Some companies claim remote work hurts wellbeing. Research shows the opposite.
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Leaders at Google and other companies are trying to get workers to return to the office, saying remote and hybrid work disrupt work-life boundaries and well-being. These arguments conflict with research on remote work and wellness.
Many leaders at top companies are trying to get workers to return to the office. They say remote and hybrid work are bad for their employees’ mental well-being and lead to a sense of social isolation, meaninglessness, and lack of work-life boundaries, so we should just all go back to office-centric work.
One example is Google, where the company’s leadership is defending its requirement of mostly in-office work for all staff as necessary to protect social capital, meaning people’s connections to and trust in one another. That’s despite a survey of over 1,000 Google employees showing that two-thirds feel unhappy about being forced to work in the office three days per week. In internal meetings and public letters, many have threatened to leave, and some are already quitting to go to other companies with more flexible options.
Last month, GM rolled out a policy similar to Google’s, but had to backtrack because of intense employee opposition. The same is happening in some places outside of the U.S. For instance, three-fifths of all Chinese employers are refusing to offer permanent remote work options, according to a survey this year from The Paper.
For their claims that remote work hurts well-being, some of these office-centric traditionalists cite a number of prominent articles. For example, Arthur Brooks claimed in an essay that “aggravation from commuting is no match for the misery of loneliness, which can lead to depression, substance abuse, sedentary behavior, and relationship damage, among other ills.” An article in Forbes reported that over two-thirds of employees who work from home at least part of the time had trouble getting away from work at the end of the day. And Fast Company has a piece about how remote work can “exacerbate existing mental health issues” like depression and anxiety.
For his part, author Malcolm Gladwell has also championed a swift return to the office, saying there is a “core psychological truth, which is we want you to have a feeling of belonging and to feel necessary…I know it’s a hassle to come into the office, but if you’re just sitting in your pajamas in your bedroom, is that the work life you want to live?”
These arguments may sound logical to some, but they fly in the face of research and my own experience as a behavioral scientist and as a consultant to Fortune 500 companies. In these roles, I have seen the pitfalls of in-person work, which can be just as problematic, if not more so. Remote work is not without its own challenges, but I have helped 21 companies implement a series of simple steps to address them.
Research finds that remote work is actually better for you
The trouble with the articles described above - and claims by traditionalist business leaders and gurus - stems from a sneaky misdirection. They decry the negative impact of remote and hybrid work for wellbeing. Yet they gloss over the damage to wellbeing caused by the alternative, namely office-centric work.
It’s like comparing remote and hybrid work to a state of leisure. Sure, people would feel less isolated if they could hang out and have a beer with their friends instead of working. They could take care of their existing mental health issues if they could visit a therapist. But that’s not in the cards. What’s in the cards is office-centric work. That means the frustration of a long commute to the office, sitting at your desk in an often-uncomfortable and oppressive open office for at least 8 hours, having a sad desk lunch and unhealthy snacks, sometimes at an insanely expensive cost and, for making it through this series of insults, you’re rewarded with more frustration while commuting back home.
In a 2022 survey, the vast majority of respondents felt that working remotely improved their work-life balance. Much of that improvement stemmed from saving time due to not needing to commute and having a more flexible schedule.
So what happens when we compare apples to apples? That’s when we need to hear from the horse’s mouth: namely, surveys of employees themselves, who experienced both in-office work before the pandemic, and hybrid and remote work after COVID struck.
Consider a 2022 survey by Cisco of 28,000 full-time employees around the globe. Nearly 80 percent of respondents say that remote and hybrid work improved their overall well-being: that applies to 83 percent of Millennials, 82 percent of Gen Z, 76 percent of Gen Z, and 66 percent of Baby Boomers. The vast majority of respondents felt that working remotely improved their work-life balance.
Much of that improvement stemmed from saving time due to not needing to commute and having a more flexible schedule: 90 percent saved 4 to 8 hours or more per week. What did they do with that extra time? The top choice for almost half was spending more time with family, friends and pets, which certainly helped address the problem of isolation from the workplace. Indeed, three-quarters of them report that working from home improved their family relationships, and 51 percent strengthened their friendships. Twenty percent used the freed up hours for self-care.
Of the small number who report their work-life balance has not improved or even worsened, the number one reason is the difficulty of disconnecting from work, but 82 percent report that working from anywhere has made them happier. Over half say that remote work decreased their stress levels.
