Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions. The IoT-enabled products have made a foray into our daily lives in a multitude of forms. These forms could vary in size and mobility ,2,3. ForIf we delve briefly into the history of smart wearables, the abacus rings that were popular during the Chinese Qing Dynasty era emerge as the early prototypes. Further centuries saw the first wearable timepiece that was introduced in the year 1505, or a two-piece computing device developed by two MIT professors to cheat on a game of roulette in the year 1961.Furthermore, the wristwatch containing a calculator marketed by Pulsar in the year 1975 ,11. To dThe keen acceptance of wristbands among users belonging to different demographics could be attributed to their non-intrusive wearability and potential for aesthetics. Presently they are being used not just for location and activity monitoring, but in conjunction with dedicated mobile applications, they now offer support for specific health problems ,14. For Among the vitals monitored by smart wristbands, heart rate variability (HRV) has emerged as an important parameter. HRV is defined as the variation in the time that elapses between consecutive heartbeats, measured in milliseconds ,17. BeinThe significance of HRV measurements to pre-empt critical states of an illness or ineffectiveness of a treatment has been established by several researchers. The availability and cost of diagnostic tests often discourage people from seeking help until the symptoms of an illness become extreme, and at times the illness has advanced beyond the possibility of treatment. Therefore, a well-designed system that can interpret a person’s HRV measurements, detect possible underlying health issues, and alert the wearer to seek help, could prove to be a life-saving strategy. Moreover, the availability of healthcare facilities suffers greatly during the time of a pandemic, since they get overwhelmed by the volume of patients. It could also result in the late detection of critical stages of health conditions and increased mortality figures. In the year 2020, the pandemic of SARS-Cov-2, commonly known as Covid-19, had afflicted millions of people over the world and across all demographics. Its large- scale spread could be mostly attributed to inadequate tests, late detection, and overwhelmed healthcare systems ,24. In tIn the In this section, we delve into the research that has been carried out in the field of smart wearables in the biomedical and healthcare domains. Since we focused on HRV as an important factor among the body vitals, we also studied research done over its applicability to health monitoring. Lastly, we summarize our understanding of the existing research by listing the identified gap areas that motivated us to develop the proposed framework and contribute to this area of research.2, skin temperature, and the physical activity of the patient [Smart wearables that record body vitals such as heart pulse temperature and the state of the wearer’s skin were developed in recent years. Some relevant examples include a chest belt that monitored body temperature, heart, and breathing rates to alert against Sudden Infant Death Syndrome . Another patient . This de patient . Hand trThe advent of fitness trackers and wrist-worn smart devices opened further avenues for biomedical research. The user data could be recorded over longer periods and supporting mobile applications provided means to log relevant user information. Early comparison of heart rate measurability between the Apple watch and the Polar chest strap showed that under normal conditions, their performance was commensurate . The wriIn Due to its relevance with current times, the proposed framework was implemented with the Covid-19 pandemic as the use-case. The first known instance of human SARS-Cov-2 infection was recorded in the year 2002. A cohort study on 121 patients confirmed that Corona virus infection manifested itself in the form of hypotension, cardiac arrhythmias, and even sudden cardiac death ,37. WithThe vast application prospects offered by smart wearables led us to explore further utilization of avenues in the healthcare domain. We also studied how the HRV measurements had found acceptance as a critical informative factor for pre-emption of mental and physical health issues. Yet it had not been applied for pre-emption of onset and criticality during a pandemic situation. The gap area was more apparent since the year 2020 had seen the outbreak of SARS-Cov-2 on a worldwide scale. To our best knowledge, this was the first time that a machine learning-based framework was designed to predict the onset and increased severity of an infection such as SARS-Cov-2. It was also to our best knowledge that the HRV measurements had been used by an algorithm to explore its applicability in pre-emptive diagnostics. We propose a hidden Markov model (HMM) based framework, driven by SARS-Cov-2 symptoms that were logged by the participants of a research study hosted by Welltory. The hidden states of the aforementioned HMM comprise changes in the standard deviation of NN (SSDN), rMSSD, and the low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements that the participants had recorded during the course of the study. We hypothesized and proved that the onset and exigent state of SARS-Cov-2 affliction can be predicted to a certain degree through the analysis of variations in the HRV components.The framework proposed in this paper was based on the principle of monitoring the trends followed by the use of HRV time and frequency based metrics to predict the onset and oncoming exigent state of a major illness. We had specifically focused on SSDN, rMSSD, LF, HF, and VLF for the same. To test our hypothesis we modeled the framework using data published by Welltory . Welltory invited participants who had been afflicted with SARS-Cov-2 virus to share the progress of their symptoms, and body vitals including the HRV collected by wearables like Apple Watch, Garmin, and Fitbit in collaboration with the Welltory mobile application. The data was collected over the early months of the year 2020 when the SARS-Cov-2 pandemic had started to spread worldwide. Due to the new laws that forbade the release of mobile applications with titles containing Covid-related terms, the Welltory application had to be removed from the app marketplaces, thus ending the data collection. Participants had shared HRV measurements through PPG technology, while other vitals were collected through wrist-worn smartwatches and bands that were synchronized with the Welltory app. The data that was published by Welltory for researchers was anonymized and was comprised of multiple CSV files.The proposed framework was visualized with two major functionalities. As can be seen in The second component of the proposed architecture was responsible for the conversion of reported SARS-Cov-2 symptoms into a sequence of observations. It was also responsible for the monitoring of trends followed by HRV components to discern the hidden states. The observations and hidden states were then used to train an HMM–based system and derive relevant probabilities. 1, s2,…, sN} and a generated sequence of observations X = {x1, x2,…, xN} [i = P(x1 = si) are the prior probabilities of si being the first state of a sequence and x1 the first observation. The matrix T is the collection of probabilities of transitions between two states i and j. Each element of the matrix T can be written as: The hidden Markov model is a sub-case of Bayesian networks and a generative probabilistic model. It is characterized by a set of internal hidden states S = {s2,…, xN} ,43. Whili.And lastly vector E(x) of functions summarizes the emission probabilities, that is, the likelihood of the observation x if the HMM were in state sThe three fundamental scenarios that can be addressed by an HMM are first, the estimation of the optimal sequence of hidden states if the model parameters and observations had been provided. Secondly, if the model parameters and observations had been provided, then calculate the model likelihood. And lastly, estimate the model parameters if only the observations had been provided. The first two scenarios are use-cases for the application of dynamic programming algorithms such as the Viterbi and the forward-backward algorithms. The last scenario can be solved by the Baum-Welch algorithm  that is The rationale behind the application of HMM in the proposed framework was that the SARS-Cov-2 symptoms recorded by the participants followed a logical sequence. Moreover, the variations in the five HRV components, namely SDNN, rMSSD, LF, HF, and VLF, were hypothesized to have a direct impact on the observed symptoms and could be used to predict the progression of SARS-Cov-2. The hidden states for HMM were thus labeled as 1 and 2, where label 1 was for the case when no continuous decline in the HRV measurements was noticed. The label 2 was used to convey the hidden state when the five relevant HRV components had dropped over a span of a week. For this study, the various symptoms that were logged by the participants were categorized into specific labels as shown in A hidden Markov model is characterized by the combination of probabilities for a set of hidden states and discrete observations. The Baum-Welch algorithm determines the expected maximum likelihood of parameters of an HMM if given a set of observations. It uses the expectation-maximization principle since the calculation of all possible transitions between the postulated states would result in a large-scale computation problem. The Viterbi algorithm is a dynamic programming technique to determine an optimal sequence of hidden states for a given HMM and a set of observations . FirstlyThe first CSV file contained anonymized information of the 186 participants, specifying their gender, age group, country of residence, and the date when they observed the onset of SARS-Cov-2 symptoms. The dataset had many missing values for the onset date, while a few participants had instead put their birth dates in that field. The data collection through the Welltory app had taken place from February 2020 until May 2020 and the distribution of the onset months that we plotted is shown in In a separate CSV file, the SARS-Cov-2 symptoms and the pre-existing health conditions that had been shared by the participants were listed, along with the observed severity, ranging in value from 1 to 6. The SARS-Cov-2 symptoms severities specified absence with value 1, very mild severity as 2, mild severity as value 3, value 4 as moderate, value 5 as severe and value 6 was used to indicate extremely severe symptoms. The symptoms for SARS-Cov-2 were labeled under different scales as shown in The participants also logged pre-existing conditions that specified if the person was suffering from heart conditions, high blood pressure, diabetes, arrhythmia, thyroid problems, and so on. The participants also recorded mental issues such as anxiety, insomnia, and the post-traumatic syndrome. We plotted the distribution of conditions that were highly reported by the participants, irrespective of their severity. We then collapsed the conditions over the severity and plotted their distribution. The two graphs are shown in As can be seen in To achieve a better understanding of reported conditions and SARS-Cov-2 symptoms severity, the correlation was plotted between gender, age, cardiovascular vulnerability, diabetic conditions, respiratory vulnerability, the presence of neurological disorders, participant partaking of vitamin supplements regularly, and participants having reported a severe S_COVID_OVERALL symptom. From The proposed