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Social robots — human and non-humanoid machines that can interact with people &mdash; have the potential to 

 and the proliferation of exoskeleton devices. But there are many areas that have yet to be explored.

, undertook a comprehensive review of 27 trials in which social robots delivered psychosocial interventions for health and well-being. While results were largely promising, they were hindered by small sample sizes, lack of independent randomization, blind assessment or follow-up and elementary statistical analyses. The majority of the trials focused on children, the elderly and patients with autism spectrum disorder, which also limited results. Overall, it is clear that research is still at an early stage.

When emerging robotic technologies mature, researchers believe they will be useful in delivering self-guided health programs. In addition to being more cost-effective and scalable, these interventions can remove the variable of perceived human judgment.

With social robots taking over low-intensity, routine tasks, therapists would be available to do more individualized work.

“Practitioners should be spending as much time as possible on the tasks that they are trained to do incredibly well, such as counselling or caring for patients during their treatment process,” said lead researcher Nicole Robinson, Ph.D., a Research Fellow at the Australian Centre for Robotic Vision at Queensland University of Technology.

The hope is that robots will be able to complete simple tasks, demonstrate skills and communicate information.

“In an ideal scenario, healthcare practitioners are involved in the design and programming of a social robot that will work with patients on a healthcare management task,” Robinson added. “They are not designed to replace healthcare professionals in any way.”

Social robots also have the potential to cut future healthcare costs by providing preventive care. 

“Currently, we cannot provide interventions for all those who want to change their behaviors before they reach a form of diagnosis,” said Leonie Cooper, a Ph.D. candidate at the University of Plymouth in the United Kingdom and a colleague of Robinson. “Of course, many will still need to go on to see therapists, but robots can support many who need psychological support in a range of domains from alcohol usage to weight loss support.”

Increased use of robots could offer large-scale financial benefits. If robots can service a large number of patients, they will extend the reach and impact of healthcare organizations.

But to realize the widespread use of social robots, researchers must conduct large-scale controlled trials. They must conduct more research before they can understand the psychological outcomes of using robots to improve human health and well-being. Humans must also become more comfortable interacting with the machines.

Researchers believe these issues will be addressed in the coming years as technology develops and social robots become more accessible.

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Articles

The technology could improve self-guided health programs.

Social Robots Might Improve Psychosocial Interventions, But Results Remain Murky

Women with epilepsy are 10 times more likely to die during pregnancy than those who don’t have the condition, with seizures acting as the most likely cause of death.

Many of these deaths could be avoided if there were a prognostic model for assessing a patient’s seizure status. Knowing whether a patient is high-risk would allow practitioners to develop personalized care plans to be carried out before, during and after delivery.

A group of researchers set out to develop and externally validate a predictive model that could assess the risk status of pregnant women who are on anti-epileptic drugs. As detailed in 

, researchers determined the net benefit of implementing the EMPiRE (AntiEpileptic drug Monitoring in PREgnancy) model, using different probability thresholds so that caregivers could make informed decisions when it came to monitoring patients and administering medicine. Results suggested that the EMPiRE model displayed the highest net proportional benefit for predicted probability thresholds from 12% to 99%.

Researchers from Queen Mary University of London Shakila Thangaratinam, Ph.D., John Allottey, M.S., and Javier Zamora, Ph.D., M.S., found that using the model “could reduce the number of women unnecessarily given an intervention from as little as two to as much as 54 for every 100 women seen.”

The study recruited 527 participants from 50 hospitals in the United Kingdom. They were all pregnant women with epilepsy who were taking one of the following anti-epileptic drugs: lamotrigine, carbamazepine, phenytoin or levetiracetam. The patients were recruited at the time of their first antenatal visit and proceeded to record their seizures in diaries designed for this purpose.

Using this data, researchers developed the EMPiRE model, a simple nomogram that relies on eight items to calculate the probability that a patient will have seizures. The model relies on data gathered from predictors, such as age at first seizure and mental health history. It can be implemented during the first antenatal visit.

While current protocol treats all pregnant women with epilepsy as if they will experience seizures, the EMPiRE model’s ability to differentiate promises to reduce the number of women who endure unnecessary treatment, such as taking increased amounts of anti-epileptic drugs. It could also lift unnecessary restrictions on activity — such as driving &mdash; and cut the number of women who cease taking anti-epileptic drugs during pregnancy for fear of hurting the fetus.

Researchers believe there may be financial benefits to the EMPiRE model, as well. “There could be a reduction in both actual cost of service to the healthcare provider and improvement of quality of life for the woman,” they told 

™. Being able to accurately estimate the risks could promote a more collaborative approach to treatment for both clinicians and patients, resulting in a shared decision-making process.

While the EMPiRE model is ready for use by healthcare professionals in clinical practice, there are limitations. The model is meant for patients receiving care in high-income settings and researchers caution that, for it to be effective, there should be information available for all predictors in the model.

Moving forward, the researchers also recommend “the model be validated in different settings and populations to fully appreciate its transportability.” To assess its impact in improving health outcomes, they suggest the model be assessed via randomized trials.

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The EMPiRE model could save lives and lead to increased quality of life.

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More than a million people die or are injured each year due to adverse drug reactions (ADRs). Approximately 2 million of these incidents occur in hospital settings, leading to more than 100,000 deaths — despite the fact that the drugs in question are being prescribed by doctors at regular dosages.

Many of these deaths and injuries could be prevented if it weren’t for low rates of spontaneous reporting in the pre- and post-market stages of drug development. In fact, more than 90 percent of new ADRs remain unreported — a major problem, especially when it comes to predicting the less common adverse effects of specific drugs.

Seeking to correct this issue, researchers developed a new deep neural network (DNN) model that can both identify the ADRs of existing drugs and predict the ADRs of new drugs.

, the new DNN model used machine learning and statistical methods to identify the ADRs of 746 existing drugs and predict the ADRs of 232 new drugs. The model examined the chemical and biological information of drugs from 2009 to 2012. None of the findings were reliant on past human reportage.

The DNN model has the potential to help physicians beyond the initial point of prescribing and administering drugs. Using the new information, doctors can better evaluate the risk of ADRs, prescribe the least risky medicine for their patients, monitor for possible ADRs, and take an appropriate course of action when ADRs do occur.

In addition to aiding doctors in saving lives, there are potential financial benefits to this new DNN model.

“Developing new drugs costs a lot of money and time,” says lead researcher Jung-Hsien Chiang, Ph.D., of Taiwan’s National Cheng Kung University. “If the pharmaceutical companies can get the additional information during the new drug development stage through our DNN model, it can help them to make a safer drug and reduce the risk when it launches to market.”

In terms of next steps, Chiang believes the new DNN model must incorporate more data in order to expand the number of drugs and ADRs it can detect. This will include collecting additional medical literature and retraining the model.

“We also need to reconsider the risk of ADRs for drug-to-drug,” says Chiang. To accomplish this, researchers will examine complicating factors, such as the potential for additional adverse effects when prescribing multiple drugs simultaneously.

While more research is needed, this new DNN model could represent a large step forward in terms of patient safety and pharmacovigilance.

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Physical inactivity is a pressing public health issue

Contributing to this crisis are the extended hours of sedentary behavior of office workers. Digital technology interventions are a possible solution, but researchers and developers are often unclear as to which avenues have already been explored.

, researchers examined 68 articles that addressed efforts to help reduce sedentary behavior in office workers. Researchers found 45 disparate interventions and identified six technological features that were most commonly used, shining a light on where the research must focus from here.

Lead researcher Yitong Huang, M.S., says the ultimate goal was to provide an “overview of the technological landscape, in terms of what has been designed, tested, what has been scaled up and what has not but has shown promise, and is hence worth more investment to move it downstream to further evaluation and development.”

Researchers used the United Kingdom Medical Research Council (MRC) framework for developing and evaluating complex interventions to group the studies. Categories included: development, feasibility and/piloting, evaluation or implementation.

