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How can I buy cocaine online in Druskininkai

We are one of the leading orthopaedic surgery clinics for medical tourists in the European Union. Meet our surgeon Prof. Sarunas Tarasevicius on November th in Cardiff and Belfast! Read more. The implant option is discussed in detail once the surgeon reviews your individual case. Get your surgery for free by claiming a refund from your local health board. The clinic helps patients with the documents needed to claim a refund after following the EU directive route for medical treatment abroad. It applies to patients who are insured under the systems of one of the EU countries and may not get the surgery due to long waiting times. One of the most experienced orthopaedic surgeons in Europe. More than 5. More than scientific articles co-authored by our surgeon and over 50 publications in different medical journals. More than positive reviews from our happy clients from all over the world. Patients saved this number of years by travelling to our clinic instead of waiting for surgery at home country. On average our patients from the EU countries get fully refunded by their local health board in 5 months after their surgeries. One of the most important factors for a quick and full recovery after surgery is proper physiotherapy. Physiotherapy helps recover after surgery as well as prevents formation of blood clots and helps avoid most of the postoperative complications and side effects. We offer two different physiotherapy packages:. The rehabilitation clinic is equipped with modern facilities. Individual physiotherapy programs are prepared by a kinesiologist with over 20 years of experience, Prof. Laimonas Siupsinskas. This type of physiotherapy is best suited for people who are physically active, athletes and those wishing to return to sports as soon as possible. Inpatient physiotherapy at SPA resort in Druskininkai. It is equipped with modern facilities. The professionals there have years of experience working with people after various surgeries and injuries. Usually, clinics are not able to offer this due to costs savings. Our patients can choose between two inpatient and outpatient options: physiotherapy with a physiotherapist of the Lithuanian national basketball team, prof. Siupsinskas or physiotherapy at a medical SPA. Our team of 5 orthopaedic surgeons has years of experience in the field in total performing over 1. Moreover, our surgeons are members of various prestigious surgical societies both Lithuanian and international. Our leading joint replacement surgeon S. Tarasevicius is an author of scientific publications in different medical journals, who has performed more than 3. We are trusted by our patients and we appreciate all the reviews and feedback collected over the years. Find more than testimonials here or on Google. Already more than 4. Members are welcome to share experiences about their visit to the clinic and to discuss all surgery-related matters. No other orthopaedic clinic can offer such group support. Moreover, thanks to our active participation in collecting data for the registries, the surgical technique used at our clinic ensures best surgical outcomes. Our clinic works according to the highest standards set by the European Union. This helps to guarantee the quality of medical services. We care about the safety, comfort and successful results of our patients from all over the world. Everyone in our clinic speaks English, including nurses, assistants and the surgeon. Read more here. A hip replacement , also known as a total hip arthroplasty , is an orthopaedic surgery performed in order to restore hip motion and relieve pain of the hip joint. During this surgery, a dysfunctional hip joint is changed into a prosthetic implant. A hip joint consists of a round femur head which tightly fits into a socket acetabulum. Normally surfaces of the acetabulum, as well as the femur head, are both covered with a thick layer of cartilage. It cushions the bones where they meet to form the joint. However, due to various medical conditions, such as osteoarthritis, rheumatoid arthritis, or osteonecrosis, hip cartilage is damaged and can no longer perform its function. Note that hip replacement surgery is performed when all other treatments have not provided enough pain relief. A hip replacement surgery is done under epidural anaesthesia and deep sedation. Watch full list of interview sessions on our Youtube channel. There are several important things a potential patient needs to consider as a hip replacement surgery approaches. Firstly an orthopaedic surgeon should be consulted. Approximately a month before the procedure a patient is advised a muscle strengthening exercise routine. It is essential to be physically prepared for the postoperative period, therefore, the patient can take up specific exercises that improve the strength of the muscles in the upper leg, gluteal region, hips, and arms. The medical team should be informed about the current intake of any medication. Patients are usually recommended not to take certain blood-thinning drugs e. Your doctor will give precise instructions about taking medication before the surgery. For a successful surgery and recovery, it is recommended for the patient takes certain vitamins, like v itamin D, vitamin C, and vitamin A, as well as certain minerals , such as calcium, zinc, and iron. Sufficient vitamin and mineral intake is important for the formation of bones and connective tissue. Bear in mind that following a healthy diet is essential, too. Meals should contain enough protein, non-starchy vegetables, and grains. There are no age-related requirements. Even though most of replacement surgeries are performed on patients between 60 and 80 years of age, younger patients can also be eligible. A tissue-preserving hip replacement , also known as minimally invasive total hip arthroplasty THA , is a surgical technique which is getting increasingly popular because of a shorter hospital stay and decreased rehabilitation time. It is similar to a traditional hip replacement surgery, however, it is designed to preserve muscle and connective tissue around the hip. A tissue-preserving full hip replacement is different from a traditional full hip replacement because a much shorter incision of about 13 cm is made as compared to approximately 25 cm in traditional surgery. Moreover, muscles and ligaments are detached to a lesser extent than in traditional hip replacement surgery. Special surgical tools and techniques are used to access hip joint without damaging the surrounding tissues. The implants are the same as in traditional hip replacement. Note that not every patient is recommended a tissue-preserving hip replacement surgery. This procedure is usually offered to patients who are younger and have a better physical form. The surgery will take approximately 2 hours. After the surgery, you will be moved to a recovery room as you recover from deep sedation. Once you are alert, you may feel some discomfort in the hip area. After waking up from deep sedation some patients may feel the need to clear their lungs by coughing excessively which is completely normal. Patients will have to take pain-relieving drugs or will have them administered through an intravenous catheter. A drain tube will probably be placed inside the hip at the incision site. On the first day, patients are usually allowed to drink and consume foods. Patients usually spend approximately 2 days at the hospital. After the first day, most of them can sit up on the edge of the bed and stand. On the second day, patients can walk slowly using crutches. On day two or three most patients will start physical therapy. Patients are recommended to sleep on the healthy hip with a pillow between the legs to position the operated hip comfortably. Make sure to have assistance at home, which is necessary for the first few weeks. The patients are recommended to refrain from housework and other activities, such as gardening. Patients are likely to begin doing light recovery exercises the next day after surgery. Later on, patients will be introduced to a special exercise routine which helps to regain muscle strength and mobility much faster. Even though moving or exercising might feel uncomfortable at first, actively participating in physical therapy is crucial for a successful healing of the muscles and reduced overall recovery time. A physiotherapist will adapt a recovery program and the difficulty of the exercises depending on individual progress. At the beginning of the post-operative period, patients are recommended to exercise for 20 minutes 3 times per day. Flying right after a surgery may increase the risk of deep vein thrombosis DVT. DVT is a formation of a blood clot within a deep vein in the body, usually in the legs. A blood clot blocks blood flow and in extremely rare cases can travel to the lungs and cause pulmonary embolism. Prolonged sitting time may provoke clot formation, therefore, it is essential to know how to reduce the risks of travelling. After a hip replacement is completed, the incision will be stapled and the wound dressings applied. A patient will most likely have a drain tube placed inside the wound and exiting from the bottom end of the dressing. A drain tube prevents serous fluids form collecting at the incision site. Most people have their drainage tube removed before they start walking. Staples should be removed after 10 to 14 days. Note that the patient should not shower for the first three days. From then on showering is allowed, however, a wound dressing should not be scrubbed and damaged in any way. Bathing and hot tubs should be avoided for the whole recovery period, i. When recovering at home the patient will need to change the bandages themselves. A wound dressing may need to be changed every day if a physician advised to do so. In order to change the bandage correctly, hands need to be washed well before and after removing an old dressing and a new dressing kept sterile right until placing it over the incision site. The patient will be advised on detailed measurements and types of bandages during their hospital stay. It may be tricky to get in and out of the car, therefore, it is best to have assistance until you feel comfortable