Frontiers in digital health最新文献

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices.

Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis.

Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.

Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios.

Digital mental health interventions (DMHIs) have surged in popularity over the last few years. However, adherence to self-guided interventions remains a major hurdle to overcome. The current study utilized a phased implementation design, incorporating diverse samples and contexts to delve into the engagement challenges faced by a recently launched online mental health platform in Brazil with self-evaluation forms. Employing an iterative mixed-methods approach, including focus groups, online surveys, and think-aloud protocols, the research aims to evaluate user satisfaction, identify barriers to adherence, and explore potential hybrid solutions. Engagement in the platform was evaluated by descriptive statistics of the number of instruments completed, and qualitative interviews that were interpreted thematically. In the fully self-guided mode, 2,145 individuals registered, but a substantial majority (88.9%) engaged with the platform for only 1 day, and merely 3.3% completed all activities. In another sample of 50 participants were given a choice between online-only or a hybrid experience with face-to-face meetings. 40% of individuals from the hybrid group completed all activities, compared to 8% in the online-only format. Time constraints emerged as a significant barrier to engagement, with suggested improvements including app development, periodic reminders, and meetings with healthcare professionals. While the study identified weaknesses in the number and length of instruments, personalized results stood out as a major strength. Overall, the findings indicate high satisfaction with the mental health platform but underscore the need for improvements, emphasizing the promise of personalized mental health information and acknowledging persistent barriers in a digital-only setting.

Background: Hospital readmissions pose a challenge for modern healthcare systems. Our aim was to assess the efficacy of telemedicine incorporating telemonitoring of patients' vital signs in decreasing readmissions with a focus on a specific patient population particularly prone to rehospitalization: patients with heart failure (HF) and/or chronic obstructive pulmonary disease (COPD) through a comparative effectiveness systematic review.

Methods: Three major electronic databases, including PubMed, Scopus, and ProQuest's ABI/INFORM, were searched for English-language articles published between 2012 and 2023. The studies included in the review employed telemedicine incorporating telemonitoring technologies and quantified the effect on hospital readmissions in the HF and/or COPD populations.

Results: Thirty scientific articles referencing twenty-nine clinical studies were identified (total of 4,326 patients) and were assessed for risk of bias using the RoB2 (nine moderate risk, six serious risk) and ROBINS-I tools (two moderate risk, two serious risk), and the Newcastle-Ottawa Scale (three good-quality, four fair-quality, two poor-quality). Regarding the primary outcome of our study which was readmissions: the readmission-related outcome most studied was all-cause readmissions followed by HF and acute exacerbation of COPD readmissions. Fourteen studies suggested that telemedicine using telemonitoring decreases the readmission-related burden, while most of the remaining studies suggested that it had a neutral effect on hospital readmissions. Examination of prospective studies focusing on all-cause readmission resulted in the observation of a clearer association in the reduction of all-cause readmissions in patients with COPD compared to patients with HF (100% vs. 8%).

Conclusions: This systematic review suggests that current telemedicine interventions employing telemonitoring instruments can decrease the readmission rates of patients with COPD, but most likely do not impact the readmission-related burden of the HF population. Implementation of novel telemonitoring technologies and conduct of more high-quality studies as well as studies of populations with ≥2 chronic disease are necessary to draw definitive conclusions.

Systematic review registration: This study is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), identifier (INPLASY202460097).

Introduction: The present study investigates the feasibility and usability of a sensor-based eHealth treatment in psychotherapy for pediatric obsessive-compulsive disorder (OCD), and explores the promises and pitfalls of this novel approach. With eHealth interventions, therapy can be delivered in a patient's home environment, leading to a more ecologically valid symptom assessment and access to experts even in rural areas. Furthermore, sensors can help indicate a patient's emotional and physical state during treatment. Finally, using sensors during exposure with response prevention (E/RP) can help individualize therapy and prevent avoidance behavior.

Methods: In this study, we developed and subsequently evaluated a multimodal sensor-based eHealth intervention during 14 video sessions of cognitive-behavioral therapy (CBT) in 20 patients with OCD aged 12-18. During E/RP, we recorded eye movements and gaze direction via eye trackers, and an ECG chest strap captured heart rate (HR) to identify stress responses. Additionally, motion sensors detected approach and avoidance behavior.

