There is limited research on prevention of mental ill-health of school personnel and the systematic management of school work environments. The aim of this study was to assess the effectiveness of implementing the guideline recommendations for the prevention of mental ill-health in schools, in particular, whether there was a difference in adherence to guideline recommendations between a multifaceted (group 1) and single implementation strategy (group 2) from baseline to 6 and to 12 months.We conducted a cluster-randomized controlled trial with a 6- and 12-month follow-up. Data was collected from nearly 700 participants in 19 Swedish schools. Participants were school personnel working under the management of a school principal. The single implementation strategy consisted of one educational meeting, while the multifaceted implementation strategy comprised an educational meeting, an ongoing training in the form of workshops, implementation teams and Plan-Do-Study-Act cycles. Adherence was measured with a self-reported questionnaire. Generalized Linear Mixed Models were used to assess the difference between groups in adherence to the guideline between baseline, 6-, and 12-months follow-up.There were no statistically significant differences between the groups in improvements in adherence to the guideline between baseline, 6-, and 12-months follow-up. However, among those schools that did not undergo any organizational changes during the 12 months of the study significant differences between groups were observed at 12 months for one of the indicators.The multifaceted strategy was no more effective than the single strategy in improving guideline adherence. There are some limitations to the study, such as the measurement of the implementation outcome measure of adherence. The outcome measure was developed in a systematic manner by the research team, assessing specific target behaviors relevant to the guideline recommendations, however not psychometrically tested, which warrants a careful interpretation of the results.150571. Registered 12 September 2017.ClinicalTrials.gov, The online version contains supplementary material available at 10.1186/s13012-022-01196-6. Contributions to the literatureFindings from this study contribute with knowledge on the effectiveness of two different implementation strategies on adherence to guidelines in the school settingResults demonstrate the importance of taking into consideration important school contextual factors, such as organizational stability and leadershipThis study fills the identified research gaps in the context of school-based implementation research in general and in the Swedish school context in particularThe school work environment, in Sweden and internationally, is known for a number of organizational and social work-related risk factors for common mental disorders –4. FailuIn 2015, new provisions about organizational and social work environment were introduced in Sweden by the Swedish Work Environment Authority with the purpose to promote a good work environment and prevent risks of ill health due to organizational and social conditions in the work environment . In shorEven though guidelines and regulations are an essential part of achieving sustainable working environments, research has shown that the mere existence of guidelines and regulations does not guarantee their use in practice . A natioTo support schools with the implementation of the guideline we conducted a cluster-randomized waiting-list controlled trial between 2017 and 2019 with the primary objective to compare the effectiveness of two different types of strategies for implementing the guideline within schools . SpecifiThis study reports the results of the outcome-evaluation of the cluster-randomized waiting-list controlled trial. More specifically, the research question is to evaluate whether there is a difference in the outcome measure of adherence to guideline recommendations between the multifaceted and single implementation strategy from baseline to 6 and 12 months.The project was a cluster-randomized wait-list controlled trial with parallel groups conducted between 2017 and 2019. Randomization was carried out at the school level, where schools were allocated to group 1  or group 2  on a 1:1 ratio . SchoolsThe Swedish school system comprises pre-school, compulsory school, and upper secondary school. Children can attend a voluntary pre-school until the age of 6 when they start compulsory school. Compulsory schooling is until grade 9, after which students can choose to continue a 3-year upper secondary school program . The schThe project was carried out in 19 compulsory public schools in two Swedish municipalities. Overall, the municipalities featured distinctive geographical characteristics, with one municipality situated in an urban area and another in a rural area. Schools were recruited in a two-step process. In step 1, advertisements were placed in newsletter, for e.g., the Swedish Union of Teachers and The Swedish Association of School Principals, social media channels, as well as university webpage and work environment blog. Next, interested municipalities and their schools’ principals were given a detailed oral presentation about the project.All employees of the participating schools—teachers, school administrators, recreational pedagogues etc.,—840 individuals in total—were invited to participate in the trial and complete the baseline questionnaire from September to October 2017. The exclusion criteria for participation in the evaluation were not being employed by the school, such as cleaning staff. Study participants were recruited by various means, presented in detail in the study protocol . A totalIn this study, the outcome evaluation of the school principals is not reported, which is a deviation from the protocol . Due to guideline for prevention of mental ill-health at the workplace  showed high fidelity to all components of the multifaceted implementation strategy except for one: forming a communication plan facilitating communication between the schools’ implementation teams and key persons in their municipality. Only in one municipality was such communication successfully facilitated. This could be a critical factor undermining the implementation effectiveness in the municipality by offering less support to the schools . SchoolsA second potential explanation for our findings is the feasibility of the employed implementation strategies in the school context. A recent study evaluating the importance and feasibility of implementation strategies for schools found that both educational meetings and ongoing training, which in the present study was operationalized as a workshop series, were rated as important and feasible strategies . This arA third explanation for our findings relates to the larger context in which the study has been conducted. As stated earlier, according to the provision of the Swedish Work Environment Authority, Swedish employers, including school principals, are required by law to regularly investigate and assess the risks of work-related mental ill-health and to take corrective measures to manage these risks. Moreover, school management is recommended to present results to their employees and to give them the possibility to discuss results . Even thFinally, our findings could be explained by unplanned organizational changes occurring at the school-level. During the first year of the project, two schools in group 1 and one school in group 2 underwent large organizational changes. The changes involved a transfer of the upper-secondary education level to another school at two of the participating schools likely resulting in, among others, re-assignments of personnel, increased uncertainty, and extra workload leading to high stress among school principals and personnel. In addition, at one group 1 school, the school principal resigned 6 months into the project. Results of the sensitivity analyses revealed higher ORs for schools with a stable organization for all of the recommendations compared to the analysis including all schools . Our findings are in line with the literature demonstrating that implementation strategies are more likely to succeed in a stable organization, and schools are no exception , 36. At Given that organizational and social risk factors for mental ill-health are a common challenge in school work environments worldwide , 38, resAs most of implementation effectiveness studies to date have been conducted in health care, and more research is needed on the effectiveness of implementation strategies in schools , 42. MorFinally, with the Swedish school setting sharing many similar features with other national school contexts, the knowledge on the effectiveness of the implementation strategies gained in the present study can be generalized to the school systems outside of Sweden . Still, Schools are still under-investigated settings when it comes to implementation research . To our A limitation of the study was that guideline adherence indicators developed by the research team were not tested for their psychometric properties. No validated instrument to assess the outcome measure of adherence to guideline in the school setting was found in the published literature. On the other hand, the outcome measure of adherence in this study was developed in a systematic manner, assessing specific target behaviors relevant to the guideline recommendations. Future studies should examine which measures would yield the most accurate assessment of guideline adherence in the school setting. One example of a more precise measure is the written occupational, safety, and health documentation collected from the school principals. Such written documentation can be accompanied by a corresponding checklist stating, for example, when the document was developed, which updates were made. Such documentation has previously been shown to be lacking in schools according to Swedish Work Environment Authority.Further, more studies are needed to test the reliability and validity of the instrument assessing adherence to guideline recommendations. One of the ways to improve the measure employed in the current study is through the cognitive interview process—an important step of the instrument refinement to ensure that respondents interpret the questions as intended by the researchers . AnotherFinally, analysis for some of the guideline adherence indicators may have been underpowered to reveal statistically significant intervention effects. As the sample size was considerably smaller for the adherence indicators of recommendation 3, due to a preceding filter question, these results should be interpreted with caution. However, for most of the guideline adherence indicators, confidence intervals point to the possibility of significant effects should the study be replicated on a larger sample of school personnel. Ultimately, aiming at the practical significance over the statistical one may be particularly relevant for this study , 49, whiThis study compared implementation effectiveness between two strategies of the guideline for mental ill-health at the workplace, in Swedish schools. The conclusion of the study is that the multifaceted strategy was not more effective than the single strategy in improving guideline adherence. Clearly, more work needs to be done on further development and validation of the guideline adherence instrument to be used in the school setting, both in Sweden and internationally. Future research is also needed on how to complement and sustain a positive impact of a multifaceted implementation strategy. This will certainly aid the efforts to implement guideline recommendations and thus support school personnel’s well-being. In an upcoming study, we will add strategies to the multifaceted implementation strategy and test their implementation mechanisms in a broader range of Swedish schools to further our understanding of how exactly they affect guideline implementation.Additional file 1. The TIDieR (Template for Intervention Description and Replication) Checklist*.Additional file 2. CONSORT 2010 checklist of information to include when reporting a randomised trial*.


