Effectiveness of digital and mobile-based interventions on sleep quality among nurses: a systematic review and meta-analysis.

BackgroundTo examine the effectiveness and safety of yoga of women with sleep problems by performing a systematic review and meta-analysis.MethodsMedline/PubMed, ClinicalKey, ScienceDirect, Embase, PsycINFO, and the Cochrane Library were searched throughout the month of June, 2019. Randomized controlled trials comparing yoga groups with control groups in women with sleep problems were included. Two reviewers independently evaluated risk of bias by using the risk of bias tool suggested by the Cochrane Collaboration for programming and conducting systematic reviews and meta-analyses. The main outcome measure was sleep quality or the severity of insomnia, which was measured using subjective instruments, such as the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), or objective instruments such as polysomnography, actigraphy, and safety of the intervention. For each outcome, a standardized mean difference (SMD) and confidence intervals (CIs) of 95% were determined.ResultsNineteen studies in this systematic review included 1832 participants. The meta-analysis of the combined data conducted according to Comprehensive Meta-Analysis showed a significant improvement in sleep (SMD = − 0.327, 95% CI = − 0.506 to − 0.148, P < 0.001). Meta-analyses revealed positive effects of yoga using PSQI scores in 16 randomized control trials (RCTs), compared with the control group in improving sleep quality among women using PSQI (SMD = − 0.54; 95% CI = − 0.89 to − 0.19; P = 0.003). However, three RCTs revealed no effects of yoga compared to the control group in reducing insomnia among women using ISI (SMD = − 0.13; 95% CI = − 0.74 to 0.48; P = 0.69). Seven RCTs revealed no evidence for effects of yoga compared with the control group in improving sleep quality for women with breast cancer using PSQI (SMD = − 0.15; 95% CI = − 0.31 to 0.01; P = 0.5). Four RCTs revealed no evidence for the effects of yoga compared with the control group in improving the sleep quality for peri/postmenopausal women using PSQI (SMD = − 0.31; 95% CI = − 0.95 to 0.33; P = 0.34). Yoga was not associated with any serious adverse events.DiscussionThis systematic review and meta-analysis demonstrated that yoga intervention in women can be beneficial when compared to non-active control conditions in term of managing sleep problems. The moderator analyses suggest that participants in the non-breast cancer subgroup and participants in the non-peri/postmenopausal subgroup were associated with greater benefits, with a direct correlation of total class time with quality of sleep among other related benefits.

The effect of digital health interventions in older adults with frailty: a systematic review and meta-analysis.

Poor sleep quality and insomnia are increasingly linked to chronic inflammation and obesity-related metabolic dysfunction. Aerobic exercise is a promising non-pharmacological approach for enhancing sleep quality and reducing systemic inflammation; Therefore, we aim to systematically evaluate and quantify the effects of aerobic exercise interventions on subjective sleep quality, insomnia severity, and circulating markers (IL-6 and TNF-α) in adults. A systematic search was conducted in institutional databases (UNAM, UACJ) and PubMed to identify randomized controlled trials (RCTs) examining the effects of exercise on sleep and inflammation. Risk of bias was assessed using the Cochrane RoB2 tool. Meta-analyses were performed using RevMan 5.4 with random-effects models to estimate pooled mean differences (MD) and standardized mean differences (SMD), with 95% confidence intervals. Anaerobic protocols were excluded from the meta-analysis due to the insufficient availability of data. : Eleven RCTs met the inclusion criteria. Aerobic exercise showed a significant pooled effect on sleep outcomes (MD = -2.51; 95% CI: -4.80 to -0.23; p = 0.03). However, subgroup analyses for Pittsburgh Sleep Quality Index (PSQI) (MD = -2.27; p = 0.15) and Insomnia Severity Index (ISI) (MD = -2.98; p = 0.16) were not statistically significant. Two studies on IL-6 reported a non-significant reduction (SMD = -0.17; p = 0.66), with moderate heterogeneity. TNF-α results were also non-significant (SMD = 0.60; p = 0.29) with substantial variability. Our results showed that aerobic exercise may modestly improve sleep outcomes; however, current evidence does not support its effectiveness in reducing levels of IL-6 or TNF-α. Further well-controlled trials are needed to clarify its immunometabolic effects, particularly in populations with obesity or metabolic disorders.

