patient-centered measurement into ongoing provider–patient interactions. Digital Tool Exploration can be used to evaluate a team's readiness toward digital tool adoption, and the impact of these tools. Team-based quality improvement serves as a framework for engaging teams in patient-centered quality improvement. Shared learning is a method that promotes patientprovider interactions that validate patient's perspectives of their care.

The portal illuminated new methods for the integration of patient-centered measurement in team-based care. The first three proposed patient-centered measurement methods provides ways to assess how a clinic can incorporate patient-centered measurement methods into team-based care. The latter two methods focus on the aim of patient-generated data in which patient's values and perspectives are represented and quality of patient-centered care can be evaluated. Further testing is needed to assess the utility of these patient-centered measurement methods across different clinical settings and domains.

Accurate measurement of pelvic floor muscle (PFM) strength is crucial for the management of pelvic floor disorders. However, the current methods are invasive, uncomfortable, and lack standardization. This study aimed to introduce a novel noninvasive approach for precise PFM strength quantification by leveraging extracorporeal surface perineal pressure (ESPP) measurements and machine learning algorithms.

Twenty-one healthy women participated in this study. ESPP measurements were obtained using a 10 × 10 pressure array sensor during maximal voluntary PFM contractions in a seated position. Simultaneously, transabdominal ultrasound was used to measure bladder base displacement (mm) as a reference for PFM contraction strength. Seven ESPP variables were calculated based on ESPP data and intra- and inter-rater reliabilities were assessed. Machine learning algorithms predicted bladder base displacement from ESPP variables.

The ESPP measurements demonstrated good to excellent intra-rater (ICC = 0.881) and inter-rater (ICC = 0.967) reliability. Significant correlations were observed between bladder base displacement and middle (r = .619, P < .001) and front (r = −.379, P =.002) vectors. The top-performing models for predicting bladder base displacement were the support vector machine [root mean square error (RMSE) = 0.139, R2 = 0.542], random forest (RMSE = 0.123, R2 = 0.367), and AdaBoost (RMSE = 0.123, R2 = 0.320) on the training set, and AdaBoost (RMSE = 0.173, R2 = 0.537), random forest (RMSE = 0.177, R2 = 0.512), and support vector machine (RMSE = 0.178, R2 = 0.508) on the test set. In predicting bladder base displacement, Bland–Altman analysis revealed these models had minimal systematic bias, with mean differences ranging from −0.007 to 0.066, and clinically acceptable limits of agreement.

This study demonstrates the potential of ESPP measurements and machine learning algorithms as a reliable and valid noninvasive approach for assessing PFM strength by quantifying the directionality of contractions, overcoming the limitations of traditional techniques.

The COVID-19 pandemic has highlighted various barriers to health and the necessity of having access to digital health services. The technology industry can support addressing health barriers, promoting health equity and partnering with organizations to ensure access to digital health services for underserviced communities. The main objective of this study was to 1) identify what initiatives have been developed within the technology industry to address digital health equity; and to 2) determine whether these initiatives have been effective.

A rapid review and a grey literature scan were conducted. The academic searches were performed using four databases, including Ovid MEDLINE, Scopus, CINAHL and PsychInfo. Two reviewers screened the articles for inclusion criteria. The grey literature scan was performed through Google and Million Short. Searches of technology industry initiatives were completed through scanning technology companies listed on the New York Stock Exchange, the Toronto Stock Exchange and iShares Expanded Tech Sector – Exchange Traded Fund.

Within the technology industry, 39 companies had relevant initiatives. These were identified as having one or more of the following: 1) having health-related collaborations with other companies, 2) promoting access to technology infrastructure and 3) delivering programs that supported notable inequities within the social determinants of health. Limited data are available on the effectiveness of these initiatives in reducing health inequities.

As technology in the delivery of health services continues to evolve, health equity initiatives must be supported through innovative strategies. Partnering with the technology industry may be one way of addressing these health equity challenges.

Virtual reality (VR) has become increasingly popular in clinical and health settings where it has been used for a wide range of purposes. A recent scoping review explored VR applications to assist pregnant women and found that VR was a useful method to be used for a range of different purposes in both pregnancy and labour. However, no such review exists for the period after birth.

We aimed to search for studies that used VR to support parents during birth and in the first year postpartum (Population) in different settings (Context), and finally provided data on the characteristics, reported effectiveness and experience of VR interventions (Concept). Two hundred and fifty-one studies were identified, of which ten were eligible. Two authors independently extracted data including study design, participants and results.

Findings indicate that VR has been used effectively in this context to alleviate depression anxiety, and multiple domains of pain and to improve childbirth satisfaction. The majority of the studies explored the use of VR technology on outcomes such as pain and anxiety during labour and birth. The studies included used a broad range of VR hardware and software. All of the studies reported positive experiences of using VR.