framework was implemented using a script written in Python programming language version 3.8.5 on a system running on Linux Mint OS 20 Ulyana using the JupyterLab IDE. The first part of the script pre-processed the CSV format files that were received in the dataset. The data was further explored and relevant visualizations were plotted using the Pandas and Plotly Express libraries. The subsequent part of the script focused on the extraction of observations reported by the participants and the hidden states computed using the behavior of the five HRV components. To discern the hidden states, drops in the values of the five HRV components were monitored. If a consistent decline was seen over a week for all of the five components, then the last date of the time window was saved. The algorithm saved dates for every consistent decline that spanned over a week for complete HRV measurement data shared by each participant. The significance of drop dates in the determination of hidden states was based on the theory that a symptom observation made in a time window far from a drop date would negate the hypothesis. Therefore, we considered two timeframes; the first frame covered a six-day window with three days prior and after a drop date. The second time frame covered the dates that did not lie in a relevant drop window. By traversing through the reported SARS-Cov-2 symptoms and their severity, we built a sequence of hidden states and observations. The hidden states could either have a value of 1 or 2 depending on the reporting of the SARS-Cov-2 symptom outside or within a drop window, respectively. The observations could be any of the labels that were introduced in The trained HMM instance was plotted to see the transition and emission probabilities with the help of the Graphviz library . Also, oThe proposed framework for pre-emption of onset and criticality of infection was implemented using SARS-Cov-2 data as a use-case. The behavior followed by five major HRV components were monitored for a consistent decline and thus marked for further use. An HMM instance is made up of two important sequences, the observations and the hidden states. The observed states of the developed HMM instance were achieved through the symptoms logged by the participants. The transition probabilities between the two hidden states are shown in The emission probabilities for both the hidden states and the five observations are shown in As can be discerned from To further test the performance of the trained HMM instance, observations of six randomly selected participants were used as input to the Viterbi algorithm. The Viterbi algorithm gave the probable sequence of hidden states derived from the input observations. The selected hidden state sequences that were used to train the HMM were thus compared against the Viterbi-generated state sequences. The advent of IoT in healthcare has created a playing field for researchers and developers to find innovative solutions for timeworn problems. The smart wearables, especially the wrist-worn technology not only appeals to aesthetics but also facilitate long-term wear, thus providing scope for extensive data collection. The human body is a machine that is governed by regulated processes, and disruptions in such working processes can be attributed to mental or physical stress. The evolving sensitivity of the smart wrist-worn device has therefore found extensive use in monitoring of body vitals and provides data for algorithms to discern any health issues that might afflict the wearer. We proposed a hidden Markov model-based framework that could be trained over variations of five major HRV components, namely SSDN, rMSSD, LF, HF, and VLF, to alert against onset or worsening of an affliction. Due to its significance at the time of the study, SARS-Cov-2 was taken as a case study and the HMM was trained over SARS-Cov-2 symptoms recorded by participants of research hosted by Welltory.We hypothesized that a consistent decline in the values of major HRV components could be attributed to the onset or worsening of SARS-Cov-2 infection. The probabilities reported by the modeled HMM instance proved that the emission probabilities between the hidden state of consistent decline and exigent SARS-Cov-2 observations were higher when the participant had experienced consistent drops in the five HRV components. The Viterbi algorithm was used to generate sequences of transitions between the hidden states, and the outputs were found to agree with the transitions detected from the HRV readings. The data collection process was dependent on the logging behavior of the participants; therefore, we followed the actual reported observations rather than according to the expected progression of SARS-Cov-2 infection. Despite the errant and limited observations in some cases, the developed HMM model worked well. Moreover, since the participant data could be collected for only three months, there is a vast scope for further investigations. A larger dataset would provide more variant observations and better testing strategies.The proposed framework showed that HRV measurements taken by wrist-worn smart wearables have a vast potential for future research. It also showed that such data collection and observations recorded by people with limited technical knowledge did not affect the performance of the developed system. For future work, we would test the framework for other afflictions and larger datasets. We are also hopeful that this study will encourage further research in the usability of data collected by wrist-worn fitness bands to pre-empt afflictions before symptoms appear, and thus lower the chance of complications and mortality.


In an era of global aging, spinal and other joint degeneration issues have become a major problem for many elders. Bone-related operations have become the largest percentage of surgeries, accounting for 40% of the top 10 operations in the United States. Further, these spine-related operations are now ranked second among all bone-related operations. Due to this enormous and daily increasing market demand, more and more firms have started to pay closer attention to related medical devices and products. The global venture capitalists (VCs) have also started to follow the mega trend and will continue to invest heavily in this industry. Although most VCs recognize that investing in firms that produce innovative spinal products or devices is a must, very few practical managers or research scholars have defined the appropriate evaluation methods for these firms to use. The traditional net present value (NPV) method, which does not consider operation flexibility and changes in strategy, is far from the reality. The real option method can reveal the vagueness and flexibilities of the values being embedded in the investment projects at spinal medical device firms. However, the real option method is strictly quantitative. Usually, the evaluation aspects contain qualitative factors or local criteria which are hard to quantify in monetary terms. Thus, the adoption of multiple criteria decision making (MCDM) methods that can manipulate both quantitative and qualitative factors will be very helpful in evaluating and selecting investment cases like the spinal medical device firms, where both quantitative and qualitative factors should be considered. An analytical framework that consists of hybrid MCDM methods and the real option method will thus be very useful to evaluate the newly established firms producing spinal medical devices. Therefore, the authors propose a real option valuation as well as the Decision-Making Trial and Evaluation Laboratory (DEMATEL) based analytic network process (DANP) and the modified VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) method (DANP-mV) based MCDM framework for evaluating the investment projects offered by these firms of spinal medical devices. An empirical study based on three newly established spinal medical device companies specializing in vertebral compression fracture (VCF) surgical devices was used to demonstrate the feasibility of the proposed analytical framework. Sensitivity analysis is performed to determine the influence of modeling parameters on ranking results of alternatives. This analytical framework can thus serve as a tool for VCs to use to determine the value of a potential candidate for investment. The proposed method can also serve as an effective and efficient tool for investment projects in other fields. In an era of global aging, both the marketing and adoption of interventions asserting to prevent, delay, or reverse aging have increased significantly in recent years . AccordiThe global aging era has arrived; thus, demands for health care and medical facilitation have also increased tremendously. Global spinal-related diseases are growing steadily due to the growth of the aging and obese population. Therefore, devices for spinal degeneration and other joint degeneration health concerns will play dominant roles in the global market of bone-related medical devices. According to the statistics by Steiner, Karaca, Moore, Imshaug, and Pickens , the numThe human spine is made up of 33 vertebrae and can further be divided into cervical (7 vertebras), thoracic (12 vertebras), lumbar (5 vertebras), sacral, and coccygeal elements. According to the different sites of disease occurrences, spinal surgeries can further be divided into five categories: fusion surgery, non-fusion surgery, spinal decompression surgery, vertebral compression fracture (VCF) surgery, and spine biologics surgery. VCF is believed to account for the third-largest market for spinal surgical devices. That global VCF market size is expected to reach $1.109B USD in 2022, with a CAGR of 5.7% from 2014 to 2022 )In summary, this decision-making framework consists of five major phases: (1) deriving determinants using the modified Delphi method introduced in The Delphi method was designed by Dalkey and Helmer  to summaThe DEMATEL method was proposed by Gabus and Fontela  from theThe major purpose of the DANP is to transform the total influence matrix being derived by the DEMATEL to an unweighted super matrix, and then derive the influence weight associated with each aspect and criteria accordingly. The major goal of introducing the DEMATEL technique is to utilize the graph theory of discrete mathematics to investigate complicated practical problems. The qualitative and mutually influenced aspects of decision-making problems can then be resolved. The DANP can reflect the essence of a decision problem by preventing the loss of any information from the trimming of any influence relationship caused by defining a threshold value and the simplifying of the investigation process by reducing the survey time required for the conventional ANP processes. Please refer to The binomial method is an outstanding technique for making financial decisions. This method offers an effective structure wherein alternatives can be evaluated. The method further helps establish a correct and balanced picture of the risks and rewards from a specific choice. To introduce the binomial method for ROV, the analytic procedure is defined by modifying the work done by Kodukula and Papudesu . The stesa), project cost (x), and option life (t). r is risk-free rate corresponding to the option life. The risk-neutral probability is denoted by p. At first, the input parameters should be identified. The elementary input parameters for the binomial method used to evaluate any type of option include revenue  and moves to the right by taking one year as the interval. The asset values will then be calculated over the whole lifetime of the option. Assume the value of the asset is u