The association of thyroid dysfunction and thyroiditis with the risk of thyroid cancer is an important issue in clinical practice, and is controversial due to various confounders, including screening effects. In this large-sample, population-based, nationwide study, we demonstrated that the screening effect likely significantly contributed to the positive associations of thyroid dysfunction and thyroiditis with thyroid cancer. After adjustment for this confounder, thyroid cancer risk was positively associated with hypothyroidism and thyroiditis but negatively associated with hyperthyroidism and Graves’ disease. Multiple subgroup analyses showed consistent results. Given these overestimated associations, we suggest that unnecessary and excessive concerns or screening tests for thyroid cancer could be reduced in patients with thyroid dysfunction or thyroiditis.n = 28,330) and the entire data from 2002 to 2019  of the Korean National Health Insurance Service database, and performed logistic regression and subgroup analyses with various covariates. In Study I, hypothyroidism, thyroiditis, autoimmune thyroiditis, hyperthyroidism, and Graves’ disease showed positive associations with thyroid cancer. In Study II, after adjustment for covariates including the number of thyroid function tests, the ORs for thyroid cancer were significantly reduced in all thyroid diseases. Hypothyroidism, thyroiditis, and autoimmune thyroiditis were positively associated  1.28 (1.25–1.32), 1.36 (1.31–1.42), and 1.17 (1.11–1.24), respectively), whereas hyperthyroidism and Graves’ disease were negatively associated with thyroid cancer (adjusted OR (95% CI) 0.80 (0.77–0.83) and 0.69 (0.65–0.74), respectively). Multiple subgroup analyses in both studies showed consistent results. In this large population-based, nationwide study, we confirmed that thyroid disease workup leads to overestimation of associations of thyroid dysfunction and thyroiditis with thyroid cancer risk.We aimed to assess the relationships of functional thyroid disease and thyroiditis with subsequent thyroid cancer, which is controversial due to various confounders, and the effect of thyroid disease workup on this association. We used the cohort data from 2002 to 2015 (Study I,  The associations between functional thyroid diseases and thyroid cancer are controversial. While some studies have reported that hypothyroidism increases the risk of thyroid cancer ,2,3, othThyroid dysfunction and thyroiditis may be involved in the carcinogenesis of thyroid cancer. Thyroid hormone and thyroid-stimulating hormone (TSH) can enhance thyroid cancer cell proliferation ,13 and aRecently, a meta-analysis of 15 observational studies reported that hyperthyroidism was associated with an increased risk of thyroid cancer, and hypothyroidism was associated with an increased risk of thyroid cancer within the first 10 years after hypothyroidism diagnosis . HoweverIn the current study, we hypothesized that functional thyroid disease and thyroiditis might be associated with an increased risk of thyroid cancer, and the screening effect could significantly contribute to the associations. Therefore, we investigated the associations of benign thyroid diseases with thyroid cancer using nationwide cohort data (Study I), and in order to determine whether these were real causal relationships or relationships due to increased detection, we evaluated the screening effect using nationwide data covering the entire population of Korea (Study II).The ethics committees of Hallym University (IRB number: 2019-10-023) and CHA Bundang Medical Center (IRB number: 2020-01-039) permitted this study. Written informed consent was waived by the Institutional Review Board. All analyses followed the guidelines and regulations of the ethics committee of Hallym University and CHA Bundang Medical Center.This study was divided into Study I, which used Korean National Health Insurance Service (NHIS)-Health Screening Cohort data, consisting of a 10% random sample of all health screening participants ,27, and n = 5769). The control group was selected from all participants who were not thyroid cancer patients . To include only patients who were newly diagnosed with thyroid cancer, we excluded patients with thyroid cancer who were diagnosed in 2002 (n = 102). Among the thyroid cancer patients, a patient without total cholesterol data was excluded (n = 1). Among the control participants, we excluded those who died before 2003 or who were missing records after 2003 (n = 34) and those who had an International Classification of Diseases Revision 10 (ICD-10) code of C73 without thyroidectomy (n = 2054). Thyroid cancer patients were 1:4 matched with control participants for age, sex, income, and region of residence. To diminish selection bias, the control participants were chosen randomly with a random number method. The index date of each thyroid cancer patient was defined as the time of diagnosis of thyroid cancer. The index date of each control participant was determined as the index date of their matched thyroid cancer patient. During the matching process, 484,345 control participants were excluded. Conclusively, 5666 thyroid cancer patients were 1:4 matched with 22,664 control participants . Thyroid cancer patients were excluded if they did not undergo thyroidectomy . Control participants were excluded if they were diagnosed with ICD-10 code C73 (n = 5249). We excluded the participants who did not have health check information . To include only thyroid cancer patients who were newly diagnosed, we excluded participants with thyroid cancer who were diagnosed in 2002 and 2003 (n = 5345). Eleven thyroid cancer patients were eliminated due to an error in the death date. Thyroid cancer patients were rematched with control participants in a 1:1 ratio according to age, sex, and region of residence due to the exclusion of participants. As in Study I, the index date for the thyroid cancer patients was defined as the date of thyroid cancer diagnosis, and the index date for the control participants was the index date of their matched thyroid cancer patients. During the matching process, 144,749 control participants and 3 thyroid cancer patients were excluded. Finally, 213,282 thyroid cancer patients were 1:1 matched with 213,282 control participants Hypothyroidism was defined as the presence of ICD-10 codes E02  and E03 (other hypothyroidism). Among such patients, we selected patients with ≥2 records of these codes.(2)Hyperthyroidism was defined as the presence of ICD-10 code E05 (hyperthyroidism (thyrotoxicosis)). Among such patients, we selected patients with ≥2 records of this code.(3)Thyroiditis was defined as the presence of ICD-10 code E06 (thyroiditis). Among such patients, we selected patients with ≥2 records of this code.(4)Autoimmune thyroiditis was defined as the presence of ICD-10 code E063 (autoimmune thyroiditis). Among such patients, we selected patients with ≥2 records of this code.(5)Graves’ disease was defined as the presence of ICD-10 code E050 (thyrotoxicosis with diffuse goiter) and treatment with antithyroid medication ≥ 3 months in Study I, and the condition of treatment was not applied in Study II.Thyroid cancer was confirmed if the patient was diagnosed with thyroid cancer (ICD-10 code C73). Among such patients, we selected only patients treated with thyroidectomy , following our previous studies ,30.2), were categorized in the same ways as in our previous study [Age groups were divided into 5-year intervals: 40–44, 45–49, …, and 85+ years old  in Study I, and 21–25, 26–30, …, and 76+ years old  in Study II. The region of residence was classified as urban or rural following our previous study . Income us study . Systolius study . Among tGeneral characteristics were compared between the thyroid cancer and control groups using the chi-square test for categorical variables and the Wilcoxon rank-sum test for continuous variables, as the distribution of continuous variables was non-normal.To estimate the odds ratios (ORs) with 95% confidence intervals (CIs) considering the sum of dates of each thyroid disease for thyroid cancer, logistic regression was used. In this analysis in Study I, the conditional logistic regression models were stratified by age, sex, region of residence, and income. A crude model, model 1 , model 2 , and model 3  were constructed. In Study II, instead of stratifying age, sex, region of residence, and income, we included these variables as covariates in the adjusted models because unconditional logistic regression was performed, and we added the number of thyroid function tests as a covariate. As autoimmune thyroiditis (E063) was included under thyroiditis (E06) and Graves’ disease (E050) was included under hyperthyroidism (E05), we constructed model 3 after calculating the ORs for hypothyroidism, hyperthyroidism, and thyroiditis in model 2.For the subgroup analyses, we divided participants by age, sex, income, region, thyroid disease, obesity, smoking, alcohol consumption, CCI score, total cholesterol, blood pressure, and fasting blood glucose, and performed logistic regression.p-value less than 0.05. SAS version 9.4  was used for the statistical analyses.Two-tailed analyses were performed, and significance was defined as a p < 0.001; p < 0.001).The prevalence rates of all thyroid diseases, such as hypothyroidism, hyperthyroidism, thyroiditis, autoimmune thyroiditis, and Graves’ disease, were significantly higher in the thyroid cancer patients than in the controls .Most of the characteristics of the study subjects of Study II were similar to those of the subjects in Study I . NeverthPatients who were diagnosed with any thyroid disease within one year before the index date accounted for 9.3%  and 0.9%  of the thyroid cancer group and the matched control group respectively, and thyroid disease was approximately 11 times more prevalent in the thyroid cancer group than in the matched control group. Moreover, in all the thyroid disease groups, the duration from thyroid disease diagnosis to the index date was shorter in the thyroid cancer group than in the control group . These findings suggested that detection bias could be a major potential confounder of the relationships between thyroid diseases and thyroid cancer.Therefore, we attempted to overcome detection bias by adding the number of thyroid function tests as a covariate in the analysis models in Study II . As in SThe subgroup analyses also confirmed that the thyroid cancer patients had higher rates of hypothyroidism, thyroiditis, and autoimmune thyroiditis and lower rates of hyperthyroidism and Graves’ disease . Even afIn this large-sample, population-based, nationwide study, we found that thyroid cancer patients were two to three times more likely to have had previous thyroid diseases, such as hypothyroidism, hyperthyroidism, thyroiditis, autoimmune thyroiditis, or Graves’ disease, than the controls. However, when we added the number of thyroid function tests as a covariate to the analysis models to adjust for the effect of thyroid disease evaluation, the risk for thyroid cancer was significantly reduced in all thyroid diseases. Hypothyroidism, thyroiditis, and autoimmune thyroiditis remained positively associated, whereas hyperthyroidism and Graves’ disease were negatively associated with thyroid cancer. The subgroup analyses according to various covariates showed consistent results, and male thyroid cancer patients had markedly greater odds of previous hypothyroidism and thyroiditis than controls.Unlike thyroid goiters and benign nodules, which have been demonstrated to have a relatively strong association with thyroid cancer, thyroid dysfunction has a weak association with thyroid cancer ,5,6,7,8,The causal relationship between inflammation and cancer is widely accepted, and many cancers arise from sites of infection or chronic inflammation ,35. ThyrOur study has several strengths. First, we analyzed large-sample, representative, nationwide population data. Not only cohort data but also entire NHIS data were used for validation. Second, we adjusted for the effect of thyroid disease workup, which is a major potential confounder, and confirmed its significant effect. In addition, to control for various other confounding factors, the participants in the control group were randomly selected and matched, and a number of subgroup analyses were performed. Finally, we distinguished thyroid dysfunction and thyroiditis to analyze the association of each disease with thyroid cancer, as these have not been differentiated in most other studies.There are also several limitations. First, since thyroid ultrasonography for screening purposes is not covered by insurance and is not included in the NHIS dataset, we could not adjust for the frequency of thyroid ultrasonography tests. Instead, we used the number of thyroid function tests, which is the most representative workup in patients with thyroid dysfunction and thyroiditis. Second, there was no information on the treatment and severity of thyroid disease. In particular, patients with diagnoses of both hyperthyroidism and hypothyroidism may have been treated for hyperthyroidism in the past or may have developed hypothyroidism after the thyrotoxic phase of thyroiditis. There were patients with coexisting thyroid diseases such as thyroid dysfunction and thyroiditis. Therefore, we analyzed thyroid diseases by adjusting for other thyroid diseases in a logistic regression model and controlled for these effects in the subgroup analyses.The screening effect likely significantly contributed to the positive associations of thyroid dysfunction and thyroiditis with thyroid cancer. After adjustment for this confounder, thyroid cancer risk was positively associated, but weaker, with hypothyroidism and thyroiditis, and negatively associated with hyperthyroidism and Graves’ disease. Our results suggest that over-screening for thyroid cancer may be reduced in patients with thyroid dysfunction and thyroiditis, particularly in hyperthyroidism and Graves’ disease.


Avian Metapneumovirus (aMPV) is a causative agent of respiratory disease complex in turkeys and chickens that has recently been detected in Vietnam. Due to its novelty, this study was conducted to elucidate the distribution of aMPV in several provinces in northern Vietnam. By the application of Enzyme-Linked Immunosorbent Assay (ELISA) and nested Reverse Transcription-Polymerase Chain Reaction (RT-PCR), this study demonstrated the circulation of aMPV in 12 out of 14 cities/provinces with positive rates of 37.6% and 17.2%, respectively. All nested RT-PCR positive samples were aMPV subgroup B. By pairing the detection results with age groups, it was observed that aMPV infections occurred in chickens of all ages. Additionally, by genetic characterization, aMPV strains were demonstrated to not be attenuated vaccine viruses and to belong to at least two genetic clades. Overall, the obtained results provided insights into the prevalence of aMPV and indicated a greater complexity of respiratory diseases in chickens in Vietnam. During the transformation from small-scale livestock production to high-density industrial farming, common respiratory diseases among poultry have been occurring regularly. Poultry may become infected with several types of respiratory illnesses caused by bacteria, viruses, and mycoplasma ,2. In teMetapneumovirus that can infect both domesticated  [aMPV is a member of the genus d ducks) ,5 and wid ducks) ,7, amongd ducks) ,9, causid ducks) . The disd ducks) ,12. In ad ducks) ,14 and cd ducks) .Mycoplasma gallisepticum), ORT , and IBV (Infectious bronchitis virus) [Regarding the spatial distribution, since first reported in the 1980s, aMPV has been present in Africa , the US s virus) ,22,23, as virus) . Due to For this study, commercial layer farms and indigenous slow-growing broilers farms in which the aMPV vaccine had not been employed were selected. Furthermore, to enhance the likelihood of detecting aMPV, farms were narrowed down to those with respiratory problems. Due to the relatively short viral shedding time ,26, respChoanal cleft swab was taken from each bird, then put into Eppendorf tubes containing 0.5 mL of phosphate-buffered saline. At necropsy, organs of the respiratory system including nasal turbinate, larynx, trachea, lungs, and air sacs were collected from each bird. The pooled organs were minced, manually homogenized to a 10% suspension in phosphate-buffered saline by grinding with a mortar and pestle. The suspension was centrifuged at 4000 rpm for 5 min to collect the supernatant. For blood collection, a 3-mL syringe was used to take 1 mL of blood from the brachial wing vein, placed on a slanted surface for 10 min to allow for clotting. After centrifugation at 4000 rpm for 5 min, serum samples were obtained. All samples  were stored at −30 °C.A total of 138 farms were sampled from 14 northern provinces and cities. From these, 256 tissues/swabs and 327 serum samples were tested for aMPV nucleic acid and for antibodies against aMPV, respectively. In some farms, only one type of sample (either tissue/swab or serum) was available; thus, out of the 138 farms, only 34 had samples tested by both nested RT-PCR and ELISA methods, while 15 farms were tested solely by ELISA and 89 farms solely by nested RT-PCR assay.Specific antibodies against aMPV were detected by indirect ELISA with the IDVET_MPVS-5P kit  following the manufacturer’s instructions. The ELISA plates were pre-coated with purified aMPV antigen and were suitable for screening antibodies against aMPV subgroups A and B. The final serum dilution was 1/500 and samples were seropositive if S/P > 0.2.The total RNA and DNA from samples were extracted with the Patho Gene-spin DNA/RNA Extraction kit , following the manufacturer’s instructions. Reverse transcription was performed using the HiSenScript RH(-) RT PreMix kit  at 45 °C for 60 min. The nucleic acid of aMPV was detected by nested RT-PCR following a previous publication’s methodology , using MThe samples with the strongest aMPV PCR band density were selected for genetic characterization. The same PCR premix was used to amplify G gene in the format of nested PCR by combining the primer pairs shown in Analysis of genetic variation along the G gene was performed using SimPlot software  with theThis study assessed the presence of aMPV in chickens by both serological and virological tests. Of the serology results, 123/327 (37.6%) sera were positive for anti-aMPV antibodies A. At theAside from the serology test, clinical specimens were examined for the presence of aMPV. To improve the sensitivity, the nested RT-PCR with primers specific to the subgroups A and B  was applIn the literature, many factors are known to affect the prevalence of aMPV such as season, breed, and age ,33,34. BFor the serology positive rates, it was observed that almost all examined age groups were positive. Additionally, it seemed that the older group (≥9 weeks old) tended to have more positive samples (>50%) than the younger group . For the virology positive rates, For the genetic characterization, 14 aMPV positive samples (showing strongest PCR band density) were selected to sequence the gene encoding attachment glycoprotein (G gene). Since attenuated vaccines against aMPV have been registered in Vietnam over the past 15 years and are still available in the market, the genetic characterization of aMPV strains in this study was first inferred based on the comparison with two attenuated vaccine virus strains, namely Nemovac  and Hipraviar (Hipra). The results shown in The genetic comparison with a vaccine strain was further evaluated by nucleotide similarity along the G gene .It was revealed that the G gene contained a region (nucleotide 1–460) that has high similarity (>96%) between strains  of the G gene. To classify Vietnamese aMPVs to the previous known field-, vaccine-, and vaccine-derived aMPV strains , the phySince the most variable genomic region of the G gene was from the 460th nucleotide, a phylogenetic tree based on the nearly complete G gene was reconstructed for a more precise classification. For this analysis, five Vietnamese aMPVs were excluded due to the partial G gene. As shown in To date, few studies have focused on aMPV infection in Vietnam . Thus, t2.7 TCID50/mL) [In the literature, the reported prevalence of aMPV was low (<30%) in many countries; for example, it was 1.04% in Ethiopia , 2.8% inID50/mL) . AdditioID50/mL) ; meanwhiID50/mL) ,25,26.Of the subgroup distribution, the aMPV subgroups A and B are generally the most common and have been detected in Asia, Europe, and South America, but not in North America; subgroup C was first discovered in North America and then France  and KoreSampling chickens of various age groups and performing serological and virological examinations enabled the finding that aMPV infections occurred in chickens of all ages in northern Vietnam. That finding was in line with several published studies that reported the detection of aMPV in chickens from different age groups, such as: 3–8 weeks ,53, 6–16The G gene was selected because it is one of the most variable genes of aMPV; it has been often selected for aMPV phylogenetic classification  and becaRegarding the genetic indications of aMPV pathogenicity, the comparison between virulent aMPV strain (VCO3/60616) and attenuated virus strain (VCO3/50) identified 18 distinct nucleotides distributed in 7 of 8 structural genes and a non-protein-coding region . Of thesTo date, several reports ,56 have Taken together, the present study’s results confirmed the wide distribution of aMPV subgroup B in the chicken population in northern Vietnam. The virus differed from attenuated vaccine viruses and belongs to at least two genetic clades.