The main purpose of this study was to analyze the effects of Chief Executive Officer (CEO) Key attributes on the financial performance of banks. Current literature gives little attention to the important characteristics of CEOs, therefore, this paper investigates the effects of characteristics of CEOs, such as education, experience, nationality, military background (MTB), and political connectedness (PC), on the financial (return on assets) performance of listed private commercial banks in Pakistan. This research sample included 20 private commercial banks of Pakistan and used Secondary data that was derived from 2011 to 2020, which contained 200 sample observations. This paper used the Fixed effect model, Normality test, Breush–Pagan, white test, multi-collinearity, and Augmented Dickey–Fuller test to investigate the study hypotheses. The main results revealed that CEO MTB and PC significantly and positively affected the financial performance of the bank. It is also found that the CEO's education and Experience have a significant and positive relationships with bank profitability. In contrast, the nationality of the CEO has no significant relationship with the financial performance of the bank. Financial and economic crises in Asian countries and few organizational scandals turn the concentration of researchers toward Corporate Governance (CG). The performance of organizations depends on their governance, and an economy is financially stable when all the enterprises in that economy are better organized and have a good CG. Many organizations fail due to poor CG because CG attracts the concentration of investors and controllers. An efficient and effective regime becomes a primary variable to contend with and endure in the world of competition. Good CG practices assure the achievements of individual firms and strengthen economic advancement Vinten, . TherefoBoard diversity is a significant part of CG, and much literature exists on the correlation between board variables and the financial performance of a bank, e.g., . Hambrick and Mason  arrangedRQ: what attributes of CEOs could affect the private commercial bank's performance?This research contributes to the literature in numerous ways. We do not get any broad study in the literature that takes such like CEO attributes at, e.g., the effect of CEO on bank profitability of private commercial banks in Pakistan. We include the exogenous literature changes in CG by concentrating on crisis-initiated regulatory changes in the banking area. Our sample covers the post-crisis, large sample size, and a broader era of the time difference to explore the effect of a CEO on bank profitability under normal and in crisis financial conditions. Past studies focused on board size (Bsize), board meetings, and other board structure variables. Still, the present research incorporates CEO attributes accordingly, from which expected to give new knowledge. This study concentrated on all the private commercial banks listed in the SECP, expected to give an overall image of the private investment in the banking industry. A lot of work has been done on the CEO characteristic, but no one can use such a variables combination, especially in Pakistan. In 2002, an objective was assigned to SECP for the background of good CG structure for all corporations in Pakistan, whereby CEOs monitor and control devices under best CG practices to accomplish the goals of investors. When these reforms were implemented in the corporate sector, its performance increased  are collected from the audited published annual reports and balance sheets of the chosen banks, just as from SBP and LinkedIn publications. Political data were collected from the certified webpage Election Commission of Pakistan (ECP), which holds elections for the National and Provincial Assemblies. A few banks were excluded because the data were not extensively accessible.ROA is a dependent variable that shows bank performance. ROA is known as the income produced by the bank according to its resource base. It is calculated to control the operating performance of a bank Yim, . This meChief Executive Officer with an MTB: In this research, an MTB of CEO is represented by MTB, a dummy variable that takes one if a CEO is an MTB in a given year and zero if otherwise. Some past investigation has also used these variables  is an important control variable because large banks are ideal for increasing their performance by guaranteeing proficient usage of their considerable assets  model cannot be a perfect estimator. Every board data model is not viewed as the equivalent of time series and cross-section data since it labels to have pairs of subscripts-i and t—where i shows the unit of investigation and t demonstrates time measurement Baltagi, . The errWhere Uit = μi + λt + vit μi = cross-section property λt = time-specific property vit = other random errorsBased on these kinds of impacts of pool data, two distinct models are presented to manage cross-sectional effects:The FE model investigates the relationship between predictor and response variables inside an entity . Every organization has its separate attributes that may affect the independent variables. For example, being a male or female could affect the thinking toward a particular problem, or the political structure of a specific state could have a few impacts on business or GDP, or the trade approaches of a corporation may affect its stock price. When using FE, we accept that something within the individual may affect or bias the independent or dependent variables, and we have to control for this. The justification behind the presumption of the relationship between the error term and independent variables of the organization. The FE model eliminates those time-invariant characteristics to assess the net effect of the independent variable on the result variable.r = 0.09).P-values are β = −0.0121, P = 0.0012, respectively. When the CEO of a bank has an MTB, the financial performance of a bank is low. The beta coefficient and its P-values are significant at the 01% level. Hence, the financial performance of banks led by CEOs with MTB is lower contrasted with those managed by the CEO with a non-MTB. The results also show that a CEO with military experience conditionally has a negative effect on the performance of a bank. The effect of politically connected CEOs on bank performance is shown in P-values are, respectively, β = 0.0060, P = 0.0044. This result is related to the required results. These results confirm and accept the first hypothesis H3 of the study. A significant positive impact suggests that a CEO with a higher education level (Ph. D. or Master Degree) in a board gives more significant bank profitability levels. This study is consistent with past research studies ; experience values is less than education level. Even though both descriptive variables are positively and statistically significant, but education is stronger in deciding ROA. Therefore, we can accept the fourth hypothesis, H4. However, it cannot support the recommendation of Phan (P = 0.3405) with bank performance. However, according to the past literature, the foreign experience of a CEO contributes to the management abilities and other specialized talents, which guarantees the efficiency and expertise lead of the organizations, which improves financial performance  data of private commercial banks for this research, from 2011 to 2020, that restricted our model size to ensure data accessibility. We used 20 private commercial banks in Pakistan. If other banks have chosen, then the consequences should be more attractive. The data relating to just a single country and banking sector of the economy, the relationship between CG and financial performance, can be investigated by taking multiple countries' data and including other significant variables of the CEO's attributes and ownership structures.The negative effect could be attributed to the high borrowing costs of the Pakistani markets. In regression analysis, When the size of the error term contrasts across values of independent variables, then heteroscedasticity is present in the data.The normality test results in Augmented Dickey-Fuller test (test for data Stationery)The fact that Prob(F-statistic) is <0.05 indicates that these study variables are stationary, as shown in Board is one of the significant components of CG. CEOs are responsible for the overall bank performance to ensure the interest of the shareholders and stakeholders. This paper examines the effect of CEO attributes on the financial performance of private commercial banks regarding a rising and developing economy like Pakistan. This investigation finds a significant and negative relationship between CEO MTB and politically connected CEOs with the financial performance of private commercial banks, which raises questions on the role of CEO MTB and politically connected CEOs on the board of Pakistani banks. The presence of such CEOs in a board becomes at risk, and there should take place an occurrence of the poor financial performance of a bank. These findings show consistency with the past research that if a CEO with an MTB and politically connected CEOs working on top of the bank boards, it will negatively affect the investors and lead the bank performance toward a decline in Pakistan. The findings set up a new perspective for Pakistani investors to look at CG practices of banks while making investment decisions. This investigation results reveal that the negative effect of MTB and politically connected CEO states that there is a need to have transparency in the appointments of CEOs. The sample result also finds a significant and positive relationship between the education level of a CEO and experience with the financial performance of private commercial banks. This study recommends that Pakistani private commercial banks consider the appointment of experienced and well-qualified CEOs in the future. In contrast, Foreign CEO does not show any significant relationship with bank financial performance. Based on the insignificant relationship with bank financial performance, this study recommended decreasing foreign CEOs in Pakistani private commercial banks and giving opportunities to local CEOs. Moreover, this study suggests that Pakistani banks should give opportunities to Pakistani CEOs by conveying their international goals and coursework. Banks suggested increasing their human capital investment by providing conventional education and training. Besides, Pakistani banks suggested considering experience and education when arranging board members and providing more opportunities to the local CEO instead of foreigners.This research has updated regulators and various policymakers with essential bits of knowledge to improve future strategies and performance. This study is more important for Pakistan and for all those countries where there are decisive political and military interventions in the corporate policies; in Pakistan, there is a strong presence of political and military approaches. Next, this study recommends that policymakers further endorse CEO characteristics in Pakistan and other developing countries and limit the political and military interventions in corporate policies, such as India, Bangladesh, other Asian countries, etc.