as d as The construction of the binomial tree See  for t yeFinally, the option values associated with every node can be calculated by backward induction from the very end of the right-hand side of the binomial tree; the decision rule is applied at each node. Heading to the left-hand side of the tree, the option values associated with each node can be calculated by introducing the option values from the succeeding nodes, then discounting them using a risk-free rate and the risk-neutral probability factor. This process is repeated until the far left end of the tree is reached. The option value can thus be derived. First, the values of the terminal nodes representing the last time step should be derived. Then, the values associated with the intermediate nodes which is one step away from the last time step is derived. The expected asset value for any intermediate node can simply be derived by the discounted (at the risk-free rate) weighted average of potential future option values using the risk-neutral probability. That is, the value at year x by using the risk-free rate as the discount rate. The decision-maker can evaluate what the value-maximizing decisions would be at the nodes that represent the earlier years.The S) and the minimum of the maximum individual regret of the opponent (as represented by min Q). The modified VIKOR method further extends the concept of the VIKOR by replacing the shortest distance to the optimal solution with the shortest distance to the aspired solution. The proposed approach can prevent the problem of selecting the best solution from a group of unsatisfied alternatives. For the detailed process of the modified VIKOR, please refer to [The VIKOR method was proposed by Professors Opricovic and Tzeng  to derivrefer to  and Apperefer to . Based oν has been introduced as weight of the strategy of the maximum group utility. By referring to refer to [ν plays an important role in the ranking of three investment projects. Generally, the value of ν is taken as 0.5. However, ν can take any value from 0 to 1. Therefore, it is necessary to conduct a sensitivity analysis on the parameter ν for validating the obtained results. Overall, a total of 160 experiments were conducted.The goal of sensitivity analysis is to determine the sensitivity (or influence) of modeling parameters (criteria weights in this study) on the final results of the decision-making process. In order to assess the impact of criteria weights on the rankings of the alternatives obtained from the modified VIKOR method, five set of experiments were conducted based on the analytic process being proposed by . In the refer to ,103, theIn this Section, the empirical study case is introduced in Three emerging Taiwanese companies, S, W, and B, will be the focal firms for the empirical study cases. The VC managements would like to evaluate these three companies as their basis for investment. The background of these three companies is offered below. Company S was established in August of 2013 with the current capital of $4.73M USD. Its main product is orthopedic medical equipment, which is mainly used in VCF and includes Vessel-X. Osteo-G, PKP, PVP, etc. In 2015, the company accounted for 40% of the total market sales, and other regions had 60%. The new product for the company, Vessel-X, was developed using the existing technology of PVP and PKP. Thus, doctors can easily learn and operate that new technology. The medical risk is low, and the efficacy of the medical services is improved. Vessel-X has been granted a sales license by the Governments of Taiwan and China. The firm is now in the process of applying for 510(k) from the Food and Drug Administration (FDA) in the United States.Company W was established in December of 2009. The firm is a spin-off of the Industrial Technology Research Institute (ITRI), in Taiwan and the INTAI Technology Co. The W company is dedicated to the research and production of a wide variety of high-end, implantable, Class II, and Class III medical devices that include bone graft substitutes, spinal fixation systems, an interbody fusion system, and minimally invasive surgical instruments. The R&D strategy of this company emphasizes designing products with safety and great efficiency to meet the needs of clinicians. Their Good Manufacturing Practice (GMP) plant can manufacture and supply the best and latest medical devices and market their own product lines. The product line for the bone graft substitutes has accounted for 20% of their market share in Taiwan. Nowadays, many of their products have received certifications and licenses from the Governments of Europe, United States, China, South-East Asia, and more. Many of their products have also been certified by the EU. B company was established in 2009 and started to sell their own product, the ReBorn Essence in September 2009. Based on the experiences of the foundry and the manufacturer of medical implants for more than 20 years, the company has become the manufacturer of competitive orthopedics medical equipment. Their product lines include but are not limited to spinal cord implants, orthopedic materials, and surgical instruments. Based on the 7 patents granted by the world’s major economies, the company’s goal is to provide products and facilitate equipment to simplify the surgery procedure and optimize the quality of medical treatments. Since company B’s product was approved in September of 2014, the firm has attended numerous global exhibitions of medical equipment and received awards, e.g., the 10th Industrial Innovation Award granted by the Taiwanese Government, and many other positive reactions. The firm expects to develop their own brand-name products to increase its domestic market share and also market their products globally. Due to their great potential of growth, the three spinal surgical device firms above have become the investment targets of VCs that focus on high growth industries. Before that investment(s), VCs should evaluate and select the appropriate firm(s). The analytic procedure that uses the proposed ROV based MCDM framework will be demonstrated in the following sub-sections.D8) being associated with the firms will be valued by using the proposed ROV method being introduced in The DANP-mV based method will be able to evaluate the actual value of an investment proposal precisely and evaluate the qualitative factors concurrently. To evaluate the investment projects, the research will first confirm the constructs and criteria in To derive the aspects and factors for evaluating and selecting the investment projects of spinal medical device firms, the modified Delphi method is applied to confirm the criteria based on the experts’ opinions. Nine senior managers from the Taiwanese VC firms see  were invD was obtained based on the opinions of nine experts. The experts were asked to indicate the direct effect that they believe a factor i will have on factor j, as indicated by D can be derived based on Equation (A2) of [N  element i on factor j. This current research collected expert questionnaires from nine VC experts. The experts come mainly from Taiwanese VC firms are at least Deputy Managers. Based on these experts’ opinions, the causal relationship and their weights versus the criteria are derived as follows. The aspects and criteria being derived are based on literature review results see  that wer (A2) of  as a nor[N refer . The tot T refer  and the  T refer  are deriT. The influence relationship will be defined based on the threshold value. The values of i plays in the decision problem. If Further, the causal relationship can be structured based on the total relationship of matrix Thus, the influences among the criteria and the ranking of the criteria can be derived accordingly. At first, the potential growth of the market impacts the following criteria: The investment project’s scale and investment strategies, the estimated ROI ratio of the investment projects, the investment projects that have a shorter recall period (about 2–3 years), the potential of being merged and the acquired, the withdrawal mechanisms of the investment project. Second, market demand impacts the following criteria: The investment project’s scale and investment strategies, the estimated ROI ratio of the investment projects, the investment projects that have a shorter recall period (about 2–3 years), and the withdrawal mechanisms of the investment project. Third, the market acceptance influences the following criteria: Patent protection, unique products and technologies, a competitor’s entry barriers, great growth potential or operation profit, technical level of the products, gross profit of the products. Market acceptance impacts the investment project’s scale and the investment strategies, the estimated ROI ratio of the investment projects, the investment projects that have a shorter recall period (about 2–3 years), the potential of being merged and acquired, and the withdrawal mechanisms of the investment project. Based on these analytical results, the top ranking criteria are as follows: Potential growth of the market, market demands, market acceptance, the estimated ROI ratio of the investment projects, the investment project’s scale and investment strategies, the withdrawal mechanisms of the investment project, the investment projects that have a shorter recall period  of [After deriving the causal relationship, the criteria weight can be derived by using the DANP. First, the total-relation matrix (A14) of . These t(A14) of , the infx). Based on the experts’ opinions, the initial investment for S company is $100,000 million USD dollar; that for W company is $90,000 million USD dollar, and that for B company is $80,000 million USD.This research uses the binomial method to derive the value of the ROV. The process was introduced in σ) by assumption is 30%. The risk-free rate corresponding to the option life (r) is assumed as 5%. Finally, the incremental time step to be considered over the option life  and (2), respectively, as 1.350 and 0.741. Then, the risk-neutral probability, p, can be derived as 0.510 using Equation (3). Then, the binomial tree can be constructed based on the derivations of the asset values of each node. So, by starting with After the parameter definition, the option parameters The option value on each intermediate node is derived using the procedures of the backward induction introduced in For a 5-year option life, the ROV at the fifth year can be derived using Equations (4)–(6) as follows:The ROV is equal to the discounted value derived using the risk-free interest rate of 5% as the discounted rate. Finally, the values of the real options being derived by using the ROV will be normalized. The normalized values will then be multiplied by 5 to fit the 5-Point Likert Scale refer to . being lAfter the derivations of weights versus each aspect and its criterion, the three firms are ranked using the performance scores as graded by the experts and the influence weights as derived using the DANP method. Based on the average performance scores graded by the nine experts  by integc25, c42, and c62 were set to the maximum value, 100%, respectively, while the remaining criteria weights being set equal to 0. Meanwhile, the values for firms S and W are equal in the first set of experiment.  