PurposeThis study aimed to evaluate the effects of cognitive behavioral therapy for insomnia (CBT-I) on sleep quality and insomnia severity in menopausal women through a systematic review and meta-analysis.MethodsA comprehensive literature search was conducted up to October 2024 using PubMed, EMBASE, Cochrane, and CINAHL, with additional searches of Chinese and Korean databases to include East Asian studies. Randomized controlled trials (RCTs) assessing the effects of CBT-I on sleep outcomes in menopausal women were included. Eleven RCTs (n=973) met the inclusion criteria. The interventions comprised face-to-face, telephone, and internet-based CBT-I programs, with session counts ranging from 4 to 12 and follow-up durations extending from post-intervention to 52 weeks. Data were analyzed using Review Manager 5.4, and effect sizes were expressed as standardized mean differences (SMDs) and mean differences (MDs) with 95% confidence intervals (CIs).ResultsCBT-I significantly improved sleep quality (n=795, SMD=−1.01; 95% CI, −1.27 to −0.75) and reduced insomnia severity (n=504, MD=−4.49; 95% CI, −6.12 to −2.87). Subgroup analyses indicated that CBT-I was effective regardless of delivery mode (face-to-face or remote), follow-up duration, or baseline insomnia severity.ConclusionCBT-I is an effective non-pharmacological intervention for improving sleep quality and reducing insomnia severity in menopausal women. These findings support the integration of CBT-I into clinical practice, particularly as a nurse-led intervention that can be delivered in both face-to-face and remote formats. To enable broader implementation, standardized CBT-I training programs and clinical protocols should be developed. Future studies should investigate long-term effectiveness and cultural applicability in diverse populations, including Korean women.

To investigate the effects of digital health interventions for improving adherence to oral iron supplementation in pregnant women. Five databases were searched from their inception to October 2023 with no date restrictions. Randomized controlled trials (RCTs) that assessed the effects of digital health interventions on adherence to oral iron supplementation (e.g., tablets and capsules) compared to non-digital health interventions for pregnant women were eligible. We calculated standardized mean differences (SMDs) and mean differences (MDs) with 95% confidence intervals (CIs) for continuous variables using the inverse variance method. We calculated odds ratios (OR) with 95%CI for categorical variables using the Mantel-Haenszel model. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The risk of bias of the included RCTs was assessed using the Cochrane risk of bias tool 2.0. Ten trials with 1,633 participants were included. Based on 7 trials, digital health interventions can improve objective adherence rate comparing with non-digital health interventions (1,289 participants, OR = 4.07 [2.19, 7.57], p < 0.001, I2 = 69%) in pregnant women. Digital health interventions can improve subjective adherence behavior comparing with non-digital health interventions (3 trials, 434 participants, SMD = 0.82 [0.62, 1.01], p < 0.001, I2 = 0%) in pregnant women. Based on 3 trials, digital health interventions can improve tablets consumption comparing with non-digital health interventions (333 participants, SMD = 1.00 [0.57, 1.42], p < 0.001, I2 = 66%) in pregnant women. Digital health interventions can improve hemoglobin level comparing with non-digital health interventions (7 trials, 1,216 participants, MD = 0.59 [0.31, 0.88], p < 0.001, I2 = 93%) in pregnant women. Digital health interventions were effective at improving adherence to oral iron supplementation and hemoglobin levels in pregnant women.