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This paper was funded by the National Social Science Fund of China (Grant No. 16BGL094), Science and Technology, Guangzhou Province, China (Grant No. 2017A040403072), The Innovation Team Project of Guangzhou, China (Grant No. 201831799), and Foundation of Humanities and Social Science Research Program, Ministry of Education (Grant No. 15YJCZH225).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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During the COVID-19 emergency, IRST IRCCS, an Italian cancer research institute and promoter of no profit clinical studies, adapted its activities and procedures as per European and national guidelines to maintain a high standard of clinical trials, uphold participant safety and guarantee the robustness and reliability of the data collected. This study presents the measures adopted by our institute with the aim of providing information that could be useful to other academic centers promoting clinical trials during the pandemic.After an in-depth analysis of European and Italian guidelines and consultation and analysis of publications regarding the actions implemented by international no profit clinical trial promoters during the emergency, we monitored the way in which the institute managed clinical trials, verifying compliance with regulatory guidelines and clinical procedures, and evaluating screening and recruitment trends in studies. During the pandemic, our center activated a new clinical trial for the treatment of patients with COVID-19. A number of procedural changes in clinical trials were also authorized through notified amendments, in accordance with Italian Medicines Agency (AIFA) guidelines. Patient screening and enrolment was not interrupted in any site participating in multicenter interventional clinical trials on drugs. The institute provided clear indications about essential procedures to be followed, identifying those that could be postponed or carried out by telephone/teleconference. All external sites were monitored remotely, avoiding on-site visits. Although home-working was encouraged, the presence of staff in the central office was also guaranteed to ensure the continuity of promoter activities.Some measures adopted by IRST could also be effective outside of the COVID-19 period, e.g. numerous activities relating to clinical trial management could be performed on a home-working basis, using suitable digital technologies. In the future, electronic medical records and shared guidelines will be essential for the correct identification and management of trial risks, including the protection of the rights and privacy of subjects taking part. Promoter supervision could be increased by implementing centralized monitoring tools to guarantee data quality. Closer collaboration between promoters and local study staff is needed to optimize trial management.The online version contains supplementary material available at 10.1186/s12874-021-01291-0. Italian legislation no. 23, art. 40, of April 8, 2020 established urgent regulations for pharmacological clinical trials during the COronaVIrus Disease 19 (COVID-19) emergency. The Italian Medicines Agency , the Italian competent authority for pharmacological clinical trial evaluation and, in particular, for studies on patients with COVID-19 , published several press releases on the matter. The most recent was that of April 7, 2020, which provided indications for the management of clinical trials and of substantial trial amendments following the exceptional restrictive measures introduced by the Italian government to cope with the pandemic. The measures adopted were to be valid until further notice and were closely linked to the state of emergency approved by the Council of Ministers on January 31, 2020.On April 28, 2020, the European Medicines Agency (EMA) published specific guidelines  establisIstituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS is an Italian institute for cancer research and treatment. The center is part of the Emilia-Romagna Regional Health Service and represents the hub of the Cancer Network of Romagna. In 2012, IRST was formally recognized by the Italian Ministry of Health as a Scientific Institute for Research, Hospitalization and Healthcare (IRCCS). The institute promotes, coordinates and manages academic trials that arise from the innovative ideas of its researchers.In Italy, non-profit trials are managed according to the ministerial decree of December 17, 2004. This legislation stipulates the general requirements and conditions for the execution of pharmacological clinical trials, in particular those on the improvement of clinical practice, as an integral part of healthcare. The legislation focuses on clinical trials whose promoter is a no profit organization and as such cannot be a marketing authorization holder. The ownership of the data pertaining to the trial, its execution and the results obtained belong to the promoter and the clinical trial cannot be profit-based or used for the industrial development of a drug. If these conditions are met, drug costs are paid by the National Health Service, Ethical Committee and Competent Authority fees are waived, and there are economic (i.e. minimum insurance requirements) and organizational benefits.During the COVID-19 pandemic, clinical trial participants were in self-isolation/quarantine, with limited access to public institutes because of the risk of the spread of infection. Medical staff involved in clinical studies were also engaged in critical tasks related to the containment of the virus. Within this context, IRST, as a promoter of no profit studies, adapted its activities and procedures in accordance with European and national guidelines in an effort to maintain a high standard of clinical trials. Exceptional measures were implemented and trials were amended.https://pubmed.ncbi.nlm.nih.gov) in September 2020 using the keywords “COVID-19”, “Promoter”, “No profit”, “Management”, “Clinical trials”, “pandemic” and “guidelines”, without limits in terms of language or type of publication, revealed very little published data regarding the actions implemented by no profit clinical trial promoters during the pandemic. This prompted us to present our own experience of the measures adopted by the institute, some of which could also be used outside of a pandemic context. Our aim was also to provide useful information and strategies for other academic promoters for the management and conduction of clinical trials in a critical period such as that of the COVID-19 pandemic.A search carried out in PubMed  identified only two methodological articles dealing with the impact of the COVID-19 pandemic on clinical trial management. Wang et al.  analyzedGiven the lack of published data on the management of clinical trials during the pandemic, we decided to analyze the guidelines of the Italian regulatory authorities and to describe the actions taken by our institute in an effort to facilitate the work of other academic promoters.IRST has a Biostatistics Unit (UBSC) that manages independent studies and clinical trials sponsored by pharmaceutical companies. The Coordinating Center (CC) for IRST-promoted trials, a subunit of the UBSC, has been active since 2012. At the start of the pandemic, the CC was managing around 20 interventional studies on drugs, medical devices and radiotherapy, 13 of which  were actively recruiting. Three of these were national multicenter trials involving 50 centers, with a total of 576 patients enrolled in active studies on March 11, 2020 , many of whom were undergoing treatment  for AIFA evaluation. Preliminary authorization was granted by AIFA’s Technical Scientific Committee (CTS), after which the study documents were assessed by AIFA’s Clinical Trials Office and a National Ethical Committee specialized in infectious diseases. Submission was handled through The National Observatory on Clinical Trials of Medicines  platform. We requested a pre-evaluation by the CTS on March 31, 2020 and received a suspended judgement after only 2 days . We submitted a response on April 7, 2020 and received a  favorable opinion on April 10, 2020. The National Ethical Committee’s favorable opinion and AIFA authorization arrived on April 20, 2020 and April 28, 2020, respectively. It took a total of 28 days for the study to be authorized compared to the 60 or more days normally required for clinical studies.In accordance with AIFA guidelines indicating the need to reduce the number of patient accesses to trial centers, IRST successfully submitted a notified amendment via the OsSC platform and was able to authorize the participating centers of the IRST Seneca trial  to supply patients with a quantity of oral drugs sufficient for more than a single therapy cycle (> 4 weeks) of “CAPTEM treatment” . In an effort to facilitate patients who lived far away from the experimental centers, IRST also authorized “pre-cycle” blood tests to be performed in public medical centers near patients’ homes, with laboratory results sent to the principal investigators of participating sites. Special procedures were implemented to manage and document drug supply and accountability because of the increased amount of drugs supplied to patients. When necessary, telephone and/or video calls were used to provide detailed instructions to patients, and all communications were documented.For the IRST 174.19 KENDO study (Eudract no. 2016-004107-31), the institute submitted a substantial amendment to notify the authorities of the temporary transfer of the Oncology Day Hospital of the Guastalla participating center to San Sebastiano Hospital in Correggio. Guastalla Hospital was re-purposed to receive the growing number of patients with COVID-19. Study staff were transferred to the new facility but blood tests continued to be performed in the Clinical Analysis Laboratory of Guastalla Hospital. The transfer to Correggio Hospital was implemented after a careful assessment of the risk of COVID-19 