and withdrawal mechanisms for the investment project (c75) are the more influential ones. These two criteria influence investment scale and strategies (c71) and the estimated ROI ratio of the investment projects (c72). The potential of being merged and acquired (c74) is independent of the others. The aspect of nature of the investment project c73) is an important criterion. Further, according to Carter and Van Auken [c73); and (3) making more investments than those VCs with business background do. The argument by Carter and Van Auken [c73) influences the investment scale and the strategies (c71). At first, the main investment goal of most VCs is high expected return and shorter payback period . This isan Auken , VCs witan Auken  is consic75) as an important factor and about 53% of the respondents regarded the payback period as important [c75) is relatively more important than the ability of the VC to extract rents; hence there exists a cut-off level for expected profits such that firms with expected profits above this level will be exited using IPOs and firms below that will be exited using trade sales [According to the survey on business angels by Hindle and Lee , about 2mportant . For lowde sales .c75) is consistent with earlier works [c75) or deliver a put option can be an important strategy for VCs when they make decisions about a sequential investment that is under uncertainty [First, the research result for the withdrawal mechanism  influence the estimated ROI ratio for the investment projects (c72).According to Li and Chi , interveThe aspect of business model  could dc41) is the most influential. The value proposition (c41) affects customer relations (c44) and the main source of profit (c46). The brand of the products has an impact on customer acquisition, retention, and profitability [c41), marketing channels, customer relations, key resources and partnerships, revenue streams and cost structure   (see ). Thus, c73) should be considered by the VCs while evaluating new ventures. The aspect of product erations . Accordierations , dependierations , a businc51) and great growth potential (c54). The analytic result is consistent with those of other scholars. Most of the researches on the role of IP in the evaluation and selection of new ventures by VCs mainly have focused on patents; i.e., new ventures that already have patents are more likely to receive VC funds  and be Keilbach , Baum anKeilbach , Hsu andKeilbach ). Additionally, new ventures with patents have greater performance throughout the VC cycle compared to other start-ups, in terms of both their survival possibilities and the total funds they receive ,147. Patc52), according to Moini [c52) is one of the most important of these criteria [For unique products and technologies , numerocriteria . A real option-based valuation Weitzman ; McGrathWeitzman ). According to McGrath and Nerkar , for a dto Mason  and Bockto Mason , the unjnvestors ,158. VCsnvestors . c11) and integrity of the management team (c14). Managers’ integrity influences the capability of the firm to focus on the task constantly (c15) and the details (c16). The venture investment is characterized by high uncertainty and high levels of information asymmetry. Managers’ characters and integrity may mitigate the potential adverse selection problems. Our results are consistent with those in the prior literature. Sahlman [According to  Sahlman  indicate Sahlman  indicatec25). The ability of the management team to implement the business plan affects the ability to arrange R&D activity and promote new products. The result is consistent with the extant studies. MacMillan et al. [The most influential criteria for Aspect 2 is the ability to execute the business plan  is the most influential criterion of Aspect 3. The result is consistent with the prior research. MacMillan et al. [The actual operating performance . Product efficacy will affect customer relations and further affect the firms’ profitability. Our result is consistent with the prior studies. Cooper [c42), key resources (c48), and key activities (c49). Cooper [The most influential criterion for Aspect 4 is value proposition such as product efficacy, brand, or price (. Cooper  indicate. Cooper  further c51) and great growth potential (c54). The rationality was already discussed here in the earlier c63). Market acceptance influences growth potential (c61) and market demand (c62). Our results are consistent with other studies. The probability of product and market development failure will be lower when a product has demonstrated market acceptance. Thus, VCs prefer to fund those firms whose products already have clear market acceptance in an existing market [c73) and the withdrawal mechanisms or the investment project (c75). The rationality of these research results was already discussed in The most influential criteria for the Aspect 5 are patent protection (g market . The mosKey decision-making factors that can influence the decision of investment by VCs include the nature of the management team, the professional competencies of the management team, related experience of the management team, the business model, products, market, and the nature of the investment project. By summarizing the experts’ opinions using the modified Delphi method, the key constructs that are more dominant in the evaluation of a VC investment project are summarized in Our results indicate four dominant influence relationship. First, business model ings See , the ranBusiness Model Impacts the Nature of the Management TeamJohnson et al.  indicateProducts Impact the Nature of the Management TeamAccording to Ulrich and Eppinger , the orgThe Market Impacts the Nature of the Management TeamRuekert  considerThe Nature of the Investment Project Impacts the Nature of the Management TeamAccording to institutional theory, new ventures are very likely to duplicate both the organizational structure and activities of focal firms within their environment in order to gain legitimacy and offset the liability of newness ,169. FurPotential Growth of the Market Impacts the Nature of the Investment ProjectTyebjee and Bruno  indicateMarket Demands and Market Acceptance Impact the Nature of the Investment ProjectHall and Hofer  indicateIn this work, a novel analytical framework consisting of the DANP-mV and ROV methods was proposed. The proposed analytical framework can overcome the limitations of the traditional ROV methods, which do not consider qualitative and local criteria or factors. The integrated model also overcomes the limitations of the MCDM framework. Further, the adoption of DANP can raise the assumptions of the independence between aspects and criteria. The introduction of the modified VIKOR method can overcome the restrictions of the TOPSIS method uncovered by . FurtherAccording to the empirical study results, the business model, nature of the investment, product, and value of the investment are the most dominant aspects. These aspects can be used to evaluate and select an appropriate spinal medical device firm for investment. To prevent the possibility of picking the best apple from a barrel of rotten apples, after the evaluation and selection of the target firm for investment, the selected spinal medical device firm should be enhanced further through the most influential aspects. Based on the analytical results derived by DEMATEL, the professional competencies of the management team and the nature of the management team were the most influential ones. Thus, the target firm can further be enhanced by strengthening the professional competencies of the management team and improving the nature of the management team. Then, the target level of professional competencies and the nature of the management team can be enhanced further to achieve the target level.It is important for VCs to evaluate a potential investment that can produce high returns while minimizing risk. Traditionally, VCs use DCF methods to evaluate the feasibility of investment projects. We propose that VCs use the real option- and DANP-mV-based MCDM method, which takes different levels of risk and market conditions into consideration. VCs can use the integrated method to choose a promising investment project more accurately than they could do using DCF. In addition, VCs can focus on the most influential criteria and the mutual impacts among different criteria to evaluate the investment project by using the integrated method. We suggest that the most important criteria for VCs to choose a good investment project include the character of the management team, integrity of the management team, the capacity to execute the business plans, operating performance, value proposition, patent protection, great growth potential, and market acceptance. We also suggest that VCs need to consider the mutual impacts. The nature of the management team affects the quality of business models, new product development, and the nature of the investment project. The nature of the investment project is affected by potential market growth, market demands, and market acceptance.This study has several limitations. First is the accuracy of the financial statement. The S company in this study is a private startup that is not listed on the Taiwan Stock Exchange or Over-the-Counter market in Taiwan. Due to the nature of the company, its research and development information is also considered to be a trade secret. Thus, we were unable to obtain the company’s public information. The financial data of the S company were forecasted by experts based on information provided by the S company. This study eliminates any preventable variables and searches for as much related and similar public information from similar industries as possible in order to obtain the most accurate information. However, the end result of this study might still be compromised.Second are the systematic errors and risks of the real option method. Taking the real application into consideration, this study situates the S company so as to face four different strategic scenarios: defer, abandon, contract, and expand. However, S company might face more than four types of scenarios after it puts its investment project into practice. Further, this study does not consider the possible interference among four offered scenarios. The research method of this study used the evaluation mode of a bi-normal option with redefined variables. Due to the simple scenarios and assumptions in this study, it is believed that the results might be different from reality.For any future research possibilities, using both actual historical financial data and forecasted financial information of investment projects will definitely help VCs make better judgments on the future development potential of a company, have better control over the environmental changes within the industry, and make more realistic assessments of the outcomes with a broader range. Furthermore, besides newly established medical device companies, the research methods of this study can also be applied to other industries. It is believed, therefore, that this study will help VCs achieve better judgments of their future investment evaluations.Spinal surgical device firms with great growth potentials have become the investment targets of VCs, who always focus on high-growth industries. It is important for VCs to evaluate a potential investment that can produce big returns while minimizing risk. Traditionally, VCs use DCF methods such as NPV or IRR to evaluate the feasibility of investment projects. We propose that VCs use the real option- and DANP-mV-based MCDM method, which takes different levels of risk and market conditions into consideration. By using the proposed real option analysis-based MCDM framework, VCs can more precisely evaluate these investment proposals. Accurate valuation results will not only help provide important information on strategic decision-making for the CEO but also help VCs obtain an accurate value of an investment proposal. Through the integrated method, VCs can make correct decisions about newly established spinal medical device firms.


