The daily average viral loads of the Delta VOC in hosts have been reported to be between ~109 copies mL−1 and ~1010 copies mL−1 during the four days after symptom onset in 1848 cases of the Delta VOC infection. Owing to the high viral load, the SARS-CoV-2 Delta VOC has the potential to effectively spread through aerosols. COVID-19 vaccination can decrease aerosol transmission of the SARS-CoV-2 Alpha VOC by reducing the viral load. The viral load can explain the conundrum of viral aerosol spreading. The SARS-CoV-2 Delta VOC aerosol clouds have been assumed to be formed in restricted environments, resulting in a massive numbers of infected people in a very short period with a high spreading speed. Strong control methods against bioaerosols should be considered in this SARS-CoV-2 Delta VOC pandemic. Large-scale environmental monitoring campaigns of SARS-CoV-2 Delta VOC aerosols in public places in many countries are necessary, and these activities could contribute to controlling the coronavirus disease pandemic.This study analyzes the reasons the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) spreads so rapidly. Novel topics such as universal conditions for the rapid spread of respiratory viruses, minimum viral loads for viral aerosol generation, effects of vaccination on viral aerosol generation, and viral aerosol clouds were studied. The analyses were based on experimental results and analytic model studies. Four universal conditions, namely asymptomatic host, high viral load, stability of viruses in air, and binding affinity of viruses to human cells, need to be satisfied for the rapid spread of respiratory viruses. SARS-CoV-2 and its variants such as the Alpha VOC and Delta VOC satisfy the four fundamental conditions. In addition, there is an original principle of aerosol generation of respiratory viruses. Assuming that the aerosol–droplet cutoff particle diameter for distinguishing potential aerosols from earthbound respiratory particles is 100 μm, the minimum viral load required in respiratory fluids to generate viral aerosols is ~10 This theoretical analysis on minimum viral loads for viral aerosol generation is based on the assumptions of a homogeneous distribution of viruses in respiratory fluids, considering one gene copy as a single virion, and a spherical volume ratio model for both respiratory particles and viruses  and the aerosol–droplet cutoff particle diameter  (v) decreases   4]. The. Thev, wIn the coronavirus disease (COVID-19) pandemic, questions are arising regarding the underlying mechanisms responsible for the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants such as the Alpha variant of concern (VOC) (formerly known as B.1.1.7) and the Delta VOC (formerly known as B.1.617.2). This study introduces the universal principles that explain the rapid spread of respiratory viruses. These principles apply to all contagious diseases caused by respiratory viruses.There are four fundamental conditions that need to be satisfied for the rapid spread of respiratory viruses. The first is asymptomatic hosts with viral shedding that peaks during the early stage of infection. Symptomatic hosts  have limited contact with other people because of self-recognition of infection. However, asymptomatic hosts interact with other people freely, and viruses can easily move from asymptomatic hosts to non-infected individuals. The second is massive shedding of the virus from the infected cells of hosts in their respiratory fluids, which is a high viral load condition. Small respiratory particles have a greater chance of carrying viruses  under high viral load conditions than under low viral load conditions. Thus, viral aerosols can be generated from small virus-laden respiratory particles under high viral load conditions and spread in the nearby environment. The third is the stability of respiratory viruses in the outside air. For long distance transmission over a long period, the stability of airborne viruses is crucial. The fourth is a strong binding affinity of viruses to human cells at the receptors of entry points such as the nose, mouth, and eyes. The fundamental conditions can even facilitate each other, for example, if a virus has an extremely strong binding affinity to human cells, it can compensate for lower concentrations of the virus in the air under low viral load conditions. These four fundamental conditions are universal requirements for the rapid spread of respiratory viruses.10 copies mL−1 [6 copies mL−1) required for aerosol generation of respiratory viruses [Concerning SARS-CoV-2, viral shedding by asymptomatic hosts has been reported to peak during the early stage of infection . The viries mL−1 , which i viruses . A study viruses . Therefo viruses ,8. SARS- viruses . SARS-Co viruses .7 copies mL−1 to ~109 copies mL−1 [Concerning the SARS-CoV-2 Alpha VOC (formerly known as B.1.1.7), viral concentrations in respiratory samples of hosts have been reported to range from ~10ies mL−1 , which iies mL−1 . The SARies mL−1 . These cies mL−1 . The aboies mL−1  support 9 copies mL−1 in China [9 copies mL−1 and ~1010 copies mL−1 during the four days after symptom onset (based on an investigation of 1848 cases of infection with the Delta VOC) in Korea [The SARS-CoV-2 Delta VOC has shown very high transmissibility , with hiin China . The daiin Korea . These fTherefore, SARS-CoV-2 and its variants such as the Alpha VOC and the Delta VOC satisfy the four fundamental conditions for universal principles of rapidly spreading respiratory viruses.It has been reported that vaccination reduced the viral loads in individuals infected with the SARS-CoV-2 Alpha VOC in the United Kingdom . TherefoThe viral load principle for viral aerosol transmission is an important key in supporting that viral aerosols in the environment can be dominant factors in the COVID-19 issues. The viral loads and viral aerosols can explain the conundrum of viral spreading, including super-spreading events, during the pandemic ,23,24,25In Tehran in Iran, SARS-CoV-2 ribonucleic acid (RNA) has been detected in the air in public environments such as airports, subway stations, subway trains, and shopping centers . It has −3) in Italy [SARS-CoV-2 Delta VOC aerosol clouds are assumed to have been formed in India and the United Kingdom in 2021, based on the rapid spread and the large number of people infected within a short period . Transmiin Italy .Large-scale environmental monitoring of SARS-CoV-2 Delta VOC aerosols at many locations over a long period is necessary ,26. ThesThe universal principles of rapidly spreading respiratory viruses, which have been studied in this analysis, can be applied to all contagious diseases caused by respiratory viruses. The overwhelming fulfilment of the four fundamental conditions can lead to generation of viral clouds, which can result in rapid large-scale infections. Control methods against bioaerosols should be considered in such situations ,35. Face


Health promotion intervention participants also reported more muscle-strengthening PA (p = 0.001), vigorous-intensity aerobic PA (p = 0.049), and moderate-intensity aerobic PA (p = 0.010) than control participants. The rise in self-reported adherence to PA guidelines and improvements in muscle-strengthening and aerobic PA considered separately suggest that a relatively brief behavioral intervention can increase PA among African American men aged 40 years and older living with HIV and potentially curb their risk of NCDs that PA can prevent.HIV and its treatment with antiretroviral therapy increase the risk of noncommunicable diseases (NCDs) tied to physical inactivity. Older African American men are also at high risk for NCDs. We tested the efficacy of a theory-based intervention to increase adherence to federal aerobic and muscle-strengthening physical activity (PA) guidelines among African American men aged 40 years and older living with HIV. We randomized African American men aged 40 years and older living with HIV to a three-session social cognitive theory-informed health promotion intervention targeting PA or a one-session health awareness control condition. The primary outcome was PA guideline adherence assessed (self-reported) preintervention, immediate postintervention, and 3, 6, and 12 months postintervention. Secondary outcomes were the number of days on which participants reported moderate-intensity aerobic PA, vigorous-intensity aerobic PA, and muscle-strengthening PA in the past 7 days. Of 302 participants, 255 completed the 12-month postintervention measures. Generalized estimated equation logistic regression indicated that the health promotion intervention participants had higher odds of meeting PA guidelines than health awareness control participants, adjusting for baseline adherence ( For instance, African American men have the highest HIV prevalence among racial/gender subgroups in the United States.10 PLWH have low rates of PA,11 as do African Americans.12 Studies have shown that regular PA is associated with reduced risk of diabetes mellitus and with lower cardiovascular and all-cause mortality rates.14 Further, low muscle mass is associated with impaired performance of activities of daily living and increased mortality among middle-aged African Americans.15 The Department of Health and Human Services (DHHS)-provided guidelines for PA indicate that adults should perform both aerobic PA and muscle-strengthening PA weekly.16 However, African Americans are less likely to meet the DHHS PA guidelines. Only 21% of African American adults meet the DHHS guidelines compared with 25% of non-Hispanic White adults.12 These findings highlight the need for interventions to encourage PA among African Americans.Although PA reduces the risk of NCDs,17 Evidence indicates that aerobic and resistance exercises improve cardiorespiratory fitness, strength, and body composition in PLWH.19 However, recent literature reviews have documented that many PLWH do not meet PA guidelines.11 Some evidence suggests that rates of PA among PLWH may be lower than their counterparts not living with HIV.20 Moreover, a meta-analysis reported high dropout rates in PA interventions with PLWH.21Engaging in healthful behaviors, including regular PA, is an essential aspect of lifestyle optimization for PLWH.22–24 In this study, we report the results of a randomized controlled trial (RCT) to address this gap in the literature. We tested the efficacy of a theory-based intervention in increasing PA in African American men aged 40 years and older living with HIV. We randomly assigned them to the theory-based intervention or a brief intervention control condition. We hypothesized that the intervention group would be more likely to report adherence to PA guidelines than the control group during the 12 months postintervention, adjusting for baseline adherence.Only a few trials have tested interventions to help PLWH improve their PA.All participants provided written informed consent. Institutional Review Board No. 8 of the University of Pennsylvania approved the study (Protocol No. 813202). Men aged 40 years or older, receiving ART for HIV, and self-identifying as Black or African American were eligible to participate. We excluded men who (1) had a blood pressure of 180/110 mm Hg or higher, (2) reported participating in a health promotion intervention trial targeting PA, diet, or prostate or colon cancer screening in the past 12 months, or (3) did not have a mailing address or planned to move beyond a reasonable distance from the study site within the next 18 months.We recruited the