infection for the patients.The above notified amendments will require formal revocation when the emergency has finished.IRST, as a promoter, contacted the participating centers by e-mail and telephone to evaluate the needs of each site, supervise the actions taken by investigators during the COVID-19 emergency, and give advice on how to handle specific situations. We monitored all centers to evaluate screening and recruitment trends and compliance with trial procedures. On the basis of the AIFA press release describing the possibility of modifying the management of active trials (including temporary protocol changes) to limit the risk of coronavirus infection, IRST carried out a risk assessment for each active clinical trial, introducing specific measures to reduce the number of patient accesses to hospital, continue with essential procedures, postpone or assess by telephone/teleconference (TC) all non-essential procedures, and avoid on-site visits. This remote monitoring was carried out in 50 centers participating in multicenter interventional clinical trials with drugs promoted by IRST. It revealed that patient screening was not interrupted in any site and that 16 patients were enrolled regularly by 5 centers Table . When neIRST, a participating center in all the trials it promoted, did not stop recruitment or treatment. IRST administration provided clear indications on the essential procedures to be maintained during the pandemic, also specifying those that could be postponed or assessed by telephone/teleconference. Patients were contacted by phone the day before a scheduled appointment to check on their health status . On the day of the appointment, patients underwent triage at the hospital entrance, where nurses measured their temperature and verified the correct use of the surgical mask. Follow-up visits were not always carried out within the time-frame established by the study protocol, but rather based on the priorities defined by IRST Administration and on legal provisions.The information collected by directly contacting study sites was used by IRST’s CC to update risk assessment plans and implement suitable actions to mitigate risks. We drafted procedures and guidelines for correct trial management, in particular for telemedicine communications between clinicians and patient at home , the shipment of laboratory blood test results, the return of unused drugs, and drug accountability. Requests for reimbursement of costs incurred by patients for laboratory exams performed at external centers were made to participating centers and will be reimbursed by the study promoter. From March 11, 2020 to June 30, 2020, all external sites involved in trials promoted by IRST were monitored remotely (Source Data Verification (SDV) was postponed), for a total of 11 monitoring visits during teleconference. Remote visits were focused on SDV as electronic patient charts were shared with the clinical monitor. It was not possible to verify source documents for only 2 remote visits. A total of 13 site initiation visits (SIV) to activate new participating centers and authorize patient recruitment were made by telephone or teleconference  provided by IRST or their own PC with an adequate ADSL connection and equipped with good antivirus protection. Suitable home arrangements respecting privacy requirements were mandatory, and staff were required to undergo a brief training course on work methods, responsibilities and safety. One person manned the central office each day on a rotating basis to guarantee the continuity of necessary activities, in particular, the registration/randomization of new patients.Clinical trials must be carried out in accordance with Good Clinical Practice (GCP) guidelines, national and international laws, and standard common sense principles to ensure the rights, safety and wellbeing of participants. During the COVID-19 emergency, IRST strived to maintain the same values and standards, minimizing contact between patients and trial staff, but guaranteeing adequate supervision by the study investigator. All measures taken were well-balanced and aimed to avoid increasing the already heavy workload of personnel in participating centers .EMA and AIFA produced guidelines to support clinical trial stakeholders during the pandemic, authorizing home treatment. Oral drugs were widely delivered directly to patients, but no guidance was received from AIFA nor were useful indications gained through discussion with other Italian no profit promoters with regard to the home management of infusion therapies, which are very common in oncology . There are still no published experiences of home-based intravenous (IV) therapies in clinical trials and so patients were forced to go to hospital for IV treatments.Very little data has been published on how the COVID-19 pandemic has impacted the management of no profit clinical trials, making it difficult to compare our experience with that of other academic promoters. The actions taken by IRST were part of the effort to adapt the activities pertaining to clinical trial management to meet regulatory authority recommendations. The final goals of clinical trials, even in an emergency period, must be to safeguard participants and guarantee the robustness and reliability of the data collected.IRST used a home-working (HW) modality to guarantee the continuity of trial management and to maintain the regular communication between research teams in the central office and laboratory via teleconference meetings and email communications. During the emergency, continuous analysis and reporting of all protocol deviations was performed. Delays in patient screening and recruitment, drug administration, tumor restaging assessments and blood tests, these last often performed externally, had to be dealt with efficiently during the pandemic because of their potential impact on the quality of the trial results. For IRST-promoted trials, we only considered and reported in our updated procedures the protocol deviations that would have substantially impacted the primary and secondary endpoints of studies. Clinical trial risk assessment performed on a regular basis by the research team was fundamental. This epidemic could have a medium- and long-term impact on the safety of patients enrolled in clinical trials and on the quality of the data collected if adequate monitoring is not carried out. Risk mitigation plans and the implementation of specific procedures are thus needed to maintain a high level of quality in our trials.Understanding the repercussions of the pandemic on the incidence, mortality and quality of life of cancer patients is a priority for national healthcare services worldwide. The effects of delays in diagnosis, postponed local intervention (surgery and radiotherapy) and systemic treatment, and delays in evaluating response to treatment and adverse events will have to be closely monitored. Data collected during this period will only be able to be used for publication after their robustness and reliability have been confirmed and even more caution will be needed when interpreting results for the purpose of registering new drugs.Some of the implementations introduced by IRST have proven highly effective and suitable for use outside of the epidemic, e.g. home-working (HC), which was formally not a widespread practice in healthcare. Our experience has shown that it is possible to transfer many activities to HW, providing that there is close collaboration with colleagues remaining in the work place. Working from home is organized on the basis of phases, cycles and the achievement of objectives, established in a contract between the employer and the employee. It offers more flexible working hours, allowing employees to complete their work in a way that is most efficient for them and thus facilitating productivity. Travelling time to the workplace, with the associated stress and cost, is also eliminated, optimizing productivity. In addition, this work modality can save money for employers in terms of resources not used in the work place . However, some employees see a downside to working from home as they feel there is no longer a clear boundary between work and private life. They may also feel isolated, cut off from their colleagues, and experience a sense of uncertainty about the quality of their work and their productivity. Overall, this new work modality requires adequate organization, a good team spirit, and the constant exchange of information and management of shared documents.The CC staff of the UBSC underwent specific online training and held regular online meetings about the correct management of clinical trials during the pandemic to remain up-to-date and be able to compare the experience of our institute with that of other Italian stakeholders. However, the emergency highlighted that the infrastructures and technologies needed for clinical trial management must be accompanied by close collaboration between promoters and local study staff. In the future, it will be essential to have electronic medical records, possibly homogeneous at the regional/national level, and shared guidelines for a correct identification and management of risks, in particular for the protection of the rights and privacy of subjects enrolled in clinical trials.Although clinical trial study procedures are known to be extremely stringent and structured, our experience during the pandemic suggests that some could be reviewed to increase remote promoter supervision. For example, centralized monitoring tools could be implemented to guarantee data quality and facilitate cross-checking of electronic Case Report Forms (eCRFs), with a special focus on serious adverse events (SAEs), adverse events and variables involved in primary objectives. Alternative methods of informed consent collection and trial management should introduced  to meet the needs of participants, who would obviously benefit from a simplification of trial procedures. Closer and more efficient cooperation should be pursued between the promoter and staff of participating centers through the use of clear and complete reports, check-lists and dedicated forms to facilitate study management. The ability of the promoter team to handle remote data will also help to reduce the number of activities that need to be carried out on-site, thus streamlining the duties of local staff and optimizing workloads.Additional file 1: Supplementary Table S1. Extraction from clinical trial database.Additional file 2: Supplementary Table S2. Monitoring visits and Site Initiation Visits (SIV) during the “pandemic period 2020” (11/03–30/06) and the same period in 2019.


