Our results indicated that larger VT values during gait are associated with worse PROMs, suggesting that an intervention to reduce VT might be an option for clinicians trying to improve PROMs.This study aimed to assess the association between the degree of varus thrust (VT) assessed by an inertial measurement unit (IMU) and patient-reported outcome measures (PROMs) in patients with knee osteoarthritis. Seventy patients  were instructed to walk on a treadmill with an IMU attached to the tibial tuberosity. For the index of VT during walking (VT-index), the swing-speed adjusted root mean square of acceleration in the mediolateral direction was calculated. As the PROMs, the Knee Injury and Osteoarthritis Outcome Score were used. Data on age, sex, body mass index, static alignment, central sensitization, and gait speed were collected as potential confounders. After adjusting for potential confounders, multiple linear regression analysis revealed that the VT-index was significantly associated with the pain score (standardized β = −0.295;  The prevalence of knee osteoarthritis (OA) is increasing worldwide, and it is one of the leading causes of disability and decline in quality of life (QoL) ,2,3,4. PPatients with medial knee OA often exhibit an abnormal gait pattern called varus thrust (VT), which is observed in a significant proportion of patients, ranging from 16% to 46% ,14,15,16Both VT and PROMs have been reported to be affected by many factors, including demographic factors or disease severity. Hence, older age, women, higher body mass index (BMI), and severe Kellgren and Lawrence (K-L) classification were found to be associated with both the presence of VT and worse PROMs ,23,24,25As an objective and quantitative method for assessing the degree of VT, the method using inertial measurement units (IMU) has been reported recently ,36,37,38Therefore, this study was designed to examine the association between the degree of VT assessed using an IMU and KOOS (one of the most commonly used PROMs) after adjusting for the potential confounding factors in patients with knee OA.In this study, 70 patients diagnosed with knee OA participated. The subjects were recruited from outpatients scheduled for tibial osteotomy in an orthopedic hospital. The patients with symptomatic medial knee OA diagnosed according to the clinical or radiographical classification criteria of the American College of Rheumatology and with K-L classification ≥ II were included in this study. The exclusion criteria were as follows: (1) patients younger than 40 years; (2) those with lateral knee OA; (3) those with osteonecrosis of the femur or tibia; (4) those with severe pain in areas other than the knee; (5) those with severe neurological diseases, such as stroke or Parkinson’s disease; and (6) those with severe psychiatric diseases, such as depression or dementia, as these diseases could affect the gait pattern or self-reported outcomes.The protocol to assess the degree of VT was the same as that previously reported . Briefly2) and a three-axis gyroscope (range: ±2000 degrees/s) . The IMU was secured to the tibial tuberosities on the affected side using elastic belts . For the index of VT during walking (VT-index), the adjusted root mean square (RMS) of the acceleration at the tibia in the ML direction was calculated. The typical signals of acceleration from the IMU and the calculation method have been described elsewhere . In brieAs the PROMs, the KOOS for the Japanese population was used in this study ,42. The We assessed some potential confounders: demographic data, structural changes and static alignment from radiographical images, and CS. The demographic data of the subjects, including age, sex, height, and weight, were extracted from the medical records. BMI was calculated by dividing the weight by the square of the height. All subjects underwent radiography, and the severity of structural changes was evaluated using the K-L classification. To assess the static alignment of the affected knee, we calculated the Mikulicz line score. The score (%) was calculated by the length from the tibial medial joint surface to the Mikulicz line to the tibial plateau width . FurtherThis cross-sectional study set six confounding variables a priori. Hence, seven variables, including the VT-index and the confounding variables, were planned to be included in the multivariate models to examine the association between VT and PROMs. To determine the required sample size for this study, we used two methods. First, according to the traditional method of determining the variables used in the multivariate model (10 subjects per variable) , a samplAll statistical analyses were performed using JMP, version 14.3.0 . Data descriptive statistics are presented as means ± standardized deviations (ranges) or numbers and proportions (%), as appropriate. To examine the association between the VT-index and each KOOS score, a single linear regression analysis was first performed. Pearson’s correlation coefficient was also calculated in each examination. Then, multiple linear regression analysis was used to clarify the associations between the VT-index and each KOOS score independently of the potential confounders. Age, sex, BMI, Mikulicz line score, CSI, and gait speed during the assessment of the VT-index were forced to be included in the multiple regression model as confounders. The variance inflation factor (VIF) was calculated to investigate multicollinearity. The level of statistical significance was set at <5%.2, and 40 (57.1%) subjects were obese (BMI ≥ 25.0). According to the K-L classification, the number of class II, III, and IV subjects was 19 (27.1%), 45 (64.3%), and 6 (8.6%), respectively. The mean KOOS score ranged from 34.8 for the QoL score to 71.3 for the ADL score. The mean VT-index was 0.036 ± 0.012 m/s/deg . Most subjects  exceeded the cutoff value (0.027 m/s/deg) of VT from previous studies [ studies .p = 0.07), −0.19 (p = 0.12), −0.26 (p = 0.03), −0.04 (p = 0.74), and −0.13 (p = 0.30) for the pain, symptoms, ADL, Sport, and QoL scores, respectively.The association between the VT-index and each KOOS score, and the results of the linear regression analyses are shown using scatter plots and regression lines in p = 0.026), symptoms , and ADL  scores, but not the Sport  and QoL  scores. The VIFs of all variables entered into the regression model were <5; thus, there was no multicollinearity.The results of the multiple regression analyses are shown in In this study, we examined the relationship between the degree of VT assessed using an IMU and each KOOS score. The subjects walked on a treadmill, and the VT-index was calculated from the acceleration and angular velocity data measured using the IMU attached to the tibia. As a result, significant associations between the VT-index and the pain, symptoms, and ADL scores were observed after adjusting for potential confounding factors, such as demographic factors, static knee alignment, CS, and gait speed. These results indicate that severer VT was independently associated with worse PROMs (severer pain and symptoms and more disabilities).Studies have supported our results, showing associations between the degree of VT and PROMs. Some reports have found that patients with VT rated by experts had worse PROM scores ,14,15,46Among the KOOS scores, significant associations with VT were found for the pain, symptoms, and ADL scores after adjusting for potential confounders, but not for the Sport and QoL scores in this study. The study by Bensalma et al. also showed that larger VT was associated with lower pain, symptoms, and ADL KOOS scores . These rThe univariate analysis for pain and symptom scores revealed that the association did not reach a significant level, despite reaching a significant level in the multiple regression models. As no multicollinearity was found between the variables in the multivariable model, the lack of a significant association in the univariate analysis could be due to confounding effects that weakened the association. Particularly, higher CSI or lower gait speeds may have such a confounding effect. It has been noted that patients with CS have a tight control gait pattern and limited stability . FurtherBoth the VT-index and the CSI were significantly associated with pain, symptom, and ADL scores of KOOS in the multiple regression models. Although it is not intrinsically acceptable to evaluate the effects of CSI on outcomes because it was included in the model as a covariate, the impact of CSI was greater (standardized β: from −0.465 to −0.516) than that of the VT-index (standardized β: from −0.256 to −0.296). These results are reasonable given that CS has been proven to have a considerable influence on PROMs ,27,28. NThis was the first study to demonstrate the robust associations between the degree of VT and PROMs after adjusting for potential confounders, to the best of our knowledge. To properly infer associations between clinical data, confounding bias must be removed as much as possible. In this study, the associations between VT and PROMs were examined after the a priori setting of possible confounding factors and the calculation of the sample size. Although discussing the causality is difficult due to the cross-sectional study design, an intervention to reduce VT might be an option for clinicians trying to improve PROMs. For example, several systematic reviews showed that foot orthosis, such as the lateral wedge insole, could reduce the degree of VT and knee adduction moment, which is strongly related to VT ,52. AddiThis study suggests that the VT-index measured using IMUs is a valid parameter for capturing changes in PROMs. As previously demonstrated, the VT-index could discriminate well healthy subjects without VT from patients with knee OA presenting VT, and a cutoff value was determined . In thisSeveral limitations should be noted in this study. First, the subjects were limited to patients with knee OA scheduled for tibial osteotomy in an orthopedic hospital. Therefore, the disease severity among the subjects was unevenly distributed; particularly, very severe or mild patients were few. Thus, caution should be exercised in generalizing the results of this study. Second, there might be several additional confounding factors that were not measured. To confirm the relationship between VT and PROMs, further validation with a larger sample, including more measurements and patients of varying severities, will be needed.This study demonstrated that severer VT assessed using an IMU was independently associated with worse PROMs (severer pain and symptoms and more disabilities) with a moderate level of standardized β after adjustments for potential confounders, such as age, sex, BMI, static knee alignment, CS, and gait speed, in patients with knee OA. The results suggest that assessing the degree of VT quantitatively using IMUs would be useful in clinical situations involving knee OA. An intervention to reduce VT may be an option for clinicians trying to improve PROMs, but further investigations are warranted to fully understand the effects of the intervention.