men from a database of PLWH who consented to be contacted for research studies, through referrals from local HIV service providers and AIDS service organizations, and through ads in a local newspaper and social media sites, including Facebook, Twitter, and Craigslist. We enrolled the participants over 35 months, beginning in January 2015 and completing all data collection by December 2018. We held the intervention and data collection sessions in university conference rooms.The study used an RCT design. After completing Visit 1 baseline assessments, men were scheduled for Visit 2 for randomization and intervention. Using computer-generated random number sequences, we randomly assigned them in a 1:1 ratio to the Men Together Making a Difference Health Promotion or health awareness control condition. One researcher generated random assignments, and the project director implemented them. Data collectors, but not the facilitators and participants, were blinded to random group assignment.25 Feedback from focus groups with African American men living with HIV helped us revise the intervention to ensure that the activities focused on and emphasized African American men aged 40 years and older living with HIV. Since some activities were designed to be implemented in sessions with individual HIV-serodiscordant couples,26 we revised them to be appropriate for groups of men. To reduce costs, we employed a single facilitator instead of the cofacilitators who implemented the Eban intervention.The Men Together Making a Difference Health Promotion Intervention is an adaptation (for men) of the Eban Health Promotion Intervention, which increased adherence to DHHS PA guidelines in a multi-site RCT with African American HIV-serodiscordant couples.27 and the reasoned action approach28 integrated with formative research was designed to increase attitudes, self-efficacy, and intention supportive of behaviors tied to decreased cardiovascular disease, diabetes, hypertension, and cancer risk. 29 were used to increase adherence to guidelines for PA, 5-a-day diet, and colon cancer screening. It encouraged 30 min or more of moderate-intensity aerobic PA on 5 days or 20 min or more of vigorous-intensity aerobic PA on 4 days each week and muscle-strengthening PA on 2 or more days each week. Participants identified their barriers to engaging in the recommended behaviors and strategies for surmounting those barriers.The intervention based on social cognitive theoryThere were nine 1-h modules delivered in three sessions over three consecutive weeks. Sessions 1 and 2 included homework assignments on a healthy diet or PA, and the subsequent session addressed their barriers to completing the assignment and strategies to overcome the obstacles. Participants learned about moderate and vigorous aerobic activity and muscle-strengthening activity, including DHHS guidelines. They learned how to exercise safely using exercise bands and practiced with a muscle-strengthening exercise video depicting three levels of intensity depending on the participant's level of fitness, with the lowest intensity involving exercising while sitting in a chair. They brainstormed barriers to aerobic and muscle-strengthening exercises and generated solutions to the obstacles. Participants received pedometers to monitor their steps and discussed how they could increase their daily steps. They participated in an aerobic exercise workout to music and received an exercise-at-home DVD with both aerobic and muscle-strengthening activities that they could perform independently.Participants played review games such as Health Jeopardy and Health Basketball to reinforce the information they covered in the curriculum. They discussed the benefits of a support system and identified people in their lives who could be supportive of healthy eating and exercise habits. They wrote a letter to self, promising to practice healthy behaviors, including reasons for engaging in healthy behavior and examples of how they will engage in such behavior. They placed it in a self-addressed envelope to be mailed to them 6 weeks later as a reminder of their commitment. Finally, they participated in a closing ceremony and received a certificate of completion.The health awareness control condition consisted of one 60-min small-group session led by a trained facilitator. Participants viewed and discussed video clips that encouraged PA, fruit and vegetable consumption, and colon cancer screening.30 On how many of the past 7 days did you exercise or participate in PA for at least 20 min that made you sweat and breathe hard, such as basketball, soccer, running, swimming laps, fast bicycling, fast dancing, or similar vigorous PAs? On how many of the past 7 days did you exercise or participate in PA for at least 30 min that did not make you sweat and breathe hard, such as walking, slow bicycling, skating, pushing a lawn mower, or anything else that caused small increases in breathing or heart rate? On how many of the past 7 days did you exercise to strengthen or tone your muscles, such as push-ups, sit-ups, or weightlifting?We administered self-report assessments at baseline, immediate postintervention, and 3, 6, and 12 months postintervention using audio computer-assisted self-interviewing (ACASI). We assessed PA using three open-ended questions developed by the Centers for Disease Control and Prevention.25 Later, we created an alternative operationalization of this outcome, which we believe is more accurate, namely a binary measure indicating whether the participant met the criterion of the number of minutes specified in the PA guideline. This new measure (PA minutes guideline met) was also calculated based on the three items, but indicated whether the participant met PA guidelines of engaging in at least 150 min of moderate-intensity PA, 75 min of vigorous-intensity aerobic PA, or an equivalent combination of moderate- and vigorous-intensity PA in the past 7 days plus muscle-strengthening PA at least twice in the past 7 days.16 Time spent (minutes) in moderate-to-vigorous PA was defined as the sum of the self-reported number of days of moderate-intensity aerobic PA times 30 min and the self-reported number of days of vigorous-intensity aerobic PA times 20 min times two because minutes of vigorous PA count twice as much as minutes of moderate PA.The a priori primary outcome was adherence to PA guidelines, a binary variable that reflected whether the men reported meeting the guideline of engaging in muscle-strengthening activity on 2 or more days and engaging in either 20 min of vigorous-intensity activity on at least 4 days or 30 min of moderate-intensity activity on at least 5 days in the past 7 days.Secondary outcomes included self-reported days of moderate-intensity aerobic PA, days of intensive aerobic PA, and days of muscle-strengthening PA in the previous 7 days.31 and drug dependency from a score of 3 or more on the Texas Christian University Drug Screen.32 We defined ART adherence as the proportion of pills taken in the past 3 days.33 We also collected measures of PA mediators, healthy diet, healthy diet mediators, anthropometric variables, and colon cancer screening, which we will report in separate articles.Participants also completed measures of sociodemographic characteristics, including age, marital status, education, income, housing stability, employment status, use of alcohol and drugs, and ART adherence. We determined alcohol dependency from a score of 2 or greater on the CAGE questionnaireA priori, we estimated the sample size required to detect a clinically meaningful effect of the health promotion intervention on the primary outcome, adherence to PA guidelines, compared with the health awareness control condition, with adequate power (>80%), accounting for the repeated measures design of our planned longitudinal assessments. An absolute increase of 9.1 percentage points, assuming greater adherence in the health promotion intervention condition, was selected as clinically meaningful. We estimated that the average correlation among PA guideline adherence rates immediate postintervention and 3, 6, and 12 months postintervention was equal to 0.10. Assuming a two-tailed test, α = 0.05, 20% attrition, and a 9.1% increase in adherence to PA guidelines (from 15.7% in the control group to 24.8% in the intervention condition), with 323 men randomized, the estimated statistical power is 87%.We summarized the participants' baseline sociodemographic characteristics with descriptive statistics and analyzed attrition with chi-square tests and logistic regression analyses. For the primary binary outcome, adherence to PA guidelines, we tested the efficacy of the health promotion intervention compared with the health awareness control condition with logistic generalized estimating equation (GEE) models, with and without adjusting for the baseline of the outcome. We used a similar GEE approach for continuous secondary outcomes by specifying an identity link function. The models included condition, follow-up time , and the outcome's baseline. We used an independent working correlation matrix and robust standard errors. We present efficacy estimates as odds ratios (ORs) or mean differences with corresponding 95% confidence intervals (CIs) to test the hypotheses of interest.We used an intention-to-treat approach, analyzing participants based on their assigned intervention, irrespective of the number of intervention sessions or postintervention assessments they attended. We used SAS 9.4 for all analyses.The health promotion intervention session attendance was high: 152 (100%) attended session 1 and 148 (97%) attended sessions 2 and 3. All participants in the control condition attended their single intervention session. Postintervention assessment completion was also high, with 296 (98%) completing the immediate post-test and 272 (90%) completing the 3-month, 264 (87%) completing the 6-month, and 255 (84%) completing the 12-month postintervention assessments. The intervention (95%) and control (97%) conditions did not differ in the percentage completing one or more postintervention assessments.Attending one or more follow-ups was unrelated to baseline adherence to the PA guideline or the sociodemographic variables in The descriptive statistics on PA outcomes by condition and assessment time are presented in There were no adverse events.The high rates of HIV and NCDs, coupled with low levels of PA adherence, pose a challenge for African American men living with HIV. The present study found that a behavioral intervention increased self-reported adherence to DHHS PA guidelines among African American men aged 40 years and older living with HIV compared with a health awareness control intervention. The intervention also caused significant increases in self-reported aerobic PA and muscle-strengthening PA considered separately. The results are significant because African American men living with HIV commonly do not engage in sufficient PA, and to our knowledge, no other interventions have increased their PA.24 The study differed from the present study, in that 30% of the participants were women and they were followed for only 6 months postintervention. A study with South African PLWH found no effect of a 6-month behavioral intervention on pedometer step counts at the 12-month assessment compared with a standard of care control group.23 An RCT of a 12-week theory-based PA intervention among older PLHW with functional limitations found that intervention increased PA behavior immediately postintervention compared