In 2015, the United Nations established the Agenda 2030 for sustainable development, addressing the major challenges the world faces and introducing the 17 Sustainable Development Goals (SDGs). How are countries performing in their challenge toward sustainable development? We address this question by treating countries and Goals as a complex bipartite network. While network science has been used to unveil the interconnections among the Goals, it has been poorly exploited to rank countries for their achievements. In this work, we show that the network representation of the countries-SDGs relations as a bipartite system allows one to recover aggregate scores of countries’ capacity to cope with SDGs as the solutions of a network’s centrality exercise. While the Goals are all equally important by definition, interesting differences self-emerge when non-standard centrality metrics, borrowed from economic complexity, are adopted. Innovation and Climate Action stand as contrasting Goals to be accomplished, with countries facing the well-known trade-offs between economic and environmental issues even in addressing the Agenda. In conclusion, the complexity of countries’ paths toward sustainable development cannot be fully understood by resorting to a single, multipurpose ranking indicator, while multi-variable analyses shed new light on the present and future of sustainable development. The Agenda, ratified by 193 countries, addresses through sustainable development the major challenges the world faces, such as environmental problems, climate change, economic growth, water, food and financial security, poverty and inequalities5; these also recently exacerbated by the Sars-CoV-2 pandemic7. The world is not new to the request of ‘a global agenda for change’. Back in 1987, the report “Our Common Future” already introduced the key idea of a common action plan to address economic growth in equilibrium with the people and environment, thus preserving our world to meet human needs for today’s and future generations8. The beginning of the XXI century marked a shift in the way countries started being actively engaged in the implementation of sustainable development, with the establishment of the Agenda 2015, allowing the joint forces of UN and governments to achieve significant milestones in poverty and inequalities reduction, as well as in improved water access10. In light of these achievements, and also of the limitations and gaps of such experience, the Agenda 2030 inherits and enlarges the views and objectives of the Agenda 201510. In practical terms, today’s Agenda addresses a more equal, just, and sustainable future by introducing the 17 Sustainable Development GoalsSDGs1. The 17 Goals are constructed upon five pillars: people, prosperity, planet, peace and justice, and partnership; and connections and spillover effects among the Goals are unavoidably present20. In line with the Charter of the United Nations, the Sustainable Development Goals have no pyramidal structure, and there is no Goal prioritised with respect to the others, thus advocating for equal efforts in the designing of appropriate policies to meet these goals (Art. 40 of the Agenda)1. In fact, each Goal targets the implementation of policies, totalling 169 targets across the 17 Goals18. Targets also mark the need for data and measurements of the status of countries with respect to the achievement of the Goals. Countries ratifying the Agenda are encouraged to pursue sustainable development by defining national strategies with a global vision of their actions2, thus contributing to the common action plan necessary to foster change21 and embracing the cornerstones of the Agenda. Nevertheless, the Agenda is not a legal condition, and governments maintain sovereignty in choosing the most appropriate strategy to be placed in the field1. Moreover, on the one hand, countries exhibit remarkable heterogeneity in the challenges they have to face6; on the other hand, the interconnections among SDGs and their targets, also define trade-offs and synergies within different sectors of development22, which are enhanced by the strategies each country implements24. These factors unavoidably create different responses at the country level26.Universality, integration, and inclusion: these are the principles and cornerstones upon which the United Nations (UN) have constructed, in 2015, the Agenda 2030 for sustainable development27 , which requires proper mathematical approaches to monitor the status of countries, and able to account for their heterogeneity and the interconnections across the Goals. Indeed, such interconnections among the Goals and, no less, the synergies and trade-offs among development sectors, can be unveiled thanks to the use of complex network theory . At the same time, within the development topic, the strategy of indexing is often used to rank countries for their performances, thus making the creation of aggregate scores necessary30 , and the Agenda 2030 makes no exception. To create aggregate scores of performances entails mathematically valuing each Goal’s contribution to the overall countries’ output, according to which compute a final score. In the construction of aggregate indices, many options can be pursued to weight these contributions34. A possible strategy would be to mathematically implement the egalitarian principle of the Agenda , thus entailing assigning the same weights of SDGs ; nevertheless, other suitable strategies may exist .It is clear then that the ensemble of countries and Goals within the Agenda 2030 is a complex system of its ownSo far, the complex network analysis of the SDGs system and the creation of aggregate scores have been treated in parallel, without relevant overlaps. Instead, we argue that the combination of data and network science may help in disentangling the dynamics of development and defining data-driven weights to create more refined and comprehensive aggregate scores. In this work, we propose to tackle the definition of rankings of countries by promoting the use of the hidden bipartite network structure of the system defined by countries and Goals performances to highlight and unravel the intrinsic complexity of this system. Such representation of the Agenda 2030 allows one to use network methodologies to provide data-driven solutions to the problem of indexing of countries and weighting of the Goals.11, progresses in the Sustainable Development Goals  are estimated using a set of indicators providing quantitative information about countries performances; each indicator measures the attainment of certain targets across the 17 SDGs. Let k-th value of the indicator I within Goal g recorded in country c. For the sake of comparison across indicators and Goals, most applications consider the 38  Notice that, in this countries-SDGs network, the link 1. We recall that, in light of this principle, the SDG Index by Sachs et al.26 is defined as36The bipartite network representation offers the chance to borrow mathematical tools of network’s centrality to define the importance of the nodes in the system and rank them accordinglyentclass1pt{minima41). Therefore, although in line with the principle of the equal importance of SDGs, to rank countries with equal Goal weights entails not accounting for the complex interconnections in sustainable development we aforementioned.The degree only measures the local information of nodes’ connections, and so it does not depict the global structure of the network  This fact determines higher EC scores of more knowledge-intensive goods.The use of the centrality metrics defined within the field of Economic Complexity (EC)53.In a similar manner, we can adapt the EC theory and methods to the network of countries and SDGs, therefore introducing new reasoning about how countries act in sustainable development. In tailoring the EC framework to the SDGs one, we assume that, if within a Goal only a few countries record near to optimal performance values, this Goal is more knowledge-intensive than the others, thus resulting in a higher EC score. Countries recording such optimal performances are those in more favourable conditions to attain the Goals. In fact, here, we translate ‘knowledge’ into policy and intervention designs and implementations; awareness and preparedness to face the challenges, all well-known factors for affecting countries performances in sustainable developmentGENeralized Economic comPlexitY framework, said GENEPY, which has been shown to reconcile the contrasting methodologies on economic complexity, and it is also a reliable method for processing non-binary incidence matrices as the one of the countries-SDGs bipartite system48. For the sake of clarity, in the following, the adaptation of the GENEPY framework to the context of the Agenda 2030 is defined as SDGs-GENEPY. To the best of our knowledge, Cho et al.38 is the only existing example in literature proposing to adapt EC methodologies and centrality metrics to score countries performances within the Agenda 2030. However, our work differs from that one in both methodology , and data, since that work is limited to the Asian Region.In this work, we adopt the g accounting for the relative performances of countries within it ; nevertheless, the complexity of the Agenda 2030 should not be neglected when defining aggregate scores. In light of this complexity, in this work, we have introduced a novel perspective on sustainable development in which we addressed–within a network science framework–the need for ranking countries for their status concerning the Agenda. In particular, we show that the countries-SDGs system can be structured as a bipartite network and that, by using the centrality tools, different weighting approaches naturally emerge for the computation of aggregate scores to rank countries.Defining aggregate scores in sustainable development is a recurrent problem, which needs to be addressed to track the path toward achieving the Goals within the Agenda 2030. Many strategies can be pursued for their computation  tend to emulate each other performances55, and it explains why, when ranked for their degree, nearby ranking-positioned countries show similar behavioural patterns 48.Thanks to this network representation of the system, we show that the SDG Index identified by Sachs et al.see Fig. . Neverth43, thus providing useful insights about the countries’ status in sustainable development. These characteristics of the SDGs-GENEPY framework can be interpreted in light of some further considerations concerning the Agenda 2030. The adjusted degree values g, there are many large performance values The SDGs-GENEPY approach we propose for the creation of one aggregate score brings two main positive advancements. Firstly, the weights entclass1pt{minima57 . Norway is one of the worldwide leader exporters of crude oil58, a fact that puts under the spotlight the country’s shared responsibility in Climate Action and the permanent presence of trade-offs between economic and environmental issues at the world level59. Therefore, in the SDGs-GENEPY