In this study, a novel flower pollination algorithm (FPA) has been suggested for optimal tuning of P and PI controllers included in a variable speed drive VSD system control circuit. In addition to the manual tuning of controllers’ parameters, several optimization algorithms like GA, PSO, GWO have been used for controllers optimal tuning in different studies. In this study, the errors caused by grid side current harmonic distortion in the VSD system have been used depending on SPWM technique to generate switching signals for a four-quadrant chopper circuit that acts like a filter and compensates the harmonic content in the grid current and smooth ripples in both DC-link current and voltage. The studied drive system and control circuit have been modeled using Matlab/Simulink, then it was linked with the proposed algorithm and tested under different operation conditions. A hardware laboratory model has been built and tested in order to confirm the validity of this study. Comparing the proposed algorithm with other optimization algorithms shows that the suggested algorithm outperforms other algorithms in improving the time response of the VSD system, reducing total harmonic distortion THD of grid current, and reducing ripple factor PF. Electrical engineering; Energy; Industrial engineering; Computer-aided engineering; Variable speed drive systems (VSDS); Four quadrant chopper; Power quality; Flower pollination algorithm (FPA); Optimization algorithms. The main core of such systems is electronic devices, which have great potentials manifested in the increased production, reduced costs, high efficiency and optimum investment of energy. The development of integrated, lower-cost drive systems is expected to impact both the grid and the combined motor in the VSD system, so there are some specifications that must be considered when developing such systems. For example, the grid currents must be within certain limits and the currents injected into the machines should not cause overheating. In addition, the electric drive should not cause any kind of disturbance to the other loads connected in the point of common coupling PCC. The performance of the grid side in a VSD system can be evaluated using the value of THD factor of the grid currents. THD is defined using the ratio between the R.M.S value of any particular harmonic H and the R.M.S value of the total current. In addition, the ripple factor RF of both DC-link Currents and voltages effects the overall performance of the VSD system. When studying the performance of grid side in a VSD, it is necessary to study the disturbances in the grid currents in both steady and transient states under different operation conditions. Time response of the VSD grid side can be improved by reducing the grid currents distortions to a tolerable level and smoothing ripples in DC side voltages and currents. In order to reduce the DC-link current ripple factor, a converter must be placed at the diode rectifier bridge output in the VSD system . DiffereIn recent years, many researches introduced FPA for solving different optimization problems. The most important research in this field is one concerned in designing a scheme for soft starting in induction motor depending on FPA-based PI controller and the study found that FPA converges faster to provide near-optimal global parameters for PI controller .In this paper, a novel FPA algorithm has been used for finding the optimal parameters of P and PI controllers included in a VSD control circuit. The performed Simulation and experimental study show that this algorithm achieved a better time response of the studied VSD system and reduced Values of the grid current THD and DC-link RF.Section 2The studied system is a VSD system with a hybrid filter , which is a four-quadrant chopper, integrated in DC-link to enhance the characteristics of the input currents of this system. In order to analyze the performance of the studied VSD system under different operation conditions, a simplified model of this system is required. The model is consisted of a grid, rectifier, filter, inverter and a motor that is replaced by a resistive load in order to simplify the study. The grid is a three-phase ac voltage system which voltages are given by Eqs. The diode bridge rectifier output is given by Eqs. where:The chopper voltage is the difference between the dc-link voltage and the inverter voltage and it can be expressed by d(t): is the operation duration of the switch.The proposed control circuit consists of two parts: the first part includes a P controller that processes the current error, while the second part includes a PI controller that processes DC-link voltage error. The second part also helps to maintain a constant current in the DC-link inductor and to remove fluctuations. The sum of output control signals of both controllers forms the input signal for SPWM control technique to generate switching signals for the filter transistors. In addition, low and high pass filters have been used in this control circuit. Both P and PI controllers have been tuned using FPA depending on the integration of time multiplied by absolute error objective function. When this function reaches its minimum value, the optimal parameters of controllers are obtained. The studied VSD system and control circuit are shown in Figures 3Over millions of years, many vital systems evolved greatly by achieving evolutionary goals such as reproduction. Building on the success of vital properties, many algorithms have been created over the past years. For example, genetic algorithm depends on the development of biological systems, particle swarm optimization PSO theory is associated with the manner of crowds of birds and fish. These algorithms have been implemented in different types of applications. In many Engineering and industrial design applications, it is necessary to find the optimal solution for a specific problem under complex conditions. Finding optimal solutions is usually a non-linear process, so it may be very difficult or even impossible to find optimal solutions. Most common optimization techniques do not operate in a good way with non-linear and multi-constrained problems, so the current trend in optimization is toward using naturally inspired algorithms to discuss such problems.Genetic algorithm is an optimization method that can be used to solve both constrained and unconstrained issues based on natural selection. This algorithm starts by creating a random initial population, then generates a succession of new populations. At each step, the algorithm uses the individuals in the current population to generate the new population, which means genetic algorithm frequently modifies a population of each solution. Genetic algorithm is generally used to create high-quality solutions for optimization and search issues depending on bio-inspired factors such as mutation, crossover, and selection. In addition, it uses probabilistic selection rules instead of deterministic rules. Since this algorithm starts from a population of points, it can avoid locally trapped optimal solutions as in traditional methods that starts search from a single point. The flow chart of GA shows in Pollination of flowers algorithm (FPA) is a new optimization algorithm that is inspired by the flower pollination process. From a biological evolution point of view, the goal of pollination of the flower is the permanence of the fittest and the ideal breed in plants in terms of numbers and preference. The basic idea of flower pollination within the context of bees and populations was studied in previous algorithms, but this algorithm is only based on the pollination characteristics .3.1Statistics show that there are more than four million species of plants in nature, about 80% of them are flowering plants. The essential goal of a flower is reproduction by the pollination process. Pollination is mainly related with pollen movement, which is done by pollinators such as insects, birds, bats, and other animals. Some flowers and insects have a mutual evolutionary relationship that develops into a very special reproduction relationship. For example, some kinds of flowers can only entice and exploit a certain kind of insect to complete pollination. Pollination takes two forms: biotic and non-biotic . About 91)Biotic and cross-pollination are regarded as a global pollination. In these types of pollination, pollen carriers use levy flight search strategy.2)Abiotic and self-pollination are used for Local pollination.3)Pollinators such as birds, insects may develop the flower stability, which rises the probability of reproduction, and that relatively depends on the likeness of the two flowers included in the reproduction operation.4)The switching between local and global pollination can be controlled by switching probability value that ranges between 0 and 1 with a slight bias towards local pollination.About 10% of pollination is non-biotic and does not need any pollinator. Wind and water-assisted propagation transmit pollen to the plants in this form of pollination. Pollinators can be very diverse; studies show that there are about 200 000 kinds of pollinators. These pollinators can develop the stability of the flower. The concept of flower pollination can be simplified using the following rules:In global pollination, pollinators such as birds, winds, etc. carry pollen and transfer it over long distances. The different forms of pollination are shown in 3.2Step 1Initialize a random population of flowers (n), then find the objective function value for each flower and choose a universal flower to the best solution.Step 2Generate a random number for each flower, if the number is smaller than switching probability, then draw a vector L with d dimension and apply global pollination as in tth iteration.g∗: the current best solution among the current options in the current iteration.L: The strength of pollination which basically is a step size. In order to imitate the movement of insects over long distances Levy flight distribution is used taking into account that insects move with different distance steps, So L > 0 can be expressed using Levy distribution as in Step 3J and K among all other solutions and perform a local pollination. The local pollination and flower constancy can be given as in If the random value is greater than the probability of switching, start from a uniform distribution. Select Step 4w, r, t are new solutions. If the new solutions are better, replace them in the population then repeat the steps for all members of the population and find the current best solution. The flowchart of flower pollination algorithm is given in Finding new solutions for all flowers, where 4In this study, FPA algorithm is used to obtain optimal P & PI controllers' parameters. Firstly, nine test functions are used to confirm the validity of the proposed algorithm, then the performance of this algorithm is compared with GA, PSO, and GWO algorithms’ performances in terms of time response of the VSD system and values