Other surveys back up Cisco’s findings. For example, a 2022 Future Forum survey compared knowledge workers who worked full-time in the office, in a hybrid modality, and fully remote. It found that full-time in-office workers felt the least satisfied with work-life balance, hybrid workers were in the middle, and fully remote workers felt most satisfied. The same distribution applied to questions about stress and anxiety. A mental health website called Tracking Happiness found in a 2022 survey of over 12,000 workers that fully remote employees report a happiness level about 20 percent greater than office-centric ones. Another survey by CNBC in June found that fully remote workers are more often very satisfied with their jobs than workers who are fully in-person.
Academic peer-reviewed research provides further support. Consider a 2022 study published in the International Journal of Environmental Research and Public Health of bank workers who worked on the same tasks of advising customers either remotely or in-person. It found that fully remote workers experienced higher meaningfulness, self-actualization, happiness, and commitment than in-person workers. Another study, published by the National Bureau of Economic Research, reported that hybrid workers, compared to office-centric ones, experienced higher satisfaction with work and had 35 percent more job retention.
What about the supposed burnout crisis associated with remote work? Indeed, burnout is a concern. A survey by Deloitte finds that 77 percent of workers experienced burnout at their current job. Gallup came up with a slightly lower number of 67 percent in its survey. But guess what? Both of those surveys are from 2018, long before the era of widespread remote work.
By contrast, in a Gallup survey in late 2021, 58 percent of respondents reported less burnout. An April 2021 McKinsey survey found burnout in 54 percent of Americans and 49 percent globally. A September 2021 survey by The Hartford reported 61 percent burnout. Arguably, the increase in full or part-time remote opportunities during the pandemic helped to address feelings of burnout, rather than increasing them. Indeed, that finding aligns with the earlier surveys and peer-reviewed research suggesting remote and hybrid work improves wellbeing.
Remote work isn’t perfect – here’s how to fix its shortcomings
Still, burnout is a real problem for hybrid and remote workers, as it is for in-office workers. Employers need to offer mental health benefits with online options to help employees address these challenges, regardless of where they’re working.
Moreover, while they’re better overall for wellbeing, remote and hybrid work arrangements do have specific disadvantages around work-life separation. To address work-life issues, I advise my clients who I helped make the transition to hybrid and remote work to establish norms and policies that focus on clear expectations and setting boundaries.
For working at home and collaborating with others, there’s sometimes an unhealthy expectation that once you start your workday in your home office chair, and that you’ll work continuously while sitting there.
Some people expect their Slack or Microsoft Teams messages to be answered within an hour, while others check Slack once a day. Some believe email requires a response within three hours, and others feel three days is fine. As a result of such uncertainty and lack of clarity about what’s appropriate, too many people feel uncomfortable disconnecting and not replying to messages or doing work tasks after hours. That might stem from a fear of not meeting their boss’s expectations or not wanting to let their colleagues down.
To solve this problem, companies need to establish and incentivize clear expectations and boundaries. They should develop policies and norms around response times for different channels of communication. They also need to clarify work-life boundaries – for example, the frequency and types of unusual circumstances that will require employees to work outside of regular hours.
Moreover, for working at home and collaborating with others, there’s sometimes an unhealthy expectation that once you start your workday in your home office chair, and that you’ll work continuously while sitting there (except for your lunch break). That’s not how things work in the office, which has physical and mental breaks built in throughout the day. You took 5-10 minutes to walk from one meeting to another, or you went to get your copies from the printer and chatted with a coworker on the way.
Those and similar physical and mental breaks, research shows, decrease burnout, improve productivity, and reduce mistakes. That’s why companies should strongly encourage employees to take at least a 10-minute break every hour during remote work. At least half of those breaks should involve physical activity, such as stretching or walking around, to counteract the dangerous effects of prolonged sitting. Other breaks should be restorative mental activities, such as meditation, brief naps, walking outdoors, or whatever else feels restorative to you.
To facilitate such breaks, my client organizations such as the University of Southern California’s Information Sciences Institute shortened hour-long meetings to 50 minutes and half-hour meetings to 25 minutes, to give everyone – both in-person and remote workers – a mental and physical break and transition time.
Very few people will be reluctant to have shorter meetings. After that works out, move to other aspects of setting boundaries and expectations. Doing so will require helping team members get on the same page and reduce conflicts and tensions. By setting clear expectations, you’ll address the biggest challenge for wellbeing for remote and hybrid work: establishing clear work-life boundaries.
A mosquito under the microscope.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
In this week's Friday Five, research on the best time to wrap up eating for the night, how to use AI to predict how hospital stays will go, a new way to armor the shields of our livers against cancer, super neurons in super agers - and much more.
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers