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Frontiers in Psychology 2024To explore the intervention effect of mindfulness training on athletes' performance using meta-analysis method.
OBJECTIVE
To explore the intervention effect of mindfulness training on athletes' performance using meta-analysis method.
METHODS
A total of 11 articles and 23 effect sizes were included through retrieval of Chinese and English databases, with a total sample size of 582.
RESULT
Mindfulness training improves the level of mindfulness [SMD =1.08, 95%CI (0.30, 1.86), < 0.01], fluency (The optimal competitive psychological state of the athlete, the athlete's attention is all focused on the task, and other things no longer attract their attention) [SMD =1.47, 95%CI (0.87, 2.08), < 0.001] and performance [SMD =0.92, 95% CI (0.40, 1.43), < 0.01], reduced psychological anxiety [SMD = -0.87, 95% CI (-1.54, -0.20), < 0.05], and all reached the level of large effect size.
CONCLUSION
The effect of mindfulness training on athletes' sports performance is effective, and it can be used as an effective psychological skill intervention method to improve athletes' sports performance. In the future, we should further expand the sample size, strengthen the comparative study of different sports and intervention modes, and pay attention to the difference between the time effect and trait mindfulness level in fluency state.
PubMed: 38939219
DOI: 10.3389/fpsyg.2024.1375608 -
BMC Psychiatry Jun 2024There is uncertainty about the optimum dose of omega-3 fatty acids for anxiety symptoms. We aimed to find the dose-dependent effect of omega-3 supplementation on anxiety... (Meta-Analysis)
Meta-Analysis
BACKGROUND/OBJECTIVES
There is uncertainty about the optimum dose of omega-3 fatty acids for anxiety symptoms. We aimed to find the dose-dependent effect of omega-3 supplementation on anxiety symptoms.
METHODS
We systematically reviewed PubMed, Scopus, and Web of Science until December 2022 to find randomized trials that assessed the effects of omega-3 fatty acids supplementation on anxiety symptoms in adults. Investigators performed the literature search and screened the titles/abstracts and full-texts and between-reviewer agreement was assessed as Cohen's kappa coefficient. We conducted a random-effects dose-response meta-analysis to estimate standardized mean differences (SMD) and 95% confidence intervals (CIs) and assessed the certainty of evidence using the GRADE framework.
RESULTS
A total of 23 trials with 2189 participants were included. Each 1 gram per day supplementation with omega-3 fatty acids resulted in a moderate decrease in anxiety symptoms (SMD: -0.70, 95%CI: -1.17, -0.22; GRADE = low). The non-linear dose-response analysis indicated the greatest improvement at 2 g/d (SMD: -0.93, 95%CI: -1.85, -0.01), and that supplementation in a dose lower than 2 g/d did not affect anxiety symptoms. Omega-3 fatty acids did not increase adverse events (odds ratio: 1.20, 95%CI: 0.89, 1.61; GRADE = moderate).
CONCLUSIONS
The present dose-response meta-analysis suggested evidence of very low certainty that supplementation with omega-3 fatty acids may significantly improve anxiety symptoms, with the greatest improvements at 2 g/d. More trials with better methodological quality are needed to reach more robust evidence.
PROTOCOL REGISTRATION
PROSPERO (CRD42022309636).
Topics: Humans; Fatty Acids, Omega-3; Randomized Controlled Trials as Topic; Dietary Supplements; Anxiety; Dose-Response Relationship, Drug; Anxiety Disorders
PubMed: 38890670
DOI: 10.1186/s12888-024-05881-2 -
PloS One 2024Involving parents of children with cerebral palsy (C-CP) in home exercise programmes (HEP) is globally practiced strategy closely linked to improved physical performance...
INTRODUCTION
Involving parents of children with cerebral palsy (C-CP) in home exercise programmes (HEP) is globally practiced strategy closely linked to improved physical performance and functional outcomes for the child. Nevertheless, non-adherence to HEP is increasing at an alarming rate, and little is known about the factors influencing adherence to HEP (AHEP) especially in parents of C-CP. This systematic review aimed to identify the factors enhancing AHEP among parents of C-CP to reinforce the efficacy of rehabilitation practices proposed by health professionals, researchers, and educators.
MATERIALS AND METHODS
We conducted searches in PubMed, Scopus, CINHAL, PsycINFO, and Embase for articles published up to March 2023, that investigated the factors influencing AHEP among parents of C-CP. A narrative synthesis was conducted using the search results and pertinent material from other sources.