This study aims to evaluate the sleep quality of adolescents with PTSD related to sexual abuse and to investigate the relationship between sleep quality, PTSD symptoms, and quality of life. Our study was designed as a cross-sectional study. Forty adolescents who were diagnosed with PTSD related to sexual abuse and 40 healthy adolescents as a control group were included in the study. Structured interview scale Clinician-Administered PTSD Scale for Children and Adolescents (CAPS-CA) were applied to children by the clinician. All participants also filled out the Pediatric Quality of Life Inventory (PedsQL), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI). The analyses of the data revealed that the quality of life scores of the case group was significantly associated with worse results. Sleeplessness index (ISI) and morning sleepiness scores (ESS) were higher in the case group than the control group (P < .001; P < .001) and perceived quality of sleep (PSQI) was determined to be lower (P < .001). A statistically significant relationship between PTSD total score and PSQI (P < .001; r = 0.550), ESS (P < .05; r = 0.369), ISI (P < .001; r = 0.613), and PedsQL (P < .001; r = -0.473) were identified. PSQI, ESS, and ISI were also found to be correlated with each other (PSQI, ESS r = 488; PSQI, ISI r = 0.755; ESS and ISI r = 0.514). Moreover, PSQI scores explain the deterioration in quality of life more significantly than CAPS-CA-TOTAL scores (PSQI P = .008; CAPS P = .572). In cases with PTSD related to sexual abuse, we found that sleep affects the quality of life more than the symptoms of PTSD. Sleep-based approaches in PTSD may affect both quality of life and functionality positively, and PSQI may be used in clinical practice to assess both sleep and quality of life in the follow up of patients with PTSD related to sexual abuse.

Individuals with opioid use disorder (OUD) may experience worsening sleep quality over time, and a subset of individuals may have sleep disturbances that precede opioid use and do not resolve following abstinence. The purpose of the present study was to (1) collect retrospective reports of sleep across the lifespan and (2) identify characteristics associated with persistent sleep disturbance and changes in sleep quality in persons with OUD. Adults with OUD (n = 154) completed a cross-sectional study assessing current and past sleep disturbance, opioid use history, and chronic pain. Repeated-measures analysis of variance was used to examine changes in retrospectively reported sleep quality, and whether changes varied by screening positive for insomnia and/or chronic pain. Multivariate linear regression analyses were used to identify additional correlates of persistent sleep disturbance. Participants reported that their sleep quality declined over their lifespan. Changes in reported sleep over time varied based on whether the individual screened positive for co-occurring insomnia and/or chronic pain. In regression analyses, female sex (β = 0.16, P = .042), a greater number of treatment episodes (β = 0.20, P = .024), and positive screens for chronic pain (β = 0.19, P = .018) and insomnia (β=0.22, P = .013) were associated with self-reported persistent sleep disturbance. Only a portion of participants who screened positive for sleep disorders had received a formal diagnosis. OUD treatment providers should routinely screen for co-occurring sleep disturbance and chronic pain. Interventions that treat co-occurring OUD, sleep disturbance, and chronic pain are needed. Ellis JD, Mayo JL, Gamaldo CE, Finan PH, Huhn AS. Worsening sleep quality across the lifespan and persistent sleep disturbances in persons with opioid use disorder. J Clin Sleep Med. 2022;18(2):587-595.