Microbiome changes on the ocular surface may cause dry eyes. A metagenome assay was used to compare the microbiome composition and function of the ocular surface between diabetic children and adolescents with dry eye, diabetic children and adolescents without dry eye, and normal children.n = 10), the non-dry eye group  and the normal group . A conjunctival sac swab was collected for metagenomic sequencing, and the relationship between the microbiome composition and functional gene differences on the ocular surface with dry eye was studied.Twenty children and adolescents aged 8 to 16 with diabetes were selected from the Shanghai Children and Adolescent Diabetes Eye Study. Ten healthy children and adolescents belonging to the same age group were selected from the outpatient clinic during the same period. The participants were classified into the dry eye group . There were significant differences in α and β diversity of metabolic pathways between the two groups(P<0.05). The functional pathways of ocular surface microorganisms in diabetic children with dry eyes were mainly derived from human disease, antibiotic resistance genes, carbohydrate, coenzyme and lipid transport and metabolism-related functional genes; In normal children, the functional pathways were mainly derived from replication, recombination, repair, signal transduction and defense-related functional genes.The classification composition and metabolic function of the microorganisms on the ocular surface of children in the 3 groups were analyzed. It was found that children’s ocular microbiota was composed of bacteria, viruses and fungi. There were significant differences in α diversity and β diversity of microbial composition of ocular surface between DM-DE group and NDM group(The DM-DE group have unique microbial composition and functional metabolic pathways. The dominant species and unique metabolic pathways of the ocular surface in the DM-DE group may be involved in the pathogenesis of dry eye in diabetic children. In recent years, due to changes in lifestyle and everyday habits that affect the eyes, the incidence of dry eye (DE) has been increasing every year, and more causes of DE have been identified. DE resulting from systemic diseases, especially type 1 and 2 diabetes mellitus (DM), has attracted an increasing amount of attention, and the prevalence of diabetic dry eye is higher than that of non-diabetic dry eye . The eyeStaphylococcus aureus, Coagulase-negative staphylococcus and Corynebacterium were related to the prevalence of DE [Ocular surface microorganism (OSM) features gradually draw people’s attention in recent years , 5. Studce of DE , 7. Othece of DE –15. The ce of DE –18.The development of next-generation sequencing technologies and bioinformatics tools can facilitate the acquisition of human microbiome characteristics . CompareIn the SCADE study, 10 children and adolescents with diabetes who had DE (DM-DE group), 10 children and adolescents with diabetes who did not have DE (DM-NDE group), and 10 age-matched healthy controls without diabetes (NDM group) who visited the outpatient clinic in January 2022 were included in the study. As the prevalence rate of dry eye in healthy children is very low  [ The inclusion criteria for study participants were as follows: (1) both subjects and their guardians were fully informed of the study, and informed consent have been obtained from their legal guardians; (2) subjects aged 8–16 years; (3) subjects with diabetes were diagnosed with type 1 diabetes according to the diagnostic criteria of the World Health Organization (WHO) ; (4) theAccording to the International Dry Eye Workshop II (DEWS II) criteria for DE examination and diagnosis , the criOn the day before specimen collection, the teenagers and children had enough sleep; On the day of sample collection, the teenagers and children avoided washing their eyes with water. Samples were obtained from 8:00 a.m. − 10:00 a.m. at the same time. Before sampling, a saline swab was used to wipe the participants’ eyelid skin twice; subsequently, a conjunctival sac swab was used to collect ocular surface microorganisms from the eyes. The entire procedure was performed aseptically. The collected samples were quickly placed in a tube containing DNA protecting fluid , stored in a refrigerator at -20 °C  for cryopreservation, and then used for testing and DNA extraction.All children were arranged for specimen collection at Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital. Eye specimens were collected by a trained ophthalmologist (Z.C.) to ensure the consistency of the results.Total microbial genomic DNA samples were extracted using the OMEGA Mag-Bind Soil DNA Kit (M5635-02) , following the manufacturer’s instructions, and stored at -20 °C prior to further assessment. The quantity and quality of extracted DNAs were measured using a Qubit™ 4 Fluorometer, with WiFi: Q33238   and agarose gel electrophoresis, respectively. The extracted microbial DNA was processed to construct metagenome shotgun sequencing libraries with insert sizes of 400 bp by using Illumina TruSeq Nano DNA LT Library Preparation Kit. Each library was sequenced by Illumina NovaSeq platform  with PE150 strategy at Personal Biotechnology Co., Ltd. .P < 0.05 was considered statistically significant.Based on the Illumina NovaSeq/HiSeq high-throughput sequencing platform, using the whole genome shotgun (WGS) strategy, the extracted total microflora metagenomic DNA was used as an mRNA template to synthesize double-stranded cDNA that was randomly interrupted into short fragments, and cDNA libraries of appropriate length were constructed. The cDNA libraries were subjected to double-paired (2 × 150 bp PE) sequencing. A library was constructed for each sample, and 6G of original data was obtained. Quality screening was performed on the original data of double-ended sequences off the high-throughput sequencing machine to remove the target sequences and obtain clean datasets that could be used for downstream metagenomic analysis. Species annotation was carried out on high-quality datasets and nonredundant amino acid sequences to obtain species composition spectra at the fine level of species and below species, and species composition analysis, diversity analysis, difference analysis, etc. To compare the diversity of different samples, the composition spectrum of all samples at the species level was leveled at the lowest sequencing depth to correct the diversity difference caused by sequencing depth. The common and unique species of each group were analyzed. According to the composition spectrum of each group at the species level, R software was used to calculate the number of common and unique species, and the number of common and unique species of each group was intuitively presented by a Venn diagram. QIIME software was used to map 2D or 3D data from PCoA to obtain spatial distribution characteristics based on metagenomic species composition and to quantify the size of differences between groups. The high-quality sequences were corrected, spliced and assembled, and the metagenomic contig sequence set was constructed. After the redundancy was removed, gene prediction was carried out to obtain the nonredundant amino acid sequence set. Functional annotation of amino acid sequences was carried out using a variety of common databases to obtain the abundance spectrum of functional groups of each level, and functional composition analysis, diversity analysis, difference analysis, etc. were performed. QIIME software was used to obtain the relative abundance distribution table of each sample corresponding to each functional level of each database. R software was used to design a bar chart for the primary and secondary functional groups or metabolic pathways in each sample. By using R software and QIIME software, PCoA was carried out on the composition spectrum of the functional units of the samples, and the difference distribution characteristics of the samples were described by two-dimensional and three-dimensional images. QIIME software was used for ANOSIM analysis, and 999 displacement tests were performed to determine whether the differences between the groups were statistically significant. LEfSe analysis software was used to analyze the functional abundance table of the KEGG  and EggNP > 0.05).The basic information of 30 children  is shown in Table  The mass distribution, average mass and the content of four bases of the original sequencing data showed an average degree at each point, and the GC content of the sequencing data was close to a normal distribution, indicating that the data were reliable. Therefore, the process of sample collection, extraction and sequencing library preparation met expectations, and subsequent analysis could be carried out . Plasmodium ovale, Enterococcus faecalis, Streptococcus pneumoniae, S. aureus, Mycobacterium tuberculosis, Escherichia coli, Salmonella enterica, Mycobacterium leprae, Acinetobacter johnsonii, Leuconostoc sp. DORA_2, Chlamydia trachomatis, Jimgerdemannia flammicorona, Acinetobacter baumannii, Vibrio vulnificus, Klebsiella pneumoniae, Enterobacter hormaechei, Mycobacteroides abscessus, Virgibacillus profundi, Cordyceps militaris and Listeria monocytogenes were the dominant species (with an abundance greater than or close to 1%). At the phylum level, the abundance of Apicomplexa, Actinobacteria, Chlamydiae and Ascomycota in the DM-DE group was significantly higher than that in the NDM group. The abundance of Firmicutes and Proteobacteria in the NDM group was significantly higher than that in the DM-DE group. At the genus level, the abundance of Plasmodium, Mycobacterium, Escherichia, Vibrio, Leuconostoc, Chlamydia and Enterobacter in the DM-DE group was significantly higher than that in the NDM group. The abundance of Enterococcus, Streptococcus, Staphylococcus and Acinetobacter in the NDM group was significantly higher than that in the DM-DE group. At the species level, the abundance of P. ovale, E. coli, S. enterica, M. leprae, L. sp. DORA_2 and C. trachomatis in the DM-DE group were significantly higher than those in the NDM group. The abundance of E. faecalis, S. pneumoniae, S. aureus and A. johnsonii in the NDM group was significantly higher than that in the DM-DE group. The abundance of dominant species in the DM-DE group was the highest at the phylum, genus and species levels   Fig. f. LEfSe oup Fig. g.Fig. 3aIn this study, 15 million nonredundant functional genes were extracted from the shotgun metagenome set. According to the KEGG  database The results of the KEGG  primary R = 0.68, P = 2.975094e-05), indicate that there were differences in function and species among all groups. Functional PCoA showed that the beta diversity of microorganisms on the ocular surface was different among the three groups   (Fig.  