Across these studies, VR was found to be effective in terms of both physiological and psychological outcomes. There are many unexplored maternal and infant focused applications of VR which warrant further investigation as emerging evidence indicates this is becoming an increasingly accessible method to improve maternal and infant health outcomes from pregnancy through to parenthood.

Mobile health (mHealth) applications have the potential to support adolescents’ self-management of knee pain. However, ensuring adherence remains a barrier when designing mHealth concepts for adolescents.

This study aimed to explore barriers and facilitators for adhering to mHealth interventions to inform design principles.

Think-aloud tests were conducted with 12 adolescents (aged 12.5 years median) with knee pain, using a low-fidelity prototype. The prototype was informed by the authors previous work, rapid prototyping sessions with seven health professionals, and synthesis via the Behavioral Intervention Technology Model. The think-aloud tests were video recorded and analyzed thematically to identify design principles.

The analysis based on user testing with adolescents with knee pain identified three themes: “user experience and feedback,” “contextual challenges,” and “new features” and nine subthemes. Adolescents were able to use mHealth behavioral features such as self-tracking, goal setting, education, and data visualization to capture and reflect on their knee pain developments, which facilitated use. However, adolescents struggle with timing interventions, breaking down management behaviors, and biases towards interventions were identified as internal threats to adherence. Competing activities, parental meddling, and privacy concerns were external adherence barriers. Twelve design principles were identified for integrating these insights into mHealth designs.

Participants’ motivations for adherence were influenced by internal and external factors. While adolescents were able to use mHealth behavioral features to capture and reflect on knee pain developments, understanding how to accommodate adolescents’ cognitive abilities, competing activities, and need for independence is quintessential to enhance adherence in everyday contexts.

Teachers are at constant risk of straining their voices due to professional demands, making them more susceptible to voice disorders and therefore increased absenteeism. A widely accessible prevention method would be beneficial, leading to the development of the ReSt mobile application. The aim of this study was to evaluate its effectiveness.

A randomized controlled trial was conducted with 399 teachers, allocating 205 participants to the training group and 194 to the control group. Over a 46-day period, participants in the training group engaged with the ReSt app. Outcomes were assessed using self-reports via the Voice Handicap Index (VHI-12) and objective evaluations employing the Dysphonia Severity Index (DSI). Additionally, app use was fully tracked using log data to gain insight into user engagement.

On average, the training resulted in substantial improvements in the DSI within the training group compared to the control group ( γ = 2.22 , S E = 0.32 , p < .001 ). No training effect was observed concerning the VHI-12 ( γ = − 0.39 , S E = 0.46 , p = .388 ). The average number of days spent opening exercises in the app was 10.57 days (SD = 9.07), and time spent on exercise-related app screens averaged 2.38 hours (SD = 2.79).

The app appears to have promising effects on physiological measured voice capability, but more training time may be needed to minimize the psychosocial impact of voice problems. Our results mark an important advancement in voice disorder prevention approaches, especially given previously inconclusive evidence for traditional voice training methods.

This study aims to investigate the development of automated International Classification of Diseases (ICD) coding models using the Medical Information Mart for Intensive Care (MIMIC) dataset. This work integrates computer science and clinical perspectives to evaluate progress, identify challenges, and provide insights for future ICD coding automation.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We selected 73 studies between 2014 and 2024 and extracted key information about data preprocessing, knowledge integration, model architectures, evaluation strategies, and explainability.

In the reviewed papers, 69.57% (48 papers) focused on utilizing medical knowledge, primarily through knowledge graphs. The methods have evolved from traditional machine learning techniques to more advanced approaches, such as deep learning, knowledge reasoning, information retrieval, and generative models. Since 2019, F1-micro scores have consistently improved: Studies using the MIMIC-III full dataset have shown a 6.4% increase, while those using the MIMIC-III top-50 dataset have experienced a 10.2% improvement. Furthermore, 60.27% (44 papers) implemented strategies to enhance explainability, which included attention visualization and analysis.

Automated ICD coding tasks have improved, but ongoing challenges remain. These challenges include a lack of diverse data, inadequate use of medical knowledge, complex algorithms, and insufficient validation by clinical coders. Those issues obstruct the model implementation. Future research should focus on integrating a wider range of multimodal data, enhancing the application of medical knowledge, and improving the explainability of models.

Among hospitalised geriatric patients, only half are computer users. However, many of them refrain from using telehealth solutions. This study aimed to investigate geriatric patients’ computer and Internet habits and digital literacy and their associations with stress levels and frequency of Internet use.