of both THD and RF factors for grid current and DC-link respectively.The objective function used in this study is the integration of time multiplied be absolute error ITAE that is expressed by d system .The error e(t) used in objective function equation is the sum of DC-link current error and DC-link voltage error, and it is given by The absolute value of the sum of these two errors is multiplied by integration time to form the objective function. The FPA algorithm depends on this objective function in its iterations. When this function reaches its minimum value, it means that the algorithm found the optimal values of controllers' parameters. The smaller the value of the objective function the better the values of the controllers’ parameters.Two MATLAB scripts were written for both FPA and GA algorithm, then linked with the MATLAB/Simulink model of the studied system. The dynamic performance (rise time and overshoot) and the static performance (stabilization time and steady-state error) were taken into account in the simulation results. The parameters of FPA algorithm are shown in 5The values obtained by FPA has reduced the total harmonic distortion THD to 27% while the values obtained by GA has reduce THD to 30% which means the source current wave has been improved by using FPA. It is noticed from Both the rectifier output voltage and DC-link voltage signals have less ripples when using FPA as shown in Figures The steady-state response of the reverse voltage on the filter transistors was slightly affected by using FPA algorithm, but the transient response is remarkably improved as shown in I and DC-link ripple factor RF values in the studied system in addition to the number of iterations Iter needed by the algorithm to reach the optimal values [Previous researches proposed grey wolf optimization GWO and particle swarm optimization PSO algorithms besides manual tuning to find best controllers’ parameters in Medium power VSD systems , 16, 17.l values , 19. A cThdI and dc-link ripple factor RF. In addition, FPA requires 80 iterations to reach the optimal values, which is less than the number of iterations required by other algorithms.As noticed from In order to confirm the experimental validation of the suggested study, a hardware laboratory model of the studied system has been built to support the simulation results. The inverter output current when the system is loaded with (resistive-inductance) load is shown in The response of the implemented system has been studied at a motor power of 4kw. It is notice that the DC-link voltage has no oscillation and reduced ripples. The ripple factor after adding the chopper circuit is about 16%. The absence of oscillations and the reduction of ripples in the dc-link voltage after adding the chopper circuit, which confirms that the hardware model experimental results are identical to the simulation results and proves the validity of the proposed study.6•FPA algorithm confirmed its superiority against other optimization algorithms.•The time response of the studied system has been improved.•The THD% factor of the network current has been reduced to a value of 27%.•The DC-link current and voltage ripple factor RF has been reduced to value of 0.16%•FPA-based PI and P controllers have proved to be an efficient technique in generating control signals for filter transistors.In this study, both GA & FPA algorithms have been used for parameters tuning of P& PI controllers, these controllers are located in SPWM control circuit that generates the control signals for the filter transistors. The results of the proposed study are as follows:As a future work, we recommend using other optimization algorithms such as Imperialist Competitive Algorithm ICA to achieve the optimal time response for the studied system. We also recommend the use of the FPA algorithm for the optimal selection of inductors and capacitors values in power systems as a future work.Safwan Nadweh, Ola Khaddam, Ghassan Hayek, Bassam Atieh & Hassan Haes Alhelou: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The authors declare no conflict of interest.No additional information is available for this paper.


The sequential multiple assignment randomized trial (SMART) design allows for changes in the intervention during the trial period. Despite its potential and feasibility for defining the best sequence of interventions, so far, it has not been utilized in a smartphone/gamified intervention for physical activity.We aimed to investigate the feasibility of the SMART design for assessing the effects of a smartphone app intervention to improve physical activity in adults. We also aimed to describe the participants’ perception regarding the protocol and the use of the app for physical activity qualitatively.We conducted a feasibility 24-week/two-stage SMART in which 18 insufficiently active participants  were first randomized to group 1 (smartphone app only), group 2 (smartphone app + tailored messages), and a control group . Participants were motivated to increase their step count by at least 2000 steps/day each week. Based on the 12-week intermediate outcome, responders continued the intervention and nonresponders were rerandomized to subsequent treatment, including a new group 3 (smartphone app + tailored messages + gamification) in which they were instructed to form groups to use several game elements available in the chosen app (Pacer). We considered responders as those with any positive slope in the linear relationship between weeks and steps per day at the end of the first stage of the intervention. We compared the accelerometer-based steps per day before and after the intervention, as well as the slopes of the app-based steps per day between the first and second stages of the intervention.P=.08). Finally, in three participants from group 2, we observed an increase in the number of steps per day up to the sixth week, followed by an inflection to baseline values or even lower . The qualitative analysis showed that participants’ reports could be classified into the following: (1) difficulty in managing the app and technology or problems with the device, (2) suitable response to the app, and (3) difficulties to achieve the goals.Twelve participants, including five controls, finished the intervention. We identified two responders in group 1. We did not observe relevant changes in the steps per day either throughout the intervention or compared with the control group. However, the rerandomization of five nonresponders led to a change in the slope of the steps per day (median −198 steps/day [IQR −279 to −103] to 20 steps/day [IQR −204 to 145]; The SMART design was feasible and changed the behavior of steps per day after rerandomization. Rerandomization should be implemented earlier to take advantage of tailored messages. Additionally, difficulties with technology and realistic and individualized goals should be considered in interventions for physical activity using smartphones.Brazilian Registry of Clinical Trials RBR-8xtc9c; http://www.ensaiosclinicos.gov.br/rg/RBR-8xtc9c/. In Brazil, despite the economic crisis, the demand for smartphones has increased dramatically. There are approximately 324 million mobile devices connected to the internet in Brazil, of which 230 million are smartphones . There iIn order to optimize app-based interventions for physical activity, a novel research design, namely the sequential multiple assignment randomized trial (SMART) design, might be a rational strategy. SMART is an adaptive design, which allows for alternative treatments depending on observed success in the intervention during the research period. This strategy brings the intervention more in line with real-life situations, helping to identify people who benefit from interventions differentially and individualize the treatment. Another benefit of this intervention design is the evaluation of multiple interventions and responses in one trial. In addition, the SMART design has been recommended over the classical randomized controlled trial for technology-based interventions ,6, as itFor example, consider a SMART to evaluate behavioral interventions in eHealth for scope and intensity. Assume that there are three types of strategies , which are listed in order of dose and range. In this study, each participant would be randomized to one of two possible initial interventions (A or B). After a pre-established period, participants would be classified as nonresponders or responders, according to a previously defined criterion. Thereafter, nonresponders would be rerandomized to a subsequent intervention more rigorous in terms of the intensity and range of the initial intervention. Responders to treatments A and B would continue in their treatments to investigate the longer follow-up effect. Nonresponders to treatment A would be rerandomized to both receive treatment C and experience treatment B. Nonresponders in B, in turn, would be rerandomized to C or would change treatment by going to A. Six interventions are embedded in this design.Given the need to evaluate the SMART design development and implementation process, as well as the preliminary results of each participant’s response to the proposed intervention, a feasibility study is appropriate and allows the identification of methodological aspects that may be adapted before more extensive randomized controlled trials. Moreover, conducting a pilot study favors the exploration of crucial outcomes beyond the evaluation of the study protocol implementation process .Despite the high potential and feasibility  of a SMAWe conducted a feasibility study about the effectiveness of the SMART protocol using a smartphone app (free of charge) for the level of physical activity in adults. Our feasibility study expected to evaluate the recruitment capacity and resulting characteristics of sampling, evaluation, and refinement of data collection procedures; outcome measures; intervention and study acceptance and adequacy procedures; resources and ability to manage and implement the study and intervention; and preliminary responses to the intervention. The interdisciplinary team that developed the feasibility SMART study protocol included a psychologist, physical therapists, and professionals in physical education.This feasibility trial was a 24-week intervention with a two-stage SMART in which the allocation sequence of the randomization was concealed using opaque envelopes. An independent researcher performed this phase of the protocol. In the first stage of treatment decision, participants were randomized to group 1 (smartphone app only), group 2 (smartphone app + weekly tailored text messages), or a control group. After 12 weeks, based on the intermediate assessment (maintenance and increase or decrease in the number of steps), participants were classified as responders or nonresponders. The nonresponders were rerandomized into the two pre-existing groups (group 1 and group 2), and a new intervention group (group 3: smartphone app + weekly tailored text messages + gamification) was