The Pearson correlation analysis of the Shannon diversity index from both the function level and species level was conducted. The results, which are shown in Fig. S. enterica, L. sp. DORA_2, V. vulnificus, Cordyceps militaris and E. mori, while the species enriched in normal children were S. aureus, M. abscessus, P.odorifer, V. agarivorans and S. gordonii, N. nitrogenifigens, and N. sp. Fuku2 − ISO − 50. The functional pathways of ocular surface microorganisms in diabetic children with dry eyes were mainly derived from human disease, antibiotic resistance genes, carbohydrate, coenzyme and lipid transport and metabolism-related functional genes; In normal children, the functional pathways were mainly derived from replication, recombination, repair, signal transduction and defense-related functional genes. Further study on the microflora of ocular surface can accurately analyze the role of microflora in the occurrence and development of ocular diseases, and can also be used to guide more accurate drug treatment to adjust the microecological balance and rebuild the microecological environment conducive to the health of ocular surface, so as to achieve the purpose of treating ocular diseases. To date, no relevant reports have been published.In this study, considering the high prevalence and incidence of dry eye in children and adolescents with diabetes , 3, as wIn this study, compared with normal adolescents and children, the DM-DE group had significant differences in the α diversity and β diversity of the OSM, and the α diversity and β diversity were lower than those of normal children. This is consistent with the results reported by Li , who conPlasmodium ovale, Mycobacterium leprae and Chlamydia trachomatis were highly abundant in the eyes of children with diabetes, especially in children with dry eyes, which had not been reported before. The specific mechanism of these microorganisms in the eyes still needs further confirmation. This was inconsistent with the report by Li [Bacteroides and Bacteroides fragilis were obviously enriched in the dry eye group, while Pseudomonas was mainly enriched in the non-dry eye group. This finding likely due to the difference between the subjects in each study. Our study found that the core ocular microorganisms Enterococcus faecalis, Streptococcus pneumoniae, S. aureus, Acinetobacter Johnson and Acinetobacter baumannii had the highest abundance on the ocular surface of normal children, and these bacteria may be involved in the causes of dry eye in adults, as previously reported [In this metagenomic examination, we found some special microorganisms in addition to bacteria on the ocular surface. In addition to the main phyla composed of Proteobacteria, Firmicutes and Actinomycetes, Apicomplexa and Chlamydia were also found to be important microbial phyla in the ocular surface, with more species than 16Sr RNA gene sequencing. Additionally, our study found that rt by Li  that Bacreported , 15. ThiAn important finding of this study was that through the analysis of functional genes, the unique metabolic pathways and functional groups in the DM-DE group were reduced, which was consistent with the decrease in microbial diversity in the ocular surface. The inflammatory response caused by the high glucose environment in the ocular surface inhibits the growth of some microorganisms, leading to a reduction in the metabolic requirements of these microbial substances. Additionally, the increase in viral and bacterial functional genes related to infectious diseases enhanced the production of inflammatory factors and virulence, and the microenvironment of ocular surface homeostasis was destroyed, leading to dry eye. Our study also found that in the DM-DE group, functional genes related to the transport and metabolism of carbohydrates, enzymes and lipids were reduced, which would lead to abnormal metabolism and transport of substances on the ocular surface, thereby damaging the normal components of tears and leading to dry eyes. Compared with the NDM group, the DM-DE group had fewer functional genes related to replication, recombination, repair, signal transduction and defense, and the normal cell metabolism of the ocular surface was inhibited. This abnormality may also be the cause of diabetic dry eye in children, but the specific mechanism needs to be further confirmed. In 2017, Chen Hong  et al. cThis study had some limitations. First, due to the impact of the COVID-19 epidemic, the number of children with diabetes who were able to participate in screening for eye diseases was small, resulting in a small sample size. Second, this is the first study to analyze the microbial composition and function of the ocular surface through metagenomic sequencing, as no previous studies have used this approach. Third, due to the low microbial content on the ocular surface and because shotgun metagenomic sequencing removed the influence of host factors, the microbial quantity obtained was lowered. Fourth, metagenomes require a high-quality sample and cannot determine the expression of microorganisms. Fifth, metagenomes require higher sequence coverage than 16Sr DNA sequence analysis, and although the sampling process was performed carefully, host contamination was inevitable.In summary, this study found that dry eye in children with DM was accompanied by characteristic changes in OSM and metabolic pathways. The microbial composition and structure of the ocular surface in patients with dry eye were changed, and the change in microflora was an important factor affecting dry eye. The role of microorganisms in dry eye is complex, and more and larger sample studies are needed to clarify the role of microorganisms in the course of dry eye disease and the pathogenic mechanism. In the future, multicenter clinical studies can be carried out to expand the sample size. Microorganisms and functional genes closely related to the development of DM-DE can be screened to further explore the pathogenesis of DM-DE in children.


On the basis of self-efficacy theory, self-control theory and psychological resilience theory, this paper discusses the relationship between physical exercise, self-efficacy, self-control, psychological resilience and Internet addiction among adolescents in Beijing. A convenience sampling method was used to conduct a questionnaire survey on physical activity and Internet addiction among 466 adolescents from first to third year in 10 high schools in Beijing, of which 41% were girls and 59% were boys; 1.9% of students were 14 years old, 42.5% were 15 years old, 23.4% were 16 years old, 31.3% were 17 years old and 0.9% were 18 years old. Using the research methods of the literature, correlation analysis and multiple intermediary structure model, this paper constructed and tested the multiple intermediary model between physical exercise and Internet addiction. The results show that physical exercise can significantly predict self-efficacy, psychological resilience and self-control; self-efficacy, psychological resilience and self-control significantly interfered with Internet addiction behaviour; there was a significant difference in the total effect of multiple intermediaries; the effect value was −0.173; the specific indirect effects of self-efficacy, psychological resilience and self-control had intermediary effects in the relationship between physical exercise and Internet addiction; and there was no difference in specific indirect effects. This paper puts forward some countermeasures and suggestions to prevent teenagers’ Internet addiction from cultivating, such as through good sports activities, thereby improving their Internet addiction. We should actively strive to improve teenagers’ deep understanding of the effect of physical exercise and gradually form physical exercise habits, with sports addiction replacing Internet addiction. The Law of the People’s Republic of China on the Promotion of Family Education stipulation describes that parents or other guardians of minors shall reasonably arrange a time for physical exercise for minors and prevent them from becoming addicted to the Internet . With thSelf-efficacy is one’s own obsession with his or her strength in successfully completing a task and is one of the strongest predictors of motivational factors, which can also conjecture an individual’s deliberate behaviour in any situation . IncreasMental resilience refers to the personal experience of thriving and excelling through hardship, rather than simply returning to normal functioning . PhysicaSelf-control is the ability of individuals to make adaptive adjustments to their cognition, emotions and behaviours, according to the requirements of goals and tasks , which cAccording to the theoretical hypotheses above, a theoretical model of the relationship between physical exercise and Internet addiction behaviour was constructed see .A total of 500 students in 10 grades were randomly selected from 3 grades in 3 high schools in Beijing using a convenience sampling method and tested using a physical exercise and Internet addiction questionnaire. A total of 466 valid questionnaires were collected, with an effective rate of 93%. Of these, 41% were girls and 59% were boys; 35.8% were from rural areas and 64.2% were from towns; 1.9% of students were 14 years old, 42.5% were 15 years old, 23.4% were 