Positive Psychological Coaching Tools and Techniques: A Systematic Review and Classification.
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ABSTRACT:
SUBMITTER: Richter S
PROVIDER: S-EPMC8298836 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Despite the popularity of the term Positive Psychological Coaching within the literature, there is no consensus as to how it should be defined (framed) or what the components of a positive coaching "model" should include. The aim of this systematic review was to define positive psychological coaching and to construct a clear demarcated positive psychological coaching model based on the literature. A systematic literature review led to the extraction of 2,252 records. All records were screened using specific inclusion/exclusion criteria, which resulted in the exclusion of records based on duplicates (n = 1,232), titles (n = 895), abstracts (n = 78), and criteria violations (n = 23). Twenty-four academic, peer-reviewed publications on positive psychological coaching were included. Data relating to conceptual definitions and coaching models/phases/frameworks were extracted and processed through thematic content analysis. Our results indicate that positive psychological coaching can be defined as a short to medium term professional, collaborative relationship between a client and coach, aimed at the identification, utilization, optimization, and development of personal strengths and resources in order to enhance positive states, traits and behaviors. Utilizing Socratic goal setting and positive psychological evidence-based approaches to facilitate personal growth, optimal functioning, enhanced wellbeing, and the actualization of people's potential. Further, eight critical components of a positive psychological coaching model were identified and discussed. The definition and coaching process identified in this study will provide coaches with a fundamental positive psychological framework for optimizing people's potential.
| S-EPMC7218139 | biostudies-literature
Project description:Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
| S-EPMC9611376 | biostudies-literature
Project description:Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
| S-EPMC10572538 | biostudies-literature
Project description:Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
| S-EPMC9818170 | biostudies-literature
Project description:Aim Studies show that about 60 min of moderate physical activity (PA) per day compensate for sitting all day at work. However, the workplace offers an ideal setting for health-promoting interventions such as PA coaching as a person-centered intervention aimed at achieving lasting health behavior changes. Given a good evidence base of health coaching studies in general, this systematic review aims to provide an overview of workplace PA coaching interventions. Methods This review was conducted according to PRISMA guidelines. Studies published up to July 2021 were considered based on the following inclusion criteria: (1) longitudinal intervention studies, (2) analysis of PA at work, (3) sedentary employees, (4) PA coaching in the workplace as intervention, (5) increasing workplace PA. Results Of 4323 studies found, 14 studies with 17 interventions met inclusion criteria. All 17 interventions indicated an increase in at least one PA outcome. Twelve interventions indicated significant improvements in at least one workplace or total PA outcome. There is a high variation within the different coaching parameters, such as behavior change techniques and communication channels. The study quality showed a moderate to high risk of bias. Conclusions The majority of interventions provided evidence for the effectiveness of workplace PA coaching. Nevertheless, the results are inconclusive with regard to the variety of coaching parameters and thus no general statement can be made about the effectiveness of individual parameters. However, this variety of parameters also leads to a high degree of individualization of workplace PA coaching interventions to increase PA for different groups of employees and different types of workplaces.
| S-EPMC10495277 | biostudies-literature
Project description:Objective:The purpose of this review was to evaluate the modalities (e.g., face-to-face, telephone or electronic) of pharmacist health coaching providing the greatest improvement in patient outcomes, to enable a more comprehensive evaluation to be done and quality decision-making around health coaching modalities to be undertaken by pharmacists. Methods:This systematic review followed the PRISMA guidelines. CINHAL, EMBASE, PubMed, PsychINFO and SCOPUS were searched (2000-2019). Included articles were reviewed for the modality used to health coach, the training provided, and the outcomes. Results:Twelve papers met the eligibility criteria. A majority of studies included involved a combination of modalities of pharmacist health coaching. Four papers referred to face-to-face sessions, and one study used telephone coaching. In each paper, coaching led to an improvement in clinical and non-clinical health outcomes. Conclusion:The training provided to health coaches varied and in some cases was not reported. Inconsistencies in reports led to difficulties when comparing study outcomes. Therefore, conclusions about the modality providing the greatest improvement in patient outcomes and the most pragmatic health coaching modality are not possible. Studies that document the training, the modality, the outcomes and the cost benefits of coaching by pharmacists are warranted to enable a more comprehensive evaluation to be done and quality decision-making around health coaching modalities to be undertaken by pharmacists.