Says Huang: “In my view, with screenless digital media, wireless communication technologies and data fusion, future digital interventions will be better embedded in the physical environment and can adapt to the user’s real-time status and needs, which affords really exciting opportunities for scalable delivery of BCTs (behavior change techniques) like ‘problem-solving’ and ‘changing the social or physical environment.’”

The increasing availability of digital technology means it’s easier to try these methods. For instance, unobtrusive devices that prompt the user throughout the day with subtle reminders to move their body and take breaks from their screens have the potential to create positive change.

“The development of such interventions will require interdisciplinary and intersectoral collaborations between intervention designers, technology designers and engineers, etc.,” Huang says.

While developing these technologies may be costly in the short term, Huang believes that they will be prove financially beneficial over time.

“Healthy active lifestyles can prevent or delay the onset of many diseases or personal injuries, which does not only save healthcare expenditures for individuals and families, but also brings economic benefits to employers and the broader economy,” Huang says. “For instance, businesses with (an) active workforce can benefit from higher productivity, less absenteeism due to sickness or repetitive strain injury, less presentism and lower job turnover rates.”

But while developing this technology may save money, it’s difficult to predict the exact numbers. Each intervention must pass through multiple stages of development and evaluation before its efficacy and scalability can be known. “Cost effectiveness often needs to be determined with economic evaluation on a case-by-case basis,” Huang adds.

This study is the first to provide an overview of the efforts to minimize office workers’ SB. The results are meant to serve as a roadmap for healthcare executives trying to “locate the relevant design inputs and experts working in the field.”

Collaboration between research communities and a greater understanding of the MRC framework will be key in developing these digital technologies.

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The number of patients who need liver donations far exceeds the number of healthy organs available for transplant.

To address this discrepancy, professors at the Massachusetts Institute of Technology (MIT) have developed a new model for ranking wait-listed candidates: Optimized Prediction of Mortality (OPOM). OPOM has the potential to decrease deaths by 20 percent, according to 

Scientists Create Predictive Calculator to Aid Liver Transplant Decisions

The current national standard for ranking patients awaiting liver transplants, Model for End-Stage Liver Disease (MELD), has been used since 2002. The candidates receive preference based on their likelihood to survive the next three months. In order to account for the decreased likelihood of survival due to other diseases, MELD grants exception points to certain candidates, such as patients with cancer.

But MIT Sloan School of Management Professor Dimitris Bertsimas, Ph.D., says MELD is systematically biased. The United Network for Organ Sharing (UNOS), he claims, “has created arbitrary points to favor cancer patients, which has overcorrected the problem.”

Because 75 percent of oncology patients are male, MELD scores tend to favor men. In addition, women tend to have higher sodium levels and lower muscle mass, which leads to higher MELD scores. This results in non-equitable distribution.

Using real-world historical data from past patients, OPOM uses optimal classification trees (OCTs), a state-of-the-art machine-learning method, to rank candidates. The order is based on the likelihood that the patient will either die or become unsuitable for liver transplantation in the near future.

Says Bertsimas: “OPOM fairly addresses the imbalance without biasing liver cancer patients.”

To test OPOM’s efficacy, researchers used the most recent Liver Simulation Allocation Model (LSAM), which pulled data from 2007 to 2011. Researchers compared MELD scores with OPOM scores to determine which model was more accurate and efficient.

Results showed that OPOM’s prioritization ranking proved more effective. There was a higher survival rate among all candidates — regardless of where they lived, their demographics or their diagnoses. The new model also distributed livers to a higher number of women.

“Correcting of gender bias and bias for cancer patients are the two most important aspects of OPOM, second only to saving 400 lives a year,” Bertsimas says.

There is no evidence of the large-scale financial benefits of OPOM, but he says that there are significant financial benefits to decreasing the death rate.

Similar state-of-the-art machine-learning models can be adapted to improve the mortality rate for other organ transplants wait-lists, as well. “We are currently working on kidney patients and down the road heart and lung transplant patients,” Bertsimas adds.

The next step is replacing MELD with OPOM as the national standard for ranking liver transplant candidates. The MIT team will present its work to overseers this spring.

