Background: Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day. Objective: This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes. Methods: In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants’ emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction. Results: Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings. Conclusions: This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.
Technology is changing the way we make babies. The pioneering work of the scientists who invented IVF led to the birth of the first “test tube baby” in 1978. We’ve come a long, long way since then.
This week, I’ve been working on a piece about the cutting edge of IVF technologies and what’s coming next. Think AI and robots and, potentially, gene-edited embryos.
My reporting has also made me think about just how much progress has been made in the last five decades. Clinicians have improved hormonal treatments. Embryologists have devised ways to culture embryos in the lab for longer. IVF clinics today offer multiple genetic tests for embryos.
The technology has also had a huge social impact. It has allowed for changes in the structure of families and provided more reproductive choices for would-be parents. So this week, let’s consider the technologies that have transformed babymaking.
Alan Penzias, a reproductive endocrinologist at Boston IVF, has been working in IVF since the early 1990s. In those days, his lab at Yale would collect a person’s eggs, fertilize them, and culture any resulting embryos for two days, until the embryos had two or four cells.
The embryos couldn’t survive any longer outside a body, so they’d be transferred to the uterus at that point. All of them. Even if there were, say, five embryos in total. Typical healthy patients could expect a live birth rate of 12% to 15%, he says.
Then Penzias heard that other teams were managing to culture embryos for three days. “We thought, No, that’s not possible,” he recalls. He learned that scientists had achieved this by tinkering with the culture medium—the nutrient-rich fluid the embryos are grown in.
Those three-day embryos, which had around six to 10 cells, seemed to have a better chance of resulting in a live birth. The teams culturing embryos for longer saw their success rates climb to 25% among similar patient groups, says Penzias. Again, he couldn’t believe it. “We thought they were making it up,” he says.
In the years since, teams have made more improvements to culture medium. Today, most IVF embryos are cultured for five or six days—a point at which they have 80 to 100 cells. The culturing process can act a little like a stress test—the embryos that make it to day six are generally more likely to go all the way and develop into a healthy baby.
Over the same period, advances in other technologies have opened up the options for what we can do with those embryos. Scientists learned they were able to freeze embryos and use them at a later date. A little over a decade ago, clinics shifted to a “vitrification” approach that rapidly cools the embryos to a glassy state. Vitrified embryos are more likely to survive freezing and thawing, so this approach quickly caught on.
As a result, doctors no longer needed to transfer multiple embryos at once. This made it less likely that patients would have twins or triplets, which can increase the risk of pregnancy complications.
Vitrification has also made IVF safer in other ways, including by affording patients a bit of time between fertility treatments. The hormonal treatments used in the first phase of IVF are designed to increase the production of mature eggs that can be collected. These treatments carry a small risk of a condition called ovarian hyperstimulation syndrome (OHSS), which in rare cases can be life-threatening. The ability to freeze all your embryos and use them at a later date is thought to give the body a chance to recover from hormonal treatment and reduces the risk of OHSS.
And because clinics are now able to culture embryos for up to a week, they can take a few of the 100 or so cells and send them for genetic testing before freezing the embryos. People undergoing IVF can get genetic readouts of all the embryos before deciding which to implant. (It is worth noting, however, that these testing technologies are not perfect.)
“Those are really radical changes, and we take them for granted,” says Penzias.
These technologies have also changed the function of IVF. What was once a treatment for infertility is now used to preserve fertility. People who want to delay parenthood can opt to freeze their eggs or embryos and use them later. They might opt to transfer one embryo in a year’s time and a second several years later. “We’ve been able to empower women to be able to have much more reproductive choice and get more reproductive mileage from a single IVF cycle,” says Penzias.
People who are about to undergo cancer treatments that might damage the testes or ovaries can opt to store their eggs or sperm ahead of time, too. Scientists have even been able to preserve pieces of ovarian and testicular tissue and reimplant them later, enabling recipients to have healthy babies.
Today, more people than ever have access to safe IVF options that offer multiple paths to parenthood. Those options look set to expand. But if you want to find out more about the AI and IVF robots, you’ll have to read this week’s story, here!
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
In late April, the Centers for Medicare and Medicaid Services and the Food and Drug Administration announced the Regulatory Alignment for Predictable and Immediate Device, or RAPID, coverage pathway. On paper, it is exactly what the medical device community has been asking for: a synchronized process that could deliver Medicare national coverage as soon as two months after FDA market authorization, rather than the year or more families and manufacturers currently endure.