added to the protocol . ParticiThe ethics committee of the university approved this study (number: 0499/2018), and the trial was registered at the Brazilian Clinical Trials Registry (ReBEC #RBR-8xtc9c).As a rule of thumb, it has been recommended to recruit 30 participants for both pilot and feasibility studies, with samples between 24 and 50 being mathematically recommended to both calculate the standard deviation of a predetermined outcome and evaluate the rates of adherence and involvement (responder and nonresponder), as well as drop-out (for example) .We invited 39 volunteers who presented to another ongoing study called the Epidemiology and Human Movement (EPIMOV) Study. Briefly, the EPIMOV Study is another study of our research team being carried out since 2013 in the city of Santos, Sao Paulo, Brazil. It is a prospective epidemiological study to investigate the association of physical activity and sedentary behavior with the incidence of cardiorespiratory and locomotor diseases. EPIMOV Study participants were recruited through social networks, folders displayed in the community, local magazines, and newspapers. In the EPIMOV Study, we included adults (age ≥18 years) who did not have cardiopulmonary diseases, locomotor disturbances, known electrocardiographic abnormalities, or other problems that would preclude them from safely performing physical exercises. EPIMOV Study exclusion criteria were regular use of assistive gait devices, recent respiratory infections, unstable or stable angina in the last 4 weeks, bradyarrhythmia or tachyarrhythmia, and abnormalities in lung function evaluated through spirometry. Thus, we used the participants of the EPIMOV Study in the sample of this SMART design study. All eligible participants, consecutively enrolled in the EPIMOV Study, were invited to participate in this feasibility trial, and upon agreeing to participate, they were randomized to one of the groups of the SMART design . In ordeThe exclusion criteria for the feasibility SMART study were as follows: walking an average of ≥10,000 steps/day  and/or a score of ≥3000 metabolic equivalents (METs)/min/week in the International Physical Activity Questionnaire (IPAQ) . We set We considered as the primary tailoring variable the increase in the average number of steps per day in comparison with baseline. Participants were rerandomized after 12 weeks from the beginning of the intervention, based on their response to the first stage of the intervention. In case of reaching the goal in the first stage, they remained in the same group; otherwise, they were exposed to a new intervention . The parWe asked participants to increase their average daily step count by 2000 steps as much as possible. There is no consensus about the minimum increase in the number of steps (per day or per week) related to cardiovascular health improvement, although one study found that a change by 2000 steps/day was inversely associated with the risk of a cardiovascular event . MoreoveHowever, at the very beginning of the intervention (third week of the sixth participant assessment), we decided not to use the increase of 2000 steps/day for identifying responders and nonresponders in order to avoid making participants feel discouraged or uncomfortable by reaching the goal only a few times. Thus, the goal for each participant was any increase in the daily step count compared with the initial assessment. In this way, to define responders and nonresponders in this study, we fitted a linear regression for each participant with the relationship between weeks on the x-axis and the number of steps per day on the y-axis . ThereafIn group 1 (smartphone app only), participants were instructed to monitor their daily steps using the Pacer app. Additional instructions on app features were not provided, and encouraging text messages were not sent. In order to record the average number of steps per week, initially, participants were instructed to send a print screen of their step counts every Monday. After it was possible, we began to collect this information using the app interface by verifying each participant profile on the app with their agreement. Participants also received weekly questionnaires with questions, such as how they felt about that week, what was the appropriateness of the goal imposed on them, and how much they would like to remain in the study.In group 2 (smartphone app + weekly tailored messages), participants used the app to track the number of daily steps, and they received weekly text messages on their smartphones with information about their performance in the previous week and motivational messages according to their behavior change status . We prepParticipants in the control group were instructed to maintain their usual routine.In group 3 (smartphone app + weekly tailored text messages + gamification), in addition to app use and tailored text messages, participants were instructed to form groups with researchers to use the functions available in the app as described above. The researchers in this group acted both as dummy participants and as social moderators, competing and giving encouragement to the real participants. Apart from step monitoring and individual messages, participants were encouraged to join virtual challenges. For each challenge they completed, they were rewarded with a virtual badge on the app. Challenges available were as follows: target number of steps per day, target distance walked in the month, and group competitions where the total number of steps was compared among different groups of app users. In addition, there were rankings of the number of steps among all users, as well as running challenges.At the end of the sixth month, there were seven possible ways of the intervention. These ways were identified as the way in the control group (one way) and ways with the letters A to F (six different ways) as follows: A, app only during 24 weeks; B, app only during 12 weeks and app + tailored messages in the last 12 weeks; C, app only during 12 weeks and app + tailored messages + gamification in the last 12 weeks; D, app + tailored messages during 24 weeks; E, app + tailored messages during 12 weeks and app only in the last 12 weeks; F, app + tailored messages during 12 weeks and app + tailored messages + gamification in the last 12 weeks .Study outcomes were assessed at baseline and after 12 and 24 weeks by researchers blinded to group allocation. All researchers who did the assessments were blinded. The researchers who did the group allocation, sent messages, and participated in gamification were not blinded. At each scheduled assessment period, study measurements were carried out during two visits, spaced 7 days apart. In all three assessments, participants repeated the protocol of the two visits. In the first visit, participants underwent general health screening , assessment of the behavior change status for physical activity , anthrop2. We also investigated by self-report the presence of previous diagnoses of the main risk factors for cardiovascular disease, including systemic arterial hypertension, diabetes/hyperglycemia, and dyslipidemia/hypercholesterolemia. A family history of premature coronary heart disease was defined as myocardial infarction or sudden death before 55 years of age in the father or another male first-degree relative, or before 65 years of age in the mother or other female first-degree relatives. We also asked participants about current smoking.Baseline assessments included the age, sex, race, and educational level of the participants. We measured height (m) and body mass (kg) in all participants. Thereafter, we calculated the BMI and defined obesity as a BMI30 kg/mWe assessed participants’ behavior change status for physical activity according to a previously validated questionnaire . This quWe performed this evaluation with a validated triaxial accelerometer  . ParticiThe total amount of sedentary behavior was considered based on the minutes with less than 100 counts/minute (cpm), which represents <1.5 METs of energy expenditure. We evaluated sedentary behavior, light-intensity physical activity, and moderate to vigorous physical activity at baseline and at the end of the 24-week intervention. The measurements were calculated in hours per day considering the total wear time and the number of calendar days of use, as well as in percentage of the total time. The thresholds for the intensity of physical activity were as follows : (1) ligWe obtained the baseline average daily step count by using a smartphone app. Before starting the intervention, we tested several smartphone apps. Researchers installed on their smartphones the most popular free physical activity apps with a step-monitoring function that worked correctly in both Android and iOS operating systems. After a meeting, we decided to use the Pacer app. We agreed on this choice based on some features of this app. First, it handles well on the two most popular operating systems. Second, it accurately monitors daily steps. Third, it has gamification features, such as goal setting, rewards, virtual badges, progress bars, walking/running rankings, and group formation possibility. Finally, the app has social network and coaching functions.We performed qualitative analysis of the perceptions of all participants included in the SMART in terms of the protocol and the use of the app for physical activity. The exchange of text messages with participants was performed through the free instant messaging app WhatsApp. We transcribed to text all the text messages exchanged between participants and researchers. The proceedings used were adapted from qualitative research in health . To assiConversations with participants via the app occurred two times a week and were usually initiated by the researcher. The content of these conversations started with an initial greeting, talked about the participant’s inclusion, and included instructions on using the step counter app and instructions on sending a print screen with weekly steps. Conversations also involved reports on performance of the participants, health situations, and difficulties in handling the app, where assistance was provided. Participants could answer the messages sent by researchers through text and emojis (message app feature).t test for comparisons between intervention groups and the control group at baseline and at the end of the 24-week intervention. Finally, we fit linear and quadratic regressions both during the first 12 weeks of the intervention and overall to investigate the behavior of changes in the average number of steps per day throughout the intervention. As for the linear trend in the number of steps, we calculated the slopes for each participant who finished the protocol (stages one and two) and then compared the median of the slopes using the Mann-Whitney test. Moreover, we calculated the median of the weekly number of steps of the five participants who completed the protocol and compared the slopes of the step trends between the initial 12 