In addition to being transparent and accessible for doctors, Bertsimas says that OPOM “is accurate, it is data-driven, and it saves 400 lives a year. I believe it is ready for prime time.”

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Image has been cropped and resized. Courtesy of Flickr user 

Triage, the examination and ranking of large numbers of wounded victims, is intrinsic to disaster relief. Yet triage algorithms, meant to ensure efficiency, are often used incorrectly or cast aside. A 

, which examined an Android app developed for use with smart glasses, found that augmented reality can help.

, suggested that accuracy increased when paramedics used smart glasses to assign patients to a triage category. But these assessments took almost twice as long as traditional triage methods.

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The project, Augmented Disaster Medicine (AUDIME), was financed by the German Federal Government with the hope of aiding relief workers in mass casualty incidents. The study

relied upon a simulation in which 31 experienced paramedics completed 362 assessments. Twelve professional actors, pretending to be injured patients, were split into three groups.

The first group was treated with the aid of the Primary Ranking for Initial Orientation in Rescue service (PRIOR) pre-triage algorithm, which was displayed via smart glasses. The second group was treated with telemedical aid. Here, the smart glasses used an integrated camera to allow a remote senior physician to communicate with onsite paramedics.

Meanwhile, a control group was treated using conventional methods. In order to use the PRIOR algorithm, paramedics either had to recall it from memory or consult a printed pocket card, which none did.

Lead researcher Andreas Follmann, M.D., in the Department of Anaesthesiology at the University Hospital RWTH Aachen, said familiarity with smart glasses may increase if civilians begin wearing them in their daily lives. “We believe that only a regular use of the technology and a routine here can shorten the time.”

Paramedics encountered obstacles during the simulation, such as the fact that the AUDIME system relies on a local Wi-Fi connection. Yet Follmann is optimistic that technological advances, such as 5G, will make the need for Wi-Fi obsolete. “I believe that in a few years many rescue and fire brigade vehicles will be able to generate a local hotspot to enable interoperability between the installed technical devices.”

Other issues included low battery capacity and incompatibility between smart glasses and personal eyeglasses, requiring eyeglass-wearing paramedics to buy special lenses. Follmann says that recent technological developments now allow monitors with cameras to be placed inside personal eyeglasses. “Especially in the fire department and rescue service, it would also be conceivable that the monitor and the camera are integrated directly into the helmet.”

The rarity of larger disaster missions renders greater financial benefits unlikely. “Above all, the high purchase price is an obstacle to use it in civil protection.”

Even with the uncertainty of large-scale economic benefits and the aforementioned obstacles, Follmann is certain that this technology has the potential to aid both paramedics and volunteers who have not received medical training. “Due to the lack of routine, situations such as triage are very stressful and every meaningful help should be used.”

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Obesity is a widespread epidemic, responsible for myriad life-threatening illnesses, reduced quality of life and, in some cases, death. But can new digital health tools help? A 

 in which researchers analyzed the results of 11 trials found that web-based weight loss programs can provide moderate short-term results among obese individuals.

While the hope was that web-based approaches would prove more effective due to their scale and ability to provide round-the-clock support and reach remote patients, researchers found only short-term progress. The limited success of web-based interventions can be attributed to high attrition rates, heterogeneity in those who participated in the studies and low digital engagement, according to the findings, published in the 

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To be eligible for analysis, studies were required to supply data on randomized controlled trials. The majority of the 1,525 patients in the selected studies were female. All participants had a body mass index that qualified as overweight or obese and were between 18 and 65 years old.

Researchers used meta-analysis and qualitative analysis to track participants’ anthropometric changes and lifestyle shifts, respectively. Participants in the selected studies engaged in web-based interventions. The control groups responded to solely in-person intervention or were wait-listed, experiencing no intervention at all.

According to Andre Andrade, co-author of the study, and Senior Research Fellow in Digital Health at the University of South Australia, researchers need more guidance when it comes to a coherent body of best practices to guide intervention development.