Results: The results indicate a promising application of sensor-supported therapy for pediatric OCD, such that the technology was well-accepted by the participants, and the therapeutic relationship was successfully established in the context of internet-based treatment. Patients, their parents, and the therapists all showed high levels of satisfaction with this form of therapy and rated the wearable approach in the home environment as helpful, with fewer OCD symptoms perceived at the end of the treatment.

Discussion: The goal of this study was to gain a better understanding of the psychological and physiological processes that occur in pediatric patients during exposure-based online treatment. In addition, 10 key considerations in preparing and conducting sensor-supported CBT for children and adolescents with OCD are explored at the end of the article. This approach has the potential to overcome limitations in eHealth interventions by allowing the real-time transmission of objective data to therapists, once challenges regarding technical support and hardware and software usability are addressed.

Clinical trial registration: www.ClinicalTrials.gov, identifier (NCT05291611).

Introduction: Normative cognitive data can distinguish impairment from healthy cognitive function and pathological decline from normal ageing. Traditional methods for deriving normative data typically require extremely large samples of healthy participants, stratifying test variation by pre-specified age groups and key demographic features (age, sex, education). Linear regression approaches can provide normative data from more sparsely sampled datasets, but non-normal distributions of many cognitive test results may lead to violation of model assumptions, limiting generalisability.

Method: The current study proposes a novel Bayesian framework for normative data generation. Participants (n = 728; 368 male and 360 female, age 18-75 years), completed the Cambridge Neuropsychological Test Automated Battery via the research crowdsourcing website Prolific.ac. Participants completed tests of visuospatial recognition memory (Spatial Working Memory test), visual episodic memory (Paired Associate Learning test) and sustained attention (Rapid Visual Information Processing test). Test outcomes were modelled as a function of age using Bayesian Generalised Linear Models, which were able to derive posterior distributions of the authentic data, drawing from a wide family of distributions. Markov Chain Monte Carlo algorithms generated a large synthetic dataset from posterior distributions for each outcome measure, capturing normative distributions of cognition as a function of age, sex and education.

Results: Comparison with stratified and linear regression methods showed converging results, with the Bayesian approach producing similar age, sex and education trends in the data, and similar categorisation of individual performance levels.

Conclusion: This study documents a novel, reproducible and robust method for describing normative cognitive performance with ageing using a large dataset.

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.

Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.

Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.

Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.

Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Objectives: This study aims to design and test a platform of key performance indicators (KPIs) and indices emphasizing achievements and improvement and helping decision-making.

Methods: An operations research study was designed to analyze data from the Hospital Management Information System (HMIS) from July 2017 to June 2018 at the Research Institute of Ophthalmology (RIO), Giza, Egypt. The HMIS data were submitted to reform covering parameters in service delivery and corresponding indicators and indices. Data were grouped into four themes: human resources and outpatient, inpatient, and surgical operations. A total of 14 performance indicators were deployed to four specific indices and total performance indices and applied to six teams of ophthalmologists at RIO. The decision matrices were deliberated to demonstrate achievements and provide recommendations for subsequent improvements.

Results: Throughout 1 year, six teams of ophthalmologists (n = 222) at RIO provided the following services: outpatient (n = 116,043), inpatient (n = 8,081), and surgical operations (n = 9,174). Teams 2, 1, and 6 were the top teams in the total performance index. Team 4 had plunges in the outpatient index, and Team 5 faced limitations in the inpatient index.

Conclusion: The study provided a model for upgrading the performance of the management information system (MIS) in health organizations. The KPIs and indices were used not only for documenting successful models of efficient service delivery but also as examples of limitations for further support and interventions.

Background: A staff e-learning course was developed to prepare for scaling up a national take-home naloxone (THN) program in Norway. The aims of the study were to (a) describe participant characteristics for those that completed a THN e-learning course, (b) compare opioid overdose knowledge scores before and after e-learning course completion, and (c) to explore subsequent THN distribution by those trained.