Diathermy techniques embody an oscillating electrical current passaging through the body tissues generating therapeutic heat; use of this technique in the physiotherapy field has been introduced recently, and because there is scarce information, the following review is proposed, aiming to explore the available evidence on applying CRET in physiotherapy clinical practice and sports. A systematic search was led through a keyword search on PubMed, MedLine, DialNet, Scopus, PEDro, Web of Science and Clinicaltrials databases. Including randomised controlled trials and quasi-experimental studies, which applied radiofrequency diathermy in sports and physiotherapy fields, without any restrictions on dates, published in Spanish, English, Portuguese or Italian. Data extraction was conducted through the Cochrane data extraction form and presented in tabular format; 30 articles were included for analysis, and assessment of methodological quality was made through the PEDro scale with a “Good/Fair” general quality score. The nature of existing articles does not allow a quantitative analysis. Conclusion: identified fields of applications were musculoskeletal physiotherapy, treatment of pelvic floor and sexual dysfunctions, as well as dermato-functional physiotherapy and sports, evidencing an increase of skin temperature, enhanced skin and muscle blood perfusion, as well as reporting an increase in oxyhaemoglobin. Further research is needed. Prospero registration number: CRD42020215592. Electrophysical agents, specifically within the radiofrequency spectrum, include capacitive-resistive electric transfer (CRET), which consists of oscillating energy at specific frequencies generating therapeutic heat in body tissues, a technique known as diathermy .In a preliminary search for evidence, we found that this type of therapy has various effects at the cellular level ,3,4,5. CGiven that these physiological effects could benefit the field of sports, the use of CRET should be extended to the sports population. Due to the technique’s novelty and its potential use in sports, a highly sensitive search that includes sports populations is justified, not limiting the search to a population with pathological characteristics or injuries. A literature review that includes international recommendations while not excluding factors significantly relevant to the use and proper management of diathermy technology is, therefore, essential. These factors have not been examined in previous reviews  and coul(A)Areas of application within physiotherapy clinical practice;(B)Areas of application within the sports field;(C)Demonstrated effects of radiofrequency diathermy;(D)Study quality;(E)Identification of gaps in knowledge for future research.To explore the available evidence on applying CRET in physiotherapy clinical practice and sports in the following aspects:This review followed the methodology recommended by Arksey and O’Mally  and LevaClinicaltrials.gov (accessed on 13 October 2021). The snowball method was included, given that the search string was designed to return the most exhaustive results possible. Authors were contacted via email for those articles that were considered incomplete and for studies registered in Clinicaltrials.gov (accessed on 13 October 2021) that had no result records.A systematic search was conducted up to October 2020 in the following databases: PubMed, DialNet, Scopus, PEDro, Web of Science and After a preliminary search, a second search was conducted with a strategy that included controlled and natural language, revised by a health science librarian, and undertaken in all the databases mentioned above. An example of our search strategy can be found in clinicaltrials.gov (accessed on 13 October 2021) were included only when the results field was completed. Publications were excluded if the study was performed on animals, corpses, or in vitro.We included randomised controlled trials and quasi-experimental studies in which radiofrequency diathermy was applied, which were published in English, Spanish, Italian or Portuguese, with no date restriction. Studies registered in www.cadima.info (accessed on 13 October 2021)) following the PRISMA flow diagram, with 2 reviewers evaluating the relevance of the studies on the same platform in an independent, standardised and unmasked manner . CADIMA also enabled a study of interobserver consistency.The eligibility evaluation was performed by employing CADIMA online software vers. 2.2.3 Julius Kühn-Institut, Erwin-Baur-Str. 27, 06484 Quedlinburg, Germany (An extraction sheet was adapted from the Cochrane “Data collection form for intervention reviews for RCTs and non-RCTs-template”. One of the researchers extracted the data from the included studies (LDS), while a second researcher reviewed them (CTS); discrepancies were resolved by a third researcher (PGF). To facilitate the presentation of the data in a systematic and synthesised manner, we presented (in a tabular format) the data relevant to our study objectives and simplified its analysis through a textual narrative synthesis.Clinicaltrials.gov (accessed on 13 October 2021) and reduce result bias by registering the protocol before conducting the review.Instead of using the Cochrane tool for data extraction, we employed the PEDro scale for the quality assessment because it provides a higher probability of reliability in the indexed scores and is a sufficiently reliable tool for use in literature reviews in physiotherapy . Due to When the search strategy was being carried out, due to the novelty of this technique, it was detected that there could be a nomenclature problem, so it was decided to include a mixture of controlled and natural language allowing a more effective search, resulting in a detailed search string to each database; when analyzing results, we were able to confirm that this nomenclature issue indeed existed. A process was opened with the U.S. National Library of Medicine suggesting indexation of a unified term inside the tree-Number “E02” labelled as “Therapeutics”, more specifically inside “Diathermy” major subject, along with terms such as “Short-Wave Therapy” or “Ultrasonic Therapy”.A total of 30 articles were included, 21 studies assessed CRET usage in the physiotherapeutic clinical practice, 7 covered the demonstrated effects of radiofrequency diathermy in concordance with secondary objectives of the study, leaving the remaining two studies to evaluate the sportive application of CRET. The 30 included articles are summarised in Except for one study performed last century, the studies were current, with more than 50% having been published in the past 2 years, denoting significant topicality. A total of 83% were published in Europe, 13% in Asia and 3% in South America. Only 22 studies were evaluated with the PEDro scale, achieving a mean score of 6.13 ± 1.75 points and were, therefore, classified as having “good” validity. Quasi-experimental reports were used to complement the identification of common usage and existing gaps.3: kilograms per cubic centimeter, Km/h: kilometer per hour, LD: lymphatic drainage, MD: mean difference, M: men, mm: milimeters, mmHg: millimeter of mercury, mmol/L: milimoles per liter, PFE: Pelvic floor exercises, PSWT: Pulsed Shortwave Therapy, pts: points, P: Score confirmed by PEDro database, Q1:first quartile, Q3: third quartile, s: seconds, ST: standard treatment, T: timepoint, TENS: transcutaneous electrical nerve stimulation, umol/L: micromoles per liter, W: women.ASIS: anterior superior iliac spine; cm: centimeters, °C: degrees centigrade, CRET: capacitive-resistive electric transfer, dm: decimeters, EC: experimental condition, Hz: Hertz, Kg/cmA total of 1057 participants were included in this review, covering healthy individuals (12.9%) and those with disease (87.1%). Some 73.3% of the experimental conditions included only CRET; the rest were applied in combination with another type of technique. The most common comparison group was placebo (46.6% of the included studies).The most often applied frequency was 448 kHz, and the mean frequency was 561.83 ± 214 kHz . The mean application time was 22.7 ± 16.9 min, with a mean usage time of 14.1 ± 22.2 min for the capacitive electrode and 11.2 ± 8.9 min for the resistive electrode.Grouped by score; 4.5% of the studies presented a total score of 9 out of 10 in the PEDro