“In the meta-analysis, we couldn’t identify which components predicted good results,” Andrade said. “Other studies suggest that theory-based interventions are more effective, but don’t point out which theory gives more effective interventions.”

Researchers are looking to solve the issues that led to lower success rates, specifically low web engagement. Private social networks, wearable activity trackers, economic and non-economic incentives, and several gamification/behavioral economics techniques are among the strategies that have been explored.

“The issue is that the evidence for any single implementation is typically very small,” Andrade said.

Another challenge is that individuals respond to catalysts differently. “We tend to treat all patients that need to lose weight as similar, but they have very different mindsets. Some patients just need a small motivational push, others require a complete environment change. Web-based interventions can help most patients, but for each need, it is a completely different solution.”

Although digital approaches have the potential to improve cost-effectiveness, Andrade cautioned that the research is not there yet. “At the moment, web-based interventions are better suited for increasing access than for saving money.”

The studies also showed a dearth of data in terms of the disadvantages of web-based interventions. More disastrous results could have included appetite disorders and muscle and bone mass reduction — all common unintended effects of obesity intervention.

Still, Andrade is optimistic. “Similar to other domains, service delivery over internet has potential of changing the entire industry.”

If done correctly, reduced cost of treatment, increased access to care, greater anonymity and the ability to save time are just a few of the possible benefits of web-based approaches to weight loss intervention.

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Wearable technology and the resultant data require a shift in thinking.

With the advent of wearable technology comes the ability to more accurately evaluate overall health by measuring the body’s resilience, according to a recent study.

Medicine usually aims to fix specific ailments, relying on targeted treatments that may solve a pressing issue while incidentally impacting other subsystems. But this approach overlooks the human body’s interconnectedness, according to 

Proceedings of the National Academy of Sciences

The human body is similar to natural ecosystems found in the wild: If a tipping point is reached in one area, it can set off a rapid decline in overall well-being. Thus, a better way to measure general health is to assess a body’s resilience, or its ability to recover from strain, sickness and injury.

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“Our work shows that healthcare professionals can use monitored dynamics of all kinds of parameters to see the ever-present regime of ‘micro-recoveries’ that reflect resilience,” says Marten Scheffer, Ph.D., lead author from Wageningen University in the Netherlands.

He asserts that these dynamic indicators of resilience (DIORs) can quantify a patient’s overall health and help doctors view the patient as a whole. Scheffer sees it as: “An intuitive approach empowered by big data.”

Among the advantages of monitoring devices, wearable technology and other sensors, is that the data they generate are accessible and easy for both medical professionals and patients to read.

“They are not some kind of black-box approach, as deep-learning approaches or algorithms that are not revealed by commercial companies,” explains Scheffer. “Instead, they are transparent and intuitively straightforward for patients and doctors. Quick micro-recoveries mean good resilience.”

While there are myriad advantages to monitoring devices, there are also inherent drawbacks, such as the worry that patients with access to this technology will feel less of a need to consult human doctors.

“Obviously, you want real doctors to see and communicate with patients,” says Scheffer. “Not only because it feels better for patients, with all the benefits of that, but also because we cannot replace doctors with algorithms.”

Emergency care is an area where monitoring devices are less likely to have a positive impact. Doctors must first establish a baseline of health for each patient before the data generated from wearable technology can be useful, says co-author Marcel G. M. Olde Rikkert, M.D., Ph.D., a professor and geriatrician at Radboud University Medical Center Nijmegen in the Netherlands. This can be difficult when treating a new patient in distress.

“Such baseline time series may become part of a planned intervention preparation,” explains Olde Rikkert. “As a result, in emergency medicine, this still may be difficult from the first moment you see a new patient.”

At this moment, the most common wearables focus on, “mobility and activity sensors, heart rate monitors, emotion sampling,” according to Olde Rikkert. “This probably will be expanded to monitoring attention, cognition and, for example, cerebral perfusion.”

He also believes that, in the future, wearables will become smaller, more user-friendly, and less noticeable. “Currently we still have watches, patches, and smartphones, but we will move towards wearables and sensors in smart clothing and smart housing.”

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