RESULTS
Overall, non-adherence rates to HEP were moderate to high, ranging from 34% to 79.2%. Strong evidence suggests that factors enhancing AHEP fall into three categories: child-related (such as younger age and better gross motor function [GMF]), the caregiver-related (including high self-efficacy and knowledge, strong social support, low levels of depression, anxiety and stress symptoms, and a low perception of barriers), and the physiotherapist-related. For the latter category, the parent's perception of a supportive and collaborative relationship with the therapist is one of the conditions most favourably influences AHEP.
CONCLUSION
Our findings highlight that factors influencing AHEP are multifactorial. Some, such as GMF or the economic and social conditions of the family, are challenging to change. However, the relationship between therapist and parent is an aspect that can be strengthened. These results underscore the importance of substantial training and psychosocial support for therapists to enhance their awareness and competence in building supportive relationship with parents.
Topics: Humans; Cerebral Palsy; Parents; Child; Exercise Therapy; Social Support; Caregivers; Home Care Services; Patient Compliance
PubMed: 38865337
DOI: 10.1371/journal.pone.0305432 -
Medical Education Online Dec 2024Non-clinical approaches such as meditation, yoga, and mindfulness are popular traditional therapeutical interventions adopted by many educational institutions to improve... (Meta-Analysis)
Meta-Analysis Review
Non-clinical approaches such as meditation, yoga, and mindfulness are popular traditional therapeutical interventions adopted by many educational institutions to improve the physical and mental well-being of learners. This study aimed to evaluate the effectiveness of yoga intervention in improving cardiopulmonary parameters such as blood pressure, heart rate, pulmonary function tests and psychosomatic symptoms such as depression, anxiety and stress in medical and dental students. Using the PRISMA protocol, a search from databases such as PubMed, Scopus, and Embase resulted in 304 relevant articles. After screening the title and abstracts, 47 papers were analyzed thoroughly and included in the qualitative analysis. 18 articles with homogenous statistical data on physiology and psychological parameters were included for meta-analysis. In comparison to the control group, the study showed a significant reduction of systolic blood pressure (SBP: 6.82 mmHg, z = -3.06, = 0.002), diastolic blood pressure (DBP: 2.92 mmHg, z = -2.22, = 0.03), and heart rate (HR: 2.55 beats/min, z = -2.77, = 0.006). Additionally, data from 4 studies yielded a significant overall effect of a stress reduction of 0.77 on standardized assessments due to the yoga intervention (z = 5.29, < 0.0001). Lastly, the results also showed a significant (z = -2.52, = 0.01) reduction of 1.2 in standardized anxiety tests in intervention group compared to the control. The findings offer promising prospects for medical educators globally, encouraging them to consider reformation and policymaking in medical curricula to enhance academic success and improve the overall quality of life for medical students worldwide.
Topics: Yoga; Humans; Blood Pressure; Heart Rate; Stress, Psychological; Anxiety; Education, Medical; Depression; Students, Medical; Respiratory Function Tests
PubMed: 38861675
DOI: 10.1080/10872981.2024.2364486 -
Annals of Medicine and Surgery (2012) Jun 2024This systematic review aimed to investigate resilience and its related factors in caregivers of adult patients with cancer.
BACKGROUND
This systematic review aimed to investigate resilience and its related factors in caregivers of adult patients with cancer.
MATERIALS AND METHODS
A systematic search of online electronic databases including Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database (SID) was performed using keywords extracted from Medical Subject Headings such as "Psychological Resilience", "Caregiver", and "Cancer" from the earliest to 6 June 2023. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
RESULTS
A total of 2735 caregivers of cancer patients participated in 15 studies. The majority of the studies found that caregivers of cancer patients had high levels of resilience. Factors related to the resilience of cancer patients' caregivers included caregivers' social support, caregivers' quality of life, patients' resilience, caregivers' family function, patients' performance, caregivers' age, caregivers' health status, caregivers' self-esteem, caregivers post-traumatic growth, caregivers religious, caregivers hope, caregivers positive affect, patients age, patients social support, patients resilience support, patients quality of life, caregivers' anxiety, caregivers' depression, caregivers' burden, caregivers level of education, caregivers financial problem, caregivers memory, caregivers negative affect, caregivers post-traumatic stress disorder, maternal distress, and patients post-traumatic stress disorder.
CONCLUSION
Therefore, healthcare administrators and policymakers can enhance the resilience of caregivers and the quality of care they provide by instituting ongoing training initiatives focused on evaluating mental well-being and implementing coping strategies for managing stress and depression.