scale, 18.2% achieved a score of 8, 31.8% a total of 7 points while 9.1% a score of 6, 13.6% scored 5 and 4 points, respectively, and the remaining 9.2% obtained the lowest score in this review with a total of 3 points.It is observed that no study scored positively for the item related to internal validity, more specifically about assessing whether they were able to blind the therapist who applies the experimental technique, it is not entirely surprising since great difficulty in blinding the therapist in behavioural and non-pharmacological interventions has been shown  demonstrTwo items were missing in 11 of the studies, one referring once again to internal validity and blindness in this case of the participants, mainly affecting subjective variables that participants must fill in and, to a lesser extent, those hard variables , and anoThe criteria where more studies coincide was reporting baseline comparison between the groups, thus that the results can be comparable, increasing the reliability in them, as well as describing results of all subjects who received treatment or who were assigned to the control group, reducing the risk of reporting bias.The external validity item is presented for guidance purposes, and it is for this reason that this criterion is not indexed in the total score.This review collected and synthesised data from the scientific literature on the use of CRET, the existing evidence of its sportive usage is limited, and the main body of the results covered a wide variety of physiotherapeutic clinical fields, such as application in musculoskeletal pathology, pelvic floor dysfunction, dermato-functional application and gastrointestinal pathology. To a lesser extent, its physiological effects on healthy subjects and standard nomenclature. The growing interest in this technique is evident in the notable increase in such studies in recent years. However, there is an identified need to methodically and critically analyse these studies’ content and quality in a broader manner than the existing review .Numerous studies have shown CRET-induced changes in skin temperature, which is more effective in the resistive case than the capacitive and more prolonged, being up to 5.3% higher in a study that compared both cases during a follow-up of 45 min  and anotIn addition to these effects, increased blood perfusion has been reported both at the skin and muscle levels. At the skin level, a study showed a 6-fold larger effect size between pre-treatment and post-treatment than placebo (−24.8% [interquartile range (IQR) 16.8] vs. −3.97% [IQR 22.01]). At the muscle level, this difference was greater than with placebo (2.06% [IQR 3.3] vs. 0.01% [IQR 0.7]) [Although the physiological effects of CRET represent a theoretical improvement in sports performance, its use has only been evidenced in a clinical trial on runners, in which tests were performed at various distances. The trial identified no physiological changes between the groups. After CRET treatment, however, the length, angle, height, and frequency of the stride increased compared with the control group . This stA quasi-experimental pre/post study suggested that this technology presents excellent clinical utility by reducing the number of sessions and treatment duration when CRET is applied to varying degrees of muscle conditions in athletes . Higher-Potential improvements in knee osteoarthritis were assessed in two studies with the Osteoarthritis Index from the Universities of Western Ontario and McMaster. The first study showed no significant intergroup improvements but managed to exceed the minimum clinically detectable difference up to 3 months later . The secThe application of CRET for neck pain is inconclusive. Two trials compared the effects of CRET on chronic  and myofNotarnicola et al.  reportedWithin musculoskeletal applications, a quasi-experimental study recently analysed the possible benefits of CRET in rotator cuff tendinopathy, indicating positive effects on resolving pain and oedema , providiOne study assessed the possibility that CRET could have beneficial effects for women after childbirth. The study found no significant differences in pain or medication intake. However, there was a reduction in discomfort during sitting, presenting a prevalence within the treatment group of 12% ± 41.1% compared with the placebo group (20% ± 64.5%) . LikewisSodré et al.  studied Noites et al.  assessedFor oedematous-fibrosclerotic panniculopathy, a randomised clinical trial revealed changes in weight, perimeters and cellulite classification only when comparing the results within the treatment group , with noIn the case of lymphedema, a clinical trial sought to demonstrate the benefits of CRET compared with pressotherapy and lymphatic drainage but found no significant differences between the study groups . More stA quasi-experimental pre/post study in a population with an intellectual disability indicated the possible use of this technique assisted with vacuum therapy to treat chronic constipation, reporting an increase in the number of stools and a reduction in irritability episodes in an evaluation period of 14 days. In improving the stool quality assessed with the Bristol scale , the phyThe Studies included in this review assess the effectiveness of CRET using a variety of experimental methods, each of them with a series of strengths and limitations. Precipitating conclusions on the efficacy of this technology for a certain clinical issue, delivered in a specific manner, to a defined population and in a particular environment, requires synthesis of a wider spectrum of sources to reach a trustworthy estimation. This process is proposed after the development of the research fields of investigation covered in this study, allowing consequent systematic reviews that address effectiveness as the main research subject. Despite the aforementioned reason overall effectiveness of CRET, is suggested from different categories, evidenced mainly in fields of temperature, pain management and biomechanical changes mostly compared over placebo or other techniques such as TENS, Laser or pulsed shortwave therapy, each field of application encouraging further comprehensive research regarding effectiveness.A significant discrepancy in terminology was observed between the articles when referring to this type of therapy. Some 16.6% of the articles referred to these interventions as “capacitive-resistive electrical transfer”. The following most repeated terms were “capacitive monopolar radiofrequency-resistive”, “tecartherapy” and “radiofrequency”, each appearing in 13% of the articles. The remaining 45.4% used a combination of terms. There is an urgent need to unify the terms to facilitate their indexing in the health descriptor thesauri, allowing for an adequate level of precision in searches, eliminating synonyms and unifying efforts.The inclusion of the snowball method might have incurred a citation bias. The nature of the articles on this topic does not allow for quantitative analysis of the compendium with its consequent partial loss of objectivity. Lastly, as mentioned before, critical scrutiny of the methodological quality was not completed, given that the objective was to collect as much information as possible on the use of CRET.The use of CRET within the field of physiotherapy was evidenced in numerous areas, including musculoskeletal pathology, dermato-functional or pelvic floor treatments, sports injury and the production of positive biomechanical changes in athletes. The proven effects of CRET include increased skin temperature, skin perfusion, muscle perfusion and increased oxyhaemoglobin levels. Evolved studies suggest clinical effectiveness in aspects such as pain management in muscle-skeletal pathology, enhancement of functional movement in knee osteoarthritis and decrease in low back pain physical disability, as well as insinuating improvement of pelvic floor pain management and Peyronie’s disease severity, finally in the dermato-functional field advising a decrease in the abdominal fold when combined with exercise. Numerous lines of future research have been identified, which, in addition to those mentioned above, include the use of CRET in gastric or visceral disease. The overall methodological quality of CRET studies can be considered good to fair, but further research is warranted.


Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression.The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression.Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days . We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression.r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status–normalized entropy and depression . The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression.Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants’ age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status–normalized entropy and depression (Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring. Depression is one of the most prevalent, complex, and heterogeneous mental health challenges of our time. In 2020, the World Health Organization (WHO) estimated that depression has impacted 264 million people worldwide , and it For the past 30 years, clinician-administered and self-reported questionnaires remain the gold standard in the assessment and diagnosis of depression . HoweverToday, smartphones and wearables offer a unique opportunity to overcome limitations in traditional depression assessment methods. Smartphones and wearables  have become ubiquitous in the global population, they are inherently personal, and people are continuously monitored through their embedded sensors  ,19. InstA growing body of research in smartphone and wearable sensing, human behavior modeling has improved our understanding of the relationship between mental health and biomarkers ,27,29-31r=–0.360), activity duration (r=–0.388), traveled distance (r=0.338), and various mental health symptoms. Similarly, Wang et al  and regularity in 24-hour movement rhythm [r=0.63]) and phone usage–based biomarkers .In the StudentLife study , for exaance r=0.8, and vaAnother promising source of biomarkers is wearable devices .Actigraphy-based biomarkers that quantify time sequences of rest and active behaviors with accelerometer sensors are known to be useful in predicting mood disorders such as depression and bipolar disorder ,35. In aTaken together, previous research has shown the potential of quantifying human behavior from smartphones and wearables data set as biomarkers. These biomarkers are insightful in understanding depression.In this study, we aim to investigate the feasibility of predicting depression using digital biomarkers quantified from a smartphone data set. To this end, we explore the relationship between digital biomarkers and depression severity with statistical methods. We investigate whether depression can be predicted with digital biomarkers using supervised ML algorithms.We utilized an existing data set collected in a longitudinal observational study with the Carat app , derivedThe Carat app is a mobile sensing app, originally developed by a team of researchers from the University of Helsinki and the University of California, Berkeley, for smartphone energy consumption research ,39. The The data set used in this study was a subset of the large-scale crowdsourced Carat app data set from anonymous volunteers. The study data set was collected for a multifaceted purpose, which includes studying the relationship between smartphone app usage and Big 5 personality traits ; studyinAll participants in this data set are Android-based smartphone users, who explicitly and voluntarily gave their consent from their mobile devices after they were informed about the purpose of the data collection, the data collection procedures, and management of the data set. The data set does not contain personally identifiable information, and was collected under the institutional review board license from the University of California, Berkeley and the University of Helsinki ,40.Besides battery consumption data, the Carat Android app unobtrusively collected participants’ time zone and time-stamped data, including foreground app usage , internet connectivity , and screen lock and unlock logs. This data set was sampled at each 1% battery change  on the participants’ smartphone. The Carat app also collected participant’s demographic information, including age, gender, education, and occupation via a self-report.In addition to the mobile sensing and demographic variables, depression severity was assessed by a self-report instrument. Participants answered the 8-item Patient Health Questionnaire (PHQ-8)  at 2-weeFor each participant’s data set, we excluded days with at least 10 missing log intervals . Next, we only included PHQ-8 responses from participants with at least 8 days of data within the preceding 2 weeks of PHQ-8 response. Consequently, the final data contained 629 participants, 1374 PHQ-8 responses with 13,898 days of participants’ data set.Our data set is primarily categorized into screen status, internet connectivity, and foreground app usage logs. For data preprocessing, we converted the time stamps of the data set to local date and time, using the participants’ time zone. We computed digital biomarkers (herein features) by quantifying the per-participant hourly and daily behavioral patterns  from these data sets with simple counts, SDs, entropy ,25,42-45We computed entropy to capture the degree of variability, complexity, disorder, and randomness in the participant behavior states from screen status , internet connectivity , and foreground app  over a 24-hour period of each day. Entropy was calculated using the Shannon entropy  formula:N is the number of states and ip is the percentage of the state i in the time series data. For example, a higher screen status entropy reflects the fact that the participant’s screen on and off pattern is more distributed between on and off states, albeit with a high degree of uncertainty and complexity in the transition between the screen on and off states in a 24-hour period. Conversely, a lower screen status entropy reflects that fact that the participant’s screen is much often in one state (on or off) over a 24-hour period. In addition to entropy, we computed normalized entropy as the entropy divided by log(N).where Regularity index quantifies routines in participant behaviors by capturing the similarity (or difference) in participant behaviors between the same hours across different days. For internet connectivity, for instance, the regularity index quantifies the routineness of the participant’s internet connectivity behavior at the same hours  for all days. We determined the hourly values as follows: for screen status, the modal screen status for each hour; for internet connectivity, the modal connectivity state for each hour; and for foreground app usage, the number of distinct apps usage for each hour.a and b using the formulaFollowing the regularity index computation method of Wang et al , we compa and b are 2-day pairs, T=24 hours, and  is the rescaled  value of hour t of day b. For each day, we computed the average regularity index for all combinations of that day and other days of the week.where The SD features capture the variance of daily behavior between 4-day epochs based on the hour of the day. We defined morning as the 6-11th hour, afternoon as the 12-17th hour, evening as the 18-23rd hour, and night as the 0-5th hour of the day. We computed the count of each screen status, the count of each internet connectivity status, and the count of foreground app usage per day epoch. With these counts per day epoch, we computed the SD per day.We also computed the day level count of each screen status, the count of each internet connectivity status, and the count of foreground app usage. Additionally, we computed the count of minutes until the first and last use of foreground app per day.Before beginning the statistical analysis, we pooled  the extracted features within the preceding 2 weeks  of each PHQ-8 response from a participant. The pooling is to ensure that the timelines of the feature variables in the analysis are aligned with those of the PHQ-8 assessment window. The pooling was done as follows: for each PHQ-8 response, we pooled all entropy and regularity index features by computing the average feature values for all days within the PHQ-8 assessment window. Instead of average values for SD, we took a different approach due to the additive properties of SD measures. For SD features, we computed the pooled SD ,47.P values for multiple testing, with a false discovery rate of 0.05.For correlation analysis, we used the pooled data to quantify the linear relationship between the features and depression severity . The correlations were computed using the Pearson correlation coefficient. Full information maximum likelihood -50 was uFor association analysis, we used the bivariate linear mixed model (LMM) ,44,52-55P values in the LMM to control the false discovery rate with the Benjamini–Hochberg procedure [P value <.05 was considered to be significant.In the LMM, we used multiple imputation to handle missing data, taking into account the nested structure of the data set . Using procedure ,61. AdjuWe developed population-based supervised ML classifiers to explore how the digital biomarkers/features predict the depression state of an individual. We also explored whether including participants’ self-reported demographics as features would improve the ML classifier performance.To this end, we used 5 supervised ML models: random forest (RF), support vector machine (SVM) with radial basis function (RBF) kernel, XGBoost (XGB) , K-neareAll the ML modeling was performed using stratified and nested cross-validation ,72. The The hyperparameter optimization in the inner cross-validation was done with grid search over a grid of parameters, where all combinations of hyperparameters are exhaustively considered. We use the F1 (macro averaged) score to select the most optimized hyperparameters.It is worth noting that in the ML setup, we used stratified sampling in the nested cross-validation. The stratified sampling ensures the splitting of the data set into folds that have an equal proportion of each class . However, the proportion of each class is still dependent on its availability in the data set. We handled class imbalance in the training data set with the synthetic minority over-sampling technique (SMOTE) ,73,74, wWe used the permutation importance method ,76 to coWe created 2 baseline classifiers to benchmark the performance of the ML classifiers. The first baseline is a random weighted classifier (RWC) with 10,000 randomly generated predictions based on a multinomial distribution of the nondepressed and depressed classes. The second baseline is a decision tree (DT) classifier trained using the same approach as the ML classifiers, but with age group and gender as features. The performance of the ML classifiers and baseline classifiers was measured using the following performance metrics: accuracy, precision, recall, AUC, F1 score, and Cohen κ. The precision, recall, and F1 scores were computed with an emphasis on predicting the depressed score .Data preprocessing and feature extraction pipeline were created with Python (version 3.7.6) and R (version 4.0.2) programming languages, using Snakemake  and RAPISelf-reported demographic data from the 629 participants included in our analyses show that 69/629 (10.97%) were females, 546/629 (86.8%) were males, and 14/629 (2.2%) were nonbinary or preferred not to disclose their gender.For the participants’ age distribution, 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. The participants were distributed across at least 56 different countries, including 91/629 (14.5%) from unknown countries, 199/629 (31.6%) from the USA, 66/629 (10.5%) from Finland, 32/629 (5.1%) from Great Britain, 42/629 (6.7%) from Germany, and 29/629 (4.6%) from India. The data set also has