I want this pathway to succeed. I have spent more than a decade helping small companies bring novel devices through FDA review, including several that earned breakthrough device designation. I have watched reimbursement delay strangle technologies that children desperately need. Faster, more predictable coverage is a real problem, and RAPID is a real step.
But this announcement does not fix the way pediatric and orphan devices chronically lag behind their adult counterparts. In some ways, in fact, it deepens that gap.
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
Forty-eight years ago this July, Louise Joy Brown became the world’s first person born with the help of in vitro fertilization. Millions more IVF babies have entered the world since then. And that’s partly thanks to advances in technology that have made IVF safer and more effective.
But it’s still not perfect. The process can be slow, painful, and expensive—and that’s for the lucky people who are able to access it in the first place. And by at least one measure, IVF success rates have been declining in recent years.
Reproduction is complex, and there’s a lot that embryologists and gynecologists still don’t know and can’t control. They don’t know why many healthy-looking embryos don’t “stick” in the uterus, for example. They don’t always have an explanation for why their patients can’t get pregnant. And they can’t always account for vast differences in IVF success rates between individuals and between fertility clinics.
Scientists are working on all those questions and more. They’re wrestling with complex ethical questions about how new genetic tools will be used to analyze or even alter embryos. Meanwhile, technologies designed to standardize treatment, eliminate human error, boost success rates, and make IVF more accessible are already beginning to usher in a new era for assisted reproduction—one aided by AI and robots.
1. Helping embryos stick
Some of those technologies are being developed at the Carlos Simon Foundation in Valencia, Spain. When I visited in March, researchers gave me a tour of the labs and showed me a device that had been used to keep a human uterus alive outside the body for the first time.
While some members of the team dream of building artificial uteruses that might one day be able to carry a fetus to term, they first want to use such devices to learn more about implantation—the moment at which a fertilized egg makes contact with the lining of the uterus, burrows inside, and essentially “hatches,” triggering the start of a pregnancy.
Despite decades of advances in IVF, that process is still poorly understood. Even healthy-looking embryos stick no more than 40% to 60% of the time.
In IVF techniques used today, clinics can create early-stage embryos and wait until the uterus is deemed most receptive, but once they insert the embryo into the uterus, it’s on its own. Xavier Santamaria, senior clinical scientist at the Carlos Simon Foundation, and his colleagues are trialing a different approach. They’ve developed a device that, at the press of a button, injects the embryo into the uterine lining.
JESS HAMZELOU / MITTR
In a demonstration I watched with a prototype, Santamaria picked up his speculum and turned to face the vaginal opening of his “patient,” which in this case was just a model of the real thing—a plastic bottom with labia, a vagina, a uterus, and ovaries, two short stumps representing what would normally be a pair of legs held in stirrups.
He hunched over and peered inside. “Embryo,” he called. His colleague Maria Pardo, an embryologist, passed him a thin needle containing a mouse embryo she had recently collected from a petri dish.
Santamaria’s device allows for the embryo-containing needle to be connected to a delivery tube. This tube also has a camera, a light, and a sensor that lets the doctor know when the needle reaches the uterine lining. Once it has been fed into the uterus, the gynecologist can see the inside of the organ and direct the tube to the lining.
JESS HAMZELOU / MITTR
“When everything is ready, you just press the button,” Santamaria said as he activated it using a foot pedal, allowing the embryo to be injected. “There it goes.”
The team has just started a trial of the device; so far, fewer than 10 women have undergone the procedure, and none of those have become pregnant. But foundation director Carlos Simon is hopeful, noting that the inventors of IVF had to perform over 160 cycles before Louise Brown was born (between 1969 and 1978, that team performed 457 cycles in 250 people, resulting in only two live births). “The trial is ongoing,” he says.
2. Picking the “best” eggs, sperm, and embryos
One long-running challenge of IVF has been selection. Say you manage to collect 10 eggs from one partner and a decent-looking semen sample from the other. How do you choose which cells to use? The same question comes up once the resulting embryos have been cultured in a dish for a few days: Which should you transfer to the uterus?
Traditionally, these judgments have been made by eye. Embryologists literally pick the ones that look the best in terms of their shape or, in the case of sperm, how they move. But scientists have been working on alternatives. And over the last decade or so, many have turned to genetic testing to hint at which embryos have the best chances of creating a healthy baby.
The most commonly used test is called PGT-A, which stands for preimplantation genetic testing for aneuploidy. Aneuploidy essentially means having an “incorrect” number of chromosomes, and it is thought that embryos with such characteristics are more likely to be lost through miscarriage or potentially develop into babies with genetic conditions.
