Recent studies demonstrated that blood pressure (BP) can be estimated using pulse transit time (PTT). For PTT calculation, photoplethysmogram (PPG) is usually used to detect a time lag in pulse wave ...propagation which is correlated with BP. Until now, PTT and PPG were registered using a set of body-worn sensors. In this study a new methodology is introduced allowing contactless registration of PTT and PPG using high speed camera resulting in corresponding image-based PTT (iPTT) and image-based PPG (iPPG) generation. The iPTT value can be potentially utilized for blood pressure estimation however extent of correlation between iPTT and BP is unknown. The goal of this preliminary feasibility study was to introduce the methodology for contactless generation of iPPG and iPTT and to make initial estimation of the extent of correlation between iPTT and BP “in vivo.” A short cycling exercise was used to generate BP changes in healthy adult volunteers in three consecutive visits. BP was measured by a verified BP monitor simultaneously with iPTT registration at three exercise points: rest, exercise peak, and recovery. iPPG was simultaneously registered at two body locations during the exercise using high speed camera at 420 frames per second. iPTT was calculated as a time lag between pulse waves obtained as two iPPG’s registered from simultaneous recoding of head and palm areas. The average inter-person correlation between PTT and iPTT was 0.85 ± 0.08. The range of inter-person correlations between PTT and iPTT was from 0.70 to 0.95 (
p
< 0.05). The average inter-person coefficient of correlation between SBP and iPTT was -0.80 ± 0.12. The range of correlations between systolic BP and iPTT was from 0.632 to 0.960 with
p
< 0.05 for most of the participants. Preliminary data indicated that a high speed camera can be potentially utilized for unobtrusive contactless monitoring of abrupt blood pressure changes in a variety of settings. The initial prototype system was able to successfully generate approximation of pulse transit time and showed high intra-individual correlation between iPTT and BP. Further investigation of the proposed approach is warranted.
Objectives
We assessed peri‐implantitis prevalence, incidence rate, and associated risk factors by analyzing electronic oral health records (EHRs) in an educational institution.
Methods
We used a ...validated reference cohort comprising all patients receiving dental implants over a 3.5‐year period (2,127 patients and 6,129 implants). Electronic oral health records of a random 10% subset were examined for an additional follow‐up of ≥2.5 years to assess the presence of radiographic bone loss, defined as >2 mm longitudinal increase in the distance between the implant shoulder and the supporting peri‐implant bone level (PBL) between time of placement and follow‐up. “Intact” implants had no or ≤2 mm PBL increase from baseline. Electronic oral health record notes were reviewed to corroborate a definitive peri‐implantitis diagnosis at implants with progressive bone loss. A nested case–control analysis of peri‐implantitis‐affected implants randomly matched by age with “intact” implants from peri‐implantitis‐free individuals identified putative risk factors.
Results
The prevalence of peri‐implantitis over an average follow‐up of 2 years was 34% on the patient level and 21% on the implant level. Corresponding incidence rates were 0.16 and 0.10 per patient‐year and implant‐year, respectively. Multiple conditional logistic regression identified ill‐fitting fixed prosthesis (OR = 5.9; 95% CI: 1.6–21.1), cement‐retained prosthesis (OR = 4.5; 2.1–9.5), and radiographic evidence of periodontitis (OR = 3.6; 1.7–7.6) as statistically associated with peri‐implantitis. Implant location in the mandible (OR = 0.02; 0.003–0.2) and use of antibiotics in conjunction with implant surgery (OR = 0.19; 0.05–0.7) emerged as protective exposures.
Conclusions
Approximately 1/3 of the patients and 1/5 of all implants experienced peri‐implantitis. Ill‐fitting/ill‐designed fixed and cement‐retained restorations, and history of periodontitis emerged as the principal risk factors for peri‐implantitis.
The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our ...analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of ...exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self‐monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7‐day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.
Recent studies described association between chronic obstructive pulmonary disease (COPD) and increased risk of cardiovascular diseases (CVD). In their analysis none of these studies accounted for ...sociodemographic factors, health behaviors, and patient comorbidities simultaneously.
To study whether COPD diagnosis is an independent risk factor for CVD.
Subjects aged 40 years and older (N = 18,342) from the sample adult file of the 2002 National Health Interview Survey (NHIS) were included in the analysis. Chi-squared tests and odds ratios (OR) were utilized to compare the data. Multiple logistic regression was employed to analyze the association between COPD and CVD with simultaneous control for sociodemographic factors (age, gender, race, marital status, education, income), health behaviors (tobacco use, alcohol consumption, physical activity), and patient comorbidities (diabetes, hypertension, high cholesterol, and obesity). The analysis employed NHIS sampling weights to generate data representative of the entire US population.