participants from varied educational and occupational backgrounds. We had 1374 PHQ-8 responses. For the distribution of the PHQ-8 scores, 1143/1374 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231/1374 (16.81%) were depressed scores (PHQ-8 score ≥10). The mean PHQ-8 score is 5.19 .The number of smartphone data set days was 13,898 for all 629 participants. screen_offCount feature is the count of all screen off states during the day. We summarize the engineered features in In all, we computed 22 features from the smartphone data set. All features were aggregated at the day level. For example, the r=0.14, P<.001). We found no significant correlation between other screen, app, and internet connectivity features and PHQ-8 depression score. P values, with the Holm–Bonferroni method, between features and PHQ-8 depression score.We found a significant positive correlation between screen status–normalized entropy and depression (P=.03). We found no significant association between other screen, app, and internet connectivity features and depression. P values calculated using the Benjamini–Hochberg method.Regarding the association analysis, we found an ICC of 0.7584; thus, 75.84% of the variations in the features are explainable by the interindividual differences. We found a significant positive association between screen status–normalized entropy and depression  is listed in As shown in In terms of precision, recall, and F1 scores, which were computed with an emphasis on the predictive performance of the positive label , XGB was the best performing classifier, followed by RF and KNN. XGB, RF, and KNN performed better than the RWC and DT baselines, as shown in Likewise, with AUC and Cohen κ performance metrics, which take into consideration both positive and negative labels , XGB, RF, and KNN classifiers had the best performance, as shown in As shown in When age group and gender were included with features as predictors, we observed a general improvement in all performance metrics for all classifiers, as shown in We present the mean permutation feature importance in predicting PHQ-8 depression score across the 10-fold cross-validation with the top 3 performing ML classifiers  in For the XGB classifier in For the RF classifier in Likewise, for the KNN classifier in By ranking all important features for KNN, XGB, and RF classifiers, the top 5 most were the screen regularity index, screen status entropy, internet regularity index, screen status–normalized entropy, and the screen off count SD. App count SD is the least important feature for all classifiers -3, and cOur objective was to investigate the feasibility of predicting depression using multivariate digital biomarkers quantified from smartphone data sets collected in a real-world study. In this study, we used 13,898 days of smartphone data set, and 1374 PHQ-8 depression assessments from 629 participants to explore the feasibility of detecting depression from participants’ behavioral markers  quantified from their smartphones. We focused on finding the relationship between repeated measures of depression scores and participant’s digital biomarkers and developing predictive models to classify depressed and nondepressed symptom severity scores.This data set was collected from a heterogeneous geographic , occupational, and educational population, with high interindividual differences .Despite this heterogeneity, digital biomarkers extracted from participants’ smartphone data set were able to predict participants’ depression state  with high predictive performance using ML models. The ML models achieved the following: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; AUC, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. These findings show that predictive modeling of mental health using digital biomarkers is not only possible in small homogenous populations ,84, but Moreover, we found that the predictive performances of ML classifiers improved when demographic characteristics were included among predictors, indicating that such variables should also be included in clinical applications. Previous studies suggest a relationship between demographic factors, smartphone usage behavior, and depression ,85,86. Tr=0.14, P<.001). The screen status–normalized entropy biomarker quantified the frequency and distribution  in the transition of the participants’ phone screen on and off states. All other indicators were nonsignificant. Moreover, correlation coefficients only measure the extent of the linear relationship between variables [Information theory [Interestingly, tree-based, nearest neighbor–based classifiers had superior performance over linear classifiers, including SVM with RBF kernel, corroborating the existence of nonlinear relationships between digital biomarkers and depression. This finding further supported the correlation finding, which failed to replicate previous results reported in Saeb et al . We coulariables ,88. Instn theory -89. The P=.03), but all other variables remained nonsignificant. This suggests that conventional statistical methods  may not depict the complex nonlinear relationship between digital biomarkers and depression, and indeed more powerful methods such as ML models  are needed to make better predictions [Given the high ICC, we tested whether LMM, a much robust method for finding linear relationships, can identify additional linear relationships. However, the results from the association analyses further showed that a unit increase in the screen status–normalized entropy positively increases the average depression score  in the field are common issues . Until nNevertheless, both the correlation findings and the feature importance analysis in the prediction models clearly showed that participants’ phone screen (lock and unlock) behaviors, such as routinely and randomly locking and unlocking phone screen, and internet connectivity behaviors played the most important role in predicting their depression state. The findings in this study are also supported by prior research that investigated the relationship between screen interactions and mental health ,27,28,33Given the crowdsourced nature of the deployment of the Carat app, the sample size in the data set is small (N=629) and may not be representative of the general population. Despite the data set having a fair distribution of age groups, with a spread over several countries, it is biased toward highly educated and professional occupations. The data set is also biased toward males in gender distribution. Future research with a larger sample size and a balanced gender distribution could explore correlations, associations, and prediction performance for population subgroups.Clinical diagnosis of depression was not an inclusion criterion for our sample population. The data set also does not contain a clinical or self-reported baseline assessment of depression and has scarce high depression scores. Because of the crowdsourced rolling recruitment nature of participants, the data set contained an unequal number of repeated depression assessments for all participants. Future research should benefit from replicating the experiment in a clinical population and a more controlled experimental design. With a clinical baseline data set, future research could study the differences in features between depressed and nondepressed groups.The correlation and association between behavioral patterns extracted from the data set in this study and depression do not necessarily imply causal relationships. For example, the correlation between screen status–normalized entropy and depression may be caused by other confounding variables. In addition, the correlation and association between screen status–normalized entropy and depression are not strong and may not generalize in other populations. Further research is needed to establish the extent to which such behaviors cause or are a consequence of depression.Lastly, the data set was collected from Android participants only, and the long-term use of the Carat app could influence participants’ behavior ,39. ReseReplicating the findings from this study with additional biomarkers from GPS and wearable sensor data sets and comparing their correlation and biomarker predictive importance will be interesting in future work. There would be a major design implication for depression intervention development if the behavioral markers from screen and internet connectivity achieve similar promising results as biomarkers from GPS and wearable devices. We hypothesize that screen interaction and internet connectivity data sets alone are less privacy intrusive, could better capture behaviors of immobile persons, and people may be more willing to donate such data sets to science. For example, Apple’s Screen Time and Google’s Digital Wellbeing app are processing and presenting such data to users to inform where and how users spent their time on smartphones.In summary, this study sought to find whether we can detect changes in human behavior that would be indicative of depression using smartphones. In addition, we sought to find what objective measures of human behavior from smartphones are insightful in understanding depression. Our results established a positive statistically significant linear correlation and association between depression and screen status–normalized entropy behavior quantified from smartphone data sets. Our findings also establish that behavioral markers extracted from smartphone data sets can predict whether or not a participant is depressed based on the PHQ-8 depression score, and that phone screen and internet connectivity behaviors were the most insightful behaviors that influence depression in participants. The findings in this study are supported by previous research findings and contribute to compelling evidence on the utility of digital biomarkers in augmenting traditional assessment of depression, thus enabling continuous and passive monitoring of the complex vectors of depression.