Once embryologists have created embryos in the lab, they can pinch off a few cells and test them for aneuploidies. The tests are especially beneficial for women over the age of 38, says Alan Penzias, a reproductive endocrinologist at Boston IVF. “You start to see an improvement: more babies and fewer miscarriages,” he says. The tests can shorten the time to pregnancy.
This type of genetic testing is possible thanks to multiple advances in technology—not just in genomics, but also in the ability to keep embryos alive in a dish for five to six days and the technique of freezing embryos while the cells undergo testing and thawing them once the results are in. And it has become hugely popular—some clinics do PGT-A tests on all their embryos.
But PGT-A won’t give you a perfect readout of a future baby’s genetics, says Sonia Gayete-Lafuente, a reproductive endocrinologist at the Center for Human Reproduction in New York City. And some of the abnormalities might be able to self-correct with time. Gayete-Lafuente and her colleagues have transferred some of those “abnormal” embryos into patients’ uteruses and seen them develop into perfectly healthy children, she says.
Other forms of PGT are even more controversial. PGT-P tests are designed to predict an embryo’s chances of developing complex traits that rely on multiple genes, including medical disorders but also physical characteristics like height or cognitive factors like IQ. These tests are new, and they are illegal in some countries, including the UK. But they are gaining ground in the US. Nucleus Genomics—a company that invites customers to “have [their] best baby”—promises to predict traits running the gamut from eye color and intelligence to left-handedness and risk of Alzheimer’s.
When I asked IVF practitioners how they might respond if a patient asked for this service, most dodged the question and told me there’s not enough evidence that any of these tests actually work. They also cautioned that selecting for one trait might inadvertently introduce new risks. None seemed especially keen on the idea of using genetic testing for anything other than preventing serious disease.
3. Speeding things up with AI
Some seemed more excited about the potential for AI. After all, AI tools are generally good at recognizing patterns. Many researchers have attempted to train tools to spot healthy sperm, eggs, and embryos.
And they’ve had some success. A team at Columbia University Medical Center in New York has developed a device that uses AI to examine semen samples from men who have only tiny numbers of healthy sperm. An embryologist might struggle to find a single healthy sperm in such a sample. But the Sperm Tracking and Recovery (STAR) system can analyze over a million microscope images in an hour. It has already been used to create healthy embryos. The team behind the work announced the first pregnancy resulting from the treatment in November last year.
Other teams are using AI tools to advance IVF in more dramatic ways. Around a decade ago, a reproductive endocrinologist named Alejandro Chavez-Badiola began developing an AI tool trained to rank embryos, another to rank eggs, and another to select sperm. He recalls being struck by a realization that these tools were “the brains that have the potential to drive robots in the future,” he says.
4. Using robots to standardize IVF
In the early 2020s, Chavez-Badiola and his colleagues decided to combine technologies and develop an automated system for IVF. In theory, a robotic system loaded up with AI tools could undertake most of the steps required in the IVF process: selecting the eggs and sperm, fertilizing eggs to create embryos, culturing those embryos in a dish, and selecting the “best” one for transfer. Such a system could “do everything in a standard way” without ever getting tired, he says.
Chavez-Badiola, who is now founder and chief medical officer at Conceivable, started building prototypes by motorizing regular IVF equipment and connecting it to computers. He and his colleagues started testing their system with animal cells before eventually moving on to human ones. “We were able to prove that integrating robots to automate different steps in IVF is doable,” he says.
The device is now being used to prepare sperm and eggs and create embryos. At least 19 children have been born following the automated IVF. It is early days, but Chavez-Badiola is hoping that future iterations of the machine could each process thousands of IVF cycles in a year, potentially making the procedure more affordable and accessible.
Many in the field are excited about the potential for automated devices like Conceivable’s. “This is all time saved for the embryologists,” says Laura Rienzi, a clinical embryologist and scientific director of the IVIRMA network of fertility centers in Italy. She also hopes it will help standardize IVF treatments. “Automation [will allow for] every patient to be treated in the same way in every single lab in the world,” she says.
5. Controversial edits are on the table
There’s a catch, however: All these technologies rely on the availability of at least some healthy sperm, eggs, and embryos at the outset. Embryologists and IVF patients have to work with what they’ve got. And sometimes, what they’ve got won’t result in a healthy baby.
That’s why some scientists are proposing a controversial idea: using gene-editing technologies like CRISPR to tinker with the genome of an IVF embryo before it is implanted. The biophysicist He Jiankui infamously took this approach to create embryos that resulted in the births of three children in the late 2010s. He was widely condemned by the scientific community and ultimately spent three years in a Chinese prison.
His former romantic partner Cathy Tie, who now leads startup Origin Genomics, is pursuing the technology as a potential way to prevent serious disease in children. At a recent event held at the Hastings Center for Bioethics, Tie made the case for using embryo editing to prevent diseases like cystic fibrosis, Huntington’s, and sickle-cell.
It won’t be straightforward from a technical, legal, or ethical perspective. Diseases that are known to be caused by single-gene mutations are good first candidates, but as the Center for Human Reproduction’s Gayete-Lafuente points out, most diseases are much more complicated than that. “I wish we could understand the genetic basis of every disease to be able to prevent it,” she says. So far, we can’t. Besides, most diseases can be influenced by our diets, behaviors, and environments as well as our genes.
As things stand, no one knows if editing a human embryo to eliminate the risk of one disease might increase a future child’s risk of some other disorder. And some scientists worry that such edits might be a slippery slope to genetic enhancement or eugenics.
Rienzi hopes that the technology might be developed in a safe way with regulatory oversight, and only for a specific list of diseases. “It has to be within a legal context,” she says. “But to me, it’s a dream.”
In the meantime, the field looks set to keep transforming with the development of new technologies that are already creating healthy babies. Watch this space.
Synergy Spine Solutions designed its Synergy Disc cervical implant like a ball and socket hip joint to improve alignment while preserving range of motion for patients with degenerative disc disease. Synergy Spine engineers created Synergy Disc using a new patented geometry paired with time-tested materials. “One of the secrets to great design is to just…
We’re bringing the whole team to DeviceTalks Boston 2026 on May 27 and 28 at the Thomas Michael Menino Convention and Exhibition Center (formerly known as the Boston Convention and Exhibition Center). We’ve got a great lineup of keynotes, interviews and panels of experts from medical device OEMs and their partners. (Go here for the full…
Background: Neurorehabilitation plays a key role in improving motor recovery for people with neurological conditions. Although 3D printing has emerged as a promising rehabilitation tool, little is known on how it is used for the rehabilitation of adults living with neurological conditions worldwide. Objective: We aimed to provide a comprehensive overview of 3D printing in neurorehabilitation and precisely explore how it is used to improve motor recovery for adults with neurological conditions living in higher- and lower-middle–income countries. Methods: We conducted a scoping review following the Joanna Briggs Institute guidelines. After searching 3 databases (MEDLINE, Web of Science, and Nursing and Allied Health Premium), 2 independent reviewers screened and selected English-language studies involving adults (≥18 years) published between 2019 and 2024 to capture the most recent advancements in this field. We extracted relevant information on neurological conditions, motor recovery outcomes, and types of 3D printing and offered a comparative analysis of 3D printing in physical neurorehabilitation from the perspective of national income levels using a modified Joanna Briggs Institute extraction form. We synthesized the findings narratively with tabular support. Results: After screening 2752 titles and abstracts and 103 (3.7%) full texts, we included 13 (0.5%) studies based on our inclusion criteria. All included studies were conducted in upper-middle–income or high-income countries, and most studies (9/13, 69.2%) focused on stroke, followed by spinal cord injury (2/13, 15.4%), Parkinson disease (1/13, 7.7%), and central nerve disease (1/13, 7.7%). The 3D-printed rehabilitation tools included orthotics (7/13, 53.8% for the upper extremities [UEs]; 3/13, 23.1% for the lower extremities [LEs]), an exoskeleton (1/13, 7.7%; UEs), a modular assistive hand device (1/13, 7.7%; UEs), and an insole (1/13, 7.7%; LEs). In total, 69.2% (9/13) of the studies targeted UE rehabilitation, measured using the Action Research Arm Test, active range of motion, the box and block test, the Fugl-Meyer Assessment, the Modified Ashworth Scale, the manual function test, range of motion, and the Toronto Rehabilitation Institute Hand Function Test, and 30.8% (4/13) targeted LE rehabilitation, measured using the 10-m walk test, anteroposterior ground reaction force analysis, the Barthel index, the Tinetti scale, the RehaWatch system, and the GaitWatch system. Conclusions: Used as a rehabilitation tool, 3D printing technology has demonstrated significant potential in improving upper and lower motor recovery for people with certain neurological conditions in high-middle–income countries. Future research should explore the implementation feasibility and effectiveness of these technologies across different neurological conditions and income settings, particularly in low- and lower-middle–income countries.
