3,262 research outputs found

    Qualitative studies of insomnia : current state of knowledge in the field

    Get PDF
    Despite its high prevalence and burden, insomnia is often trivialized, under-diagnosed, and under-treated in practice. Little information is available on the subjective experience and perceived consequences of insomnia, help-seeking behaviors, and treatment preferences. The use of qualitative approaches (e.g., ethnography, phenomenology, grounded theory) may help gain a better understanding of this sleep disorder. The present paper summarizes the evidence derived from insomnia studies using a qualitative research methodology (e.g., focus group, semi-structured interviews). A systematic review of the literature was conducted using PsycINFO and Medline databases. The review yielded 22 studies and the quality of the methodology of each of them was evaluated systematically using the critical appraisal skills programme (CASP) appraisal tool. Selected articles possess at least a very good methodological rigor and they were categorized according to their main focus: “Experience of insomnia”, “Management of insomnia” and “Medicalization of insomnia”. The main findings indicate that: 1) insomnia is often experienced as a 24-h problem and is perceived to affect several domains of life, 2) a sense of frustration and misunderstanding is very common among insomnia patients, which is possibly due to a mismatch between patients' and health care professionals' perspectives on insomnia and its treatment, 3) health care professionals pay more attention to sleep hygiene education and medication therapies and less to the patient's subjective experience of insomnia, and 4) health care professionals are often unaware of non-pharmacological interventions other than sleep hygiene education. An important implication of these findings is the need to develop new clinical measures with a broader scope on insomnia and more targeted treatments that take into account the patient's experience of insomnia. Greater use of qualitative approaches in future research may produce novel and more contextualized information leading to a more comprehensive understanding of insomnia

    AI Modeling Approaches for Detecting, Characterizing, and Predicting Brief Daily Behaviors such as Toothbrushing using Wrist Trackers.

    Get PDF
    Continuous advancements in wrist-worn sensors have opened up exciting possibilities for real-time monitoring of individuals\u27 daily behaviors, with the aim of promoting healthier, more organized, and efficient lives. Understanding the duration of specific daily behaviors has become of interest to individuals seeking to optimize their lifestyles. However, there is still a research gap when it comes to monitoring short-duration behaviors that have a significant impact on health using wrist-worn inertial sensors in natural environments. These behaviors often involve repetitive micro-events that last only a few seconds or even microseconds, making their detection and analysis challenging. Furthermore, these micro-events are often surrounded by non-repetitive boundary events, further complicating the identification process. Effective detection and timely intervention during these short-duration behaviors are crucial for designing personalized interventions that can positively impact individuals\u27 lifestyles. To address these challenges, this dissertation introduces three models: mORAL, mTeeth, and Brushing Prompt. These models leverage wrist-worn inertial sensors to accurately infer short-duration behaviors, identify repetitive micro-behaviors, and provide timely interventions related to oral hygiene. The dissertation\u27s contributions extend beyond the development of these models. Firstly, precise and detailed labels for each brief and micro-repetitive behavior are acquired to train and validate the models effectively. This involved meticulous marking of the exact start and end times of each event, including any intervening pauses, at a second-level granularity. A comprehensive scientific research study was conducted to collect such data from participants in their free-living natural environments. Secondly, a solution is proposed to address the issue of sensor placement variability. Given the different positions of the sensor within a wristband and variations in wristband placement on the wrist, the model needs to determine the relative configuration of the inertial sensor accurately. Accurately determining the relative positioning of the inertial sensor with respect to the wrist is crucial for the model to determine the orientation of the hand. Additionally, time synchronization errors between sensor data and associated video, despite both being collected on the same smartphone, are addressed through the development of an algorithm that tightly synchronizes the two data sources without relying on an explicit anchor event. Furthermore, an event-based approach is introduced to identify candidate segments of data for applying machine learning models, outperforming the traditional fixed window-based approach. These candidate segments enable reliable detection of brief daily behaviors in a computationally efficient manner suitable for real-time. The dissertation also presents a computationally lightweight method for identifying anchor events using wrist-worn inertial sensors. Anchor events play a vital role in assigning unambiguous labels in a fixed-length window-based approach to data segmentation and effectively demarcating transitions between micro-repetitive events. Significant features are extracted, and explainable machine learning models are developed to ensure reliable detection of brief daily and micro-repetitive behaviors. Lastly, the dissertation addresses the crucial factor of the opportune moment for intervention during brief daily behaviors using wrist-worn inertial sensors. By leveraging these sensors, users can receive timely and personalized interventions to enhance their performance and improve their lifestyles. Overall, this dissertation makes substantial contributions to the field of real-time monitoring of short-duration behaviors. It tackles various technical challenges, provides innovative solutions, and demonstrates the potential for wrist-worn sensors to facilitate effective interventions and promote healthier behaviors. By advancing our understanding of these behaviors and optimizing intervention strategies, this research has the potential to significantly impact individuals\u27 well-being and contribute to the development of personalized health solutions

    An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

    Get PDF
    The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

    National eHealth system – platform for preventive, predictive and personalized diabetes care