Inpatients and outpatients aged 65 years or older, all computer users, were consecutively surveyed. Besides information about computer and Internet habits, computer support, and computer stress, the survey also collected information about digital literacy using the electronic Health Literacy Assessment toolkit.

A total of 124 computer users with a mean age of 80.6 ± 7.4 years participated in the study from 1 October to 1 December 2019. Most patients received computer support from their children and grandchildren, whereas 6% did not seek support. They found themselves ‘most familiar with using a keyboard’ (79%), 59% ‘were unfamiliar with the Copy Paste function’, and only one-third ‘were open to new ways of using computers’. Digital literacy was associated with the frequency of Internet use (P = 0.001), and higher digital literacy was associated with less computer stress (P = 0.01).

Geriatric computer users are challenged by their basic computer skills, which may influence their choice of participation in telehealth solutions. If telehealth solutions are to succeed among geriatric patients, individualised computer support based on their basic computer skills and user-friendly computer devices are a prerequisite. For ongoing support, it is also necessary to introduce people close to the patient to telehealth solutions.

As a new medical service mode, the value of mobile health (mHealth） services has received increasing attention and recognition. However, compared with the owners of mobile devices, the user scale of mHealth services is still small. It is well known that doctors’ recommendations have an important impact on what kind of medical service patients choose. To explore the key factors affecting doctors’ recommendation of mHealth services to patients, and to provide countermeasures for mHealth service providers and hospital managers, so as to promote doctors to recommend mHealth services to more patients.

Through literature review, expert consultation and pre-test, a questionnaire including 22 questions was designed, and 114 valid questionnaires were collected by online research. Net Promoter Score (NPS) was used to evaluate doctors’ recommendation willingness, and multivariate logistics analysis was used to evaluate the key factors affecting doctors’ recommendation willingness.

The NPS of doctors was 6.06%, among which the recommenders, neutrals and critics accounted for 29.56%, 46.96% and 23.48%, respectively. The attitude towards mHealth services and whether they pay attention to and/or are willing to try new technologies are the key factors affecting the doctors’ recommendation, and the usefulness for patients most often emphasized by mHealth service providers to doctors does not affect doctors’ recommendation willingness. In addition, whether mHealth services can help doctors establish personal brands may be a potential factor to enhance doctors’ recommendation willingness.

In order to improve the recommendation willingness of doctors, mHealth service providers and hospital managers should focus on doctors who have a positive attitude towards mHealth services and are highly innovative (which often means younger and lower professional levels). At the same time, they should think about how to use mHealth services to help doctors establish personal brands in the future.

Despite the benefits of digital transformation, many healthcare organisations still rely on manual, traditional processes to transfer clinical images and videos to electronic patient records (EPRs), consuming valuable nurse–patient-facing time. Our study aimed to outline tasks performed by nurses to traditionally transfer clinical images and videos to EPRs, assess the impact on nurses’ time and efficiency cost, and report on nurse experiences when transitioning to a digitised process.

Observations spanned two one-week periods in the ENT outpatient department of Royal Devon University Healthcare NHS Foundation Trust, using direct observational time and predetermined motion time system analysis to map nurses’ tasks pre-and post-implementation of a Medical Video Recorder and Centralised Medical Content Management. Two staff experience surveys were conducted, and data was aggregated to evaluate the impact of digitisation.

Nurses traditionally followed two processes to transfer clinical images and video to EPRs. Digitisation saved 43 seconds per endoscopy (a 10% efficiency gain) compared to Process 1, in which nurses print, attach, scan, and digitise images off-site to EPRs, and 3 minutes and 8 seconds (a 33% efficiency gain) compared to Process 2, where urgent content is recorded on an iPad and transferred to EPRs. Digitising could save nurses 26 working days annually by reducing non-patient-facing tasks in the ENT department, leading to an annual time efficiency cost-saving of £6780. Furthermore, 75% of nurses reported significantly improved morale, 63% strongly agreed that digitisation improved confidence in the accuracy of patient details, and all paper processes were eliminated.

Our findings highlight the inefficiency of traditional methods in transferring images and videos to EPRs. Digital transformation could enhance nursing efficiency and morale, improving care quality. Future studies should evaluate the effectiveness of Medical Video Recorders and Centralised Medical Content Management in other departments.

Pain is a common adverse event in survivors of breast cancer (sBCs). As there is no gold standard to assess pain experience predominantly related to central sensitization (CS) symptoms, we designed the PaiNEd app, which includes an algorithm to report whether patients are under predominant CS pain mechanisms.

We aimed to assess the reliability of the PaiNEd app to estimate whether sBC pain experience is predominantly related to CS symptoms.

An observational, descriptive reliability design was employed to assess the inter- and intrarater reliability of the PaiNEd app. This app includes an algorithm that considers the number of painful body parts and some questionnaires related to pain, such as the Numeric Pain-Rating Scale, the Brief Pain Inventory, the Tampa Scale for Kinesiophobia, the Pain Catastrophizing Scale, and the Central Sensitization Inventory (CSI).

A total of 21 sBCs with persistent pain were recruited. We observe a general trend of close agreement between the paper-based and app-based formats (ICCs ranged between 0.802 and 0.972; Cronbach's alpha ranged between 0.797 and 0.971). Test–retest reliabilities were moderate to excellent (ICCs ranged between 0.510 and 0.941; Cronbach's alpha ranged between 0.499 and 0.938). The agreement between the categorization of the CS algorithm and the CSI (cut-off point ≥ 40 for CS symptoms) was 95.24%.

The PaiNEd app emerges as a robust tool for evaluating pain experience predominantly related to CS and pain-related symptoms in sBCs. Its demonstrated reliability not only bolsters its utility but also signifies its potential as a valuable asset for healthcare professionals engaged in pain education programs.

Dietitians report insufficient time and low-quality educational materials limit patient-centered care for conditions like diabetes. In other clinical practice areas, technology facilitates patient-centered care, but few practice-based technologies have been developed for dietetics. Thus, little is known about what dietitians need to incorporate technology-based solutions into practice.

The objective of this study was to understand dietitians’ design needs for a novel education material clinical decision support system (CDSS) to customize materials for improved patient-centered care.

Design needs were evaluated with qualitative user interviews during which participants responded to mockups of the proposed technology. Interviewers coded transcripts to identify themes around three key constructs known to drive technology adoption: perceived usefulness, usability, and workflow integration.

Dietitians (n = 9) who were licensed in Texas and provided nutrition education to patients as part of their practice participated in the interviews.

Customization drove perceived usefulness, and efficiency drove perceived usability of the CDSS. Participants indicated that workflow integration would depend on if the CDSS met both customization and efficiency needs. Some participants indicated that efficiency was more important than patient-level customization; these participants suggested the possibility of customizing handouts for their typical patient population instead of individual patients.

Dietitians are interested in a CDSS that customizes education materials, with efficiency being essential to adoption. Incorporating these insights into future CDSS design could improve the successful adoption of technology for supporting patient-centered dietetic care.

The rising prevalence of chronic diseases and the widespread use of smartphones have led to a surge in preventive health mobile applications (mHealth apps) for corporate wellness. However, user retention and the intention to continue using these apps remain suboptimal. This study examines factors influencing corporate employees’ intention to continue using a preventive health mHealth app in Thailand, using the Health Belief Model (HBM) as the guiding framework.

A cross-sectional survey was conducted among a sample of corporate employees in Thailand. The survey questionnaire measured constructs from the HBM, including perceived severity of disease, perceived benefits of using mHealth apps, cues to action, and self-efficacy in using mHealth apps. Structural equation modeling (SEM) was employed to analyze the relationships between these constructs and the intention to continue using preventive health mHealth apps.

Perceived benefits were the strongest predictor of intention to continue using the app. Cues to action and self-efficacy were also significant predictors, whereas perceived severity was not a significant factor.

The findings underscore the importance of highlighting the benefits of mHealth apps and leveraging cues to action and user confidence in the intention to continue using. Strategies in corporate wellness should focus on these factors to enhance engagement. However, because this study evaluated a single wellness app among pre-selected users already engaged with the app, the results should be interpreted with caution and may not be generalizable to other settings or populations.

Direct-to-consumer (DTC) electronic prescription services (EPS) are a novel addition to the Australian healthcare landscape. This study aimed to explore consumers’ perceptions on how this model of care supports the delivery of best-practice care.

Focus groups participants were recruited through social media and included adults aged 18 years or older, Queensland (Australia) residents, and interested in DTC EPS. Focus groups were conducted via Zoom^® and repeated until data saturation. Inductive thematic analysis was undertaken to elicit consumer perception themes from focus group discussions and field notes.

Three focus groups were conducted between July and August 2022 and included 13 participants of which two (15%) had previously used DTC EPS. Four major perception themes were induced: (a) Consumer responsibility. There is an assumed level of consumer health literacy leading to an unacceptable burden of responsibility on the patient; (b) System processes appear to be underdeveloped to support best-practice care; (c) Access to convenient and timely healthcare will be improved for many patients, however, out of pocket expenses may promote inequity; and (d) Service model improvements can address safety and quality concerns including integration of the model within existing national digital health platforms.

Participants believed that DTC EPS was a valuable addition to the Australian health care landscape increasing convenient and timely access to medicines for consumers. Participants were concerned that a heavy reliance on health literacy and underdeveloped system processes may lead to unsafe prescribing.