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Background: Wearable activity trackers are useful tools to track and monitor physical activity (PA), especially considering their use in free-living environments. Users often see moderate improvements in step count, but consistent increases at various intensities of PA are inconclusive. While wearable research is growing, no known studies specifically examine the relationship between how the use of self-selected social features on wearables affects PA and exercise self-efficacy. Objective: This study aims to compare weekly PA, approximating moderate-to-vigorous intensity, of adults from the New York City metropolitan area assigned to either use or not use social engagement PA features on their device. Exercise self-efficacy was also measured. Additionally, a preliminary examination into the use of 3 different social features was conducted to inform where controlled parameters on feature use may be needed in future work. Methods: The researchers conducted a real-world pilot study by recruiting wearable users aged 18 years and older in the New York City area to wear their devices in free-living environments. After consent, participants were randomized into 1 of 2 conditions: the condition that involved use of the social engagement PA features or the condition that did not for 8 weeks. Participants submitted objective data from their device and completed a self-efficacy measure at baseline, week 4, and week 8. Those in the intervention group also answered questions about which social feature they used the most throughout the study. Results: Data from 123 participants were analyzed using mixed methods analysis. Principal findings included no difference between wearable social feature users and nonusers in weekly PA (=.55) or exercise self-efficacy (=.47). There was an overall effect of time across the repeated measures on PA (=.006) with an average increase of 72 (SD 3) minutes. Secondary findings highlight the need to control for the use of only a single social feature to identify more concrete effects. An effect of time was found across the repeated measures (=.01) in the intervention group, showing an increase of 49 to 126 minutes of PA, depending on the feature used most. The mixed methods analysis also found that exercise self-efficacy did not significantly change based on which social feature was used most (=.24). Conclusions: Consistent with other literature, this pilot study demonstrates that using wearables can lead to increases in PA and that sharing one’s PA data with others may amplify the effect. However, the novelty of this study is that although carefully implied, specific social features on a wearable may have a greater effect than others. This study identified the need for further investigation into which features may be more effective. With the increased prevalence of device ownership, knowing if certain social features lead to greater increases in PA may help those encouraging PA behavior change.
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Background: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where nonsmokers may pass through or remain nearby. Although prior studies have quantified outdoor SHS concentrations, fewer have examined how many people may be present within a plausible exposure setting. Estimating the exposure-opportunity level requires methods that are feasible, scalable, and minimally intrusive. Objective: This study aimed to evaluate the feasibility of using passive Wi-Fi packet sensing, calibrated with brief on-site observation, to estimate the number of smokers and passersby within a plausible SHS exposure range at a public outdoor smoking area in Japan. Methods: We conducted a formative field study at a designated outdoor smoking area at the Asia Pacific Trade Center in Osaka, Japan. A passive Wi-Fi packet sensor collected timestamps, anonymized device identifiers, organizationally unique identifiers, and received signal strength indicator (RSSI) values from October 13 to 29, 2023. The main analysis focused on October 28, 2023, a high-footfall event day selected for direct calibration. Episodes were classified using empirically derived RSSI thresholds, and class-specific calibration ratios were applied to estimate day-level counts. Results: Of 128,313 anonymized detections recorded on October 28, 90.3% (115,950/128,313) occurred during business hours. Among these, 8.6% (n=11,068) identifiers were detected more than once. Dwell time could be calculated for 1.4% (n=1817) of the identifiers, and 0.5% (n=659) eligible presence episodes remained after preprocessing. During a 30-minute validation window, smokers and passersby were counted manually within a 25-m radius. During the validation window, 6230 signal records formed 104 stays, with a mean stay duration of 9.89 (SD 7.89) minutes. During the validation window, direct observation recorded 14 smokers and 207 passersby within the 25-m radius. Applying the rule-based classification and calibration ratios to business hours data yielded estimated day totals of 262 smokers and 3907 passersby within the plausible SHS exposure range. Estimated smoker counts showed 2 peaks, around noon and 4 PM, whereas passerby volume peaked around midday. In an exploratory analysis, a random forest model using stay duration, mean RSSI, and RSSI variability achieved an accuracy of 0.95, sensitivity of 0.75, specificity of 0.97, and area under the receiver operating characteristic curve of 0.99. Conclusions: This formative field study suggests that passive Wi-Fi packet sensing, combined with brief on-site observation, can be used to estimate population-level exposure opportunity around an outdoor smoking area. The method identified substantial numbers of potentially exposed passersby in a high-footfall public setting. Although the findings are site specific and preliminary, they indicate that exposure-count metrics may complement concentration-based and survey-based SHS research. Further studies incorporating repeated validation, direct pollutant monitoring, and multiple sites are needed to refine the method and strengthen its usefulness for tobacco control and public health decision-making.
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