indexing approach, the heterogeneity of countries and contrasting policy implementations are naturally embedded through the data and brought up by the algorithm, determining the weights of SDGs. This hierarchy testifies the shared global responsibility in sustainable development and the intrinsic compromise among political willingness, opportunities and capacities to move toward sustainable development61. This compromise is even more evident in countries with more favourable conditions to fulfil the Agenda, resulting in higher ‘knowledge’ .In fact, evidence of the validity of such analysis can be found in Norway’s development strategies, among the most relevant examples in this study. Norway is currently diversifying its industrial sector by enhancing investments in the Research and Development area, so to face the reduction in prices of crude oil55, we argue that identifying role-model countries is rather relevant, and it can pave the way to a new strategy for boosting sustainable development in the next decade. In particular, our ranking can be used as an ‘ex post’ and complementary tool to the Rapid Integrated Assessment–RIA–analysis2 which the United Nations conduct to monitor the willingness of countries in integrating the Goals within their national strategies. In this sense, our analysis would effectively provide insights about the implementation of such plans, also providing a tool for comparing the efforts across countries. Moreover, such an approach can be suitably adapted to the sub-national level by using regional data on sustainability performance, thus revealing crucial features of countries’ regional efficiency in sustainable development.In light of these considerations, we can interpret the SDGs-GENEPY ranking of countries as a picture of shared responsibilities, where it emerges the possibility for nations to act as role-models and promote the achievement of global sustainable development. In light of the emulation phenomena among countries62 among the many63). Moreover, the burgeoning literature in the field of SDGs assessments suggests the presence of different ideologies about how to properly measure the status of countries along their sustainable development path. We realise that the understanding of such paths should not be shrunk to a single indicator analysis. Therefore, to fully understand countries’ paths toward sustainable development, we suggest using different and complementary mathematical approaches, such as e.g., the computation of both the degree and SDGs-GENEPY rankings. The adoption of more classical methods (such as the degree-like ones) combined with the SDGs-GENEPY would provide a bird’s-eye view of the conditions of countries to achieve sustainable development while providing a list of change-making places and actions that can help meet the 2030 deadline.In conclusion, we acknowledge the complexity of the system defined within the Agenda 2030. In light of such complexity, and for the sake of providing more complete analysis, we address future work to embed models of interaction across the sustainable development sectors  clearly show that work is still needed to have a comprehensive, homogeneous, and extensive database covering all countries and years under the Agenda 2030 and beyond. For this reason, the input data we are using are taken from the 2020 SDG Index and Dashboard26, which represent a commendable step forward in data collection, homogenisation and assessment of countries progress in sustainable development. The aim of the Dashboard is to provide yearly rankings of UN countries based on an aggregate score of all Goals’ performances. The score is intended to be readable as a percentage of achievement of all the Goals, ranging from 0 to 100; therefore, countries close to 100 are approaching the complete fulfilling of the Agenda’s Goals according to the indicators used to compute the score36. The score is constructed upon a number of indicators providing quantitative information about countries performances. All listed indicators are normalised according to an optimum and a minimum value of indicator performance to ensure comparability and aggregation of measurements (we refer the reader to36 for further details). Listed indicators are updated every year, accounting for advances in monitoring and research. In order to provide statistical-sound results, we only refer to 2020 data, thus not inferring any possible missing data back in other years’ Dashboards. The 2020 dataset constitutes of 115 indicators across the Goals, 30 of which are specifically defined for the members of the Organization for Economic Co-operation and Development (OECD). The Dashboard only includes countries covering at least g, the term Notwithstanding the call for efforts toward the standardisation in the data collection by all National Statistical Systems, NSSs, launched by the Cape Town Global Action Plan in 201736. Notice that, as pointed out by the Authors of the Dashboard65, this assumption might have implications in any analysis.In line with the methodology exemplified with the SDG Index, we replace countries’ missing data with the Regional score in that same Goalc and g. By definition, the eigencentrality value for country c is its degree weighted by the centrality of all Goals, and vice-versa39. In this work, the centrality score for countries 66. The system in Eqs.  provides a more straightforward mathematical rationale than the one presented in the original work for trade. Building upon the export data, the GENEPY index in48 is a multidimensional centrality score for economic complexity in which two eigenvectors of the matrix For further details, we refer the readers to Sciarra et al.wing Eq. , respectwing Eq. . FinallySupplementary Information.


Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people’s HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic. Heart rate variability (HRV) consists of the periodic oscillation between heartbeats, and these oscillations are modulated by the Autonomic Nervous System ,2. Over One of the results of the aging process is the decline in the ability of the autonomic nervous system to adjust blood flow in response to external situations. This autonomic control can be assessed and monitored in a non-invasive way through the heart rate variability being considered as a parameter to estimate the interaction between the brain and the cardiovascular system in elderly individuals .Changes in HRV patterns provide a sensitive and early indicator of possible declines in health. Efficient autonomic mechanisms provide a high HRV, which is a sign of good adaptation to intrinsic and extrinsic factors, characterizing a healthy individual. Conversely, low HRV reflects an abnormal and insufficient adaptation of the autonomic nervous system, which may indicate the presence of physiological changes relevant to the individual’s health. It is necessary to monitor and investigate the reasons why this variability is altered .Therefore, HRV can be considered a promising early biomarker of cognitive impairment in the older population. This index must be evaluated within a preventive perspective to minimize the impacts caused by aging and the risks of developing cognitive impairment ,5.Traditionally, HRV is measured in clinical or laboratory settings, using equipment such as the electrocardiogram (ECG), cardiac belt, or Holter. These tools have some characteristics that prevent their use in day-to-day life and hinder the continuous and routine monitoring of HRV. In general, problems of biocompatibility, discomfort, unsatisfactory design, and limited access to clinical or laboratory environments are factors that make the use of these instruments less appropriate for health assessments and unlikely to be adopted in the individual’s daily routine ,7,8.On the other hand, sensors for capturing physiological data in wearable devices emerged quickly in the market and became popular among its users and in the field of scientific research, due to technological advances of companies that stand out in this market of wearable devices, focusing mainly on the creation of smartwatches .The combination of wearable devices and health systems has proven over the years to be a viable strategy for resolving health conditions efficiently, characterizing a new era for the monitoring, diagnosis, treatment, and prevention of many diseases, through the detection of early patterns of potential health declines ,10.The appearance of wearable devices with photoplethysmography sensors make it possible to capture the cardiac pulse in minutes. However, due to manufacturers’ design decisions, wearable device APIs provide time series of heart rate with missing data, compromising the achievement of heart rate variability indicators, considering that the acquisition of HRV information is performed through identification of heartbeats every second ,12.seniorsaudemovel.com.br accessed on 23 January 2022), which has features to monitor the health and wellbeing of older persons and is being experimented in several pilot studies in Brazil. Among these features, we also explored smart algorithms to build a model that aims to provide accurate HRV parameters trying to overcome the current technological limitations of a large scale commercially available wearable device. We defined a workflow that involves data pre-processing, modeling, and model’s execution.The goal of this study is to introduce an intelligent model for HRV based on time series provided by commercial wearable devices. In this study, the chosen devices were from the Fitbit brand. This was achieved through the introduction of the Sênior Mobile Health (SMH) platform , square root of the square mean of the differences between adjacent normal RR intervals, expressed in milliseconds, that is, the standard deviation of the differences between adjacent normal RR intervals (RMSSD), and the percentage of adjacent RR intervals with a difference in duration greater than 50 milliseconds (pNN50), which are common HRV scores; being identical or smaller than the critical value, the p-value was greater than the significance level meaning that there is no change in HRV in HRV parameters when using the SMH. Since this study was motivated by the COVID-19 pandemic scenario that imposed isolation constraints to elderlies, these results show that commercial wearable devices combined with the SMH platform can be used as an effective tool for HRV monitoring.The overall method is summarized as follows: Regarding the quality of data pre-processing, we ensured that missing data were filled in the best way. In this sense, we tested several different conventional time series imputation methods comparing the time series of Fitbit smart watch  and Polar H10  heart rate monitor chest strap. After identifying the best filling approach, the Piecewise Cubic Hermite Interpolating Polynomial method (PCHIP), the modeling approximated the data obtained from Fitbit to the data coming from Polar H10. Therefore, we compared different prediction methods and the best one was the Kamath Rule, with similar