16 years old, 31.3% were 17 years old and 0.9% were 18 years old.The Physical Exercise Rating Scale (PARS-3), revised by Liang Deqing et al. , was useThe two subscales of six items of self-efficacy and five items of mental resilience from the psychological capital entry developed by Luthans et al.  were useThe Internet Addiction Inventory (CIAS-R) developed by Chen Shuhui  was usedThe self-control scale developed by Tan Shuhua et al.  was usedThe data collected were entered and collated using IBM SPSS 26.0 ; relevant variables were analysed; a model of physical activity and Internet addiction was developed using AMOS 24.0 software ; model fit tests, direct effect path tests and, finally, multiple mediated effect tests using bootstrap and whether there were differences in specific indirect effects were tested.To prevent possible common method bias in the collected data, the administration process was controlled accordingly. The Harman one-way method was used to test for common bias. The results showed that there were 11 factors with eigenvalues greater than 1, and the variance explained by the first factor was 10.828%, which was less than the critical threshold of 40%, and the amount of common variance extracted was higher than 70%, indicating that there was no common method bias .The data were tested for convergent validity and compositional reliability using SPSS 26.0 see , and Crop < 0.01 or p < 0.05); withdrawal response was significantly associated with interpersonal relationships and health, time management problems, healthy habits, resisting temptation, entertainment control, physical exercise, self-efficacy and mental resilience (p < 0.01 or p < 0.05); relationships and health were significantly associated with time management, health habits, impulse control, entertainment control, physical exercise and self-efficacy (p < 0.01 or p < 0.05); time management was significantly associated with entertainment control, physical exercise and self-efficacy (p < 0.01 or p < 0.05); impulse control was significantly associated with health habits, temptation resisting, learning concentration, entertainment control, physical exercise and mental resilience (p < 0.01 or p < 0.05); health habits were significantly associated with temptation resisting, learning concentration, entertainment control, physical exercise and mental resilience (p < 0.01 or p < 0.05); learning concentration was significantly associated with entertainment control, self-efficacy and mental resilience (p < 0.01); physical exercise was significantly associated with self-efficacy and mental resilience (p < 0.01); and self-efficacy was significantly associated with mental resilience (p < 0.01). The diagonal AVE open root sign scores were all greater than the dimension and dimensional correlations.Compulsive Internet surfing was significantly associated with frustration tolerance, withdrawal response, relationships and health, time management, health habits, temptation resisting, recreational abstinence, physical exercise, self-efficacy and mental toughness , and the unstandardised coefficient results indicated that each pathway of influence was significant and valid. The standardised path coefficients showed that among the direct influence paths of the latent variables on Internet addiction, the absolute value path coefficient of self-control was the largest, with a standardised absolute value path coefficient of 0.15, while the standardised absolute value path coefficients of self-efficacy and mental resilience were both 0.136. Among the direct influence path coefficients of physical exercise as the dependent variable, the standardised coefficient of mental resilience was the largest at 0.643. The analysis of the above data indicates that the direct path coefficients for Internet addiction are significant, and the effect sizes of the path coefficients vary.The results of the direct pathways for the relationship between physical exercise and Internet addiction see  showed tThe intermediary variable is closer to the outcome than the predictor variable, and the intermediary variable itself is a causal variable. The indirect effect is an independent variable influencing the dependent variable through the intermediary variable. The way to test the effect of the indirect effect consists of the causal path intermediary effect test and the product of the indirect effect coefficients test. The causal path test , in whicIn fact, researchers can easily obtain path multiplication coefficients, only to obtain the value but not the standard error, which cannot be calculated. The standard error of the equation used to seek causal steps coefficients is more suitable for use in an intermediary. Complex models, especially, contain multiple variables, and complex methods can only be solved with SEM  and moreA bootstrap was used to test for multiple intermediary effects and whether there were differences in specific indirect effects. The results of the tests were as follows see : the ovePhysical exercise has the effect of regulating the body’s tension, improving the psychological state, enabling individuals to adapt to the developing environment, improving their cognition, emotional responses and volitional behaviour in a positive state and maintaining normal regulation . PhysicaThe intermediary latent variable factors—self-efficacy, mental resilience and self-control dimensions—each had a significantly negative predictive effect on Internet addiction. Internet addiction was negatively correlated with self-efficacy, and the results may help schools, families and society to design appropriate Internet addiction prevention programs for adolescents ; mental The results of the 5000 bootstrap intermediary effect tests for self-efficacy performed in this study show that the effect size of self-efficacy was −0.064, bias-corrected, and the Percentile 95% confidence interval did not contain 0. The test results indicate that the self-efficacy intermediary effect was significant. According to self-efficacy theory, it is known that college students’ adherence to physical activity can enhance self-efficacy ,50,51, iThe results of the 5000 bootstrap intermediary effects test performed for mental resilience in this study show that the effect size of mental resilience was −0.037, bias-corrected, and the Percentile 95% confidence interval did not contain 0. The test results indicate that the intermediary effect of mental resilience was significant. The theory of mental resilience refers to the ability of adolescents to activate protective factors against undesirable influences . The characteristics of recreation, competition and interest in physical exercise can enhance adolescents’ mental resilience ,57, a spThe results of the 5000 bootstrap intermediary effects tests for self-control execution in this study suggest that the effect size of self-control was −0.072, bias-corrected, and the Percentile 95% confidence interval did not contain 0. The test results indicate that the intermediary effect of self-control was significant. The increase in the level of self-control motivation was related to individuals’ correct perception and evaluation of themselves, and adherence to regular physical exercise could improve self-control, which was negatively related to mobile phone dependence . The selIn conclusion, family education is the starting point and root of the whole education, playing a more important role than school education and social education. The family has a great influence on young people’s Internet addiction, and there is a significant relationship between Internet addiction and family parenting. In the absence of realistic interactions, children become obsessed with the internet in order to find support. Optimising family parenting styles is an effective way to stop students’ Internet addiction . ExercisOverall, this study proposes a multiple mediation model analysis of physical exercise and Internet addiction, which reveals the mechanism of the effect of physical exercise on Internet addiction in adolescents: physical exercise influences the level of Internet addiction in adolescents through self-efficacy, mental toughness and self-control. Therefore, there is a mediating effect between physical exercise and Internet addiction. In the process of participating in physical exercise, adolescents should choose their favourite sports, pay attention to the time and intensity of exercise and persist in completing physical exercise so as to enhance their self-efficacy, mental toughness and self-control in physical exercise and effectively intervene in the occurrence of Internet addiction. There are also shortcomings in this study. Due to the ethical requirements of the survey and the limitations of the research resources, the sample of this survey is mainly young people, excluding both university students and primary school students, and the research results do not yet cover the whole scope of school sport, so it is hoped that more scientific research methods and theoretical perspectives can be explored in subsequent studies to make up for the shortcomings.Physical exercise had a positive predictive effect on self-efficacy and mental resilience, while self-control, self-efficacy, mental resilience and self-control were important factors in intervening in Internet addiction. The empirical test showed that the total indirect effect existed with a total effect size of −0.173, and the three path-specific indirect effects of self-efficacy, mental resilience and self-control also had significant effects, with self-control having the highest intermediary effect size, accounting for 42% of the total indirect effect; followed by self-efficacy at 37%; and mental resilience had the smallest intermediary effect size, accounting for 21%. The empirical tests indicate that the 95% confidence intervals for the bias-corrected and Percentile values for the three pathway-specific indirect effects contained 0, and that there was no significant difference in the comparison of the differences in the three pathway-specific indirect effects.


The stress–strain index (SSI) is a measure of corneal material stiffness, which is obtained using the Corvis ST algorithm based on dynamic corneal response parameters. The reduced SSI corresponds to the longer axial length (AL). In a previous study, we found SSI increases as the corneal curvature flattens, whereas a flatter corneal curvature indicates a longer AL (emmetropia or myopia). Therefore, in this cross-sectional study, we aimed to address these contradictory findings. First, we characterized the features of SSI, curvature radius of the anterior corneal surface (CR), and AL and analyzed their correlation with advanced myopia. Next, we compared the relationship between AL and SSI after adjusting for the effect of CR. We found a significant positive correlation between SSI and CR, which contradicts the developmental law of axial myopia. Furthermore, after accounting for the effect of CR, we observed a stronger correlation between SSI and AL than that in the unadjusted model. In conclusion, CR is an independent influencing factor for SSI in addition to AL, which masked the decrease in SSI caused by prolonged AL in axial myopia. The biomechanical properties of the cornea are often described using parameters such as material modulus and structural stiffness . The CorHowever, our previous study revealed a stronger correlation between SSI and spherical equivalent refractive error (SER) or axial length-to-corneal radius ratio (AL/CR) than between SSI and axial length (AL) alone . This suTherefore, in this cross-sectional study, our aim was to confirm whether the relationship between SSI and CR depends on the dioptric properties of CR, thereby establishing an indirect relationship between SSI and CR. We first characterized the features of SSI, CR, and AL and analyzed their correlation with advanced myopia. Subsequently, we compared the relationship between AL and SSI after adjusting for the effect of CR.This cross-sectional comparative study was conducted at the Eye Institute of Shandong First Medical University in Qingdao, China. The study followed the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Qingdao Eye Hospital of Shandong First Medical University.Participants included healthy individuals and patients scheduled for refractive surgery at the Qingdao Eye Hospital of Shandong First Medical University between July 2021 and April 2022. To account for the effect of axial myopia on SSI, participants were grouped based on their AL. The study aimed to investigate the relationship between corneal curvature and corneal biomechanics in individuals with myopia; therefore, we excluded participants who met any of the following criteria: 1) astigmatism of 3 diopters (3D) or higher, 2) use of contact lenses, 3) history or suspicion of corneal diseases such as keratoconus, and 4) history of eye surgery. Only data from the right eye of each individual were analyzed due to the high correlation of corneal parameters between the right and left eyes.To be eligible, participants’ medical records needed to include a complete medical history and results of ophthalmic examinations conducted on the same day, including comprehensive optometry results after mydriasis. The mean anterior corneal curvature radius within a 3-mm diameter range of the corneal apex (CR) and AL were measured using the OA 2000 device . The corneal biomechanical parameter, SSI, was determined using the Corvis ST device . Only measurements with “OK” quality specifications were included in the analysis.p < 0.05. Continuous variables were summarized as mean (standard deviation), while categorical variables were presented as sample size (percentage). Pearson’s correlation tests were conducted to examine the relationships between SSI, AL, and CR, and a matrix of plots was created to analyze the correlations between these three variables. Linear regression models were used to examine the associations between the variables: Model 1 treated SSI as the dependent variable and AL as the independent variable, Model 2 included an additional adjustment for CR, and Model 3 further adjusted for the AL×CR interaction term. The Akaike Information Criterion (AIC) . The significance level was set at on (AIC)  and modeon (AIC)  were perr = −0.212, p < 0.001), a positive correlation between SSI and CR , and a positive correlation between AL and CR .To examine the relationships between SSI, AL, and CR, Pearson’s correlation tests were performed. As shown in β = −0.02, 95% CI = −0.04 to −0.01, p < 0.001) after adjusting for age and gender. For model 2, we found that AL was still negatively associated with SSI  after additionally adjusting for CR, while the slope (β) changed from −0.02 (model 1) to −0.04 (model 2). In addition, we further performed a linear regression model with an interaction term (AL×CR) to study whether the relationship between AL and SSI was dependent on CR. In Model 3, CR was treated as a continuous variable, while CR was categorized into three groups based on its mean and standard deviation to facilitate the interpretation of the interaction term , indicating that the relationship between AL and SSI was not dependent on CR. The AICs of Model 1, Model 2, and Model 3 were −244.8, −271.4, and −269.5, respectively. The model likelihood ratio tests also suggested that Model 2 was the best-fitting model .As stated in the section on Statistical analyses, three linear regression models were performed to study the associations between SSI, AL, and CR. The results of the three regression models were summarized in ion term . As showβ = −0.02, 95% CI = −0.06 to 0.01, p = 0.13), while the adjusted model  showed that AL was negatively associated with SSI . However, among participants with AL ≥26, we did not observe a significant association between AL and SSI neither in the unadjusted model  or in the adjusted model .Finally, additional four linear regression models with SSI as the dependent variable were performed separately for individuals with AL < 26 and AL ≥ 26. The model results are shown in This study aimed to investigate the correlation between central corneal curvature and corneal biomechanical behavior parameters acquired using DCRs from a dioptric perspective, to address conflicting conclusions reported in previous studies regarding the relationship between SSI and AL in myopic eyes. For this purpose, linear regression models were established to examine the associations between the variables. The comparison of model performance using AIC and model likelihood ratio tests provided valuable insights into the performance of the three models . Based oDCRs obtained from Corvis ST are influenced by internal structures and scleral stiffness . SSI hasThe SSI algorithm was developed by using the least squares method based on numerical modeling input and output parameters CCT, bIOP, and stiffness parameter at highest concavity (SP-HC), while corneal curvature was fixed at 7.8 mm . TherefoIn addition, the phenomenon of the better correlation of SSI with SER, AL/CR, and astigmatism of the anterior corneal surface, rather than AL  can be eFrom the perspective of dioptric optics, this study analyzed the relationship between SSI and CR and found it to be contrary to the characteristic changes associated with increasing axial myopia. These results suggest that CR is an independent influencing factor for SSI, in addition to AL, indicating that the acquisition of SSI may be influenced, to some extent, by corneal geometry or that corneal flattening may be related to an increase in the material stiffness of the corneal stroma. Furthermore, we confirmed that the effect of CR did not alter the association between AL and the corneal material stiffness parameter obtained through DCRs. Our findings contribute to understanding the influence of corneal curvature on the development of algorithms for the corneal biomechanical behavior parameter acquired by DCRs. However, it is important to note that the average curvature radius of the anterior corneal surface within 3 mm of the apex, as an essential optometry parameter, cannot fully reflect the characteristics of the noncentral corneal region, especially when it is smaller than the applanation length of the air pulse. Therefore, this finding does not represent the effect of global corneal curvature on SSI acquisition. Given the nature of cross-sectional studies, we cannot exclude the possibility that corneal flattening may be related to the increased material stiffness of the corneal stroma, which could lead to the observed positive correlation between CR and SSI. Future studies should focus on examining the relationship between SSI and the best fitting surface of the anterior and posterior corneal surfaces while controlling for myopia-related variables.CR is an independent influencing factor for SSI in addition to AL. CR masks the decrease in SSI caused by prolonged AL in axial myopia.


Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks.This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated . The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation.The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks. The human upper limb function is crucial to perform daily living activities. The loss of one or both arms causes severe disability that greatly affects a person's ability to perform essential daily activities  is a device designed to make and biofeedback signal detection reliable and easy. The system transmits signals from Trigno Avanti^TM sensors to a receiving base station using a time-synchronized wireless protocol that minimizes latency in data transmission across sensors. In this study, eight sensors were placed around the forearm near the elbow to capture muscle signals during experiments, as depicted in The TrignoFour representative activities of daily life were specifically chosen for the experiment focusing on pronation-supination (PS) movement or dart-throwing movement (DTM). Specifically, PS and DTM were chosen for our prosthetic emulator in Poddar et al.  and PoddFor each trial, the procedure was as follows: First, the participant started from the T-pose position which stretches the arm shoulder height with palms facing down and feet on designated marks on the floor. The participant's toes were ~40 cm away from the edge of the table, with the distance adjusted based on the reach range of each individual. Recording began after a voice cue. After 2 s of recording, the participant was visually/orally prompted to begin. In the Screwdriver/Hammering/Cup Drinking task, the participant reached forward to pick up the screwdriver/hammer/cup and performed the screwing/hammering/drinking action 10 times. The procedure for the Bulb twisting task was slightly different. The twisting was performed 10 times in a clockwise direction and 10 times in a counterclockwise direction. After the participant completed the final movement, the tools were returned to the initial position on the table.For each activity of daily living task, the trial was repeated three times by incrementally increasing the height of the table. The height of the table for the first trial started at 78.5 cm and increased by 5 cm each time, ending at 88.5 cm. A verbal cue was given before each trial to start. The participant was instructed to perform the movements at a consistent speed to maintain uniformity and integrity of data. A practice trial was conducted prior to the recording sessions to familiarize the participant with the steps involved in each trial. Participants performed four tasks sequentially in random order.The data collection system consisted of a motion capture system, eight Delsys wearable sensors, a height-adjustable table, and four sets of tools for conducting experiments. In the experiment of this study, upper limb motion is measured using ten motion capture