The COPD population had increased prevalence of CVD (56.5% vs 25.6%; P < 0.0001). Adjusted logistic regression showed that COPD patients (N = 958) were at higher risk of having coronary heart disease (OR = 2.0, 95% CI: 1.5-2.5), angina (OR = 2.1, 95% CI: 1.6-2.7), myocardial infarction (OR = 2.2, 95% CI: 1.7-2.8), stroke (OR = 1.5, 95% CI: 1.1-2.1), congestive heart failure (OR = 3.9, 95% CI: 2.8-5.5), poor circulation in lower extremities (OR = 2.5, 95% CI: 2.0-3.0), and arrhythmia (OR = 2.4, 95% CI: 2.0-2.8). Overall, the presence of COPD increased the odds of having CVD by a factor of 2.7 (95% CI: 2.3-3.2).
These findings support the conclusion that COPD is an independent risk factor for CVD.
Depression significantly impacts quality of life, affecting approximately 280 million people worldwide. However, only 16.5% of those affected receive treatment, indicating a substantial treatment ...gap. Immersive technologies (IMTs) such as virtual reality (VR) and augmented reality offer new avenues for treating depression by creating immersive environments for therapeutic interventions. Despite their potential, significant gaps exist in the current evidence regarding the design, implementation, and use of IMTs for depression care.
We aim to map the available evidence on IMT interventions targeting depression treatment.
This scoping review followed a methodological framework, and we systematically searched databases for studies on IMTs and depression. The focus was on randomized clinical trials involving adults and using IMTs. The selection and charting process involved multiple reviewers to minimize bias.
The search identified 16 peer-reviewed articles, predominantly from Europe (n=10, 63%), with a notable emphasis on Poland (n=9, 56%), which contributed to more than half of the articles. Most of the studies (9/16, 56%) were conducted between 2020 and 2021. Regarding participant demographics, of the 16 articles, 5 (31%) exclusively involved female participants, and 7 (44%) featured participants whose mean or median age was >60 years. Regarding technical aspects, all studies focused on VR, with most using stand-alone VR headsets (14/16, 88%), and interventions typically ranging from 2 to 8 weeks, predominantly in hospital settings (11/16, 69%). Only 2 (13%) of the 16 studies mentioned using a specific VR design framework in planning their interventions. The most frequently used therapeutic approach was Ericksonian psychotherapy, used in 56% (9/16) of the studies. Notably, none of the articles reported using an implementation framework or identified barriers and enablers to implementation.
This scoping review highlights the growing interest in using IMTs, particularly VR, for depression treatment but emphasizes the need for more inclusive and comprehensive research. Future studies should explore varied therapeutic approaches and cost-effectiveness as well as the inclusion of augmented reality to fully realize the potential of IMTs in mental health care.
Consumer sleep tracking devices are widely advertised as effective means to monitor and manage sleep quality and to provide positive effects on overall heath. However objective evidence supporting ...these claims is not always readily available. The goal of this study was to perform a comprehensive review of available information on six representative sleep tracking devices: BodyMedia FIT, Fitbit Flex, Jawbone UP, Basis Band, Innovative Sleep Solutions SleepTracker, and Zeo Sleep Manager Pro. The review was conducted along the following dimensions: output metrics, theoretical frameworks, systematic evaluation, and FDA clearance. The review identified a critical lack of basic information about the devices: five out of six devices provided no supporting information on their sensor accuracy and four out of six devices provided no information on their output metrics accuracy. Only three devices were found to have related peer-reviewed articles. However in these articles wake detection accuracy was revealed to be quite low and to vary widely (BodyMedia, 49.9±3.6%; Fitbit, 19.8%; Zeo, 78.9% to 83.5%). No supporting evidence on how well tracking devices can help mitigate sleep loss and manage sleep disturbances in practical life was provided.
Background
Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients’ quality of life, and improving clinical outcomes. Stationary ...biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions.
Objective
The aim of this project was to design and test two low-cost wireless interfaces for the telemonitoring of home-based biking exercise.
Methods
We designed an interactive biking system (iBikE) that comprises a tablet PC and a low-cost bike. Two wireless interfaces to monitor the revolutions per minute (RPM) were built and tested. The first version of the iBikE system uses Bluetooth Low Energy (BLE) to send information from the iBikE to the PC tablet, and the second version uses a Wi-Fi network for communication. Both systems provide patients and their clinical teams the capability to monitor exercise progress in real time using a simple graphical representation. The bike can be used for upper or lower limb rehabilitation. We developed two tablet applications with the same graphical user interfaces between the application and the bike sensors but with different communication protocols (BLE and Wi-Fi). For testing purposes, healthy adults were asked to use an arm bike for three separate subsessions (1 minute each at a slow, medium, and fast pace) with a 1-minute resting gap. While collecting speed values from the iBikE application, we used a tachometer to continuously measure the speed of the bikes during each subsession. Collected data were later used to assess the accuracy of the measured data from the iBikE system.