results to the chosen gold standard, and a performance better than other methods. Finally, in the model’s execution, our For the present research, the Fitbit brand device, model Inspire HR, was used. This device is already widely available on the market and meets important criteria such as biocompatibility of the material; thus, reducing the possibility of allergic processes due to constant contact with the skin. Furthermore, criteria such as comfort, design and usability are provided by these devices, which are considered important factors in the adhesion to the solution.Another important aspect found on Fitbit is that there are incentives for researchers to use their devices in clinical studies worldwide, as the company makes available the data of users who have provided consent to researchers, both in the form of summary data and time series. The mechanisms of automatic wireless synchronization with other devices are also very practical for the daily use of the elderly and their caregivers. Finally, an open Application Programming Interface (API) is available, providing the possibility to create new code and functionalities for third-party applications or even new applications for some versions of their products.The provision of open APIs is a great advantage for this research, allowing the integration of data coming from the different sensors to the developed platform, granting a monitoring aligned to the desired goals. Not all devices on the market make their APIs open and available, making it impossible to obtain data from sensors and analysis. This was one of the main criteria for excluding other wearables.The Fitbit Inspire HR data are transmitted via Bluetooth Low Energy (BLE) to the wearable device provider smartphone application (Fitbit app), which makes the necessary data processing and sends it to the wearable device provider server (Fitbit server), where it becomes available for consultation by third parties via web services. Due to the manufacturer’s internal policies and security measures, third-party applications are not allowed to collect data directly from the wearable device; therefore, the sole party responsible for the collection is the wearable device provider application.After data are collected by the manufacturer’s application and made available on its server, third-party applications can collect these data, if they have the appropriate permissions. We used these data in a mobile application and web dashboard, providing smart algorithms to predict adverse health conditions and promote real-time and continuous monitoring of elderly health. Such algorithms are currently analyzing, besides HRV (which is the main focus of this study), steps, nocturia, quality of sleep, and gait speed, which are closely linked to adverse effects in the health of older people.Most biological signals are defined as quasiperiodic, which means that they can vary repetitively at almost regular time intervals. In this sense, a periodic variable can be analyzed as a function of time or as a function of the frequency at which the event takes place. Therefore, an HRV analysis can be performed in the time domain and in the frequency domain. In this study, we implemented algorithms for HRV metrics only in the time domain due to lesser complexity and pragmatism for delivering the SMH platform ,14.For the HRV analysis in the time domain, so called because it expresses the results in time units (milliseconds), each normal RR interval (heartbeat) is measured during a certain time interval and, from there, based on statistical or geometric methods, we calculate the metrics that reflect fluctuations in the duration of cardiac cycles ,13.The statistical metrics used to assess HRV can be divided into two categories: metrics based on the measurement of RR intervals individually  and metrics based on the comparison between two adjacent RR intervals (pNN50 and RMSSD) .The statistical metrics pNN50 and RMSSD predominantly represent vagal tone ,4,13,15.The metrics based on the measurement of RR intervals individually, such as SDNN, SDANN , and SDNN index  depict the global variability, comprising the parasympathetic and sympathetic autonomic nervous system ,4,13,15.Through the SMH platform it is possible to carry out continuous, remote, and real-time monitoring of the data acquired from the Fitbit Inspire HR of the sample participants, monitoring the data of interest in the present study.The development of the SMH platform was established with services related to user and information management, data science, and IoT. Artificial intelligence algorithms and time series analysis were employed to monitor important data on the health of the older person.Fitbit brand devices offer data through time series of active minutes, number of daily steps, heart rate measurements and sleep stages. These data are obtained through the APIs and integrated into the SMH platform, where they are stored, analyzed, and displayed by means of graphs, indexes, and values. The next figures show features in the mobile app and in the dashboard related to the HRV monitoring. All the figures show fictitious data because the purpose is just to illustrate the functionalities.Now we focus more specifically on the HRV feature in the SMH dashboard. swagger.io, (accessed on 23 January 2022)), with three important routes that compose the HRV service. The first route, GET/metrics, retrieves all the existing HRV metrics in the database, regardless of the patient. The second route, GET/patients/{id}/metrics, retrieves all the HRV metrics for a specific patient. In this route, we can also filter the HRV metrics by period. Finally, the third route, DELETE/patients/{id}/metrics/{date}, allows to delete the HRV metrics provided for a specific day.For the acquisition of data from the heart rate time series and construction of intelligent algorithms, 10 older persons were recruited.The study was conducted with community-dwelling elderly people aged 60 years or over, residents on the city of Campina Grande (Brazil). The sample was recruited for convenience and includes active individuals without chronic-degenerative diseases and individuals with neurofunctional disorders such as Parkinson’s and Alzheimer’s or sequelae caused by stroke. Individuals with cognitive impairment were assessed by the Leganés Cognitive Test following scores below the cutoff level for dementia (≤22 points), those who did not have the ability to use a smartphone, those with severe visual problems, and individuals bedridden were excluded from the sample. Data collection was carried out from May 2020 to November 2021. All participants were informed of the research objectives and procedures and signed the Free and Informed Consent Form. The study protocol was approved by the Ethics and Research Committee of the State University of Paraiba .The data collection procedure was performed with the elderly positioned in a chair with an arm and backrest for the spine, in an environment with controlled temperature and without any visual or sound stimulus. Participants were asked to remain in a sitting position for about 15 min.The heart rate data acquisition was obtained through the Polar H10, which is recognized as the gold standard instrument for classification of HRV. At the same time, the research participants used the Fitbit Inspire HR in the left wrist region, located two fingers above the styloid process of the ulnar bone. Both devices were placed in constant contact with the skin during the signal acquisition process.All collected data were sent to the SMH platform for storage, and then the data analysis and processing procedures, construction of intelligent models and deployment process were carried out.The process of building the intelligent model of the SMH platform is presented in As shown in According to ,17 HR moTo mitigate the inaccuracy of PPG sensors, which may be caused by a number of different factors  , we adopBefore trying to learn the mapping function, we needed to assess other issues inherent from the data. The Instantaneous Heart Rate (IHR) data acquired through Fitbit consists of a time series that presents gaps (missing data) due to possible measurement failures. In order to guarantee the integrity of the data, it is necessary to fill in those gaps. The analysis of certain properties of the time series, such as its stationarity, provides an indication of possibly appropriate methods for the filling process. According to the Augmented Dickey Fuller Test (ADF Test), a common statistical test used to verify whether a provided time series is stationary or not, the series is non-stationary; implying that mean, variance, and autocorrelation are not constant over time, as depicted in For that reason, methods such as replacing the missing values with the mean of the series would probably provide a poor result, as can be checked in Mean filling: mean filling replaces the gaps with the mean value of the series;Forward fill: this method fills in the missing values by propagating the last valid observation forward;Backward fill: this approach fills the missing values of the data frame backwards;Linear interpolation: the linear interpolation method fits a line of polynomials to the end points of the gap using a linear formula to estimate the missing values;Quadratic and cubic spline interpolation: Type of interpolation in which the interpolant is a special type of piecewise polynomial named spline. This method fits lower degree polynomials, in this case quadratic or cubic, to a small subset of values rather than fitting only one polynomial with high degree to all the values;Piecewise cubic Hermite interpolating polynomial (PCHIP): PCHIP consists of a spline third-degree piecewise polynomial function specified by its values and first derivatives at the end points of the corresponding domain interval;Akima interpolation: The Akima interpolation is a continuously differentiable spline interpolation, formed from piecewise third order polynomials, in which data from the next neighbor points is used to determine the coefficients of the polynomial;Exponentially weighted moving average (EWMA): EWMA estimates the missing values as a weighted average of the historical observations, giving older observations lower weights that decrease exponentially as the data points age;Interpolation by nearest neighbor: in this method, the imputed value is equal to that of the closest known point;Mean of the nearest neighbors: This approach fills in the missing points with the average value for the k nearest neighbors. To select the optimum K, which corresponds to the number of neighbors considered, we run the algorithm several times with different values of K, choosing the value that reduces the number of errors committed.To ensure data were filled in the best way, we tested several different conventional time series imputation methods. In order to measure the performance of each approach, missing values were randomly introduced to a complete time series, obtained from Polar H10 and filled in with the different methods comparing the predicted