PubMed: 38846864
DOI: 10.1097/MS9.0000000000001469 -
Frontiers in Psychiatry 2024Methamphetamine is currently one of the most commonly used addictive substances with strong addiction and a high relapse rate. This systematic review aims to examine the...
INTRODUCTION
Methamphetamine is currently one of the most commonly used addictive substances with strong addiction and a high relapse rate. This systematic review aims to examine the effectiveness of physical activity in improving negative emotions, cognitive impairment, and drug craving in people with methamphetamine use disorder (MUD).
METHODS
A total of 17 studies out of 133 found from Embase and PubMed were identified, reporting results from 1836 participants from MUD populations. Original research using clearly described physical activity as interventions and reporting quantifiable outcomes of negative mood, cognitive function and drug craving level in people with MUD were eligible for inclusion. We included prospective studies, randomized controlled trials, or intervention studies, focusing on the neurological effects of physical activity on MUD.
RESULTS
Taken together, the available clinical evidence showed that physical activity-based interventions may be effective in managing MUD-related withdrawal symptoms.
DISCUSSION
Physical exercise may improve drug rehabilitation efficiency by improving negative emotions, cognitive behaviors, and drug cravings.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024530359.
PubMed: 38827441
DOI: 10.3389/fpsyt.2024.1402533 -
BMC Medical Informatics and Decision... May 2024Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.
METHOD
A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.
RESULTS
Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.
CONCLUSIONS
The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.
Topics: Humans; Machine Learning; Suicide; Risk Assessment; Algorithms; Risk Factors
PubMed: 38802823
DOI: 10.1186/s12911-024-02524-0 -
JMIR MHealth and UHealth May 2024Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping... (Review)
Review
BACKGROUND
Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious.
OBJECTIVE
This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges?
METHODS
We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded.
RESULTS
We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression.
CONCLUSIONS
This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression.
Topics: Humans; Depression; Stress, Psychological; Anxiety; Phenotype; Smartphone
PubMed: 38780995
DOI: 10.2196/40689 -
Frontiers in Psychiatry 2024Midwives may be key stakeholders to improve perinatal mental healthcare (PMHC). Three systematic reviews considered midwives' educational needs in perinatal mental... (Review)
Review
BACKGROUND
Midwives may be key stakeholders to improve perinatal mental healthcare (PMHC). Three systematic reviews considered midwives' educational needs in perinatal mental health (PMH) or related interventions with a focus on depression or anxiety. This systematic review aims to review: 1) midwives' educational/training needs in PMH; 2) the training programs in PMH and their effectiveness in improving PMHC.
METHODS
We searched six electronic databases using a search strategy designed by a biomedical information specialist. Inclusion criteria were: (1) focus on midwives; (2) reporting on training needs in PMH, perinatal mental health problems or related conditions or training programs; (3) using quantitative, qualitative or mixed-methods design. We used the Mixed Methods Appraisal Tool for study quality.
RESULTS
Of 4969 articles screened, 66 papers met eligibility criteria (47 on knowledge, skills or attitudes and 19 on training programs). Study quality was low to moderate in most studies. We found that midwives' understanding of their role in PMHC (e.g. finding meaning in opening discussions about PMH; perception that screening, referral and support is part of their routine clinical duties) is determinant. Training programs had positive effects on proximal outcomes (e.g. knowledge) and contrasted effects on distal outcomes (e.g. number of referrals).
CONCLUSIONS
This review generated novel insights to inform initial and continuous education curriculums on PMH (e.g. focus on midwives' understanding on their role in PMHC or content on person-centered care).
REGISTRATION DETAILS
The protocol is registered on PROSPERO (CRD42021285926).
PubMed: 38711873
DOI: 10.3389/fpsyt.2024.1345738 -
Frontiers in Psychiatry 2024Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the... (Review)
Review
Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the same time, the expression of human language plays a central role in the detection of mental health problems. Whereas spoken language is implicitly assessed during interviews with patients, written language can also provide interesting insights to clinical professionals. Existing work in the field often investigates mental health problems such as depression or anxiety. However, there is also work investigating how the diagnostics of eating disorders can benefit from these novel technologies. In this paper, we present a systematic overview of the latest research in this field. Our investigation encompasses four key areas: (a) an analysis of the metadata from published papers, (b) an examination of the sizes and specific topics of the datasets employed, (c) a review of the application of machine learning techniques in detecting eating disorders from text, and finally (d) an evaluation of the models used, focusing on their performance, limitations, and the potential risks associated with current methodologies.
PubMed: 38596627
DOI: 10.3389/fpsyt.2024.1319522