In 1938, Harvard began an in-depth study of the secrets to happiness. It's still going, and in today's podcast episode, the study's director, Bob Waldinger, tells Leaps.org about the keys to a satisfying life, based on 85 years of research.
What makes for a good life? Such a simple question, yet we don't have great answers. Most of us try to figure it out as we go along, and many end up feeling like they never got to the bottom of it.
Shouldn't something so important be approached with more scientific rigor? In 1938, Harvard researchers began a study to fill this gap. Since then, they’ve followed hundreds of people over the course of their lives, hoping to identify which factors are key to long-term satisfaction.
Eighty-five years later, the Harvard Study of Adult Development is still going. And today, its directors, the psychiatrists Bob Waldinger and Marc Shulz, have published a book that pulls together the study’s most important findings. It’s called The Good Life: Lessons from the World’s Longest Scientific Study of Happiness.
In this podcast episode, I talked with Dr. Waldinger about life lessons that we can mine from the Harvard study and his new book.
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More background on the study
Back in the 1930s, the research began with 724 people. Some were first-year Harvard students paying full tuition, others were freshmen who needed financial help, and the rest were 14-year-old boys from inner city Boston – white males only. Fortunately, the study team realized the error of their ways and expanded their sample to include the wives and daughters of the first participants. And Waldinger’s book focuses on the Harvard study findings that can be corroborated by evidence from additional research on the lives of people of different races and other minorities.
The study now includes over 1,300 relatives of the original participants, spanning three generations. Every two years, the participants have sent the researchers a filled-out questionnaire, reporting how their lives are going. At five-year intervals, the research team takes a peek their health records and, every 15 years, the psychologists meet their subjects in-person to check out their appearance and behavior.
But they don’t stop there. No, the researchers factor in multiple blood samples, DNA, images from body scans, and even the donated brains of 25 participants.
Robert Waldinger, director of the Harvard Study of Adult Development.
Katherine Taylor
Dr. Waldinger is Clinical Professor of Psychiatry at Harvard Medical School, in addition to being Director of the Harvard Study of Adult Development. He got his M.D. from Harvard Medical School and has published numerous scientific papers he’s a practicing psychiatrist and psychoanalyst, he teaches Harvard medical students, and since that is clearly not enough to keep him busy, he’s also a Zen priest.
His book is a must-read if you’re looking for scientific evidence on how to design your life for more satisfaction so someday in the future you can look back on it without regret, and this episode was an amazing conversation in which Dr. Waldinger breaks down many of the cliches about the good life, making his advice real and tangible. We also get into what he calls “side-by-side” relationships, personality traits for the good life, and the downsides of being too strict about work-life balance.
Show links
- Bob Waldinger
- Waldinger's book, The Good Life: Lessons from the World's Longest Scientific Study of Happiness
- The Harvard Study of Adult Development
- Waldinger's Ted Talk
- Gallup report finding that people with good friends at work have higher engagement with their jobs
- The link between relationships and well-being
- Those with social connections live longer
Avalanche rescue dogs train to find and dig out people buried in snow slides
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”