Digital health technology provides opportunities to leverage artificial intelligence and other digital applications to promote cardiovascular health. Digital health technologies include artificial intelligence (such as machine learning [ML], neural networks),1 analytic systems, mobile apps, wearables, email, text messaging, and telemedicine.2 In this article, we review the role of digital technology in cardiovascular health and a selection of recent studies to evaluate the evidence of its effectiveness. Artificial intelligence is broadly defined as the capability of computer systems to perform tasks similar to humans.3 Examples include vision, speech, pattern recognition, and decision making. Machine learning is the ability of the computer program to learn from experience. This typically occurs from analysis of large sets of data processed through human-derived algorithms to enhance, predict, and explain outcomes.4 An example of the use of ML in clinical care is cardiovascular disease (CVD) prediction and electrocardiographic interpretation. Neural networks, named after the human nervous system, are nonlinear statistic models that control where signals are sent. Neural networks can be used for decision making such as cardiovascular diagnosis confirmation. Digital Technology Use in Cardiovascular Risk Assessment Several studies have demonstrated improved CVD risk factor identification using ML compared with traditional risk assessment tools. Researchers developed an ML risk calculator and compared it with the American College of Cardiology/American Heart Association CVD risk calculator in 6459 participants from the Multi-Ethnic Study of Atherosclerosis.5 Study participants were free of CVD at baseline and followed for 13 years. Results revealed that the American College of Cardiology/American Heart Association risk calculator was less precise: statin therapy was recommended to 46% of the sample, with 23.8% of CVD events occurring in those not recommended a statin. In comparison, the ML risk calculator recommended a statin to 11% of the sample, with 14.4% of CVD events occurring in those not recommended a statin.5 Similarly in 3 cohorts from Australia, 4 ML models were developed and compared with the 2008 Framingham model. The ML models provided 2.7% to 5.2% better predictions across all 3 cohorts.6 Taken together, the authors of these studies suggest ML provides promise in providing more precise estimates of CVD risk. Digital Health Interventions for Cardiovascular Disease Prevention Digital health interventions have the potential to provide a personalized approach to promote cardiovascular health. Behavior change theory is a key component of digital interventions and includes theoretical frameworks such as supportive accountability,7 self-efficacy theory,8 social cognitive theory, and the health belief model.9 Precision healthcare has been promoted for decades. Many of the challenges in operationalizing precision healthcare are healthcare accessibility, scheduling, care continuity, and inadequate knowledge exchange between provides and patients.10 Thus, promotion of healthy lifestyles and lifestyle risk factor reduction remain inadequately addressed in patients with CVD.11 To achieve sustainable change, individual-level personalized strategies may be leveraged through digital health interventions. Evidence of the effectiveness of digital health interventions has varied but is promising overall. Text messaging has been successfully used to provide information regarding healthy diet and physical activity recommendations, monitoring, and individual feedback. Text messaging has resulted in improvements in diet and activity in many (TextMe,12 Mobile MyPlate,13 MyQuest,14 Text-To-Move15), but not all studies.16 Smartphone/mobile apps have been designed to improve dietary and physical activity behavior. Examples include apps that track dietary patterns and activity through user input of text or visual images.17,18 Users can set their own goals and receive feedback on progress toward goals. Reviews of smartphone apps have had variable results with many demonstrating short-term improvement. Villinger et al19 conducted a systematic review and meta-analysis of the effectiveness of mobile app interventions on nutrition behaviors (41 studies, 27 randomized controlled trials [RCTs]). Findings revealed significantly improved nutrition behaviors and nutrition-related outcomes (P = .004 and P = .043, respectively). A second systematic review of 27, primarily RCTs, found significant between-group improvements in 19 of the 27 studies.20 A meta-analysis of 6 RCTs in adults using a smartphone app as the primary component of the intervention revealed a trend for more steps per day in the intervention compared with the control groups, with programs lasting less than 3 months more effective than