driving alone again. It is important to practice applying the brake quickly and forcefully as well as slowly as may be required in particular situations. Bear in mind that some pain relieving medications can have a sedative effect and should not be taken before driving. Right after the surgery, you will be able to walk, however, it might feel quite uncomfortable. Since you will have to use a cane, walker, or crutches, arrange the furniture at home so that it is not difficult to move around. Avoid the stairs for the first few weeks. Therefore, if your bedroom is located on the upper floor, you might need to consider sleeping in a room that does not require you to walk up and down. Patients will be provided with a special exercise routine that focuses mainly on strengthening muscles in the hips and upper legs. Bear in mind that walking, although highly beneficial, is not a substitute for the exercises. Being physically active is a crucial part of a successful recovery. Patients are encouraged to return to an active lifestyle around three months after surgery. Mild physical activities like swimming, golfing or cycling will not only aid in the recovery of your joint but will also positively affect your general health. Highly active sports like running or skiing should be avoided. Total Hip Replacement Abroad: Lithuania. Our patients and clinic in the media. Price in GBP. Prices in GBP. What is included. Refund for EU patients. Prices in EUR. Hip replacement case analysis. Patient stories. Only we can offer:. Clinic videos. Leading orthopaedic surgery clinic. Greatest gift — happy patients. Clinic tour. Nordorthopaedics Center Of Excellence. Experienced hip and knee surgeon. Physiotherapy packages. Option 1: outpatient physiotherapy. Option 2: inpatient physiotherapy. Our clinic. Self-catered accommodation with medical care. Unique, integrated post-surgery physiotherapy. A team of highly professional and educated surgeons. One of the leading orthopaedic surgery clinics for medical tourists in the European Union. Center of excellence. A 5-star clinic. Community of our patients. Our clinic uses implants based on their performance in international registries. Our clinic is seen on different media mentions. EU clinic with European standards. Our European Union patients can get a full reimbursement of surgery costs. We provide customer service in 9 foreign languages. Athletes treated at Nordorthopaedics. Official clinic of Lithuania national football teams. Highest quality implants. Direct flights to Lithuania. Read More. Dublin, Shannon. Madrid, Barcelona, Palma de Mallorca, Alicante. Stockholm, Gothenburg. What is a hip replacement? Firstly, a surgeon makes an incision along the side of the hip and divides and repositions the muscles that cover the joint. Then a socket acetabulum is prepared by removing damaged cartilage and bone spurs. Depending on the implant and technique, a surgeon may use bone cement to implant and tightly anchor a metal socket in the acetabulum. A femoral part of the joint requires preparation as well. The surgeon excises the head of the femur and replaces it with a stem implant as well as a round head implant which are inserted into the femur bone. Finally, implants of the socket and the head are both joined and secured together to form a new functional joint. FAQ about hip replacement with our surgeon. Our orthopaedic surgeon Dr Sarunas Tarasevicius explains what to expect after hip replacement surgery. More FAQ videos from our surgeon:. Travelling after hip replacement surgery How long will the hip implant last? Sports after hip replacement surgery Technique for hip replacement Preparing home for the period after hip replacement Driving after the hip replacement. Preparing for a hip replacement surgery. Exercise routine. What are the eligibility criteria for a hip replacement surgery? Overweight patients are recommended to lose a few kilograms before the surgery In addition, there are normally no body mass index BMI criteria. However, overweight patients are recommended to lose at least a few kilograms before the surgery, since one kilogram of body weight is almost equal to 4 kilograms of strain on the hips. It is easier to adapt to a new hip implant if there is less body mass to carry. Tissue-preserving technique. What to expect after hip replacement surgery? In the hospital. At home. The importance of physical therapy. How to reduce risks of flying after a hip replacement surgery? In order to prevent any complications, patients are advised to wear compression stockings , especially during a flight which is longer than 4 hours. Compression socks tightly compresses the legs, reducing vein diameter, and therefore, increasing venous blood flow velocity and valve effectiveness. Walking whenever possible and doing anti-DVT exercises foot pumps, ankle circles, leg lifts, and knee pull-ins will lower DVT risk too. A physician will prescribe blood-thinning medication as well. The patient should drink plenty of water and avoid consuming alcohol. Wound care. Drain tube. Driving after a surgery. Physical activity after surgery. Active lifestyle. Send us your enquiry.

Total Hip Replacement Abroad: Lithuania

How can I buy cocaine online in Druskininkai

Official websites use. Share sensitive information only on official, secure websites. Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system iOS and Android platforms. The mobile sensing system mPulse collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment EMA of state impulsivity and constructs related to impulsive behavior ie, risk-taking, attention, and affect. Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. Keywords: mobile sensing, digital phenotyping, impulse control, impulsivity, self-regulation, self-control, mobile health, mHealth. Mobile health mHealth technology has demonstrated the ability of smartphone apps and sensors to collect high-fidelity and high-frequency data pertaining to patient activity, behavior, symptoms, cognition, and context \[ 1 \]. Mobile sensing, in particular, has the ability to collect data objectively and continuously during the lived experience of individuals. Building on this potential, prior research using mobile sensing technology focused on specific psychological disorders \[ 6 - 11 \] or general mental and physical well-being \[ 12 - 14 \]. One construct that has not been rigorously examined is impulsivity and impulsive behavior. Impulsivity is a multidimensional construct primarily characterized by the inability to inhibit acting on short-term temptations despite long-term consequences or loss of potential gains. Consequently, it is the hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health \[ 15 - 18 \]. Prediction of impulsive behavior is nevertheless challenging due to the multidimensional and heterogeneous nature of the impulsivity construct and different manifestations of state impulsivity \[ 20 , 22 \]. Such impulsive behavior includes the traits of urgency, lack of planning or premeditation, lack of perseverance, inattention, present and future discounting, response inhibition, and sensation seeking. Passive detection of impulsive behavior is a crucially important research goal given the widespread negative consequences of impulsivity. Potential behavioral biomarkers of impulsive behavior are intuitively present in most interactions with digital technology. Mobile sensing may be especially useful for assessing impulsive behavior indicative of digital addiction, such as loss of control over mobile phone use, interference with other activities, and repeated phone checking. Objectively quantifying phone usage can further help inform the debate on the existence of digital addiction \[ 23 \] and identify distinct problematic uses of smartphones. Preliminary evidence suggested a link between impulsivity traits and use of mobile devices. Studies of self-reported phone usage conducted by Billieux et al \[ 24 , 25 \] revealed a direct relationship between the inability to delay gratification and different patterns of mobile phone use. In other studies, mobile analytics features, such as latency to respond to a text, were shown to predict personality traits associated with impulsivity, such as extraversion and neuroticism \[ 26 - 29 \]. We developed a mobile sensing system—mPulse—to remotely monitor impulsivity on both Apple operating system iOS and Android platforms. Our system was designed based on data that are pervasive and available across both iOS and Android platforms and can be used to measure signals of daily activities, social interactions, and digital addiction. We selected call logs, battery charging, and screen checking as the mobile sensor data sources. We conducted a 3-week exploratory study with 26 participants as part of a larger mHealth study of impulsive behavior called the Digital Marshmallow Test DMT \[ 30 \]. To validate the mobile sensing model, we used mobile sensing features to predict common self-reported impulsivity traits, objective behavioral and cognitive measures, and ecological momentary assessment EMA of impulsivity and constructs related to impulsive behavior ie, risk-taking, attention, and affect. The DMT study by Sobolev et al \[ 30 \] was designed to develop and test remote assessment of impulsivity using both iOS and Android applications for widespread dissemination to researchers, clinicians, and the general public. Additional details can be found in the paper describing validation of the DMT app \[ 30 \] and on the Open Science Framework \[ 31 \]. Of the participants enrolled in the DMT study, a subsample of 26 participants enrolled in this passive sensing study. The subsample included 14 females, 10 males, and 2 participants who refused to disclose, and the average age of the participants was We compared the baseline subjective trait assessments of trait impulsivity and impulsive behavior between the current subsample of participants and the full sample and found no significant differences between the groups. The DMT study included three main data sources, which we used as dependent variables in this study: 1 subjective, self-reported trait impulsivity assessments performed at baseline in the lab; 2 behavioral and cognitive active tasks performed daily on the DMT mobile app; and 3 self-reports, ecological momentary assessments EMAs , and the Photographic Affect Meter PAM performed daily on the DMT mobile app. The BIS \[ 32 \] measures three aspects of impulsivity: attention inability to focus attention or concentrate , motor acting without thinking , and nonplanning lack of future orientation or forethought. The UPPS impulsive behavior scale \[ 33 \] assesses impulsivity on subscales pertaining to urgency acting rashly under conditions of negative affect , lack of premeditation difficulty in thinking and reflecting on consequences of an act , lack of perseverance inability to remain focused on a task , and sensation seeking tendency and openness to try and enjoy exciting or dangerous activities. The mobile versions are exploratory and were partially validated as part of the DMT study see the DMT study \[ 30 \] for more details on each of these measures. The mBART measures how individuals balance the potential for reward and loss via a simulated test where the participant can earn virtual money by pumping a balloon. It is based on the BART \[ 34 \]. The mBART includes 15 trials and lasts approximately 2 minutes. We recorded the number of pumps, which indicates risk taking, and the total gains in the task for each trial. The mGNG is a measure of attention and response control. It is based on the GNG task \[ 35 \]. The mGNG included 75 trials, each of which had the following sequence: fixation cross ms , blank screen ms , vertical or horizontal cue white rectangle for 1 of 6 stimulus-onset asynchronies ms, ms, ms, ms, ms, and ms , go or no-go target green or blue rectangle, respectively until participant responds or ms, and an intertrial interval ms. Participants were instructed to respond by pressing the screen as fast as possible to green, but not to blue, targets. We recorded the commission and omission errors and response latency before they reacted to the targets. The mDD task is used to measure the ability to delay immediate, smaller, and shorter monetary and time-based rewards for longer, time-lapsed, but larger rewards. It is based on DD tasks that were used in research on addiction \[ 36 \]. We used the algorithm as described by Frye and colleagues \[ 37 \]. In the mDD task, participants were given five choices between a smaller, hypothetical monetary or time-based reward that varied from trial to trial based on the previous response and a larger, fixed reward that remained the same throughout all of the trials. We recorded the propensity of choosing an immediate, smaller reward in each trial. EMAs were based on a semantic differential scale and questions consisted of two opposite feelings, thoughts, or behaviors \[ 38 \]. We measured five items from 0 most positive to 10 most negative : 1 focused—distracted, 2 intentional—impulsive, 3 cautious—thrill-seeking, 4 engaged—bored, and 5 determined—aimless. These items were measured twice daily with respect to the feeling in the present moment in the morning AM and evening PM. Self-reported questions were also based on a semantic differential scale \[ 38 \]. We measured five items from 0 most positive to 10 most negative : 1 positive—negative, 2 intentional—impulsive, 3 productive—unproductive, 4 relaxed—stressed, and 5 healthy—unhealthy. These items were self-reported based on the general feeling in the previous day. PAM was designed for momentary response where users choose an image that best represents their emotion at a given time \[ 39 \]. Overall, our results corresponded to previous research on impulsivity by demonstrating high correlations between different self-reports but low correlations between behavioral measures and self-reports \[ 22 \]. A full description of these results can be found in the paper describing the DMT study \[ 30 \]. AWARE Framework is an open-source framework used to develop an extensible and reusable platform for capturing context on mobile devices \[ 41 \]. It is available on both iOS and Android platforms as an installable app that collects phone sensor data eg, activity and screen checking. Our goal was to create sensing models that can effectively transform raw sensor data collected from mobile phones into measurable outcomes of clinical interest. We focused on data that are pervasively available across both iOS and Android platforms while minimizing battery consumption beyond the normal use of mobile devices and protecting user privacy. Therefore, despite the relevance of data sources such as accelerometers and location data for physical activity, mobility, and motor impulsivity, we elected not to include these data sources in the passive sensing model in this study. Eventually, three types of sensor data were identified and implemented in the mPulse system Figure 1 for these purposes: call logs, battery charging, and screen checking. Call logs are indicators of social interactions \[ 42 \] and are frequently used in mobile sensing studies. Prior research, for example, identified negative correlations between frequency of incoming and outgoing calls and depressive symptoms in both clinical \[ 43 \] and nonclinical \[ 44 \] samples. In the mPulse system, we recorded time stamps of each call the participants sent or received and their durations. Battery logs are an indicator of daily activities \[ 42 \]. We identified battery management as a potential indicator of self-regulation in the context of phone usage and planning. In the system, we recorded the time stamps and durations of battery charging events. We observed several instances of a charging event with a duration of 1 second followed by a longer charging event, which we suspected were caused by system error. Thus, we removed charging events that were shorter than 10 seconds. Using these criteria, Screen checking can serve as an indicator of digital and mobile addiction. For example, a previous study demonstrated that individuals with smartphone addiction presented with some symptoms common to substance- and addictive-related disorders such as compulsive behavior, tolerance, and withdrawal \[ 45 \]. In the mPulse system, we measured screen checking by collecting the number of screen unlocks and the duration of each unlock session. Notification-induced screen-on events were intentionally excluded. We removed screen unlock sessions longer than 2 hours, which are triggered by unrelated usage, such as continuous use of the phone for navigation while driving. This resulted in the removal of only 0. From the passive data, we extracted the same set of features for all sensor data, namely usage, frequency, entropy, mean, and standard deviation. This resulted in 15 passive features for the analysis:. Use duration and frequency per hour: normalized duration and frequency for each hour—that is, the summation of sensor event durations and occurrences divided