    Get PDF
    National eHealth System, covering all citizens and all healthcare levels in Republic of Macedonia, was introduced in July 2013, has been internationally recognized System for successful reduction of waiting times and instrumental in the management of national healthcare resources. For the first time, National Diabetes Committee, formed in February 2015 according to the Law on healthcare and being overall responsible for the diabetes care in the country, was able to derive exact figures on the national diabetes prevalence from the System, instead of extrapolations used before, serving as a basis for development of strategies for prediction and prevention of diabetic complications, as well as for personalized diabetes care. Number of diabetes cases identified through the National eHealth System in June 2015 was 84,568 (4.02 % of total population), 36,119 males (3.42 % of total male population) and 48,449 females (4.61% of total female population). Age stratified diabetes prevalence was as follows: less than 20 years – 549 cases (0.11 % of respective population), 20-39 years – 3,202 (0.49 %), 40-59 years – 26,561 (4.58 %), 60-79 years – 48,470 (14.57 %), 80 years or more – 5,786 (12.96 %). Addition of parameters for metabolic control and diabetic complications in the System is under way, further facilitating the modeling of diabetes treatment, metabolic control and the outcomes. Inclusion of pre-diabetes patients (IGT and IFG) is also planned, thus providing opportunity to also focus healthcare activities for prevention of progression into overt type 2 diabetes

    Medical data processing and analysis for remote health and activities monitoring

    Get PDF
    Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

    Development of a tailored, telehealth intervention to address chronic pain and heavy drinking among people with HIV infection: integrating perspectives of patients in HIV care.

    Get PDF
    BACKGROUND: Chronic pain and heavy drinking commonly co-occur and can infuence the course of HIV. There have been no interventions designed to address both of these conditions among people living with HIV (PLWH), and none that have used telehealth methods. The purpose of this study was to better understand pain symptoms, patterns of alcohol use, treatment experiences, and technology use among PLWH in order to tailor a telehealth intervention that addresses these conditions SUBJECTS: Ten participants with moderate or greater chronic pain and heavy drinking were recruited from a cohort of patients engaged in HIV-care (Boston Alcohol Research Collaborative on HIV/AIDS Cohort) and from an integrated HIV/primary care clinic at a large urban hospital. METHODS: One-on-one interviews were conducted with participants to understand experiences and treatment of HIV, chronic pain, and alcohol use. Participants’ perceptions of the infuence of alcohol on HIV and chronic pain were explored as was motivation to change drinking. Technology use and treatment preferences were examined in the fnal section of the interview. Interviews were recorded, transcribed and uploaded into NVivo® v12 software for analysis. A codebook was developed based on interviews followed by thematic analysis in which specifc meanings were assigned to codes. RESULTS: A number of themes were identifed that had implications for intervention tailoring including: resilience in coping with HIV; autonomy in health care decision-making; coping with pain, stress, and emotion; understanding treatment rationale; depression and social withdrawal; motives to drink and refrain from drinking; technology use and capacity; and preference for intervention structure and style. Ratings of intervention components indicated that participants viewed each of the proposed intervention content areas as “helpful” to “very helpful”. Videoconferencing was viewed as an acceptable modality for intervention delivery CONCLUSIONS: Results helped specify treatment targets and provided information about how to enhance intervention delivery. The interviews supported the view that videoconferencing is an acceptable telehealth method of addressing chronic pain and heavy drinking among PLWH.UH2 AA026192 - NIAAA NIH HHSPublished versio

    Treatment Response in Depression: Predictors and Moderators of Outcome

    Full text link
    Major Depressive Disorder (MDD) is a highly prevalent psychological disorder that affects an estimated 20.6% of adults in the United States. Despite significant research efforts, treatment response rates remain unacceptably low. The Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study aimed to address this problem through the search for "biosignatures" that include clinical, contextual, and biological measures to identify a more personalized approach to identifying appropriate treatment recommendations. Through three distinct investigations, this dissertation aims to utilize prior research to study “biosignatures" that may be relevant for predicting antidepressant treatment response. Results from this dissertation may inform future personalized approaches to depression care that may reduce the time to receiving adequate treatment.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147538/1/rcardina_1.pd

    Quantifying Quality of Life

    Get PDF
    Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

    Predicting cardiovascular risk in diabetic patients: arewe all on the same side?

    Get PDF
    Cardiovascular diseases are the main reason for morbidity and mortality in diabetic patients, and cardiovascular risk is increased at least twofold in men and at least fourfold in women with diabetes compared to non-diabetic populations. Predictive medicine is of the utmost importance in the clinical care of diabetic patients, since predicting cardiovascular risk is essential for modification of risk factors aimed at prevention or delay of future cardiovascular events. The prediction of cardiovascular risk is a valuable tool within the context of patient-centered care, as it includes active participation of diabetic patients in the decision-making process, resulting in higher compliance with the treatments agreed. However, there are differences among the current guidelines of various international authorities, such as the International Diabetes Federation (IDF), European Society of Cardiology (ESC) / European Association for Study of Diabetes (EASD), American College of Cardiology (ACC) / American Heart Association (AHA), American Diabetes Association (ADA), and National Institute for Health and Care Excellence (NICE), for the prediction of cardiovascular risk in diabetic patients. Furthermore, the clinical use of models with classic risk factors and novel biomarkers that would predict cardiovascular risk in diabetic patients from various populations with acceptable precision poses a challenge. Taking into consideration the global diabetes pandemic and its close association with cardiovascular diseases, there is an urgent need for streamlining of current guidelines on the prediction of cardiovascular risk and its use in clinical practice
    corecore