longer programs.21 Taken together, text messaging and smartphone/mobile apps have the potential to improve lifestyle behaviors associated with cardiovascular health. The addition of strategies to increase sustainability of the effects needs to be assessed. Digital Health Interventions: Primary and Secondary Prevention Widmer et al2 conducted a meta-analysis of 51 RCTs and cohort studies using digital health interventions for the prevention of CVD events and risk factor modification. Subgroup analyses of primary prevention studies (2 studies) did not provide evidence of a statistically significant reduction in CVD outcomes. However, evaluation of individual risk factors in primary prevention studies found a significant reduction in weight (11 studies; −3.35 lb), systolic blood pressure (23 studies; mean difference, −2.12 mm Hg), total cholesterol (13 studies; mean difference, −5.19 mg/dL), low-density lipoprotein cholesterol (8 studies; mean difference, −4.96 mg/dL), and glucose (6 studies; mean difference, −1.38 mg/dL).2 A subgroup analysis of secondary prevention studies demonstrated a significant impact of digital interventions on CVD outcomes (relative risk, 0.60; a 40% relative risk reduction), improvement in body mass index (6 studies; mean difference, −0.31 kg/m2) but no improvement in weight, systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and glucose. Taken together, this meta-analysis suggested that digital interventions were beneficial not only in lowering CVD events in higher-risk patients but also in lowering risk factors in primary prevention approaches.2 In a second meta-analysis conducted by Akinosun et al,11 researchers analyzed 25 RCTs in patients with traditional CVD risk factors who received a digital intervention versus usual care.11 Findings revealed benefits in total cholesterol (mean difference, −0.29), high-density lipoprotein cholesterol (mean difference, −0.09), low-density lipoprotein (mean difference, 0.18), physical activity (mean difference 0.23), physical inactivity (relative risk, 0.54), and diet (relative risk, 0.79). There was no significant improvement in body mass index, systolic and diastolic blood pressure, hemoglobin A1C, alcohol intake, smoking, and medication adherence. Authors concluded that digital interventions were more effective at improving healthy behaviors than reducing unhealthy behaviors. In patients who experienced a myocardial infarction, a digital health intervention providing medication reminders, vital sign and activity tracking, education, and outpatient care coordination resulted in a 52% lower 30-day readmission rate compared with usual care.22 Sociodemographic characteristics (age, sex, and race) did not influence use of the digital intervention, highlighting a potential role for digital interventions in the promotion of equity in social determinants of health.23 Digital Health Interventions in Cardiac Rehabilitation Cardiac rehabilitation is an essential component of secondary prevention of CVD.24 Some patients face barriers in participation in cardiac rehabilitation due to physical accessibility, time, and travel.25 Digital health interventions have the potential to bridge these barriers and increase participation. Digital delivery of cardiac rehabilitation therapy with real-time personalized support has several advantages.26 In a systematic review of 31 studies in which authors examined digital health interventions for cardiac rehabilitation, the results revealed that cardiac rehabilitation program adherence was greater in patients using digital interventions than traditional methods alone. Secondary benefits were found in self-efficacy, weight management, diet, and quality of life. Taken together, digital cardiac rehabilitation was feasible and effective whether used alone or in combination with traditional cardiac rehabilitation.26 Conclusion Digital health technology is an evolving field with tremendous potential to improve cardiovascular health. Cardiovascular disease remains the major cause of death in the United States. The age-adjusted mortality rate has increased in the last decade. More people died from CVD causes in 2020 (nearly 900 000 deaths) than any year since 2003.27 Opportunities to reduce CVD and CVD risk have not been fully leveraged, and digital technology interventions have the potential to meet this need. Digital health technology also has the potential to provide equitable and personalized care. Device data, electronic medical record data, and social determinants of health data provide an opportunity to combine and identify longitudinal trends and risk factors before CVD begins. In the future, large data sets can be created that can be analyzed using ML to identify patterns and structures within and among the data to provide a more robust risk assessment to promote CVD prevention.

Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes.Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage).Results: 15% of the sample spent < 1 month, 27% spent 1–2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6–1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3–2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI (p < 0.001; MD: 1.25; 95% CI: 0.87–1.63) and ISI (p < 0.001; MD: 2.5; 95% CI: 1.72–3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month (p < 0.001; MD: 1.28; 95% CI: 0.26–2.3).Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athletes. Lockdown duration had further disrupting effects on elite athletes’ sleep behavior. These findings could be of relevance in future lockdown or lockdown-like situations (e.g., prolonged illness, injury, and quarantine after international travel).

Introduction: One of the most common sleep disorders, insomnia is a significant public health concern. Several psychiatric disorders, such as anxiety disorders and depression, have shown strong relationships with insomnia. However, the clinical impact of the combination of these two conditions on insomnia severity and sleep quality remains unknown. We investigated the relationship between sleep disturbance and psychiatric comorbidities in subjects with high risk for insomnia.Methods: We analyzed data from a nation-wide cross-sectional survey of Korean adults aged 19 ~ 69 years conducted from November 2011 to January 2012. The survey was performed via face-to-face interviews using a structured questionnaire. We used the insomnia severity index (ISI) to evaluate insomnia and defined respondents with ISI scores of ≥10 were considered to be at high risk for insomnia. To diagnose anxiety and depression, we used the Goldberg anxiety scale (GAS) and Patient Health Questionnaire-9 (PHQ-9), respectively.Results: Of the 2,762 respondents, 290 (10.5%) were classified as subjects with high risk for insomnia; anxiety [odds ratio (OR), 9.8; 95% confidence interval (CI), 7.3–13.1] and depression (OR, 19.7; 95% CI, 13.1–29.6) were more common in this population than in participants without insomnia. Of the participants with insomnia, 152 (52.4%) had neither anxiety nor depression, 63 (21.7%) only had anxiety, 21 (7.2%) only had depression, and 54 (18.6%) had both anxiety and depression. The group with both anxiety and depression was associated with worse scores on sleep-related scales than the other groups [high ISI, Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale]. The relationship between outcome measures (ISI and PSQI) and psychiatric problems was significant only when anxiety and depression were present. The PSQI has a significant mediation effect on the relationship between psychiatric comorbidities and insomnia severity.Conclusion: Among the respondents with insomnia, psychiatric comorbidities may have a negative impact on daytime alertness, general sleep quality, and insomnia severity, especially when the two conditions are present at the same time. Clinicians should, therefore, consider psychiatric comorbidities when treating insomnia.

Effect of exercise on sleep quality and insomnia in middle-aged women: A systematic review and meta-analysis of randomized controlled trials

Objective To investigate the correlation between insomnia and sleep quality in adolescents. Methods According to Insomnia Severity Index (ISI) Chinese Version, 3342 students technician training in school were divided into non insomnia group (N = 2345) and insomnia group (N = 997). Sleep and emotional state were assessed by ISI Chinese Version, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Self?Rating Anxiety Scale (SAS) and Beck Depression Inventory (BDI). The social demographic data were collected simultaneously. Results The number of insomnia, daytime sleepiness, anxiety and depression in the population was 997 (29.83%), 568 (17.00%), 243 (7.27%) and 1287 (38.51%), respectively. The comparison of social demographic data between 2 groups showed that the proportion of female ( P = 0.000), poor physical condition ( P = 0.000), non?only child ( P = 0.006), high learning pressure ( P = 0.000) and smoking ( P = 0.027) in insomnia group were significantly higher than those in non insomnia group. The total scores of ISI Chinese Version ( P = 0.000), ESS ( P = 0.000), SAS ( P = 0.000) and BDI ( P = 0.000) in insomnia group were significantly higher than those in non insomnia group. Pearson correlation analysis showed that ISI Chinese Version and PSQI scores were positively correlated with ESS score ( r = 0.361, P = 0.000; r = 0.064, P = 0.000), SAS score ( r = 0.326, P = 0.000; r = 0.069, P = 0.000) and BDI score ( r = 0.529, P = 0.000; r = 0.067, P = 0.000), and ISI Chinese Version had higher correlation ( r = 0.300-0.600) with the above scores than PSQI ( r < 0.100). Further partial correlation analysis showed that ISI Chinese Version score was negatively correlated with PSQI score ( r = ? 0.056, P = 0.001). Conclusions Higher proportion of female, worse physical condition, more non?only child, greater learning pressure and higher smoking rate were observed in insomnia group. Daytime sleepiness, anxiety and depression in insomnia group were more serious than those in non insomnia group, but PSQI score can not distinguish the above differences. Compared with PSQI, ISI Chinese Version is more closely related to daytime sleepiness, anxiety and depression, and might be more suitable for assessing insomnia in adolescents. DOI: 10.3969/j.issn.1672-6731.2017.09.007