by total hours of data collected from each individual, respectively. Use mean and standard deviation: used to measure individual usage baselines and variances. We calculated the means and standard deviations of the event durations unit in hours across the study for each participant. Entropy: calculated from the possibility distribution of event occurrences over 24 hours. The intuition is that if the occurrences of the events distribute more uniformly across the day, the pattern is more random higher entropy ; otherwise, if the events occur more frequently at certain hours of the day, the pattern is more controlled lower entropy. This was inspired by the use of the entropy feature in prior mobile sensing research to measure variability of time the participant spent at the location clusters \[ 8 \]. Ent: entropy; Freq: frequency per hour; Mean: use mean; SD: use deviations; Use: use duration per hour. Means and standard deviations across individuals for the mobile sensing features are presented in Table 1. To predict assessment of trait impulsivity and impulsive behavior BIS and UPPS , we used averages across individuals as predictor variables. For predicting daily features, such as active tasks and EMA questions, we used the hour window before the morning assessment. In this section, we evaluate the value of mobile sensing in explaining and predicting trait impulsivity. We first examined the correlations between mobile sensing features and different components of trait impulsivity. Next, we compared the goodness of fit for regression models using mobile sensing features as predictors. Finally, we validated the predictive power of such models using leave-one-subject-out LOSO cross-validation. The full correlation table is shown as Figure 2. We performed a multivariate regression analysis to examine the power of extracted mobile sensing features from day-to-day phone usage to explain components of trait impulsivity. Features were standardized across samples. Given our small sample size, we first used Lasso regularization to prevent overfitting by selecting the most important features. We then used a linear regression model with ordinary least squares to estimate the trait impulsivity scores from the selected features. Model performance was evaluated against adjusted R 2 and is summarized in Table 2. Descriptive statistics of laboratory subjective impulsivity and impulsive behavior trait, and regression analysis of mobile sensor data as predictors of impulsivity trait scales and subscales. LOSO cross-validation was performed to further examine the predictive power of the passive sensing features for out-of-sample data. We trained a separate linear support vector regression model for each set of passive features for 25 participants and tested it on the 1 remaining participant. We ran the same procedure 26 times to obtain predicted scores for all 26 participants. Model performance was evaluated against mean absolute error MAE and Pearson r. We found that the passive model predicted only the sensation-seeking trait with a MAE of 0. Descriptive statistics on daily variables used for prediction of state impulsivity are presented in Table 3. We used a generalized estimating equation GEE model to take into account the intraclass correlations for individual differences. We performed a multivariate regression analysis for five daily semantic differentials and positive and negative affect measures. We used a logistic regression model and LOSO cross-validation. The full results are reported in Table 4. Regression analysis and classification of mobile sensor data as predictors of daily ecological momentary assessment questions for semantics differentials and the Photographic Affect Meter PAM. We used a GEE model to take into account the intraclass correlations for individual differences. The full results are reported in Table 5. Regression analysis and classification of mobile sensor data as predictors of daily active behavioral and cognitive tasks. This exploratory study examined the potential of detecting and monitoring state impulsivity and impulsive behavior in daily life using continuous and ubiquitous mobile sensing. We explored the predictive power of the mobile sensing system and model we developed mPulse. We discovered relationships between passive mobile sensor data and self-reported impulsivity traits, EMA of impulsive behavior, and mobile behavioral and cognitive active tasks of risk-taking, attention, and time preference. This is the first study to examine the relationship between passive mobile phone data, daily self-reports and self-report measures of trait impulsivity, and exploratory, objective, active mobile measures of impulsivity. Overall, our findings suggest that passive measures of mobile phone use such as call logs, battery usage, and screen on-off metrics can predict different facets of impulsivity and impulsive behavior in nonclinical samples. This study adds to the emerging literature on mobile phone phenotyping using ubiquitous sensor data as well as to the measurement of impulsive behavior in daily life \[ 46 - 48 \]. Our results can further inform the development of digital interventions for individuals \[ 49 - 51 \] by identifying and intervening with potential problematic behavioral patterns before they result in consequences. First, we investigated the relationship between mobile sensing features and impulsivity traits on the individual level. Our regression models significantly explained variance in sensation-seeking, nonplanning, and lack of perseverance traits, but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting the sensation-seeking trait. The regression model indicated that overall battery charging frequency and screen-checking usage were significant positive predictors of sensation seeking, while call entropy was a significant negative predictor. Cross-validation further confirmed the validity of these mobile sensing features for predicting sensation