weeks and the final 12 weeks using analysis of covariance. We used the number of steps as the dependent variable, the intervention stage as a fixed factor, and the intervention weeks as a covariate. We set the alpha level at 5% for all analyses.Quantitative analysis involved comparison between the slopes of the average number of steps per day obtained in the first stage of the intervention (12 weeks) and the second stage (24 weeks). Because of the small sample size, we chose the following statistical procedures. We performed a Mann-Whitney test to investigate the differences in the study groups. We compared the average number of steps per day at baseline and after 12 and 24 weeks of the intervention using the Kruskal-Wallis test. We also used an independent samples In total, we invited 39 participants from the EPIMOV Study. Among them, 14 were not included  and seven were excluded  . Of the We successfully adopted the SMART design, performed randomization and rerandomization among all groups, and delivered the proposed intervention for a feasibility study. The technology-based intervention allied with the SMART design may have created conditions to favor participants’ behavior change. The delivery of tailored messages, identification and rerandomization of nonresponders, and interaction between participants and researchers were feasible. The average number of daily steps was a feasible measure of the level of physical activity for this study design.The qualitative analysis showed that participants’ reports could be classified into the following three categories: (1) difficulties in managing the app and technology or problems with the device, (2) good responses to the app, and (3) difficulties in achieving the goals . An examResearcher: Good morning! Your average number of steps this week was 8,281/8,208.Participant #14 : Good morning.Researcher: [emoji reinforcing the participant’s behavior]Participant #14: [emoji expressing happiness]Participant #14: I started a bodybuilding program.Researcher: Cool! Congratulations!Only one out of 12 participants achieved the goal of increasing 2000 steps/day in the first 12 weeks of the intervention and presented a positive slope. Therefore, the participant was classified as a responder and was not rerandomized. Among nonresponders, one out of seven participants achieved the goal in the remaining 12 weeks after rerandomization. On occasions when the goal was not reached, in 66.7% of responses, participants reported feeling bad or very bad about it. Interestingly, in 72% of weekly questions, they reported that the 2000-step goal was adequate at the time they did not achieve it. In addition, in 99% of cases, participants reported being willing to continue the study. In 95% of cases, participants reported feeling well or very well when they reached the goal of increasing 2000 steps/week. They also reported performing physical activities alone 66% of the time. Walking for exercise and leisure were the most prevalent types of physical activity.P=.10) in the average number of steps per day on comparing baseline, week 12, and week 24 (P=.08). Comparing the trend of the median number of steps between the first and second stages of the intervention in the total sample, we found a relevant inflection , a quadratic rather than a linear regression was better to predict the behavior of the average number of steps during the first 12 weeks of the intervention . They shThe primary objective of this study was to investigate the feasibility of a SMART design for assessing the effects of a community-based smartphone app intervention to improve physical activity in insufficiently active adults. We also described the participants’ perceptions regarding the protocol qualitatively. Moreover, we aimed to analyze the participants’ responses to the intervention. Supporting the primary purpose, we observed that this study design was feasible for interventions promoting behavior change in physical activity. Furthermore, multicomponent app-based interventions seem to be more effective than app-based interventions alone .We showed that the SMART design, in association with behavior change techniques and technology-based features, is feasible and shows potential to be effective in promoting more active lifestyles. The study design worked correctly and called for participants to experience positive effects on physical activity levels. Thus, the SMART protocol required adequate planning and a dedicated interdisciplinary team to deliver the intervention. Interdisciplinary interventions increase the chances of achieving the different dimensions of change and sustenance of complex behaviors, as in physical activity .After we finished this feasibility SMART study, we made changes to the protocol, and the full trial is ongoing. For the adaptive intervention trial itself, we will change the way we recruit volunteers. Initially, we were inviting participants from another study with a different focus (EPIMOV Study) to be part of the SMART study. However, we realized that we need to broadly publicize the SMART study  to recruit volunteers interested in increasing their levels of daily physical activity, which we hope will contribute to decreasing the drop-out rate.The qualitative analyses showed that participants presented an excellent response to the app, as well as important issues such as difficulty in managing the technology and difficulties in achieving the goals, which could be addressed in a future large-scale clinical trial. The possibility of interaction with the team had an essential role among participants. It is interesting to observe that participants tried to report their difficulties or their achievements/progress to the researcher. The sensibility and acceptance of the participants’ demands had an essential role in the process of behavior change among the participants of this study. Some people need more support and incentive to start the change, and some recent evidence showed that self-efficacy is a potent mediator for improving physical activity, especially considering meaning in life and peer support ,22. The Researchers in the field of psychology point out the complexity and importance of therapeutic alliance research in psychotherapy ,24. TherSmartphone devices and apps provide more awareness than motivation in practicing physical activity . The intFurthermore, the availability of researchers allied with the weekly text messages may have contributed to participants’ adhesion to the intervention since they could stop using the app or stop monitoring or trying to increase their daily steps. Although electronic devices and apps are essential tools for health interventions, real-time feedback is crucial for behavior change .In a small sample, we found a tendency to increase the average number of steps per day throughout 24 weeks of the intervention, especially in the last 12 weeks. Moreover, the tailored messages seemed to have a positive impact on the physical activity level; however, our results demonstrated that rerandomization to a new treatment strategy would be better implemented before 12 weeks of the intervention. These findings confirm previous research about the importance of the social environment ,29 and tOur findings suggest that the rerandomization itself seemed to play an important role in participants’ behavior, leading to an inversion of the tendency to decrease physical activity over a short period. While most participants were not able to achieve the goal of increasing 2000 steps/day, we observed, intriguingly, that they reported that this goal was adequate.After about 3 weeks of the intervention, we decided to redefine the goal to avoid making participants feel discouraged or uncomfortable by reaching the goal only a few times. The importance of goal setting has been discussed in the field of sports psychology. Weinberg  argues tThus, the goal for each participant was to increase their daily steps by any count compared with the initial assessment. This goal can be an essential strategy to encourage self-regulation, which plays a vital role in behavior change. Buckley et al  showed tIn addition, we consider that the presentation of personalized messages sensitive to the stage of behavior change can generate a higher positive response and incentive in the search for a goal. In the initial stage of behavior change, the possibility of the individual perceiving himself/herself as supported and encouraged is of fundamental importance for the construction of a more effective behavior change . SendingFinally, our results showed a decline in the average number of steps of participants in the first stage of the intervention with relevant positive inflection in the second stage. However, it is worth noting that an essential part of the sample that received personalized messages showed quadratic behavior in the step trend with an evident decline from the sixth week. These results suggest that if rerandomization was performed earlier, our results could have been even more consistent. Our intervention currently provides for rerandomization in the sixth week. Adaptations in technology intervention are dynamic and must be implemented quickly. In this sense, an application with sufficient artificial intelligence could automate and individualize the adaptive process to increase physical activity.As a strength of our study, we highlight its novelty. To our knowledge, this is the first study to develop an adaptive intervention based on behavior change techniques to increase the level of physical activity in adults. In addition, an interdisciplinary team was mandatory for the proposed intervention . We showed that the SMART design, in association with behavior change techniques and technology-based features, is feasible and shows potential to be effective in promoting a more active lifestyle. In addition, few participants reported problems using the app. The Pacer app was useful for step counting, as expected. Even with limited accuracy, the number of steps taken in the Pacer app was reproducible and able to predict the distance traveled during walking . TherefoWe are aware that the 34% drop-out rate of participants in this study is a possible limitation. However, this proportion has been described in other physical activity interventions in primary health care, even with a shorter intervention period  -41. MoreThe SMART design was feasible for assessing the effects of a community-based smartphone app intervention to improve physical activity in insufficiently active adults. Our results suggest that rerandomization should be implemented earlier to take advantage of tailored messages.Additionally, difficulties with technology and a realistic and individualized goal should be considered in interventions for physical activity using smartphones. We found a tendency to increase the average number of steps per day throughout the 24 weeks of the intervention, especially in the last 12 weeks. The results from the feasibility study contributed greatly to the final design of the SMART.