results with the original values:The best technique was chosen by measuring the Root Mean Square Error (RMSE), one of the most used criteria to evaluate performance of interpolation methods found in literature . The RMSAfter applying the best filling method found as described on the last section, we were ready to try to approximate the data obtained from Fitbit to the data coming from Polar H10.Multinomial logistic regression: A logistic regression that admits for more than two categories for the dependent variable. Here, the discrete numerical values of our dependent variable were considered as categories;K nearest neighbors: a method that assigns a predicted value to a new observation based on the mean of its nearest neighbors;Decision tree: a tree structure in which the data are continuously split according to a certain parameter, where the internal nodes represent the features of the dataset, the branches represent the decision rules, and the leaf nodes represent the outcome;Random forest: a method for classification and regression that works by constructing a large amount of decision trees;AdaBoost: an ensemble machine learning algorithm that combines multiple predictions from many weak learners into a single stronger prediction;Linear regression: an algorithm that assumes a linear relationship between the input variables and the output, attempting to model the relationship between the two variables by fitting a linear equation to the observed data;Neural Networks: Neural Networks are a supervised learning algorithms that seek to approximate a function which is represented by the input data. The type of artificial neural network to handle with sequential data or time series data are a recurrent neural network (RNN). Here we compared traditional single-layer RNN with a Long Short-Term Memory network (LSTM), a special kind of RNN which is capable of learning long-term dependencies.Supervised learning problems can be further grouped into regression and classification problems. Both problems have, as a goal, the construction of a succinct model that can predict the value of the dependent variable from the attribute variables. The difference between the two tasks is the fact that the dependent attribute is numerical for regression and categorical for classification. Our predictor variable (HR) is neither continuous nor categorical, but an integer number, which is a discrete value. To guarantee that the best approach was chosen different regression and classification methods were compared:The times series data were split into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The division was stratified by subject so that we can later calculate the HRV metrics for each individual.To obtain a more accurate representation of how well our model can perform on data that have not been used before, we used k-fold cross-validation for the hyperparameter tuning. With this method, we split the training data into training and validation multiple times, each time for a different subset of the training data, assigning a score to the model after each iteration and averaging all scores to obtain a better representation of how the model performs. K was set to 5. The data splitting process is detailed in The hyperparameters of machine learning algorithms are coefficients that allow the model to adapt its behavior to a specific dataset, optimizing the results. Except for linear regression, which does not offer much room for tuning, all these algorithms have a different set of hyperparameters. We used the cross-validation technique to look for the optimal hyperparameter values for each model as depicted for most models in The tuning for the Neural Networks is slightly different. Here, cross validation was employed to evaluate each model using Grid Search, a model hyperparameter optimization technique that constructs and evaluates the model for different combinations of hyperparameters. The best accuracy was achieved for a batch size of 64 and 15 epochs. The best results were obtained with the Adam optimizer, a learning rate of 0.1 and a momentum of 0.5. Further details about the Neural Networks’ models as the number of layers, neurons and activation functions are depicted in Selecting an optimum number of epochs is an important step, since too many epochs can lead to overfitting, whereas too few may result in an underfit model. Given that the goal of training is to minimize the loss, After finding optimal hyperparameters and evaluating the model’s average performance with cross validation, we can evaluate the model’s final performance. In order to perform this, we trained the models on the entire 75% of the data that we used for all of our evaluations, using the optimal hyperparameters found for each model and then compare how our models perform on the test set.For evaluating the model’s final performance, the RMSE was calculated as presented on the Although LSTM and RNN have a similar performance, LSTM has the lowest RMSE, which implies that it can fit the dataset is the best out of all models.Once the time series is completely filled, the IBI can be calculated from the HR data using the equation :HR = 600Malik Rule: IBIs differing by more than 20% from the one preceding it are removed;Karlsson Rule: IBIs diverging by more than 20% of the mean of previous and next IBI are removed;Kamath Rule: this method considers a heartbeat abnormal whenever the IBI increased by more than 32.5% or decreased by more than 24.5% when compared with the previous IBI;Acar Rule: IBIs differing by more than the 20% of the mean of last nine IBIs are removed.Then the IBI data must be adjusted before any HRV analysis method can be applied, since any artifact in the time series can significantly interfere in the analysis. This adjustment consists of removing inconsistencies such as outliers and ectopic beats (irregular heartbeats). The HRV-analysis library provides different methods to remove these inconsistencies, such as:kubios.com, (accessed on 23 January 2022)) correction methods [In order to choose the best method, we applied all the above-mentioned approaches to the IBI series obtained from Polar H10 and compared the results with the achieved by Kubios HRV  supports the healthcare professional in assessing reactions of their patients’ autonomic nervous system during normal daily activities and in response to therapeutic interventions, by comparing the patient’s daily readings to determine if any significant changes have taken place .https://pypi.org/project/hrv-analysis, (accessed on 23 January 2022)) that is distributed under the GPLv3 license. In (3) we have the SMH web dashboard consuming data from the Sênior Mobile Health server. Python scripts were written to extract in (4) information about HRV parameters and compared with the parameters calculated by the Kubios HRV tool in (5). Results are discussed further.The model execution experiment is illustrated in In the previous sections, we validated each step of the analysis workflow individually. For validating the process as a whole, we compared the HRV time-domain indexes obtained by the SMH platform using the data collected from Fitbit Inspire HR with the ones given by Kubios, for the Polar H10 data. t-test , the critical value of t obtained from the t distribution table is 1.833. For a confidence level of 99%, recommended for health applications, the critical value is 2.821.t statistic for the SDNN was 1.772, which is smaller than the critical value for both 95% and 99% confidence levels. The p-value was 0.110, which is greater than the significance level, so we accept the null hypothesis that there is no difference in the means of the SDNN metric calculated by SMH. For RMSSD, the obtained t statistic was 1.150, which is also smaller than the critical values. The p-value was 0.280, that is greater than the significance level, so we accept the null hypothesis that there is no difference between the means. Lastly, for pNN50, the t-statistic was 0.025, which is also smaller than the critical values and the p-value was 0.980, that is greater than the significance level, therefore, we accept the null hypothesis.The p > 0.05 and p > 0.01) from the ones obtained for Polar H10 data by Kubios, for individuals during rest; making it a reasonable alternative for HRV estimation when ECG measurement is impractical.The results indicate that the time domain HRV indices estimated for Fitbit data by SMH were not statistically significantly different , as motion artifacts make the estimation of HR from PPG more challenging. Future studies should take into account the correlation between indicators of frailty such as weight loss, exhaustion, level of physical activity, walking speed, and grip strength to the HRV.These findings together with ours may open new opportunities to remotely measure and track heart rate variability through telehealth technologies, strengthening strategies for early identification of adverse health conditions and conducting appropriate interventions, seeking to promote the health of this portion of the population.Our study reinforces the current scenario and contributes to technological development. The COVID-19 pandemic encouraged the need to create strategies to maintain assistance to the individual continuously, but remotely, preserving and monitoring health and disease indicators. In this way, several remote monitoring platforms using the concepts of telehealth were adopted as measures of care for the population ,31,32.Therefore, establishing measures and strategies to monitor the health of the elderly using the potential of technology becomes essential in the context of an aging society and, that needs care. In addition, the technological solutions developed during the pandemic and changes in the form of health care for the elderly will undergo continuous and perhaps, permanent changes. In this way, technology will be increasingly inserted in health care practices.In this study we reported the challenges and strategies in the process of building intelligent models to fill in missing data from heart rate time series and the methods of implementing heart rate variability classifiers, acquired using a commercial wearable device, namely Fitbit. The results were compared with complete time series as the gold standard, obtained from Polar H10 chest strap. Following a workflow, we explored several different conventional time series imputation methods in the data pre-processing phase, and different regression and classification methods in the modeling phase to approximate the data obtained from Fitbit to the data coming from Polar H10. In the model’s execution phase, we also presented the feasibility of using this type of technology as a method of long-term monitoring of HRV, focusing on the most important HRV metrics, being a useful for remote monitoring of older persons, enabling the early identification of possible adverse health outcomes and risks that may arise during the human aging process that comprise changes in HRV indicators. Finally, we conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors.