Can Mental Health Apps Work for Depression?
Even before the pandemic created a need for more telehealth options, depression was a hot area of research for app developers. Given the high prevalence of depression and its connection to suicidality — especially among today’s teenagers and young adults who grew up with mobile devices, use them often, and experience these conditions with alarming frequency — apps for depression could be not only useful but lifesaving.
“For people who are not depressed, but have been depressed in the past, the apps can be helpful for maintaining positive thinking and behaviors,” said Andrea K. Wittenborn, PhD, director of the Couple and Family Therapy Doctoral Program and a professor in human development and family studies at Michigan State University. “For people who are mildly to severely depressed, apps can be a useful complement to working with a mental health professional.”
Health and fitness apps, in general, number in the hundreds of thousands. These are driving a market expected to reach $102.45 billion by next year. The mobile mental health app market is a small part of this but still sizable at $500 million, with revenues generated through user health insurance, employers, and direct payments from individuals.
Apps can provide data that health professionals cannot gather on their own. People’s constant interaction with smartphones and wearable devices yields data on many health conditions for millions of patients in their natural environments and while they go about their usual activities. Compared with the in-office measurements of weight and blood pressure and the brevity of doctor-patient interactions, the thousands of data points gathered unobtrusively over an extended time period provide a far better and more detailed picture of the person and their health.
At their most advanced level, apps for mental health, including depression, passively gather data on how the user touches and interacts with the mobile device through changes in digital biomarkers that relate to depressive symptoms and other conditions.
Building on three decades of research since early “apps” were used for delivering treatment manuals to health professionals, today’s more than 20,000 mental health apps have a wide range of functionalities and business models. Many of these apps can be useful for depression.
Some apps primarily provide a virtual connection to a group of mental health professionals employed or contracted by the app. Others have options for meditation, sleeping or, in the case of industry leaders Calm and Headspace, overall well-being. On the cutting edge are apps that detect changes in a person’s use of mobile devices and their interactions with them.
Apps such as AbleTo, Happify Health, and Woebot Health focus on cognitive behavioral therapy, a type of counseling with proven potential to change a person’s behaviors and feelings. “CBT has been demonstrated in innumerable studies over the last several decades to be effective in the treatment of behavioral health conditions such as depression and anxiety disorders,” said Dr. Reena Pande, chief medical officer at AbleTo. “CBT is intended to be delivered as a structured intervention incorporating key elements, including behavioral activation and adaptive thinking strategies.”
These CBT skills help break the negative self-talk (rumination) common in patients with depression. They are taught and reinforced by some self-guided apps, using either artificial intelligence or programmed interactions with users. Apps can address loneliness and isolation through connections with others, even when a symptomatic person doesn’t feel like leaving the house.
At their most advanced level, apps for mental health, including depression, passively gather data on how the user touches and interacts with the mobile device through changes in “digital biomarkers” that can be associated with onset or worsening of depressive symptoms and other cognitive conditions. In one study, Mindstrong Health gathered a year’s worth of data on how people use their smartphones, such as scrolling through articles, typing and clicking. Mindstrong, whose founders include former leaders of the National Institutes of Health, modeled the timing and order of these actions to make assessments that correlated closely with gold-standard tests of cognitive function.
National organizations of mental health professionals have been following the expanding number of available apps over the years with keen interest. App Advisor is an initiative of the American Psychiatric Association that helps psychiatrists and other mental health professionals navigate the issues raised by mobile health technology. App Advisor does not rate or recommend particular apps but rather provides guidance about why apps should be assessed and how health professionals can do this.
A website that does review mental health apps is One Mind Psyber Guide, an independent nonprofit that partners with several national organizations. One Mind users can select among numerous search terms for the condition and therapeutic approach of interest. Apps are rated on a five-point scale, with reviews written by professionals in the field.
Do mental health apps related to depression have the kind of safety and effectiveness data required for medications and other medical interventions? Not always — and not often. Yet the overall results have shown early promise, Wittenborn noted.
“Studies that have attempted to detect depression from smartphone and wearable sensors [during a single session] have ranged in accuracy from about 86 to 89 percent,” Wittenborn said. “Studies that tried to predict changes in depression over time have been less accurate, with accuracy ranging from 59 to 85 percent.”
The Food and Drug Administration encourages the development of apps and has approved a few of them—mostly ones used by health professionals—but it is generally “hands off,” according to the American Psychiatric Association. The FDA has published a list of examples of software (including programming of apps) that it does not plan to regulate because they pose low risk to the public. First on the list is software that helps patients with diagnosed psychiatric conditions, including depression, maintain their behavioral coping skills by providing a “Skill of the Day” technique or message.
On its App Advisor site, the American Psychiatric Association says mental health apps can be dangerous or cause harm in multiple ways, such as by providing false information, overstating the app’s therapeutic value, selling personal data without clearly notifying users, and collecting data that isn’t relevant to mental health.
Although there is currently reason for caution, patients may eventually come to expect mental health professionals to recommend apps, especially as their rating systems, features and capabilities expand. Through such apps, patients might experience more and higher quality interactions with their mental health professionals. “Apps will continue to be refined and become more effective through future research,” said Wittenborn. “They will become more integrated into practice over time.”
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.