Participants reported all approaches they had used to reduce or maintain weight in the past year. Data were analyzed with multiple response methods. (3) Results: The most commonly reported approach was exercise, which included fitness, yoga, biking, and other physical activities (74.7% of respondents), and the second most commonly reported approach was decreased food intake (69.6% of respondents). The use of approaches differed according to respondents’ demographic characteristics. Regarding sex-related differences, in particular, men preferred to exercise, while women were more likely to decrease food intake. Among men, exercise was highest in the 40–49 years age group (48.3%). Among women, decreased food intake was highest in the same age group (16.1%). (4) Conclusions: These findings indicate that it is necessary to introduce individualized weight management approaches and measures according to target groups in obese adults. The prevalence of obesity has reached epidemic proportions worldwide . The WorAccording to the data mentioned above, the global prevalence of obesity continues to increase, with a worsening trend in health and other associated problems. Regarding this situation, a prior study suggested that new innovative approaches for obesity prevention be introduced . AnotherIn general, the cause of obesity is considered to be an adherence to unhealthy diet and lifestyle patterns . In addiMeanwhile, obesity management has recently been highlighted as an important public health issue due to the COVID-19 pandemic , and thehttps://knhanes.cdc.go.kr/knhanes/eng/index.do (accessed on 30 March 2020)). A total of 6170 respondents were included in the KNHANESϕ-3 database. For the current analysis, the sample was limited to those aged 19 years and older. After excluding pregnant women, the study sample size was 2142 .The study was a secondary analysis of data obtained from the 2018 Korea National Health and Nutrition Examination Survey (KNHANES) φ-3 by the Korea Disease Control and Prevention Agency (KDCA) . The metThe body mass index (BMI) was calculated as the ratio of weight and squared height and categorized as underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 23), pre-obese stage (23 ≤ BMI < 25), obesity class I (25 ≤ BMI < 30), obesity class II (30 ≤ BMI < 35), and obesity class III (35 ≤ BMI) . Height The survey was conducted in a mobile screening vehicle. After participants had confirmed that they were the subject of the investigation and had filled out a consent form, the investigation proceeded. The survey was conducted by a professional research team consisting of nurses, nutritionists, and health science majors. After the team had been selected, they completed practical training (theory education and practice) for 2 to 4 weeks and were then put at the survey site. Afterward, the ability to conduct the surveys was continuously verified through on-site quality management, as well as regular training seven times a year.The weight management survey was completed through self-reported questionnaires. The weight management question asked participants to select all the methods (multiple responses) that they had used to lose or maintain weight in the past 12 months. Response items included the following: exercises , fasting , reducing portion sizes or avoiding specific foods, skipping meals, taking prescription medications or nonprescription weight loss products, including herbal medicines, taking health functional food, and following a single-food diet  .Sampling weights to ensure the representativeness of the sample relative to the Korean population were available in the KNHANES database. Missing data were processed as valid values. Frequencies of sociodemographic variables and obesity levels were calculated for sex and age groups. Among subjects over 19 years of age, pregnant women and those taking prescription or nonprescription medications for weight loss were excluded from the data analysis. All analyses were performed using SPSS version 23.0 .https://knhanes.cdc.go.kr/knhanes/sub03/sub03_02_02.do (accessed on 30 March 2020)) [The KNHANES Φ-3 was approved by the Institutional Review Board of the KDCA (2018-01-03-P-A). The raw data used in this study were provided by legitimate procedures through the KNHANES website under the KDCA (h 2020)) . Persona2) was 35.0%; this was 41.9% among men and 28.2% among women. The prevalence of obesity was highest for men aged 40–49 years (23.0%), and for women aged 70 years and older (20.8%). Among the demographic characteristics, the prevalence of obesity was higher in married individuals than in singles, and in the economically active population group than the economically inactive group. In addition, obesity was more prevalent in men with higher family income and women with lower family income. Considering the level of education, the prevalence was highest in the group with a college degree or higher for men, and the group below elementary school graduation for women.The results of the study variables are presented in Among obese respondents, the most commonly used approaches for weight control were exercise (mentioned by 74.7% of respondents), decreased amount of food intake (69.6%), skipping meals (12.7%), health functional food (10.2%), one-food diets (2.6%), herbal medicines (2.5%), and fasting (2.2%). Exercise was the most common approach reported by men (42.7%), while decreased food intake was the most common approach for women (36.2%). Among men, exercise was most common in the 40–49 years age group (18.3%). Among the groups of men, the CIH access rate was in the order of exercise (42.7%), decreased food intake (33.3%), skipping meals (6.2%), health functional food (3.4%), fasting (1.0%), one-food diet (1.0%), and herbal medicines (0.6%). Among women, decreased volume of food intake was most common in the 40–49 years age group (16.1%). Among groups of Korean obese women, the CIH access rate was in the order of decreased food intake (69.6%), exercise (32.0%), health functional food (6.8%), skipping meals (96.5%), herbal medicines (1.9%), one-food diet (1.6%), and fasting (1.2%).The prevalence of obesity found in this study was 35%, which is higher than the 26.1% measured in 1998 when the KNHANES was launched . This isKey risk factors for the development of obesity have been reported to include “eating disorders, high energy-dense diets, low physical activity, and adoption of a sedentary lifestyle” ,16. The Interestingly, in this study, weight-control approaches showed differences by demographic variables such as sex, age, marital status, economic activity status, family income level, and education level. Among the groups of men, the CIH access rate was in the order of exercise, decreased food intake, skipping meals, healthy functional food, fasting or one-food diet, and herbal medicines. The CIH access rate was the highest in 40- to 49-year-old men with the highest prevalence of obesity. These results can be interpreted positively in terms of obesity management. Surprisingly, the CIH access rate was higher in the married and economically active groups of men—the higher the family income and education level were, the higher was the CIH access rate. It is evident that additional studies are needed to confirm the veritable efficacy of these approaches for obesity management.Unlike obese Korean men who showed the highest preference for exercise, the CIH access rate in obese Korean women was in the order of decreased food intake, exercise, healthy functional food, skipping meals, herbal medicines, one-food diet, and fasting. The current findings differ from reports on Jordanian women with obesity, who had preferences for commercial dietary supplements, herbal products including green tea, and folk remedies . In anotMany of the practices noted in this study can lead to other health problems. For example, younger women are particularly prone to following unhealthy practices such as skipping meals . Among oSurprisingly, only a few published studies have reported survey data on weight-control approaches in the obese population at the national level. For this reason, the present study has limitations, in that it is difficult to compare the weight management methods of the obese population in Korea with CIH approaches for weight management of the obese population applied in other countries. Another limitation of this study is the structure of questions in the KNHANES. In particular, the response of “exercise” included fitness, yoga, biking, and other physical activities. Future investigations should measure this in more detail by subdividing the “exercise” item into individual items.Despite these limitations, it should be noted that this study was based on data obtained from a rigorous national health survey. In addition, this information, particularly about differences according to demographic variables such as sex, age, marital status, economic activity status, family income level, and education level, could be useful in assessing the efficacy of specific approaches. The results could be used in follow-up studies to confirm the progress of weight management in these study participants. Finally, the KNHANES data are sufficiently rigorous to enable comparisons across countries and to be used for health policy development. If the sharing of data on obesity management accumulated in various countries is activated, this could aid the derivation, application, feedback, and improvement of global obesity management policies. CIH approaches for weight control must be continuously developed with consideration of “social, cultural, behavioral, and environmental contexts” in the future . FurtherIn conclusion, this study analyzed nationally representative data on Korean use of CIH approaches for weight control. Overall, exercise  was the most common approach used for weight management. The use of the approach differed according to demographic variables such as sex, age, marital status, economic activity status, family income level, and education level. In particular, regarding sex-related differences, men preferred to exercise, while women were more likely to decrease food intake. These findings indicate that it is necessary to individualize weight management approaches and measures according to the targeted demographic characteristics in adults with obesity. Further investigations are needed to verify the efficacy of CIH approaches used by the Korean obese population. Furthermore, these findings could be used as basic data to compare weight management before and after the COVID-19 pandemic.


Cerdocyon thous) in the Northeast of Brazil. Although this process occurred in a background of purifying selection, there is evidence of adaptive evolution -or selection of sub-consensus sequences- in internal branches after the host shift. The interaction of domestic and wild cycles persisted after host switching, as revealed by spillover and putative recombination events.The rabies virus (RABV) is characterized by a history dominated by host shifts within and among bats and carnivores. One of the main outcomes of long-term RABV maintenance in dogs was the establishment of variants in a wide variety of mesocarnivores. In this study, we present the most comprehensive phylogenetic and phylogeographic analysis, contributing to a better understanding of the origins, diversification, and the role of different host species in the evolution and diffusion of a dog-related variant endemic of South America. A total of 237 complete Nucleoprotein gene sequences were studied, corresponding to wild and domestic species, performing selection analyses, ancestral states reconstructions, and recombination analyses. This variant originated in Brazil and disseminated through Argentina and Paraguay, where a previously unknown lineage was found. A single host shift was identified in the phylogeny, from dog to the crab-eating fox ( Lyssavirus (family Rhabdoviridae), which are etiological agents of a fatal infection of the nervous system of mammals, represent an excellent case study to gain insight into virus genetic diversity and disease emergence. Although several mammalian species are susceptible to infection, most lyssaviruses are only able to sustain onward transmission in a relatively low number of host species, indicating the presence of strong barriers to host shifting [Chiroptera and Carnivora. Phylogenetically, the RABV is divided into two reciprocally monophyletic groups: a bat-related group confined to the American continent that circulates predominantly in chiropterans, with some circulation in skunks and raccoons in North America [shifting ,2,3,4. O America , and a d America . The cirUrocyon cinereoargenteus) in two disjunct areas: in Colombia and Venezuela [Cerdocyon thous), but also among other mesocarnivores such as the hoary fox  and the South American Coatí (Nasua nasua) [In South America, at least three distinct dog-related RABV phylogenetic lineages have been reported. When typed with a panel of monoclonal antibodies directed against the viral nucleoprotein , they dienezuela , and in enezuela ,9,10. Thenezuela ,9,11,12.a nasua) ,14,15,16Although several studies have been published focusing on molecular epidemiology ,15,17,18A total of 55 dog-related RABV positive samples from Argentina, Brazil, and Paraguay isolated between the years 1989 and 2017 were typed by indirect immunofluorescence