Results
Collected RPM data in each subsession (slow, medium, and fast) from the iBikE and tachometer were further divided into 4 categories, including RPM in every 10-second bin (6 bins), RPM in every 20-second bin (3 bins), RPM in every 30-second bin (2 bins), and RPM in each 1-minute subsession (60 seconds, 1 bin). For each bin, the mean difference (iBikE and tachometer) was then calculated and averaged for all bins in each subsession. We saw a decreasing trend in the mean RPM difference from the 10-second to the 1-minute measurement. For the 10-second measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.67 (SD 0.24) and 1.22 (SD 0.67) for the BLE iBike, and 0.66 (SD 0.48) and 0.87 (SD 0.91) for the Wi-Fi iBike system, respectively. For the 1-minute measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.32 (SD 0.26) and 0.66 (SD 0.83) for the BLE iBike, and 0.21 (SD 0.21) and 0.47 (SD 0.52) for the Wi-Fi iBike system, respectively.
Conclusions
We concluded that a low-cost wireless interface provides the necessary accuracy for the telemonitoring of home-based biking exercise.
•Unhealthy alcohol use is prevalent among women attending STI clinics.•We tested whether CBI or CBI-IVR-TM, reduced alcohol use among women in this setting.•Neither CBI nor CBI-IVR-TM reduced alcohol ...use more than control.•2/3 of women had an alcohol use disorder, 65% substance use, 28% depressive symptoms.•CBI is insufficient for alcohol reduction in this high severity, high comorbidity setting.
We sought to determine if a computer delivered brief alcohol intervention (CBI) with or without interactive voice response counseling and text messages (CBI-IVR-TM), reduced alcohol use and sexual risk behaviors compared to attention control.
We conducted a 3-arm RCT among women (n = 439) recruited from Baltimore City Sexually Transmitted Infection (STI) Clinics. Eligibility included: 1) consumption of >7 drinks per week or 2) ≥2 episodes of heavy episodic drinking or ≥2 episodes of sex under the influence of alcohol in the prior three months. Research assessments conducted at baseline, 3, 6 and 12 months included a 30-day Timeline Followback querying daily alcohol use, drug use, and sexual activity. We used the MINI International Neuropsychiatric Interview-DSM-IV to ascertain drinking severity. Primary alcohol outcomes included: drinking days, heavy drinking days, drinks per drinking day. Secondary sexual risk outcomes included number of sexual partners, days of condomless sex, and days of condomless sex under the influence of drugs and alcohol.
Median age was 31 (IQR 25–44 years), 88% were African American, 65% reported current recreational drug use, and 26% endorsed depressive symptoms. On the MINI 66% met criteria for alcohol use disorder (49% alcohol dependence, 18% abuse). At follow-up, all three groups reduced drinking days, heavy drinking days, drinks per drinking day and drinks per week with no significant differences between study arms. There was no difference in sexual risk outcomes among the groups.
Among women attending an urban STI clinic single session CBI with or without IVR and text message boosters was insufficient to reduce unhealthy alcohol use or sexual risk behaviors beyond control. The high severity of alcohol use and the prevalence of mental health symptoms and other substance use comorbidity underscores the importance of developing programs that address not only alcohol use but other determinants of STI risk among women.
The goal of this pilot study was to compare Alexa voice and video interfaces for home-based telerehabilitation dialog by conducting cognitive walkthrough testing. All task performance scores were ...higher in video interface as compared to the audio interface. The overall task score was significantly higher for video interface (42.4±4.6) as compared to the audio score (41.3±5.9). Comparative usability survey demonstrated higher preference of the video interface as compared to the audio interface. Based in the comparative survey, 85.7% stated they definitely prefer video interface, 85.7% felt that video introduction was simpler to understand, 71.4% felt that exercise instructions were simpler to understand with the video interface, and 78.6% felt that overall navigation was easier with the video interface. The overall time to accomplish all three tasks was significantly shorter (p<0.05) for the video interface (170.5±12.2 seconds) as compared to the audio interface (194.2±10.3 seconds). This is the first study systematically comparing two major Alexa interfaces in a telerehabilitation system. These results are instrumental for future development of Alexa-based telerehabilitation systems.
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