with a usual care control group.22 Our study with up to 12 months of follow-up also supports the efficacy of theory-based behavioral interventions for increasing self-reported PA.The present findings differ from those observed in a recent RCT that also used a behavioral intervention designed to increase PA among a predominantly African American sample of PLWH. The intervention reduced sweetened beverage consumption and weight, but did not increase PA.21 of high treatment dropout in PA interventions with PLWH, the high intervention session attendance and low follow-up assessment attrition are also strengths of this study. It is noteworthy that the intervention was relatively brief, consisting of only three 3-h sessions. Many interventions contain considerably more sessions of face-to-face contact.35 The present study suggests the possibility that brief interventions may be effective.The RCT design is a strength of the study. Considering the meta-analytic evidence36 Future research should investigate whether the effects we observed will replicate in a trial using objective PA behavior measures. Another limitation is that the participants were African American men from one city; the findings may not generalize to all African Americans aged 40 years or older living with HIV. Despite these limitations, this study's results have important implications for intervention research among PLWH and provide vital information for programs aimed at improving outcomes for PLWH.The study's limitations include the use of self-reports of PA. Imperfect memory and social desirability responding can diminish the accuracy of self-reported behavior. To reduce such bias, we used ACASI, which decreases socially desirability bias compared with the alternative of paper-and-pencil surveys and face-to-face interviews.In conclusion, a theory-based behavioral intervention increased self-reported adherence to DHHS PA guidelines during a 12-month follow-up period. This intervention shows promise for uptake, adoption, and evaluation in real-world HIV-serving clinical and community settings using implementation science research strategies. More generally, the findings have implications for interventions to increase daily PA for African American men living with HIV, a population at high risk of NCDs that PA can prevent. Specifically, the findings highlight the promise of the reason action approach and social cognitive theory integrated with formative research in designing in-person group PA interventions for African American men living with HIV. As the population ages with increased risk of NCDs, our finding suggests that health behavior interventions need to be introduced earlier to involve younger men to accrue more extensive health benefits through increasing PA in their daily lives.


Variables examined include percentage of time (%) spent in sedentary, and light, moderate, and vigorous PA. The full dataset (n = 342) indicated sedentary behavior 62% ± 11% of the time and only 10% ± 5% of the time spent in moderate-to-vigorous PA. Among children in the upper-median half of wear-time (n = 176), Hispanic children had significantly greater % sedentary time vs. Non-Hispanic children , and lower % light PA  and moderate PA . Our results highlight that PA levels were lower among our sample compared to previous studies, and that Hispanic children were more sedentary and less active compared to non-Hispanic white children.Obesity prevalence among Hispanic children is twice that of non-Hispanic white children; Hispanic children may also engage in less physical activity (PA) compared to non-Hispanic white children. A large number of U.S. preschool-aged children are cared for in Family Child Care Homes (FCCH), yet few studies have examined PA levels and ethnicity differences in PA levels among these children. We examine baseline data from a cluster-randomized trial  to improve food and PA environments in FCCHs. Children aged 2-to-5-years ( Childhood obesity has more than tripled since the 1970s and currently affects 13.9% of preschool-aged (2-to-5-year old) children .Regular PA among children also presents a myriad of health benefits independent of BMI control , attenuSocietal shifts in the workforce have resulted in an increasing demand for childcare. In the U.S., approximately 61% (12.5 million) of children receive non-parental childcare outside the home . In addiTherefore, the goal of this study is to fill critical gaps in the literature by documenting the levels of objectively measured PA among preschoolers in the context of FCCHs. Additionally, we seek to leverage an ethnically diverse sample by examining differences in PA levels among preschool-aged children by ethnicity .n = 342 children ages 2-to-5-years old completed baseline accelerometer measurements. FCCHs were recruited within 60 miles of the Providence, Rhode Island metropolitan area. Consent to measure was obtained from the children’s parents; assent was obtained from the participating children. This study was approved by the Institutional Review Boards of Brown University, University of Connecticut, and The University of Rhode Island.This study examines baseline data from a cluster randomized controlled trial, Healthy Start/Comienzos Sanos, which studies the efficacy of a multicomponent intervention to improve the food and physical activity environments in FCCHs and the diet, PA, and screen time behaviors of 2-to-5-year old children in their care . Data waChildren’s demographic information  was obtained from a parent-reported survey. Standard techniques were used to measure the height, weight, and waist circumference of participating children in accordance with CDC NHANES protocol ,14. All TM ActiGraph  accelerometers were used to measure children’s PA levels. The use of accelerometry has been validated among the pediatric population ).We report low levels of MVPA overall, and lower PA for younger children and girls compared to older children and boys. We also report significant differences in levels of PA and sedentary behavior between ethnic groups with Hispanic children having lower PA. These observed associations suggest the importance of further research to determine the reasons for these differences  and possibly the development of tailored interventions to promote PA among ethnically diverse preschool-aged children as part of obesity prevention efforts. In addition, in the FCCH setting, the examination of actual child activities, provider practices, and their association with objectively measured PA levels are needed as provider-led play is a predictor of child PA. Innovative ways to train and provide support for FCCPs are needed to increase MVPA in FCCHs, and community-based, multicomponent intervention approaches should be considered as intervention delivery methods.


Mental disorders are prevalent and cause considerable burden of disease. Exercise has been shown to be efficacious to treat major depressive disorders, insomnia, panic disorder with and without agoraphobia and post traumatic stress disorder (PTSD).the efficacy and cost-effectiveness of the manualized, group-based six-months exercise intervention “ImPuls”, among physically inactive patients with major depressive disorders, insomnia, panic disorder, agoraphobia and PTSD within a naturalistic outpatient context in Germany. A minimum of 375 eligible outpatients from 10 different study sites will be block-randomized to either ImPuls in addition to treatment as usual (TAU) or TAU only. ImPuls will be conducted by trained exercise therapists and delivered in groups of six patients. The program will combine (a) moderate to vigorous aerobic exercise carried out two-three times a week for at least 30 min with (b) behavior change techniques for sustained exercise behavior change. All outcomes will be assessed pre-treatment, post-treatment (six months after randomization) and at follow-up (12 months after randomization). Primary outcome will be self-reported global symptom severity assessed with the Brief Symptom Inventory (BSI-18). Secondary outcomes will be accelerometry-based moderate to vigorous physical activity, self-reported exercise, disorder-specific symptoms, quality-adjusted life years (QALY) and healthcare costs. Intention-to-treat analyses will be conducted using mixed models. Cost-effectiveness and cost-utility analysis will be conducted using incremental cost-effectiveness and cost-utility ratios.This pragmatic, two arm, multi-site randomised controlled trial will evaluate Despite its promising therapeutic effects, exercise programs are currently not provided within the outpatient mental health care system in Germany. This trial will inform service providers and policy makers about the efficacy and cost-effectiveness of the group-based exercise intervention ImPuls within a naturalistic outpatient health care setting. Group-based exercise interventions might provide an option to close the treatment gap within outpatient mental health care settings.DRKS00024152, 05/02/2021).The study was registered in the German Clinical Trials Register (ID: The online version contains supplementary material available at 10.1186/s12888-021-03541-3. Epidemiological data from 2019 suggests that 15.6% of the German population suffered from any mental disorder in 2019  . NotablyWorldwide, 27.2% of the DALYS attributable to mental disorders can be explained by major depressive disorders and 16.3% by anxiety disorders . In 2015Despite the severe negative impact of mental disorders, it is estimated that in Germany only 10% of all affected individuals receive evidence-based treatment and only 2.5% receive psychological treatment . In addiExercise, defined as physical activity that is planned, structured and repeated, with the primary aim to improve or maintain physical fitness , has revExercise might not only be a promising efficacious treatment but also carry the advantage of being highly efficient, since it can be delivered in group settings with relatively short durations , can be Despite the promising evidence for MVAE as an intervention for patients with mental disorders, exercise programs or professional exercise therapy are currently not provided as regular health services within the outpatient mental health care system in Germany. With the aim of combining the current evidence on the efficacy of MVAE and sustained exercise behavior change with specific demands of a real-world outpatient health care setting, ImPuls was developed as a manualized group exercise intervention , 48 for 1. Participants in the intervention condition, who have received ImPuls in addition to TAU, will show lower global symptom severity at post-treatment and follow-up assessments compared to a control condition with TAU only.2. Overall costs in the intervention condition will represent a significant saving for the public health system compared to the control condition at post-treatment and follow-up assessments.3a. The intervention will lead to significantly higher levels of MVAE at post-treatment and follow-up assessments compared to the control condition.3b. The effect of condition on the reduction of the primary outcome global symptom severity will be mediated by an increase in MVAE.4. Participants in the intervention condition will show lower disorder-specific symptoms  compared to participants in the control condition at post-treatment and follow-up assessmentsWe will further assess, if participants in the intervention condition show more instances of clinically significant change compared to participants in the control condition at post-treatment