cameras and sEMG data were collected from eight wireless Trigno sensors. In the present ADL tasks, two angles were calculated: the pronation-supination (PS) angle and the dart-throwing motion (DTM) angle. These angles were calculated by constructing pairs of vectors within the markers in 3D space and computing the angle between them as in Fazil et al. . As showO(n2·T4) time complexity. Recently, Ismail Fawaz et al. , which means the length of the sliding window is 250 frames (125 ms), and eight-channel signals were collected from eight sEMG sensors. Besides the Butterworth filter mentioned before, a scaler was used to normalize the data when generating features from the data. Same scaler was also applied to the data of the validation group, “Quick training” group, and test group.i represents the true angle (PS angel or DTM angle) at time frame i, while i. n.In this study, two common measures are used for numerical evaluations: Root Mean Square Error (RMSE) and Pearson correlation (PC), with following formulas. θwhere Pearson Correlation is a measure of linear correlation between two sets of data. It is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1.The comparisons between the measured and predicted data with Quick training of four different tasks are depicted in The Bulb task used a model with a depth of 5, which means five Inception blocks are used. In each block, there are three convolutional layers with kernel sizes of 64, 16, and 4, respectively. The number of filters is 128. The numbers of epochs for the pre-training and “Quick training” part are both set as 30. When the model is pre-training, optimizer Adam (adaptive moment estimation) is used with a learning rate starting as 1e-3 and other parameters as default. The learning rate is decayed to half of its original value every 10 epochs. On data from the pre-training group, the selected model has RMSE of 19.723 deg/s, and Pearson Correlation of 0.669. On data from the validation group, the selected model has RMSE of 21.123 deg/s, and Pearson Correlation of 0.628. For the Screw task, an eight-depth model is utilized, which employs eight Inception blocks. Each block is composed of three convolutional layers with kernel sizes of 64, 16, and 4, respectively. The number of filters used is 128. Pre-training is done for 40 epochs, while “Quick training” is done for 30 epochs, using the Adam optimizer as before. The pre-training group achieved RMSE of 9.467 deg/s and Pearson Correlation of 0.849. On the validation group data, the selected model achieved RMSE of 25.265 deg/s and Pearson Correlation of 0.727.For the Hammer task, a model with a depth of 4 is employed, utilizing four Inception blocks. Each block contains three convolutional layers with kernel sizes of 64, 16, and 4, respectively. The number of epochs for pre-training and “Quick training” is set to 30, and the Adam optimizer is used as before. The selected model achieved RMSE of 5.679 deg/s and Pearson Correlation of 0.817 on the pre-training group data. However, on the validation group data, the selected model achieved RMSE of 5.385 deg/s and Pearson Correlation of 0.166. As for the Cup task, a model with a depth of 3 is used, employing three Inception blocks. Each block consists of three convolutional layers with kernel sizes of 128, 32, and 8, respectively. The number of epochs for pre-training and “Quick training” is set to 30, and the Adam optimizer is used as before. Unfortunately, during the second trial of Subject 2, the sEMG sensors disconnected from the software, so the entire set of Subject 2 had to be dropped. The selected model achieved RMSE of 2.298 deg/s and Pearson Correlation of 0.961 on the pre-training group data. However, on the validation group data, the selected model achieved RMSE of 4.701 deg/s.In The presented study shows a new framework to use real ADL task data to train a multi-DoF prosthe tic wrist using sEMG signals. The “Quick training” shows the utilization of a large data pool for creating a generic model but applies to a new user by using only a small amount of data for improving the model performance. Four tasks were tested to create the ML models by recruiting a total of 24 participants and tested on eight participants, which showed comparable performance with other models using a larger data set or training only simple motions.Comparing between tasks, the Screw and Bulb tasks showed higher Pearson‘s correlation than the Cup and Hammer tasks. This is presumably because high variation was found in the movement in Cup and Hammer tasks for various reasons. First, participants chose different movement strategy to perform the Cup and Hammer tasks. Some participants preferred moving only their wrists when lifting the hammer, while other participants preferred only moving their wrists when dropping the hammer. Participants chose different movement coordination between the wrist, elbow, and shoulder to perform the Cup and Hammer task. Second, the end-effector (tool) movement to fulfill the task had different kinematic redundancy. The Bulb and Screw tasks required to rotate the screw or bulb exactly along the screw thread. However, the cup or hammer task was not performed with restricted end-effector as Bulb and Screw tasks. Lastly, participants had different fluency to perform the hammer task. Even though 5-min practice session was provided for each task, there were participants who never used a hammer before. This could be another factor to create deviation in the movement, resulting different sEMG patterns among participants. Even though higher Pearson's Correlation was observed in the Cup and Hammer tasks, it should be noted that the Cup and Hammer tasks had larger RMSE. This was due to the different range of motion of the pronation-supination and the dart-throwing-motion tasks. Pronation-supination tasks (Bulb and Screw) had a significantly larger range of motion than dart-throwing-motion tasks (Hammer and Cup), which naturally led to larger RMSE despite higher Pearson's correlation.A few other researchers also studied various regression models for controlling prosthetic wrist. Stival et al.  combinedR2, and the detailed numeric results for the model are listed in R2 values, it is important to note that our study performed more complicated ADL movements with only eight sensors. Additionally, our “Quick Training” process required much less training data, and the performance of LSTM models would decrease substantially over time due to its natural instincts that the model itself depends on its previous predictions, which means minor turbulence could cause large deviation. Moreover, the way they combined CNN and LSTM required separate tuning, which would affect the efficiency of the proposed method significantly.Bao et al.  also proAnother study proposed the regression Supervised Domain Adaptation (SDA) for estimation of the wrist angle of flexion/extension through sEMG data .The studies involving humans were approved by Institutional Review Board of University at Buffalo. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any identifiable images or data included in this article.JK: conceptualization and supervision. ZM: data collection and analysis. ZM and JK: visualization, writing the manuscript, and editing. All authors have read and agreed to the published version of the manuscript.


Growing evidence shows that exposure to nature and psychological engagement with nature improve health and wellbeing and promote greater proenvironmental engagement. The unprecedented situation created by COVID-related lockdowns seems to have brought both potential distress with household confinements and greater research on experiences in nature. University students may have been particularly impacted as the quality of their home arrangements can vary substantially. The aim of the study was to examine how psychological engagement with nature (nature connectedness and noticing nature), time spent in nature, and household conditions relate to psychological wellbeing and proenvironmental behavior among university students. An online survey was administered to a sample of 566 university students from Italy and Ireland. Hierarchical multiple regressions were performed to investigate the relationships between variables. The results indicate that time spent in nature and psychological engagement with nature in terms of nature connectedness and noticing nature were associated with increased wellbeing and pro-nature-conservation behavior, controlling for demographic covariates. Moreover, the perception of chaos in one’s household was related to decreased wellbeing during the prolonged COVID-19 emergency. The findings highlight the need to invest in accessible natural places for students and to focus campus sustainability practices on encouraging nature connectedness to promote wellbeing and proenvironmental engagement. In response to the global emergency related to the transmission of the coronavirus SARS-CoV-2, authorities in many countries established public health control measures, including national lockdowns and limitations to movement during one or more periods during the COVID-19 pandemic. These measures confined individuals in their homes for extended periods of time, impacting their experience of household and outdoor spaces with important consequences for health and wellbeing . It is oThere is growing evidence that exposure to nature improves health and wellbeing ,6. StudiBoth the time spent in nature (exposure) and the psychological connection with nature are key aspects of the human–nature relationship . Recent Another benefit of an increased human–nature relationship is that it can promote and motivate greater proenvironmental engagement in individuals. Nature connectedness and noticing nature in the moment have been identified as important predictors of pro-nature-conservation behaviors beyond visits to nature ,15.household chaos, or daily disruptions within the household [Since lockdowns and social distancing measures confined more people in their homes, individuals’ household living conditions may have had an important role for wellbeing and prosocial choices . Recent ousehold . It inclousehold . Consistousehold . Changesousehold . ExistinThe purpose of the study is to examine the relationship of psychological engagement with nature (nature connectedness and noticing nature), time spent in nature, and the household environment with psychological wellbeing and pro-nature-conservation behavior. Based on the existing literature, we expect that nature connectedness, current engagement with nature, and time spent in nature are associated positively with both wellbeing and conservation behavior. Moreover, we hypothesize perceived household chaos to be negatively associated with wellbeing. Since there is a lack of theoretical development and evidence of the potential relation between household conditions and youth environmental behavior, no specific hypothesis was advanced, but the association was explored as an open research question. The research was approved by the ethics committees of the University of Bologna and the University College Cork. Students from the two universities were invited to participate in the study through institutional e-mails and social media posts on the universities’ pages. The data were collected in June–July 2021, at the tail-end of an academic year marked by the COVID-19 emergency and intermittent lockdowns and restrictions. Participants were provided with consent forms before filling the survey online. The instrument was anonymous. Surveys with missing information and incomplete responses were excluded .N = 456, 80.6%) and Ireland . Female respondents were overrepresented , which may be partially related to the prevalence of female students in the universities. Respondents who indicated their gender as nonbinary or third gender were excluded from the analyses, due to the low number impeding comparisons (N = 8). Respondents’ age ranged between 18 and 65 years old, with 82.1% of the respondents between the ages of 18 and 29 years old, and 17.9% between 30 and 65 years old (6.2% were between 42 and 65 years old). Participants reported living in households where money largely covered their family needs . The sample consisted of 566 participants from Italy  and the University College Cork (Ireland) in the context of the Greenmetric World University Ranking Network. not at all to 5 = fully) [Demographic information. Participants were asked to report their age, gender, and perceived family economic situation  .completely disagree to 7 = completely agree). Nature connectedness. Nature connectedness was assessed with the item “I feel part of nature” from the Nature Connection Index (NCI) , which wcompletely disagree to 7 = completely agree). Engagement with nature. Current nature engagement was assessed with the item “I am taking more time to notice and engage with everyday nature ”  measuredTime spent in nature. Participants were asked “How many times have you been outdoors in green areas and natural places in the last 14 days? ” [true or 0 = false, with reverse coding for six items. Scores were summed and higher scores indicated a more chaotic environment. The reliability of the scale was good (α = 0.73).Household conditions. The perceived disruptions in household conditions were measured with 14 items from the Confusion, Hubbub and Order Scale (CHAOS) , assessinot at all to 10 = fully), which asked participants about their satisfaction with life, sense of worthwhile life, and their feelings of happiness and loneliness (reversed score). These were items used in previous studies on the topic to assess constructs associated with wellbeing [Wellbeing. Wellbeing was assessed with 4 items measured on a 10-point Likert scale . An example was “I sign petitions supporting nature conservation efforts” [Pro-nature-conservation behavior (PNCB). The 8-item short-form Pro-Nature Conservation Behavior Scale (ProCoBS)  was usedefforts” . The relThe means and standard deviations of the main variables in the analyses are reported in As shown in  = 21.107, p < 0.001), time spent in nature  = 5.756, p = 0.017) and perceived household conditions  = 57.423, p < 0.001). Differences were found between the Italian and the Irish sample in age ; nature connectedness, time spent in nature and engagement with nature were inserted in the second block; and finally, household conditions constituted the third block. The overall sample size provided adequate power for eight independent variables: at α = 0.05 and power = 0.80, a sample size of 109 was sufficient for a medium effect size. The assumptions for multicollinearity, independence of errors and homoskedasticity were satisfied .  = 10.593, p < 0.001, R2 = 0.134, R2Adj. = 0.122). Participants whose families were wealthier had higher wellbeing. Feeling as part of nature and taking time to notice nature were also related to higher wellbeing. Perceived disruptions in household conditions were negatively related to wellbeing.It was found that family economic situation, nature connectedness, current engagement with nature, and household conditions were significantly related to wellbeing  = 11.240, p < 0.001, R2 = 0.141, R2Adj. = 0.129). In particular, older participants were more likely to engage in pronature behavior, while male participants were less likely. Feeling as part of nature, taking time to notice nature and spending time in natural spaces were all strongly and positively related to PNCB. Household conditions did not contribute to explaining PNCB.Age, gender, nature connectedness, current engagement with nature, and time spent in nature were significantly related to pro-nature-conservation behavior .  = 8.364, p < 0.001, R2 = 0.117, R2Adj. = 0.103). Within the Irish sample, wellbeing was significantly related to engagement with nature, time spent in nature, and household conditions  = 8.351, p < 0.001, R2 = 0.374, R2Adj. = 0.329).The results suggest that there were differences between the Italian and the Irish sample in the relevance of some of the independent variables for both wellbeing and PNCB. Within the Italian sample, family economic situation, nature connectedness, and household conditions were significantly related to wellbeing  = 10.367, p < 0.001, R2 = 0.141, R2Adj. = 0.128). Among Irish participants, PNCB was significantly related only to time spent in nature  = 5.300, p < 0.001, R2 = 0.275, R2Adj. = 0.223).Among Italian participants, PNCB was associated with age, gender, family economic situation, nature connectedness, and engagement with nature (FThe present research extended previous work by examining the role of nature exposure, connection and engagement with nature, and perceived disruption in household conditions simultaneously as factors in wellbeing and pro-nature-conservation outcomes. The results indicate that time spent in nature and psychological engagement with nature in terms of sense of connectedness and noticing nature were associated with increased wellbeing and pro-nature-conservation behavior, controlling for demographic covariates. Moreover, the perception of chaos in one’s household was also related to wellbeing during the prolonged COVID-19 emergency. The findings that increased nature connectedness and current engagement and time in nature were related to wellbeing and pronature behavior are largely consistent with previous research in the field ,13,24. PA novel contribution of this study was the examination of perceived household disruptions among young people during the pandemic and its role in wellbeing and environmental engagement alongside contact with nature. As public health measures restricted movement and determined a shift to home-based online learning, the household environment took center stage for university students. Our results suggest that a negative perception of the levels of noise, confusion and crowdedness in the household are associated with an important decrease in wellbeing. This was confirmed for both Italian and Irish students. While this finding is novel, it is broadly in line with research within developmental psychology on the impacts of household chaos to families and child health ,21. The Finally, our findings point to some sociodemographic differences in the level of proenvironmental behavior, particularly among Italian students. In line with previous research ,30, we fSeveral limitations of this study should be mentioned. First, the analyses were based on cross-sectional survey data, which limited any causal inference for the identified associations. Further research should employ longitudinal designs that can show the direction of the studied effects, as well as shed light on possible long-term impacts of nature engagement and household conditions. Second, the results were based on nonrepresentative samples of university students and cannot be generalized to the student or general population. Third, the data were based on self-reports, which can be subject to recall bias. Fourth, our measures did not consider the specific characteristics and quality of natural places that participants were exposed to and future studies could address these aspects. A potential limitation to the measure of nature exposure is that it excluded the time spent in one’s private garden. Recent studies during COVID-19 suggest that private gardens could have important contributions to feelings of nature connectedness and wellbeing . MoreoveThe study has significant implications for environmental policies in universities. The findings highlight the need to invest in accessible natural places for students to promote wellbeing and environmental engagement. Beyond providing spaces, however, the results on the importance of psychological engagement with nature suggest that interventions and education should focus on encouraging nature connectedness and increasing the noticing of nature, which will be an important component of preparedness for the future. Moreover, such interventions should also consider assessing students’ household conditions and their impact on students’ wellbeing to better respond to their need for green and restorative spaces.


The risk factors of progestational anxiety, depression, and sleep disturbance in women with a history of recurrent pregnancy loss (RPL) remain controversial, additional study is needed to investigate the incidence and risk factors of progestational anxiety, depression, and sleep quality in RPL women.A cross-sectional study was conducted among 663 non-pregnant RPL women in Northeast China from October 2019 to July 2022. We assessed the state of anxiety, depression, and sleep quality before pregnancy using structured questionnaires, including sociodemographic characteristics, state-trait anxiety scale (STAI), center for epidemiological survey, depression scale (CES-D), Pittsburgh sleep quality index (PSQI), and symptom self-rating scale (SCL-90). Logistic regression was used to evaluate the association between sleep quality and anxiety, depression. Pearson’s correlation was used to evaluate the correlation between anxiety and depression. Multivariate logistic regression analysis was used to find the risk factors of depression symptoms. The receiver operating characteristic curve (ROC) was used to evaluate the predictive value of the model.The incidence of state anxiety, trait anxiety, depression, and sleep disturbance in RPL women were 60.3, 51.7, 33.9, and 31.2%, respectively. The level of anxiety and depression in RPL women varied at different stages of treatment. In a longitudinal study (25 pairs), we found the level of state anxiety and trait anxiety were significantly lower after the cause was identified. Sleep disturbance is positively correlated with anxiety and depression. Logistic regression showed that the number of miscarriages ≥4 (Odds ratio (OR) = 2.268, 95%CI 1.300–3.956), Low household family income , interval since last miscarriage <6 months  and sleep disturbance  were associated with the occurrence of depressive symptoms. At the same time, anxiety can be used as a predictor of depression.Recurrent pregnancy loss women have a certain degree of anxiety, depression, and sleep disturbance. Education level, interval since the last miscarriage <6 months, and sleep disturbance are risk factors for anxiety and depression. A history of pregnancy loss after 14 weeks and no living birth are also closely related to anxiety. Therefore, it is necessary to pay close attention to the psychological state of RPL women and provide appropriate psychosocial support to reduce the occurrence of negative emotions. Recurrent pregnancy loss (RPL) was defined as 2 or more consecutive pregnancy losses . It affeChinese people traditionally consider fertility and childbearing ability of great importance to women. At the same time, there is a tendency in society to think that not having children is always the woman’s fault . The etiAn article of previous miscarriage-related literature shows that almost half the women had features of depression disorder after pregnancy loss . RepetitHalf of the etiology of RPL is still unknown, which undoubtedly increases the psychological burden of women with RPL and leads to the emergence of adverse psychological problems. Most researches on psychological problems in RPL were small in sample sizes and varied in psychological assessment scale, thereby making the results rather mixed. The prevalence of depression in reported studies ranged from 8.6–37%, while anxiety 7–45% . And fewA cohort basing on recurrent pregnancy loss population in Shengjing Hospital Affiliated to China Medical University located in northeast China was established to study the effects of pre-pregnancy and gestational exposure on pregnancy outcomes from October 2019 to July 2022. Subjects with a history of depression, anxiety, and other psychological problems or currently using psychotropic drugs or refusing to participate were excluded. A total of 1,261 questionnaires were collected, 133 were excluded due to pregnancy, 267 patients were excluded due to incomplete sociodemographic information, and a total of 861 subjects were finally included, with an effective response rate of 74.1%. According to the number of pregnancy losses, they were divided into three groups: Group 1 represents subjects with RPL (663 cases): ①spontaneous miscarriage for two or more consecutive times. ②20–45 years old. ③No history of cognitive impairment or mental illness. ④Did not take any psychotropic drugs. ⑤Willing to accept investigators. Exclusion criteria: ①illiteracy. ②Known mental illness and major medical and surgical complications. ③Long-term use of psychotropic drugs. ④ Refuse to accept the investigators. Group 2 represents subjects with a history of one pregnancy loss (124 cases): ①a history of one spontaneous miscarriage. ②20–45 years old. ③No history of cognitive impairment or mental illness. ④Did not take any psychotropic drugs. ⑤Willing to accept investigators. Exclusion criteria are the same as the RPL group. And group 3 represents subjects with no previous miscarriage (74 cases): ①no history of previous pregnancy loss. ②20–45 years old. ③No history of cognitive impairment or mental illness. ④Did not take any psychotropic drugs. ⑤Willing to accept investigators. Exclusion criteria are the same as the RPL group.This study included a small longitudinal study in which 25 of the subjects completed two questionnaires before and after identifying the cause.The whole pregnancy is clinically divided into three periods, the first trimester occurs less than 14 weeks of gestation, the second trimester occurs between 14 and 28 weeks of gestation and the third trimester of pregnancy occurs after 28 weeks. Most miscarriages occur in the first trimester, and the psychological state of early and late miscarriage may be different. Therefore, 14 weeks was taken as the critical point in this study to investigate the influence of a history of pregnancy loss after 14 weeks on the level of anxiety and depression.The study was approved by the Ethics Review Board of Shengjing Hospital Affiliated to China Medical University . All subjects were guaranteed anonymity during data processing and informed consent was obtained.After obtaining informed consent from the subjects, a questionnaire was used to collect information from all included subjects. All included subjects completed the questionnaire at the first pre-pregnancy visit, and some of them completed the same questionnaire again at the initial stage of treatment after the etiology was identified.The questionnaire mainly includes:It consists of two subscales assessing two different types of anxiety, with a total of 40 questions to assess the current anxiety mode and personality anxiety tendencies. The State Anxiety Scale, S-AI, reflects the severity of the subject’s current anxiety symptoms, and the Trait Anxiety Scale, T-AI, reflects the subject’s consistent or usual anxiety. All scores ranged from 20 to 80. Higher scores indicate higher levels of anxiety. A threshold score of 40 is considered a clinically meaningful threshold value for identifying state anxiety, trait anxiety . The ChiThe CESD consists of 40 questions that respond to feelings or behaviors that may have occurred in the past week. A CESD score of ≥16 is often used as a threshold for clinical depression . The ChiIt consists of 7 subscales assessing sleep quality, time to fall asleep, sleep duration, sleep efficiency, sleep disorders, hypnotic drug use, and daytime dysfunction. Each subscale has a score between 0 and 3, with an overall score of 0–21. The higher the score, the worse the sleep quality. The current study uses an established threshold of scores above 5 to identify poor sleep quality . The ChiIt consists of nine subscales (90 items): somatization, obsessive–compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, and psychosis. The SCL-90 scale has good discrimination between individuals with psychological symptoms , duration of marriage, monthly household income, interval since the last miscarriage, number of previous miscarriage, pregnancy loss after 14 weeks, and living birth.T-test, one-way analysis of variance (ANOVA) in combination with LSD posthoc tests were used to compare with continuous variables, Chi-square (χ2) tests were used for the comparison of categorical variables, and unpaired t-tests for continuous variables. The longitudinal study used repeated measure design T-test. We analyze the correlation between anxiety and depression in Pearson. Logistic regression was used to adjust for possible confounding factors by including variables to be analyzed and corrected. Binary logistic regression analysis was performed to explore possible predictors for anxiety and depression in RPL women, and the resulting predicted probabilities were analyzed by ROC curves. A p-value of <0.05 was considered statistically significant.Data were statistically analyzed using the statistical software SPSS 26.0. Continuous variables were expressed as mean ± standard deviation, while categorical variables as the number of cases (percentage). p > 0.05).The basic characteristics of the study subjects for each group were summarized in p < 0.05). The SCL-90 subscales were further compared, including somatization, obsessive–compulsive disorder, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, and psychosis. Eight of these psychiatric symptoms scores were significantly higher in the RPL women than in the comparison group . And the incidence of state anxiety, trait anxiety, and depression in RPL women was 60.3, 51.7, and 33.9%, respectively.As shown in p = 0.003, p = 0.009).Among the 663 RPL women who participated in the study, 663 completed the questionnaire for the first visit before pregnancy, and only 25 completed the questionnaire for treatment after finding out the cause. The scale scores of the patients were compared between the first visit before pregnancy and the treatment after finding out the cause, as shown in The mean score of PSQI was 4.63 ± 2.771, indicating that RPL women had a good overall sleep quality. 31.2% had sleep quality disturbance . Because only one patient had poor sleep quality, thus we combined the women with PSQI scores of 6–10, and 11–21 into one group. Sleep quality is negatively correlated with anxiety and depression. As shown in In this study, multiple scales were used to evaluate anxiety and depression. S-AI, T-AI, and SCL-90 anxiety subscales were used to evaluate anxiety, and CES-D and SCL-90 depression subscales were used to evaluate depression. Correlation analysis revealed that there was a positive correlation between each dimension of anxiety scale and each scale of depression, as illustrated in Multiple regression analysis using the step-wise method was conducted. As noted in P < 0.001), as shown in The predictors of depressive symptoms were listed in Recurrent pregnancy loss is a special group of people with fragile psychology. In RPL women, feelings of guilt and loss of control were predominant . MoreoveAmong breast cancer patients, there are differences in their mental states after diagnosis, before surgery, after surgery and before chemotherapy . The samLogistic regression results showed that a bachelor’s degree or less was a risk factor for anxiety, and a monthly household income of less than 10,000 was a risk factor for depression. Patients with different educational levels have different personal expectations, social opportunities, family and social pressure, understanding of disease, and compliance with doctors . Low eduIn this study, 14 weeks was used as the cut-off point, we found that a history of pregnancy loss after 14 weeks was a risk factor for state anxiety. Consistent with previous studies that anxiety and depression in women who experienced intrauterine death in the third trimester persisted during pregnancy until nearly 3 years after delivery . PregnanAnother important finding of the study was that anxiety predicted depressive symptoms. In line with previous research, anxiety disorders often precede depression disorders . FurtherA few limitations are acknowledged. First, the current research adopts the psychological scale standard cut-off value. RPL women, as a special population, needs to be combined with clinical psychological symptoms to establish an appropriate psychological cut-off value. Second, different patients should be grouped according to the time interval of identifying etiology, and the changes of psychological state were compared. The conclusion was that the influence of identifying etiology time on mental state was concluded. Third, more data should be collected in the future to conduct longitudinal studies comparing psychological changes in RPL women at different stages of pregnancy before and after treatment. This study has the advantage of a large sample size. A variety of psychological scales were used, and the results were consistent with each other. It integrates social demographic characteristics and maternal history, discusses the psychological changes in the process of pregnancy preparation among non-pregnant women. The purpose is to detect psychological problems at the initial stage of diagnosis and treatment as early as possible.In conclusion, RPL women are a vulnerable population with a high risk for developing anxiety and depressive symptoms, which have different levels of anxiety and depression at different stages of diagnosis and treatment, so it is necessary to conduct a multi-time assessment. Education level, monthly household income, interval since last miscarriage, history of pregnancy loss after 14 weeks, and sleep disturbance are closely related to anxiety and depression, and anxiety is also a predictor of depression. Our study explored the correlation between anxiety and depression and established a model to predict depression in women with RPL, to provide a reference for the prevention and intervention of abnormal psychology in women with RPL. This study highlights the need for early identification and treatment of anxiety and depression symptoms in women with RPL. Health care professionals should make greater efforts to strengthen the social support of these women to reduce their symptoms of anxiety and depression.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of China Medical University . The patients/participants provided their written informed consent to participate in this study.YH and T-tW, and CQ conceived, designed, and initiated the study. T-tW and Y-lL contributed to the planning and implementation of the study. T-tW analyzed survey data and interpreted the results. T-tW and Y-lL drafted the manuscript. CQ and J-pL revised the manuscript. All authors contributed to the article and approved the submitted version.The current study was supported by grants from the National Key R&D Program of China (2016YFC1000404), the National Natural Science Foundation of China , the Outstanding Scientific Fund of Shengjing Hospital (201706), Science and Technology Project of Shenyang (grant number: 20-205-4-004); Leading Talents of Talent Project (grant number: XLYC2005008), Livelihood Science and Technology Joint Project of Liaoning Province (grant number: 2021JH2/10300123). These funding bodies played a role in protocol development and did not play any role in data collection, analysis, interpretation of data, or writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.