Methods: This was a quasi-experimental pre-test, post-test longitudinal cohort study of individuals completing a THN e-learning course from April 2021 to May 2022. Frequency analyses were performed for participant characteristics and subsequent naloxone distributions at 1-week and 1-month follow-up. The opioid overdose knowledge scale (OOKS) was used to measure pre-test-post-test knowledge among participants. Wilcoxon signed-rank test was performed for comparison between pre-test and post-test. Effect size was calculated using Cohen criteria.

Results: In total, 371 individuals were included in this study. Most were either nurses or social workers (n = 277, 75%). Participant knowledge increased by medium or large effect for all items measured. At 1-month follow-up, 15% reported naloxone distribution. During the study period, 94 naloxone kits were distributed. Major reasons for not distributing were "clients not interested", "workplace not distributing" and "workplace in process of distributing".

Conclusions: Our findings suggest that an e-learning course is equally effective in terms of knowledge transfer as an in-person classroom setting, and may provide engagement in terms of naloxone distribution. However, our findings also emphasize the importance of clear implementation routines, including support from central coordinators to optimize the implementation process.

Introduction: Currently, mortality by non-communicable diseases is increasing alarmingly. They account for approximately 35 million deaths each year, of which 14% are due to cardiovascular disease and 9.2% occur in Africa. Patients do not have access to healthcare services outside the healthcare setting, resulting in missed follow-ups and appointments and adverse outcomes. This study aimed to assess the willingness to use remote monitoring among cardiovascular patients in a resource-limited setting in Ethiopia.

Method: An institution-based cross-sectional study was conducted from April to June 2021 among cardiovascular patients at referral hospitals in Ethiopia. A structured interview questionnaire was used to collect the data. A systematic random sampling technique was used to select 397 study participants. Binary and multivariable logistic regression analyses were employed and a 95% confidence level with a p-value <0.05 was used to determine the level of association between variables.

Result: In total, 81.61% of the study participants were willing to use remote patient monitoring [95% confidence interval (CI) = 77.4%-85.1%]. Age [adjusted odds ratio (AOR) = 0.94; 95% CI: 0.90-0.98], having a mobile phone (AOR = 5.70; 95% CI: 1.86-17.22), and perceived usefulness (AOR = 1.50; 95% CI: 1.18-1.82) were significantly associated with willingness to use remote patient monitoring among cardiovascular patients.

Conclusion: Cardiovascular patients had a high willingness to use remote patient monitoring. Age, perceived usefulness of remote patient monitoring, and having a mobile phone were significantly associated with a willingness to use remote patient monitoring.

Introduction: Challenging behaviour (CB) is a common issue among children with autism spectrum disorder or intellectual and developmental disability. Mental health applications are low-threshold cost-effective tools to address the lack of resources for caregivers. This pre-post study evaluated the feasibility and preliminary effectiveness of the smartphone app ProVIA-Kids using algorithm-based behaviour analysis to identify causes of CB and provide individualized practical guidance to manage and prevent CB.

Methods: A total of 18 caregivers (M = 38.9 ± 5.0) of children with a diagnosis of autism spectrum disorder (44%), intellectual and developmental disabilities (33%) or both (22%) aged 4-11 years (M = 7.6 ± 1.8) were included. Assessments were performed before and after an 8-week intervention period. The primary outcome was the change in parental stress. Caregiver stress experience due to CB was also rated daily via ecological momentary assessments within the app. Secondary outcomes included the intensity of the child's CB, dysfunctional parenting, feelings of parental competency as well as caregivers' mood (rated daily in the app) and feedback on the app collected via the Mobile Application Rating Scale.

Results: We observed increases in parental stress in terms of conscious feelings of incompetence. However, we also saw improvements in parental stress experience due to CB and overreactive parenting, and descriptive improvements in CB intensity and caregiver mood.

Discussion: ProVIA-Kids pioneers behaviour analysis in a digital and automated format, with participants reporting high acceptance. Pilot results highlight the potential of the ProVIA-Kids app to positively influence child behaviour and caregiver mental health over a longer intervention period.

Registration: The study was registered at https://www.drks.de (ID = DRKS00029039) on May 31, 2022.