and follow-up assessments .The study will be led by researchers based at the University of Tuebingen in Germany, and will be conducted in 10 different study sites across Baden-Württemberg, a region in South-West Germany. The entire project will be conducted between September 2020 and February 2024. The study has been registered at the German Clinical Trial Register  and has been approved by the local ethics committee for medical research at the University of Tuebingen .A pragmatic multi-site block-randomized controlled trial with two treatment arms (ImPuls + TAU vs. TAU) and three points of assessment  will be conducted see Fig. . All outPatients will be recruited mainly via inpatient psychiatric departments, family practices, general practitioners and psychiatric and psychotherapeutic outpatient units. The project will be conducted in collaboration with two health insurances, the AOK Baden-Württemberg (AOK) and the Techniker Krankenkasse (TK), who will support the recruitment with a targeted approach of general practitioners, psychiatrists and psychotherapists. All hospitals, clinics and practices will receive information material, such as flyers and posters, to inform their patients about the project. In addition, AOK and TK will publish articles in media distributed for their members during the course of the project. The TK further will inform eligible patients directly via phone calls. Recruitment will be additionally performed via social media posts (Instagram and Facebook), newsletters of professional associations, email distribution lists of universities, local influencers and regional newspapers and television.Interested patients will first attend a preliminary telephone screening of eligibility criteria and will receive general information about the project , insomnia (F51.0), panic disorder (F41.0), agoraphobia  or PTSD (F43.1). Exclusion criteria include: Performing exercise with at least moderate intensity, more than once a week for at least 30 min each, continuously over a period of six weeks within the last three months before study diagnosis, medical contraindication established by the general practitioner or a medical specialist, acute mental and behavioral disorders due to psychotropic substances , acute eating disorders (F50); acute bipolar disorder (F31), acute schizophrenia (ICD-10 F20), acute suicidality.t-test, alpha level of 0.05, a test power of 80%, an equal cell population, and a dropout rate of 30% were assumed. This calculation resulted in N = 375, which is conservative enough to detect the lowest expected treatment effect at the post-treatment phase. However, we regarded this sample size as the minimum and targeted a total sample of N = 600, in order to have enough statistical power for further analysis that will be predefined and published in a separate study protocol regarding the evaluation of the entire implementation process based on the Medical Research Council framework  . The GSIr = 0.67 .depressive symptoms will be assessed with the PHQ-9 module, assessing symptoms over the last two weeks with nine items, each of them representing one of the DSM-5  criteria for a depressive episode  , 67. TheSleep Quality will be assessed with the global sleep quality score of the German version of the Pittburgh Sleep Quality Index PSQI; 68]. The global sleep quality score is the sum of seven sleep component scores (range of component scores: 0–3): subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. The global sleep quality score can vary from 0 to 21 with a cut-off score of 5, identifying clinically raised sleep impairment . The glo.The seven-item Generalized Anxiety Disorder scale [GAD-7] , 71 asser = 0.67)  incurred in relation to the additional unit of effectiveness [transdiagnostic symptomatology] or utility [QALY]. Quality Adjusted Life Years (QALY) are captured by using the EQ-5D-5L questionnaire.The statistical uncertainty of the incremental cost-effectiveness ratios will be evaluated by applying cost-effectiveness acceptability curves (CEAC). During the intervention period and follow up time, costs of outpatient and inpatient care, outpatient medication and days of incapacity to work are displayed as quantities. In addition, cost parameters resulting from the intervention and the implementation are considered.The perspective of the statutory health insurances is adopted and the results are examined with regard to a transferability to the statutory health insurance populations. Sensitivity analyses, additional explorative subgroup analyses and statistical uncertainty analyses will provide a differentiated illustration of the results of the economic evaluation. Differences in costs related to disparities in age or comorbidities are additionally analyzed using mixed models.Exercise therapy is currently not provided within the outpatient mental health care system in Germany, despite the promising effects of exercise found for different mental disorders , and desA recent pilot study evaluating the efficacy of ImPuls within a monocentric randomized controlled trial for patients with major depressive disorders, insomnia, anxiety disorders, PTSD and ADHD showed therapeutic effects comparable to disorder-specific exercise interventions or established treatments, such as CBT . BesidesSpecific challenges might arise during the current trial. First, recruitment success might depend on regional circumstances. Some study sites are located in big cities and some in smaller districts with smaller populations. Thus, participation demands might be higher in some study sites compared to others, which could lead to unbalanced participation rates between sites or might have further effects on the motivation of study therapists since some might wait a long time to start their first group while others might be pressured to start the intervention. However standardized training and adaptations in recruitment procedures might counteract these challenges. The effects of study sites will be considered in the statistical models. Second, recruitment and implementation started in March 2021 during the third wave of the Covid-19 pandemic in Germany. Thus, patients might be hesitant or anxious to participate in the study. There might be a bias due to a potentially low participation rates of patients with anxiety disorders. This possible selection bias will be also modelled statistically.In addition, another study protocol will be published that goes beyond this study protocol and addresses in more detail the relevant outcomes of the implementation process , following the MRC framework .Additional file 1. TIDieR-Checklist.pdf (TIDieR Checklist).


Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5–67.6% . Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient’s and clinicians' needs in prognostication. Attempts to provide prognostic markers for long-term outcomes, such as Rumke’s “praecox feeling”, have appeared throughout medical history4, but despite an abundance of outcome predictors at group-level, such as sociodemographic characteristics, clinical markers, and neurocognitive markers6, at a patient-level, no valid prediction model for long-term outcome of schizophrenia is available to clinicians at present7. An additional challenge is that “outcome” entails symptomatic, social, functional, and personal dimensions, which are only partly interrelated9, and may have differing significance for individual patients11. These matters complicate clinical decision-making, for example when considering an early switch to clozapine12, antipsychotic dose reduction or discontinuation strategies13, allocations of sheltered housing14, or occupational support15. From a public health perspective, reliable long-term outcome prediction and the resulting treatment stratification are important, as demands usually outweigh the capacity of mental health institutions, even in countries with high mental healthcare expenses16.Schizophrenia is a heterogeneous illness and its long-term outcomes are highly variable21. In clinical research, machine learning, or pattern recognition, refers to an algorithm that is able to learn from a large multivariate dataset to make an adequate prediction for a patient, for example concerning the future clinical outcome. Modern prospective multicenter studies facilitate the development of prediction models based on machine learning. They provide well-established outcome measures and large numbers of potential predictors (i.e. “features”), in study samples large enough to cover the heterogeneity of the target population19. A landmark study by Koutsouleris et al. recently demonstrated the potential of machine learning for individual outcome prediction in psychosis18. Pre-treatment data from a multicenter clinical trial were used to predict global outcomes after 4 and 52 weeks of treatment in first-episode psychosis. Predictive accuracy was found significantly above chance, at 73.8–75.0%. With an average drop of 2.8%, accuracy was retained when the models were tested on geographic sites left out of the model training procedure, suggestive of its validity in other samples. Unemployment, lower education, functional deficits, and unmet psychosocial needs were found most valuable in predicting 4- and 52-week outcomes.Machine learning potentially presents a way to develop models reliably predicting individual outcomes for multifactorial and heterogeneous illnesses such as schizophrenia22. We explore the use of a wide range of baseline markers of genetic and environmental risk and measures of past and baseline clinical state, to predict 3- and 6-year symptomatic and global outcomes. We use data-driven selection to arrive at a model containing predictors from a limited number of measures, aiming at clinical applicability. We assess its generalizability by using leave-one-site-out (LOSO) cross-validation, testing our models on geographic study-sites left out of model development. Additionally, we investigate the use of the features that have been found to predict 4- and 52-week outcomes of first-episode psychosis18, for 3- and 6-year outcomes in the GROUP sample.Here, we extend the use of data-driven model development based on patient reportable data, to long-term (3 and 6 years) symptomatic and global outcomes of patients with schizophrenia-spectrum disorders. To this end, we include a heterogeneous population of schizophrenia-spectrum patients, with variable illness duration and baseline clinical status from the Genetic Risk and Outcome in Psychosis (GROUP) cohort studyT3) and six (T6) years after baseline. Demographic and clinical baseline characteristics of the study sample and comparisons to patients excluded because of missing follow-up assessments are listed in Table T3 and T6, 49%, 37%, and 41% of patients were in symptomatic remission ) respectively; 31%, 44%, and 36% had good global functioning status  scale ≥ 65) at respective measurements. For symptomatic outcome, 65% and 64% of patients were stable at T3 and T6 relative to baseline, and 68% and 68% for global outcome , and either Camberwell Assessment scale of Need Short Appraisal Schedule  or Community Assessment of Psychic Experiences  data for multimodal modeling. Notably, this set is especially rich on indicators of clinical course until inclusion in GROUP . The choice of these modalities was based on unimodal modeling performance for the following modalities: 1) demographic variables; 2) illness-related variables; 3) PANSS; 4) substance use characteristics; (5) neurocognitive task scores; (6) social cognitive task scores; (7) Premorbid Adjustment Scale items; (8) CANSAS; (9) CAPE; (10) extrapyramidal symptoms; (11) genetic features, and familial loading of psychotic disorder, bipolar disorder, and drug abuse; (12) environmental variables of urbanicity and living situation ) including recursive feature elimination, support vector machine models were trained to predict individual patient’s outcomes based on their baseline data from four modalities. The outcome could be predicted with similar cross-validated balanced accuracies (BACs), regardless of the period, outcome, and the fourth modality included (i.e. CANSAS or CAPE), ranging from 62.2% to 67.6% Table .The 10% most influential features for symptomatic as well as global outcome based on weight and frequency of selection included items from all four modalities in the model: PANSS items, illness-related, demographic features, and either CANSAS or CAPE items , worse score on  quality of life and the use of antipsychotics were associated with multiple poor outcome endpoints. This was supplemented by a lower number of no needs and met needs, together with housing needs and unmet psychotic disorder needs in models including CANSAS items. In models including the CAPE, items of importance from the CAPE mostly included those from the depression subscale  Table .The following features were predictive of at least one-fourth of poor outcome endpoints, albeit selected in fewer models than those summed in the previous paragraph: higher age, schizophrenia diagnosis, and a higher level of various present state symptoms in PANSS subdomains of positive, negative, disorganization symptoms and emotional distress  Table .Comparison of frequently misclassified patients to those mostly correctly predicted showed the following: patients with a good outcome, who were incorrectly classified as having poor outcome (21–37% over the models) showed less favorable baseline characteristics  when compared to the correctly classified group of patients. Contrary, patients with poor outcome who were mostly incorrectly classified as having good outcome (3–14%) showed favorable baseline characteristics, such as lower PANSS and higher GAF scores, when compared to the most correctly classified group of patients . There was, again, a small difference between CANSAS-based and CAPE-based models . LOSO models validated on the Utrecht site and single-site models trained on the Utrecht site tended to perform below average, in line with the differential patient profile  found at this site relative to the other geographic sites and its smaller sample size ). These LOSO validated models had slightly (−2.3% on average) lower accuracies than models trained on the full dataset )), resulted in accuracies of 59.0–62.7% for symptomatic outcome. For global outcomes, we obtained accuracies of 56.5–66.4%  performance estimates, predictions are modestly accurate, similar to recent experimental prognostic models for depression based on machine learning predicting long-term clinical outcomes based on patient reportable data24. The models did not reach the LOSO cross-validated accuracy of 71% in the study on the one-year outcome of first-episode psychosis18, presumably due to the uncertainty introduced by time, care-as-usual setting, and the heterogeneity of baseline clinical status and illness duration within our target population.To the best of our knowledge, no prognostic models for the long-term global and symptomatic outcomes of psychosis are presently available25.Through a modality-wise learning strategy, a combination of baseline sociodemographic features and clinician-rated symptoms, complemented by self-rated lifetime psychotic experiences (CAPE items) or psychosocial needs (CANSAS items) was selected in the models. Interestingly, in unimodal models, these state-based and context-based modalities outperformed trait-based modalities, including genetic and cognitive task scores. This finding may be partly explained by the relatively large share of patients with a stable clinical state at inclusion and follow-up. We further argue that the performance of trait-based measures may improve if the interaction between genetics and environmental exposures in psychosis outcomes is taken into account18.Features offering a clinician’s integration of the clinical picture and those with a broad underlying construct  show to be the most important predictors. These resemble features found to be predictive of one-year outcome in first-episode psychosis: psychosocial needs, global functioning deficits, and affective symptoms 18. In particular, lack of insight appears predictive of poor long-term outcomes across all the models. This may be mediated through poor adherence and eventual service disengagement26. Furthermore, we note that the most important social need in our models (i.e. housing) is different from those  predicting short-term outcomes. This could be explained by the lower level of social functioning found in our study cohort, compared to first-episode patients27, suggesting that in a model suited for a functionally heterogeneous population, the entire range of social needs within the CANSAS instrument may have its relevance. In interpreting the influence of features on the predictions, it should be noted that some, frequently selected, features show large variation in weight. This variation could have its origin in the heterogeneity of the disorder and should be the subject of future research.In comparison to the aforementioned study, and our work, we also note differences, suggesting differential ways to short-term and long-term clinical management of psychosis. We found higher, and not lower symptom severity to predict poor long-term outcomeWithin our models, misclassification especially occurs in patients with unfavorable clinical baseline status combined with good outcomes. This may reflect variation in baseline clinical context and acute state at the time of inclusion (i.e. admission due to relapse vs. outpatient treatment) of these patients and/or availability of therapeutic or supportive resources. We note that the higher baseline symptom severity, lower GAF, and higher psychosocial needs found in one geographic site that underperformed in the LOSO validation relative to the others, may support his possibility. To enhance model performance, these contextual factors may be taken into account in future models.29. Models with modest accuracy may be of use, depending on the level of uncertainty parties involved in clinical decision-making are willing to accept from a model31, a level which to our knowledge is unknown for long-term outcomes of psychosis. In addition, the preferable way of interaction between model and clinician remains to be addressed32. We suggest clinicians may inform their decision making, both by the prediction itself and important features in it, for example, high core social needs, affective symptoms, or low quality of life. Apart from clinical practice, modestly accurate model predictions may serve intervention research by offering stratified randomization.Our models on long-term outcomes of psychosis perform with reasonable accuracies, but at present are not suitable as a stand-alone tool to stratify treatment. Regardless, the machine learning model trained here and a clinician would represent rather different takes on reality. The model sees the patient through the lens of a number of constructs, such as “insight”, whereas a clinician’s judgment is more globally constructed and starts from the moment the clinician meets his patient in the waiting room30. Furthermore, the significance of any predicted outcome might differ per patient, per stage of illness, and per intervention10. Hence, presenting an array of outcome dimensions with accessible features might best fuel the clinician–patient dialog on intervention33. Moreover, clinical guidance on when and how to use prognostic tools might prove essential for future dissemination of prognostic models based on machine learning in psychiatry.Future prediction tool development should be informed by end-user (i.e. patients and clinicians) needs concerning scope, predictive capacities, and potential clinical consequences. We need to learn how they weigh benefit and harm due to treatment choices against a given outcome probability34. It should be noted that the drop in performance was small (on average 2.3%), when the models were applied to patients from geographic sites not part of the training sample, suggesting transportability to samples with a comparable profile. Although we implemented a comprehensive validation procedure, we cannot rule out some overfitting not accounted for35, including that resulting from information leakage because modality selection, imputation, and scaling were performed outside the nested cross-validation pipeline. Apart from this, our approach of taking the four best performing data modalities from unimodal modeling runs together does not necessarily yield the best performing combination in a multimodal model. Instead, models may benefit from a combination of modalities containing a wider range of information, as has been suggested by studies combining patient reportable and imaging data37. We suggest future research may address what is a clinically parsimonious set of modalities, that is, an optimum between accuracy and investment to obtain data. Furthermore, we believe that imputation and scaling outside the cross-validation setup has not led to over-optimistic estimates of generalizability, because of the very low number of imputations (<0.5%) and the fact that most of our features’ scales are fixed, thus independent of our dataset.We note the following limitations. Although we present the largest machine learning study to date on outcome in psychosis, based on patient-reportable data, the sample size may not be sufficient to account for the substantial heterogeneity of the out-of-study population with a schizophrenia-spectrum disorder38. The nature of the sample included may also explain the association of antipsychotics use with worse outcomes, as antipsychotics use at baseline is likely to be confounded by history or expectation of more severe illness course. The observational sample obtained may however be more representative for clinical practice than those stemming from clinical trials. To improve model reliability, the use of multi-center samples dedicated to prognostic model development or models informed by national registry data, as has been done to predict transition to psychosis from high-risk mental states and suicidal behavior, may be needed41.The GROUP study sample is known to represent a relatively well functioning subset of a population of schizophrenia-spectrum patients in need of specialist care. Generalization to other samples further might be hindered by the exclusion of the most severely affected patients, either due to study drop-out, exclusion of patients with extensive missing data, or incompetence or unwillingness to give study consent42. We used baseline data for outcome prediction, whereas in clinical practice, decisions are typically based on longitudinal, rather than single, examinations. Longitudinally informed models are expected to result in better prediction accuracies. Furthermore, we propose that contextual information, such as baseline clinical context , or supportive resource status (e.g. family support) may further enhance model performance. The addition of biomarker modalities, including imaging data and genetic data derived from genome-wide association studies, possibly in interaction with