| S-EPMC7457552 | biostudies-literature
Project description:Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.
| S-EPMC9139416 | biostudies-literature
Project description:Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.
| S-EPMC6230720 | biostudies-other
Project description:BackgroundA wide variety of surgical techniques are used to perform vasectomy. The purpose of this systematic review was to assess if any surgical techniques to isolate or occlude the vas are associated with better outcomes in terms of occlusive and contraceptive effectiveness, and complications.MethodsWe searched MEDLINE (1966-June 2003), EMBASE (1980-June 2003), reference lists of retrieved articles, urology textbooks, and our own files looking for studies comparing two or more vasectomy surgical techniques and reporting on effectiveness and complications. From 2,058 titles or abstracts, two independent reviewers identified 224 as potentially relevant. Full reports of 219 articles were retrieved and final selection was made by the same two independent reviewers using the same criteria as for the initial selection. Discrepancies were resolved by involving a third reviewer. Data were extracted and methodological quality of selected studies was assessed by two independent reviewers. Studies were divided in broad categories (isolation, occlusion, and combined isolation and occlusion techniques) and sub-categories of specific surgical techniques performed. Qualitative analyses and syntheses were done.ResultsOf 31 comparative studies (37 articles), only four were randomized clinical trials, most studies were observational and retrospective. Overall methodological quality was low. From nine studies on vas isolation, there is good evidence that the no-scalpel vasectomy approach decreases the risk of surgical complications, namely hematoma/bleeding and infection, compared with incisional techniques. Five comparative studies including one high quality randomized clinical trial provided good evidence that fascial interposition (FI) increases the occlusive effectiveness of ligation and excision. Results of 11 comparative studies suggest that FI with cautery of the vas lumen provides the highest level of occlusive effectiveness, even when leaving the testicular end open. Otherwise, firm evidence to support any occlusion technique in terms of increased effectiveness or decreased risk of complications is lacking.ConclusionsCurrent evidence supports no-scalpel vasectomy as the safest surgical approach to isolate the vas when performing vasectomy. Adding FI increases effectiveness beyond ligation and excision alone. Occlusive effectiveness appears to be further improved by combining FI with cautery. Methodologically sound prospective controlled studies should be conducted to evaluate specific occlusion techniques further.
| S-EPMC428590 | biostudies-literature
Project description:Depression and anxiety are well-known to be associated with adverse health outcomes in cardiac patients. However, there has been less work synthesizing the effects of positive psychological constructs (e.g., optimism) on health-related outcomes in cardiac patients. We completed a systematic review of prospective observational studies using established guidelines. A search of PubMed and PsycINFO databases from inception to January 2014 was used to identify articles. To be eligible, studies were required to assess effects of a positive psychological construct on subsequent health-related outcomes (including mortality, rehospitalizations, self-reported health status) in patients with established heart disease. Exploratory random effects' meta-analyses were performed on the subset of studies examining mortality or rehospitalizations. Seventy-seven analyses from 30 eligible studies (N=14,624) were identified. Among studies with 100 or more participants, 65.0% of all analyses and 64.7% of analyses adjusting for one or more covariates reported a significant (p<.05) association between positive psychological constructs and subsequent health outcomes. An exploratory meta-analysis of 11 studies showed that positive constructs were associated with reduced rates of rehospitalization or mortality in unadjusted (odds ratio=.87; 95% confidence interval [.83, .92]; p<.001) and adjusted analyses (odds ratio=.89; 95% confidence interval [.84, .91]; p<.001); there was little suggestion of publication bias. Among cardiac patients, positive psychological constructs appear to be prospectively associated with health outcomes in most but not all studies. Additional work is needed to identify which constructs are most important to cardiac health, and whether interventions can cultivate positive attributes and improve clinical outcomes.
| S-EPMC4487518 | biostudies-literature