Background and purpose: The aim of this study is to assess the impacts of digital health interventions on quality of life and mental status in stroke patients. Stroke is one of the leading causes of death and disability worldwide, and patients are often associated with emotional problems such as depression and anxiety during recovery, hence, it is important to explore effective interventions. Digital health intervention technologies, including virtual reality (VR), telemedicine, and robotic assistance, are the focus of this study because of their innovation and potential effects. Methods: Following predefined protocols, the study searched four databases up to November 2023, screened for relevant randomized controlled trials (RCTs), and extracted data on quality of life and psychological status, including depression/anxiety. A total of 17 studies involving 1437 participants were included. The study used different digital health interventions, including VR, robotic-assisted and telemedicine, and standardized mean differences (SMD) and 95% confidence intervals (CI) were used to assess intervention effectiveness. Results: The data show that digital health interventions are more effective than conventional treatments in improving the quality of life of stroke patients and reducing the incidence of psychological disorders. In particular, significant differences were observed in the intervention groups for VR (SMD = 0.90, 95% CI = [0.07, 1.73]), robotic-assisted (SMD = -0.65, 95% CI = [-1.11, -0.19]) and telemedicine (SMD = 0.27, 95% CI=[0.11, 0.44]). In addition, the study found that digital health interventions were effective in reducing the incidence of depression in stroke patients, thereby improving their psychological well-being. Conclusions: Digital health interventions have been shown to be effective in improving the quality of life and psychological well-being of stroke patients. However, it is worth noting that anxiety levels did not significantly improve among patients with digital health interventions. This suggests that future research should adjust its focus to explore whether specific factors associated with stroke patients correlate with the effectiveness of digital interventions in improving anxiety states. It is also necessary to assess the long-term effects of digital health interventions. Further exploration is needed to optimize the approach, intensity, and frequency of digital health interventions for stroke patients.

ObjectiveInsufficient sleep duration and quality has negative effects on athletic performance, injury susceptibility and athlete development. This study aimed to assess the sleep characteristics of professional Qatar Stars League (QSL)...

Poor sleep quality, often resulting from poor sleep hygiene, is common among medical students. Educational interventions aimed at improving sleep knowledge are beneficial for sleep quality in healthy populations. However, sleep education is often given minimal attention in medical school curriculums. The aim of the study was to explore whether a short educational intervention could improve sleep knowledge, and consequently sleep quality, among medical students. We recruited preclinical- and clinical-stage medical students during the 2017-2018 academic year. Students completed a demographic survey, the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Assessment of Sleep Knowledge in Medical Education (ASKME) questionnaire. Students then attended a lecture on the physiology and importance of sleep. To assess the efficacy of the intervention, questionnaires were repeated 4 months thereafter. A total of 87 students (31 preclinical) with a mean age of 25.86 years (standard deviation [SD], 3.33), 51 of whom were women, participated in the study. At baseline, students had poor sleep quality with a PSQI mean score of 5.9 (SD, 2.37), without significant sleepiness, and a mean ESS score of 8.86 (SD, 4.32). The mean ASKME scores were consistent with poor sleep knowledge at 11.87 (SD, 4.32). After the intervention, the mean ASKME results improved to 14.15 (SD, 4.5; P < .001), whereas sleep quality did not. The effect was similar in preclinical and clinical medical students. Sleep knowledge was inadequate among medical students, who also experienced poor sleep quality. A short educational intervention improved sleep knowledge but was insufficient at improving sleep quality. Further studies are needed to determine which interventions may provide benefit in both sleep knowledge and sleep quality.