environmental exposures, holds the same promise41. However, all additions come at the expense of time investment, model interpretability, and the requirement of larger training datasets20.Regarding outcomes, prediction of longitudinal patterns, or adverse events, such as readmission to a psychiatric hospital may also add clinical relevance, especially for long-term outcomeIn conclusion, we demonstrate the feasibility of a machine-learning approach to long-term outcome prediction in a heterogeneous target population of schizophrenia-spectrum patients, based on a lean set of patient reportable features, overlapping with those predictive of short-term outcome of first-episode psychosis. Future models may benefit from considering patient’s and clinician’s needs, the appropriate nature of the training sample  features), and implementation of advancements in machine learning methodology. Individual outcome prediction based on machine learning may inform the treatment stratification needed both from a patient and a public health perspective.43; (2) age 16–50 years (extremes included); (3) Dutch language proficiency; (4) ability to provide informed consent. Extensive genetic, cognitive, environmental, and outcome data were collected at baseline (T0), and 3-year (T3) and 6-year (T6) follow-up. The full GROUP sample at baseline included 1119 patients with variable illness duration, including recent-onset psychosis22. Here, we used data of 523 participants for whom outcome assessments at both T3 and T6 were available, with a schizophrenia spectrum disorder , assessed with the Comprehensive Assessment of Symptoms and History or the Schedules for Clinical Assessment for Neuropsychiatry  psychotic disorder diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition APA psychotiThe study protocol was approved by the Medical Ethical Review Board of the University Medical Centre Utrecht and by local review boards of participating institutes. Participants provided written informed consent. Database release 5.0 was used in all analyses.T3 and T6. Symptomatic outcome was selected as it traditionally is a mainstay of clinical care. We followed the consensus definition of symptomatic remission by Andreasen et al., operationalized as a mild score  on selected items of the PANSS , maintained for at least 6 months47. For global outcome we followed Koutsouleris et al. in operationalizing global outcome with a dichotomization of the Global Assessment of Functioning (GAF) scale, considering a score of <65 poor global outcomes and ≥65 good global outcomes43, as GAF scores between 61 and 70 have been proposed as a threshold between at-risk mental states and illness and widely used as markers of recovery as part of more complex criteria49. GAF was constructed as a mean composite score of the GAF symptoms and GAF disabilities subscales assessed in the GROUP project, and normally distributed in our sample. To investigate the possibility that a threshold other than GAF 65 would better represent a border between “good” and “poor” outcomes, we tested other cut values in the GAF 50–68 range post-hoc (Supplementary Note 4.2).We selected two long-term outcome measures in a classification approach to outcome prediction: symptomatic remission and global functioning, measured at both 18. We clustered available candidate baseline predictors in modalities according to information type: (1) demographic variables, including age, sex, education, socioeconomic status, living situation, and employment; (2) illness-related variables, of diagnosis, comorbidities, illness course duration of untreated psychosis, quality of life and medication use; (3) clinician-rated, present state symptoms as measured by the PANSS47; (4) substance use characteristics  indicated by urine analysis and the Composite International Diagnostic Interview50; (5) neurocognitive task scores of IQ, memory, processing speed/attention and executive functioning, assessed with the Wechsler Adult Intelligence Scale-Third Edition short form, Word Learning Task, Continuous Performance Task-HQ and Response Set-shifting Task respectively; (6) social cognitive task scores of theory of mind, affect recognition and facial recognition, assessed with Hinting Task, Degraded Affect Recognition Task and Benton Facial Recognition Test respectively. For psychometric instrument references for cognitive testing, see Supplementary Note 1.5; (7) Premorbid Adjustment Scale items51, comprising social and cognitive functioning in childhood and adolescence; (8) need of care items, measured with the CANSAS53; (9) self-rated lifetime psychotic experiences, consisting of Community Assessment of Psychic Experiences questionnaire (CAPE) items54; (10) extrapyramidal symptoms, comprising akathisia, dyskinesia, and Parkinsonian symptoms; (11) genetic features  environmental variables of urbanicity and living situation. For global content of, and features within the modalities, see Supplementary Note 1 and Supplementary Table All clinical variables assessed at baseline within the GROUP project which permitted a sample size >250 patients were considered for inclusion as a predictor, barring the models which contained a prespecified set of features based on the best-performing features in the EUFEST study by Koutsouleris et al.  and its binary label is the outcome . SVM returns feature weights, reflecting the relative influence of predictors on outcome prediction. We used weighting by outcome class to account for unbalance between outcome group sizes and blind the algorithm to base rate distribution, to avoid model bias towards the largest outcome group. Internal validation was performed with three-layer, 10-fold nested cross-validation, where the inner cross-validation layer optimized the cost parameter, representing a penalty imposed on cases violating the margin of the decision boundary of the model. The middle layer selected the smallest predictor set with performance within 10% of the best performing set by recursive feature elimination (RFE). The outer layer provided performance estimates, reflecting the accuracy of the ensemble of k models taken together. This validation procedure was repeated 50 times to reduce dependency on the choice of train-test partitions. We employed the e1071 library (version 1.6.8) for SVM in R (version 3.4.0); and the caret package (version 6.0.76) for RFE59.We trained a linear support vector machine (SVM)ses Fig. . For a g60) which yielded comparable performance in the study sample  Data-driven model development including internal validation using repeated 10-fold nested cross-validation , positive predictive value, and negative predictive value. To give an overview of important features to predict long-term outcomes, we listed features with the highest selection chance per model (top 10%), selected in >1 model. Since the entire cross-validated RFE procedure was repeated 50 times we were able to calculate the percentage of misclassified and correctly classified patients within these 50 repeats. To explore ways to enhance future model performance, we compared the profile of ≥90% misclassified patients with that of ≥90% correctly classified patients on sociodemographic and clinical characteristics, separately for the poor and the good outcome groups. We made use of the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statementFurther information on research design is available in the Supplementary InformationReporting Summary


The COVID-19 pandemic restricted everyday life during 2020-2021 for many people worldwide. It also affected alcohol consumption patterns and leisure activities, including the use of social media.The aim of this study was to analyze whether social media use predicts increased risky drinking over time and during the COVID-19 pandemic restrictions in particular.This 5-wave longitudinal survey study, based on a nationwide sample of workers, was conducted in Finland in 2019-2021. A total of 840 respondents  participated in all 5 waves of the study. The outcome variable was risky drinking, measured using the 3-item Alcohol Use Disorders Identification Test (AUDIT-C). Multilevel linear hybrid modeling enabled the investigation of both within-person and between-person effects. Predictors included social media use and communication, involvement in social media identity bubbles, psychological distress, and remote working. Controls included sociodemographic factors and the Big Five personality traits.Increased involvement in social media identity bubbles was associated with an increase in risky drinking behavior. Of all social media platforms examined, online dating app use was associated with riskier use of alcohol over time during the COVID-19 crisis. Daily social media communication with colleagues about nonwork topics was associated with risky drinking. Female gender, younger age, university education, nonindustrial occupational field, conscientiousness, agreeableness, and neuroticism were associated with lower levels of risky drinking.Social media use during a pandemic carries some risks for alcohol consumption. Involvement in social media identity bubbles and online dating are risk factors for excessive drinking during the COVID-19 pandemic. The COVID-19 pandemic has remarkably changed everyday life for people worldwide. Restrictions placed on social gatherings, public events, bars, and restaurants are likely to affect peoples’ drinking habits . ResearcDifferent forms of social media have become a central part of communication and interaction among people across the world -21. SociThe underlying mechanisms between social media use and related alcohol use are not entirely known, although previous studies suggest that perceived drinking norms and exposure to alcohol content and marketing might be possible factors ,39-41. AResearch on social media identity bubbles  shows that people are drawn toward similar-minded individuals with whom they identify strongly and share information ,44,45. TThere are likely to be major differences in drinking during the COVID-19 pandemic at the individual level. Although the ongoing pandemic has been reported to have a negative impact on the mental well-being of people in Europe ,48, it sAccording to pre-pandemic studies, younger people and men are more likely to be risky drinkers -56. SomeThis longitudinal study focused on analyzing risky drinking behaviors before and during the COVID-19 pandemic restrictions in Finland. The first COVID-19 case in Finland was reported in January 2020, and the country reacted to the rising worldwide crisis in March 2020 by placing several restrictions: large public events were cancelled and recommendations for remote work were implemented . On MarcThis longitudinal study aimed to analyze risky drinking behaviors before and during the COVID-19 pandemic restrictions in Finland. The research questions were (1) How did alcohol use change during the COVID-19 pandemic restrictions? (2) Do social media–related factors predict alcohol use over time? (3) Are psychological and social factors, such as psychological distress, remote working, and personality, associated with drinking?Social Media at Work in Finland Survey study started in Finland in mid-March 2019, and data were collected at 6-month intervals. A total of 5 waves were collected from Finnish employees. The baseline survey data collection commenced in mid-March 2019 , with a response rate of 28.3%. The same participants were contacted for the follow-up surveys in mid-September 2019 , mid-March 2020 , mid-September 2020 , and mid-March 2021 (n=1018). The third survey was sent only to those who had responded to the second survey, whereas the fourth and fifth surveys were sent to all original respondents. Of the original 1817 survey respondents, 840 (46.23%) participated in all 5 surveys.The longitudinal The survey was designed to represent the Finnish working population. The survey was conducted in Finnish, and responses were collected from all areas of mainland Finland. All participants were working at T1, and they were from various major occupational fields  and those who responded only to some waves (n=977) showed that the final sample included slightly older respondents and more male participants. However, we found no differences in other variables included in this study, such as risk drinking, between those who had participated in all 5 surveys and others. In other words, dropout from the survey is not associated with our main points of interest. Our final sample is very close to the official census figures provided by Statistics Finland in terms of mean age (43.90 years in our sample vs 41.81 years in the Finnish population), gender , and university-level education (47.74% in our sample vs 42.46% in the Finnish population) ,63.The study protocol was reviewed and accepted by the Academic Ethics Committee of the Tampere region (decision 90/2018). All participants agreed to voluntarily participate in the web-based surveys, and they were informed about the purpose of the study and data processing procedures. Data collection was carried out in collaboration with Norstat, a data solutions provider.. Our models used AUDIT-C as a continuous variable. We also report the proportion of excess users, using a cutoff of ≥6 points for men and ≥5 points for women [We used the 3-item Alcohol Use Disorders Identification Test (AUDIT-C) to measure risky drinking. AUDIT-C is a widely used screening tool for excessive drinking -68. AUDIor women .excellent at all time points .We used the 12-item General Health Questionnaire (GHQ-12) to measure psychological distress -72. All does not describe me at all) to 7 (describes me completely). The original scale ranged from 6 to 42, but we transformed it to a scale of 0 to 1. The internal consistency of the scale was good at all time points .We measured involvement in social media identity bubbles using the 6-item Identity Bubble Reinforcement Scale (IBRS-6) . InvolveThe use of different social media platforms and apps was determined by asking the study participants questions about their general use of social media  and their use of social media for work purposes . Both questions were followed by separate items for different social media apps and internet services, such as Facebook, Instagram, Twitter, YouTube, LinkedIn, Microsoft Teams, and “Dating apps” . Answer options included “I don’t use it,” “less than weekly,” “weekly,” “daily,” and “many times a day.” All commonly used apps were first analyzed separately by using dummy variables for weekly users. In the final models, we used a dummy variable for use of online dating apps (0=no use and 1=at least sometimes). Sensitivity analyses were performed for weekly and daily users of online dating apps. The models also included a variable for general social media use. This variable was combined from the most used social media platforms , and the dummy variable was created on the basis of social media use several times a day .We used a single-item measure for daily social media communication with colleagues about nonwork topics: “How often do you use social media to keep in touch with your colleagues or work community regarding nonwork-related matters?” Answer options included “I don’t use it,” “less than weekly,” “weekly,” “daily,” and “many times a day.” A dummy variable was created indicating those communicating with their colleagues informally at least once a day. The measure was previously validated with two Finnish cross-sectional samples ,24.We measured remote work with a question on whether respondents were working remotely. Those respondents who indicated that they were working remotely at least 3 days a week were considered remote workers in this study.other gender, and none were among those who had participated in all waves (n=840). Hence, gender was used as a dummy variable . Information on participants’ education was obtained via a question including 7 categories , and occupational area . Gender included options for male, female, and other gender. Very few participants selected ries see . We usedα=.69; conscientiousness α=.67; agreeableness: α=.56; and neuroticism: α=.71) to good (extroversion: α=.87).We used the 15-item Big Five Inventory for personality . All iteDescriptive results of the study are reported in In our models, all main time-varying variables had both within-person and between-person effects. Within-person effects show how changes in predictors over time are associated with the change in the outcome variable. Between-person variables show group differences between individuals. The models also included several between-person control variables.We first focused on analyzing within-person and between-person effects of social media app use on risky drinking. We performed analyses on the use of such apps, as well as on weekly and daily use, when possible. The full model reporting our main findings included only those social media apps that were found relevant during this analysis. Different sensitivity analyses were performed, and we also checked interactions with COVID-19 time points (T3-T5) to determine whether some effects were stronger or different during the pandemic.Descriptive results showed changes in risky drinking measured were relatively small during the COVID-19 time points examined (T3-T5) compared to the pre-pandemic time points (T1-T2). Drinking decreased slightly, especially at T5 see . The proP=.04). Use of online dating apps  was strongly associated with risky drinking in all models. In particular, between-person effects were strong for users in general , weekly users , and daily users  in models adjusting for age and gender. Therefore, of all the different social media apps, online dating app use was included in our final model.Our results showed that the most used social media apps were not strongly associated with risky drinking . Additionally, those respondents who had daily social media communication with colleagues about nonwork topics drank more than others did . Women  and those with an MA degree or higher from university drank less than others did . Older age was associated with higher risky drinking . Workers in the industrial sector drank more than others did . Lower conscientiousness , lower agreeableness , and lower neuroticism  were associated with higher drinking. In addition, the full model shows that participants drank less during the COVID-19 era than they previously did .Analysis based on the full hybrid model showed that of the within-person predictors, only involvement in social media identity bubbles was significantly associated with risky drinking . Other relevant interactions with time were not observed.The last part of our analysis aimed at reviewing whether these within-person and between-person effects were stronger during the pandemic. This analysis showed that the within-person effect of Tinder use was observed during the pandemic  was also found, but only during the COVID-19 pandemic. This finding suggests that unusual circumstances have perhaps led to unusual and risky drinking habits. Online dating app users also drank more than others. Moreover, we found that daily social media communication with colleagues about nonwork topics was associated with risky drinking. Altogether, our results suggest that people engaged in drinking as part of involvement in web-based social bubbles. Furthermore, as dating in bars, clubs, and restaurants was not possible during the COVID-19 pandemic, dating apps such as Tinder were considered an alternative. These dating activities were found to be associated with risky drinking, despite the closure of bars.Previous cross-sectional studies conducted during the COVID-19 pandemic have reported decreased drinking, especially in Europe , but incOur finding on the within-person effect of involvement in social media identity bubbles offers an interesting contribution to the literature. Previous studies have especially considered social media postings as motivational determinants of alcohol use  and geneWe discovered that increased use of dating apps was associated with increased risky drinking during the pandemic. Our longitudinal evidence supports previously noted findings that dating app users drink more than nonusers, and this pattern increases during their dates -36. It iOur findings further showed that female gender, younger age, university education, nonindustrial occupational field, conscientiousness, agreeableness, and neuroticism were associated with lower levels of drinking. These results reflect the findings of previous studies ,58.Der Spiegel indicated that Finland had coped the best with COVID-19 in comparison with 154 other countries [We found no longitudinal evidence on the effect of psychological distress on risky drinking during the COVID-19 pandemic. This finding contrasts previous findings on the role of psychological distress and mental well-being in drinking during the pandemic -18. One ountries . It is fOur study was limited to Finland and its working population. Hence, some risk drinking groups, including full-time students and retired elderly people, were not part of the study. The results only cover the timeframe of 2019-2021, and it is too early to estimate how the crisis and drinking will further develop. Our results are limited to self-reported information on drinking. Despite these limitations, a major strength of our study is the use of multi-wave, longitudinal data. Data collection started before the COVID-19 crisis, enhancing estimation of the effects of the pandemic on drinking. Our data are particularly strong on social media measures, and the response rate for surveys was high. Our modelling strategy used within-between person hybrid models that provide an advanced way of estimating longitudinal effects of independent variables.This longitudinal study of workers in Finland showed a decrease in risky drinking during the COVID-19 crisis. The main risk factors for increased risky drinking were found to be related to social media use. Involvement in social media identity bubbles was associated with risky drinking over time. Dating app users were more likely to drink more during the pandemic. Moreover, daily social media communication with colleagues about nonwork topics was associated with higher risky drinking. Our results provide significant evidence of the power of social media during unusual times.The role of social media in drinking should be considered by social media companies such as Tinder to monitor and regulate alcohol-related postings and advertising on their services and promote more healthy ways of social interaction. This is especially essential during a crisis like the COVID-19 pandemic, when social media services present virtually the only possibilities for forming new social relationships due to lockdowns and restrictions on movement. The connection between social media use, involvement in social media identity bubbles, and increase in alcohol consumption are important findings for organizations and policy makers alike. Education and prevention efforts should consider these risk factors and, possibly, be targeted to at-risk groups.