seeking. Sensation seeking in itself has multiple facets from thrill-seeking to boredom proneness to disinhibition. Therefore, due to the rewarding nature of interacting with mobile devices, one would expect to discover digital biomarkers of sensation seeking in mobile sensor data. Our results suggest that individuals high in sensation and thrill-seeking may be more prone to repeated phone checking and more intense interactions with their devices when they are using them eg, less entropy. Previous studies have yielded mixed findings on the relationship between sensation seeking and psychopathology. For example, in a meta-analysis of the UPPS subscales, sensation seeking demonstrated the strongest associations with alcohol and substance use but an overall lower relationship with other clinical conditions than other UPPS traits \[ 51 \]. It could be that these relationships represent not only maladaptive behaviors but also a desire to seek information, be conscientious at work or with family requests, and stay connected to others. Future studies should collect more information on the interaction between sensation and thrill-seeking and reasons for phone checking to parse out the positive and negative relationships between these passive metrics and outcomes. Second, we explored the use of mobile sensing features to discover measures that assess state impulsivity and impulsive behavior in daily life. Our mobile sensing model successfully predicted objective behavioral measures, such as present bias in a delay discounting task, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also successfully predicted daily EMA questions on positivity, stress, health, and affect. Perhaps most intriguingly, our model failed to predict daily EMA questions designed to measure previous day and present moment impulsivity directly. This finding indicates that it might be easier to predict constructs related to trait impulse control than self-reported state impulsivity itself in our sample. While studies have revealed that trait impulsivity is highly related to state impulsivity \[ 47 , 48 \], there may be more powerful constructs that mediate the relationship between sensors and state impulsivity. For example, studies have revealed a close relationship between affect and impulsive behavior and, separately, between affect and phone sensor data \[ 44 \], which may have more robust relationships than with state intentionality—impulsivity. It is also possible that because our sample skewed toward intentional versus impulsive responses, we were less able to detect differences. Despite this surprising finding, the data does suggest that combined mobile phone use features are associated with a range of important factors related to well-being, such as perceived productivity. This further highlights the need to personalize passive detection models of state impulsivity or impulsive behavior for the appropriate context, such as substance misuse, productivity, and gambling. It also suggests the need to compare this sample against clinical populations with potentially higher impulsivity scores. Taken together, the exploratory analysis between the passive mobile phone features and daily measures of impulsive behavior revealed that the range of combined mobile phone sensors can predict certain behaviors but that identifying the individual predictors of these components is more challenging. Passive mobile sensing can be particularly useful for detecting signs of digital addiction and problematic phone usage. Digital addiction and excessive phone usage are considered other negative consequences of impulsivity and self-regulation failures \[ 24 \]. We considered this emerging theoretical relationship in the design of the mPulse sensing model, which provides ecologically valid features such as battery usage and screen checking. Our preliminary results confirmed this hypothesized relationship through the sensation-seeking trait, which can explain reward-based phone usage. The relationship between sensation seeking and screen checking was further evidenced by the significant associations between screen frequency and thrill-seeking EMA. It is also possible to use mobile sensing models to predict consequences of digital addiction, such as daily productivity. There is an opportunity to use our passive sensing models to contribute to the debate on the existence and measurement of digital addiction and distinguish between actual and problematic phone usage \[ 23 \]. Mobile sensing can help objectively detect signals of problematic phone usage and provide input into personalized interventions to reduce this impulsive behavior \[ 52 \]. Future research should model and evaluate mobile sensing features as they relate to digital addiction and problematic use of smartphones. Our inability to predict traits such as attention and urgency, which should theoretically correlate with mobile sensing features, indicates the challenge of predicting impulsivity using the sensors chosen for the current study. Similarly, our models struggled the most with predicting the EMA question that directly asked participants to self-report the general state impulsivity in the present moment and in the previous day. We suspect this finding might be due to the multidimensional nature of impulsivity and the complex interaction between trait and state impulsivity \[ 20 \]. While studies showed promising results for measuring momentary impulsivity \[ 46 - 48 \], the overall convergence between behavioral and self-report measures of the impulsivity construct remains low \[ 22 \]. Future research should ideally include larger samples of clinical and nonclinical populations and different measures to discover and model these interactions. Mobile sensing and phenotyping can provide an additional objective method of assessing impulsive behavior. This method can provide further insight into a range of new, unexplored opportunities to understand human behavior and explain impulsive