as described in Cisterna et al. .We focused on the analysis of complete N ORF sequences, since this gene is the most thoroughly studied locus ,19,20,22RABV N ORFs from 39 AgV2 and 16 AgV1 brain tissue samples were obtained by high-throughput sequencing (HTS). RNA was extracted with TriZol. RNA extracts were depleted of rRNA content and amplified by SISPA RT-PCR as described previously ,24. Amplhttp://broadinstitute.github.io/picard/ accessed on 20 March 2021) were used for adapter and SISPA primer removal, PCR duplicate removal, and quality filtering of the index (<30 Phred) and reads (<20 Phred). De novo assembly was performed with Ray [http://broadinstitute.github.io/picard/ accessed on 20 March 2021), and finally, Samtools v0.1.18 [The following pipeline was conducted to obtain full-genome consensus sequences. Open-source programs Cutadapt , Prinseqwith Ray , an algowith Ray  was usedl., 2015 . Brieflye index < Phred anC. thous, and L. vetulus in the wild lineages). Eight sequences were annotated as isolated from “Dusicyon sp.” in Genbank. Taking into consideration that Dusicyon is an extinct lineage and that L. vetulus was previously assigned to this genus, these sequences could belong to that species. However, as pointed out by Carnieli et al. [L. vetulus is not normally found [C. thous. However, there are records of L. vetulus in the state of Ceará [A total of 118 Genbank dog-related RABV sequences were included representing all lineages and sublineages following Troupin et al. , with anly found  and coulof Ceará ,33,34, wNucleotide alignments were performed with Clustal Omega  Supplem. NucleotA Bayesian phylogenetic analysis was carried out with MrBayes v3.2.7  for 1.73https://www.ibge.gov.br/ accessed on 30 November 2021), and whose regional division is adopted in this study. Similarly, the northeastern and northwestern regions of Argentina are characterized by contrasting biomes: the Yungas (Mountain forests) and Puna (high-elevation grasslands), and the Alto-Paraná Atlantic forests  and Chacoan forest and Espinal (shrubland forest).For the phylogeographic analysis, we included the 39 AgV2 N gene sequences obtained in this study and retrieved from Genbank all AgV2 Brazilian samples (198) with corroborable geographic and temporal information . With th 10.53%) . A totalTo determine the degree of the clock-like structure in the AgV2 data set, we employed root-to-tip linear regression as available in the TempEst program. The analysis of the Bayesian phylogenetic tree based on the 237 AgV2 N gene sequences yielded a negative correlation between nucleotide divergence and time , suggesting that the data contains little temporal signal, therefore being unsuitable for inference using phylogenetic molecular clock models. The observed pattern could be caused by intrinsic factors such as continuous fluctuations of the evolutionary rate through time, or by data quality issues such as errors in sampling dates annotation or incomplete/over-sampling . EvidencTo make a rapid inspection of geographic structure and time of isolation, a Median-joining network was inferred using PopART v1.7  with theAncestral state reconstruction was performed with MBASR , which uequences . AncestrTo identify candidate recombinants, the alignment including the 237 AgV2 nucleoprotein sequences, was tested for recombination using the Recombination Detection Program RDP version 4 (RDP4) . DefaultTo assess possible historical recombination, we also tested a dataset containing all AgV2 N gene sequences as well as South-American AgV1 N gene sequences from the northern and southern distributions .To assess if natural selection affected the evolution of AgV2 lineages, and particularly if host shifts were accompanied by changes in selective pressure, we employed codon-based and lineage-based Bayesian and Maximum Likelihood approaches to estimate rates of non-synonymous (dN) to synonymous substitutions (dS). Two datasets were analysed in the Datamonkey webserver : (1) TheP ≤ 0.10 for SLAC, FEL, and MEME, P≤ 0.05 for aBSREL, and posterior probability ≥0.90 for FUBAR.Selection was inferred using the following codon-based methods: Single sites under selection were identified using Single Likelihood Ancestral Counting (SLAC) , Fixed EThe aBSREL analysis was conducted in two ways: (1) an exploratory analysis was run, where all branches were tested for positive selection; (2) A priori selecting a set of “foreground” branches to test for positive selection. In this case, we selected both branches leading to the Wild Brazilian lineages (Brazil Wild A+B) as well as the MRCA of the AM3b lineage, taking into account that in one of these two nodes the host shift from dogs to wild mesocarnivores took place (see Results). We ran a third aBSREL analysis testing the whole Brazil Wild A + B clade, as well as the internal branch leading to AM3b. For the FEL analysis, we used this last setting as test branches and the rest of the AgV2 tree as reference.Selection was also tested using tree-based methods at the Datamonkey server: RELAX  and BUSTThe amino acid ancestral reconstructions obtained in the SLAC test were analysed to find convergent or parallel changes for different clusters or lineages of the AgV2 variant. An amino acid replacement was operationally considered internal if it involved three or more descendant sequences, indicating that these are fixed mutations. Alternatively, an amino acid replacement was considered terminal if it was conserved in two or fewer descendant sequences, which denotes a transient (unfixed) amino acid polymorphism.The Bayesian phylogenetic analysis revealed the existence of two different RABV terrestrial lineages occurring in Argentina showing total correspondence with antigenic variant typing . The samC. thous and N. nasua) from Argentina and Paraguay. Following the current classification scheme ([http://rabv-glue.cvr.gla.ac.uk/ accessed on 30 August 2021), this lineage should be named AM3c. In Paraguay, sequences belonging to lineages AM3a and AM3c were found, evidencing that both cycles circulate in that country.Within the AgV2 clade, a novel terrestrial rabies lineage occurring in the Northeast of Argentina and Paraguay was found . This li scheme . Two sister clades composed of mainly wild canid samples, although there are some spillovers to dogs, complete the AM3b lineage. The Brazil Wild A sublineage is characterized by relatively shorter branches and concentrates all the spillovers reported to dogs. The main host for this lineage is C. thous, although there are several isolates from L. vetulus . The Brazil Wild B sublineage has the longest branches compared with all other clades in the phylogeny. The AM3c lineage has no clear host structure, although most samples belong to domestic dogs. All AM3a, AM3b, and AM3c lineages and sublineages have posterior probabilities >0.9  spillovers are observed. In the northeast of Argentina, a total of seven dog→C. thous and three dog→N. nasua spillover events were recorded. As expected, the ancestral host reconstruction of all main AgV2 clades reflected the dominant host. The ancestral host of the AM3b lineage is ambiguous being the domestic dog (45.22%) and C. thous (41.23%) nearly equiprobable species. The host shift should have taken place in the AM3b MRCA or, most likely, in the subsequent branch which leads to the Brazil Wild (A+B) lineage  two simultaneous replacements occurred in different residues. Interestingly, in a C. thous→dog spillover, three amino acidic replacements took place, which would reflect a high rate of evolution in a terminal branch . Remarkably, none of the internal replacements occurred in the branches involving the host shift , which overlap in space and time [In this study, we present the most comprehensive RABV AgV2 phylogenetic and phylogeographic analysis confirming previous findings and adding new results contributing to a better understanding of the origins, diversification, and the role of different host species in the evolution and dissemination of this dog-related variant. Although the lineage of which AgV2 descends is not clear, we confirm that this variant is monophyletic. Even though it has been reported that Brazilian lineages were monophyletic ,12, the and time ,12,16. Tand time . We poinThe AgV2 variant originated in dogs in the northeast of Brazil and dispersed to the south and to the west as a result of urbanization and human migrations ,12,15 t. After tC. thous are actual reservoirs for RABV AgV2, the former being an urban and the latter a sylvatic reservoir. However, although C. thous is able to maintain successful transmission cycles in Brazil [L. vetulus (and “Dusicyon sp.”), N. nasua, and domestic cats, which can be occasional hosts but not reservoirs, as denoted by the lack of host-specific monophyletic groups that would represent prolonged infection transmissions between members of the same species , which is congruent with a context of strong purifying selection . Both ints alone . Moreoveon rates . Populatpulation .C. thous and C. thous→dog spillovers were found in the northeast of Brazil, which demonstrates the close contact between domestic and wild canids in this region (C. thous→dog spillovers (9 cases), while there are no such events in the Brazil Wild B lineage. In addition, a total of 10 domestic→wild spillovers were found in the Argentinian clade, denoting high levels of contact between dogs and wild mesocarnivores. Although there is no evidence of a wild cycle in this region, the existence of C. thous cycles in Brazil together with the high levels of dog-related infections leads to considering this species as a potential reservoir in Argentina.Two lines of evidence account for ongoing interaction between the domestic and wild cycles: spillovers and the presence of potential recombinants. Several spillovers were corroborated between geographically overlapping lineages. Both dog→s region . NotablyRecombination is a common event in RNA viruses, and its detection is crucial to minimize the disruptive impact that this phenomenon can have on subsequent phylogeny-based analyses of molecular evolution ,66. FurtIn our analysis, five putative recombinants were found, originating from three independent recombination events (three sequences derive from a single recombination event) . In a prC. thous→dog spillovers were found, showing that in this lineage there is also some degree of contact with dogs (The sequences involved in the recombination events were retrieved from Genbank and were obtained by a single laboratory, so we cannot completely rule out the possibility of contamination. However, it is noteworthy that in the recombined segments the potential recombinants share mutations with respect to the minor parental sequences. As proposed in the guidelines for recombination identification in negative-stranded viruses, evidence that recombinant sequences form a distinct circulating lineage  and haveith dogs . HoweverC. thous took place. This variant was disseminated through Brazil, Argentina, and Paraguay, originating three lineages: AM3a, AM3b, and AM3c. All this process occurred in a background of purifying selection, although some sites may have gone through adaptive evolution -or selection of sub-consensus sequences- in internal branches after the host shift. The interaction of domestic and wild cycles persisted after host switching, as revealed by the presence of putative recombinants and by spillover events. As argued elsewhere [In this study, we have inferred the origins and diversification of the AgV2 RABV variant, showing that it originated in dogs in the northeast of Brazil, while in the same region, a host shift to lsewhere ,77, the