In the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data.We applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata.We investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission.We identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high  and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions.Early spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies. Following the first detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019, a global pandemic evolved within months . In westThis is the first pandemic when capacity of next generation sequencing (NGS) is widely available, allowing sharing of sequences at unprecedented speed from various countries with more than 155,000 sequences submitted to GISAID by 21 October 2020 . In a geClinical specimens were collected from all coronavirus disease (COVID-19) cases, i.e. PCR-confirmed SARS-CoV-2-infected patients and healthcare workers (HCW) of the LMU Klinikum Munich tested between 29 February and 27 May 2020 at the diagnostic laboratory at the Max von Pettenkofer Institute in Munich, Germany.https://www.djangoproject.com) and a PostgreSQL database. Information on every index patient or index HCW was captured by an assigned clinician, recording onset of symptoms, test results, contacts including risk categories, date, place and type of contact. For the investigation of transmission clusters within the clinic, persons who had contact with a COVID-19 case were classified in three risk categories following the definitions from the German public health institute, the Robert Koch Institute, according to duration, closeness and context [Supplementary Table S1.Epidemiological links were analysed by chart reviews and structured interviews. These metadata were collected using a software system developed in-house called SARS-CoV-2 Infection Surveillance (SCoVIS), based on Django , the Maxwell RSC 48 RNA kit ) or the EZ1 Virus Mini Kit (Qiagen). For diagnostic testing, the following PCR assays were used: The protocol from the US Centers for Disease Control and Prevention, the Charité protocol, the Seegene Allplex 2019-nCoV Assay or the Cobas SARS-CoV-2 assay  as described previously .Supplement for methodological details) [Amplicon pools spanning the SARS-CoV-2 genome were prepared based on the ARTIC network nCoV-2019 sequencing protocol v2 and analysed using the ARTIC bioinformatics protocol (http://artic.network/ncov-2019) (see the details) . The conPatients were part of the COVID-19 Registry of the LMU Klinikum  trial identification number DRKS00021225). Staff members were part of the Care-Corona-Immune Study (CCI). All data were anonymised for analysis and the study was approved by the local ethics committee (CORKUM No: 20–245 and CCI No: 20–247).Supplementary Table S2 for GISAID accession numbers and metadata). Genome coverage was related to viral load of the original respiratory sample  with a median genome coverage of 99.5% (IQR: 98.8–99.7). Sequenced isolates were obtained directly from nasopharyngeal swabs (n = 144), endotracheal aspirates (n = 23), bronchoalveolar lavage fluid (n = 2) or sputum (n = 5).We sequenced clinical specimens collected from 289 SARS-CoV-2-infected individuals between 29 February and 27 May 2020 at the LMU Klinikum in Munich, which is the second largest university hospital in Germany. The majority of COVID-19 cases were male (205/289 (70.9%)) with a median age of 55 years (interquartile range (IQR): 37–71). Of note, 61 of these cases (21.1%) were HCW. We obtained 174 near full-length SARS-CoV-2 genomes with a genome coverage of more than 90% that were further used for phylogenetic studies (see Supplementary Table S3). The SARS-CoV-2 sequences from the LMU Klinikum were distributed throughout the global phylogenetic tree, suggesting multiple independent introductions in the Munich Metropolitan Region .To explore the source of the early introductions, we combined SARS-CoV-2 sequence data with detailed patient history. Most early COVID-19 cases at the LMU Klinikum had a travel history to either Austria or northern Italy, which prompted us to perform a regional phylogeographical analysis of these 174 isolates together with subsampled sequences at GISAID from Italy (n = 61), Austria (n = 88) and the rest of Germany (n = 92) , indicating subsequent local spread in the Greater Munich Area. We also performed an analysis including additional SARS-CoV-2 sequences available from France and Switzerland. However, the cases in Munich did not cluster with sequences from these countries to the same extent as with Italian and Austrian sequences .The patients’ travel history together with unsupervised clustering of isolates using the maximum likelihood phylogeny revealed multiple introductions from various skiing areas in the Alps with subsequent community transmission for some cases. Of note, three unconnected individuals, including the first COVID-19 case at the LMU Klinikum, returned from a skiing vacation in the same skiing area in the Dolomites in Northern Italy, in the period 22 to 28 February 2020  diagnosed in the LMU Klinikum by the Max von Pettenkofer Institute during this early period of the first pandemic wave (weeks 9–22 of 2020), we observed cases both among HCW  and patients . The 174 SARS-CoV-2 genomic sequences derived from samples from patients and HCW were closely related and clustered together in the phylogeny . Among tTo spatio-temporally resolve presumed nosocomial transmission chains, we investigated clusters of infection at the LMU Klinikum, combining detailed case histories based on anamnestic workup and patient files with the interactive phylogenetic viral genome analysis platform using the Auspice software. As an example, we report here the outbreak investigation of the earliest cluster of cases at our hospital.The chronological order of events is summarised in On 4 March, this patient , who had been in contact with this patient (Patient 0) at some stage, became symptomatic and tested PCR-positive for SARS-CoV-2 . Among these screened persons, three infected HCW and one patient were identified. Patient 1 was admitted to the LMU Klinikum on 28 February for a COVID-19-unrelated reason and spent one night (1 March) in the same room with Patient 0 before the latter was diagnosed with COVID-19 and protective measures were implemented, thus classifying this as a risk category I contact. Patient 1, while testing PCR-negative on 3 March, tested positive on 13 March, at a point when they were free of any COVID-19-related symptoms. The virus sequence obtained from Patient 1 was identical to the consensus sequence obtained from Patient 0.On 13 March, HCW 6, who had treated Patient 0 before and after his isolation, became ill with fever and tonsillitis and tested positive for SARS-CoV-2. While a transmission directly from Patient 0 or this cluster seemed likely, sequence analysis revealed that their infection was unrelated and transmission must have occurred from another source (Another HCW (HCW 8), who was treating Patient 0, tested positive on 14 March, yet with a sequence clearly distinct to that derived from Patient 0  was in the same range as in other institutions during the early pandemic phase, e.g. 15% in a London teaching hospital, but substantially higher than in some other reports, e.g. < 1.7% at a hospital in the Boston area, indicating that well established IPC, among other factors such as the burden of infectious patients, can have a significant impact on nosocomial spread ,23. In oIn this analysis, SARS-CoV-2 infection of five HCW was likely to be linked to the index case (Patient 0), while the infections of three other HCW and two other patients were definitely not linked to Patient 0. One likely nosocomial infection (Patient 1) was identified. The analysis of this outbreak exemplifies that the combination of detailed spatio-temporal metadata on interactions and NGS-based virus phylogeny are powerful in establishing likely transmission trees, refuting incorrect assumptions about transmission events and identifying likely risk constellations. It is possible that SARS-CoV-2 transmission may have occurred from a COVID-19 patient to HCW despite the correct use of personal protective equipment, for example during aerosol-generating procedures including NIV. NIV was only later recognised as a potential high-risk procedure in the context of COVID-19, although the true risk level still remains debated ,25. Of nNGS has been employed previously to investigate outbreaks of other pathogens such as multidrug-resistant bacteria and recently also for nosocomial SARS-CoV-2 outbreaks ,27. NGS Our study has a few important limitations. The information we obtained about case and contact histories was extracted semi-automatically as well as manually from medical records and supported by directed interviews. To facilitate future investigations of outbreak scenarios, structured standardised interviews and contact tracing should ideally be performed, enhanced by machine learning methods such as contact prediction via interaction network analysis, tightly integrating documented interactions and genetic evidence. This is currently in development in a nationwide collaborative project as part of the medical informatics initiative. This initiative aims at the development of cross-institutional infrastructure and data-sharing effort by the German Ministry of Research and Education. In addition, spatio-temporal tracing of interactions between HCW and patients could be supported by proximity detection methods using contact tracing apps on mobile phones, although privacy protection remains a major concern for such approaches. Our analysis was limited to cases that presented to our healthcare institution and other sequence data available at GISAID. We realise that the number of available sequences from Germany, especially southern Germany, was low during the investigated time period. We have incorporated all available GISAID sequences from Germany in the analysis during that period. Given the low genetic divergence and incomplete sampling in the early phase of the pandemic, the validity of inferring of transmission and geographical origins of introductions is limited . InterprWe demonstrate how the combination of case histories and genomic data can be integrated to resolve the spread of SARS-CoV-2 on a global, regional and local level. These findings can be used to implement and adjust containment strategies at the population and institutional level.