behavior. One of the primary goals of this study was to design a mobile sensing system and model, supporting both iOS and Android platforms. The majority of foundational research on mobile sensing was examined on a single platform, which limits the generalizability and real-life applicability of the findings. Cross-platform research services more diverse populations and offers different opportunities for passive and active assessment. Given differences between the two operating systems, compromises are required when considering passive sensor data sources to only collect the subset of sensor data that are available on all devices. Android devices in particular offer a wider range of passive sensing modalities, such as app usage and keyboard typing, compared with iOS devices. The mobile sensing capabilities of different platforms, however, continue to evolve and new restrictions might limit future research and replicability of our findings. Passive sensing can only be useful if the environments used to collect the data do not cause the user more burden than other methods of data collection. The intention of collecting passive sensing active behavioral tasks and EMA data was to build and validate digital biomarkers that can assess impulsivity for future intervention and management, and the preliminary results show the promise of such data. Yet, there exist very real possibilities for such data to be used to exploit a user, for example through stimulated impulsive purchasing \[ 53 , 54 \] or targeted advertising. These passive sensor data, including call logs, battery charging, and screen unlocks, were easy to collect and commonly used in other mHealth studies for monitoring sleep, mental health, and depression \[ 7 , 8 , 55 \]. Researchers should be aware of possible exploitation and privacy concerns as we design similar health-related studies. At the same time, there is evidence that these data are already being collected by large companies. Developing individualized interventions directed at the person to increase awareness of vulnerability and potentially developing protective measures may be needed to combat the onslaught of socially engineered content. There are several limitations to the study design that may have affected the performance of passive sensing models. One of these limitations is that the passive sensor data collection was noisy in the sense that user intentions were not fully captured by the current system. For example, it is potentially useful to distinguish screen checks in response to notifications from screen checks initiated by the users. Another limitation is that this study was based on a small sample size, as was the case with previous exploratory passive sensing studies. In addition, due to the cross-platform iOS and Android implementation of the mPulse system, the passive sensing and range of mobile sensing modalities were limited. Relevant data sources, such as keyboard and SMS logs, could potentially be used to examine behaviors but were not included in this study because they were only available on the Android platform. Another limitation is that our preference to protect user privacy and reduce battery drain led to the exclusion of relevant mobile sensor data sources, such as location and accelerometer data for motor impulsivity. Future work should pursue replication of promising measures as well as explore novel sensing modalities with larger samples. Mobile sensor data sources, such as global positioning systems and accelerometers, can be explored to detect mobility and physical activity as predictors of motor impulsivity. Such future work should directly address technical limitations, including battery drain, privacy concerns with regard to location sharing, and the generalizability of mobile sensing models to both iOS and Android platforms. Similarly, physiological sensing modalities from wearable devices, such as heart rate variability, can provide multimodal sensing capabilities. These explorations can reveal more information and improve the prediction accuracy of state impulsivity and impulsive behavior. We developed a mobile sensing system called mPulse for both iOS and Android smartphones to remotely detect and monitor state impulsivity and impulsive behavior as part of the DMT study. The design of our mPulse system was based on data that are pervasively available across both iOS and Android platforms: call logs, battery charging, and screen checking. In the exploratory study, we used mobile sensing features to predict trait-based, objective behavioral, and ecological momentary assessment EMA of impulsivity and related contacts ie, risk-taking, attention, and affect. Our findings suggest that passive sensing features of mobile phones can predict different facets of trait and state impulsivity. For trait impulsivity, the models significantly explained variance in sensation, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. On the daily level, the model successfully predicted objective behavioral measures such as present bias in a delay discounting task, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Overall, the study highlights the potential for continuously, passively, and remotely assessing impulsive behavior in daily life to advance the science of self-regulation and awareness. All authors reviewed the final manuscript. As a library, NLM provides access to scientific literature. Find articles by Hongyi Wen. Find articles by Michael Sobolev. Find articles by Rachel Vitale. Find articles by James Kizer. Find articles by J P Pollak. Find articles by Frederick Muench. Find articles by Deborah Estrin. Contributed equally. Open in a new tab. List of features from ecological momentary assessments and active tasks. Conflicts of Interest: None declared. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Generalized estimating equation regression summary Pearson r , within-group correlation.

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