9 Tips for Online Mental Health Therapy
Telehealth offers a vast improvement in access and convenience to all sorts of medical services, and online therapy for mental health is one of the most promising case studies for telehealth. With many online therapy options available, you can choose whatever works best for you. Yet many people are hesitant about using online therapy. Even if they do give it a try, they often don’t know how to make the most effective use of this treatment modality.
Why do so many feel uncertain about online therapy? A major reason stems from its novelty. Humans are creatures of habit, prone to falling for what behavioral scientists like myself call the status quo bias, a predisposition to stick to traditional practices and behaviors. Many people reject innovative solutions even when they would be helpful. Thus, while teletherapy was available long before the pandemic, and might have fit the needs of many potential clients, relatively few took advantage of this option.
Even when we do try new methodologies, we often don’t do so effectively, because we cling to the same approaches that worked in previous situations. Scientists call this behavior functional fixedness. It’s kind of like the saying about the hammer-nail syndrome: “when you have a hammer, everything looks like a nail.”
These two mental blindspots, the status quo bias and functional fixedness, impact decision making in many areas of life. Fortunately, recent research has shown effective and pragmatic strategies to defeat these dangerous errors in judgment. The nine tips below will help you make the best decisions to get effective online therapy, based on the latest research.
Trust the science of online therapy
Extensive research shows that, for most patients, online therapy offers the same benefits as in-person therapy.
For instance, a 2014 study in the Journal of Affective Disorders reported that online treatment proved just as effective as face-to-face treatment for depression. A 2018 study, published in Journal of Psychological Disorders, found that online cognitive behavioral therapy, or CBT, was just as effective as face-to-face treatment for major depression, panic disorder, social anxiety disorder, and generalized anxiety disorder. And a 2014 study in Behaviour Research and Therapy discovered that online CBT proved effective in treating anxiety disorders, and helped lower costs of treatment.
During the forced teletherapy of COVID, therapists worried that those with serious mental health conditions would be less likely to convert to teletherapy. Yet research published in Counselling Psychology Quarterly has helped to alleviate that concern. It found that those with schizophrenia, bipolar disorder, severe depression, PTSD, and even suicidality converted to teletherapy at about the same rate as those with less severe mental health challenges.
Yet teletherapy may not be for everyone. For example, adolescents had the most varied response to teletherapy, according to a 2020 study in Family Process. Some adapted quickly and easily, while others found it awkward and anxiety-inducing. On the whole, children with trauma respond worse to online therapy, per a 2020 study in Child Abuse & Neglect. The treatment of mental health issues can sometimes require in-person interactions, such as the use of eye movement desensitization and reprocessing to treat post-traumatic stress disorder. And according to a 2020 study from the Journal of Humanistic Psychology, online therapy may not be as effective for those suffering from loneliness.
Leverage the strengths of online therapy
Online therapy is much more accessible than in-person therapy for those with a decent internet connection, webcam, mic, and digital skills. You don’t have to commute to your therapist’s office, wasting money and time. You can take much less medical leave from work, saving you money and hassle with your boss. If you live in a sparsely populated area, online therapy could allow you to access many specialized kinds of therapy that isn’t accessible locally.
Online options are much quicker compared to the long waiting lines for in-person therapy. You also have much more convenient scheduling options. And you won’t have to worry about running into someone you know in the waiting room. Online therapy is easier to conceal from others and reduces stigma. Many patients may feel more comfortable and open to sharing in the privacy and comfort of their own home.
You can use a variety of communication tools suited to your needs at any given time. Video can be used to start a relationship with a therapist and have more intense and nuanced discussions, but can be draining, especially for those with social anxiety. Voice-only may work well for less intense discussions. Email offers a useful option for long-form, well-thought-out messages. Texting is useful for quick, real-time questions, answers, and reinforcement.
Plus, online therapy is often cheaper than in-person therapy. In the midst of COVID, many insurance providers have decided to cover online therapy.
Address the weaknesses
One weakness is the requirement for appropriate technology and skills to engage in online therapy. Another is the difficulty of forming a close therapeutic relationship with your therapist. You won’t be able to communicate non-verbals as fully and the therapist will not be able to read you as well, requiring you to be more deliberate in how you express yourself.
Another important issue is that online therapy is subject to less government oversight compared to the in-person approach, which is regulated in each state, providing a baseline of quality control. As a result, you have to do more research on the providers that offer online therapy to make sure they’re reputable, use only licensed therapists, and have a clear and transparent pay structure.
Be intentional about advocating for yourself
Figure out what kind of goals you want to achieve. Consider how, within the context of your goals, you can leverage the benefits of online therapy while addressing the weaknesses. Write down and commit to achieving your goals. Remember, you need to be your own advocate, especially in the less regulated space of online therapy, so focus on being proactive in achieving your goals.
Develop your Hero’s Journey
Because online therapy can occur at various times of day through videos calls, emails and text, it might feel more open-ended and less organized, which can have advantages and disadvantages. One way you can give it more structure is to ground these interactions in the story of your self-improvement. Our minds perceive the world through narratives. Create a story of how you’ll get from where you are to where you want to go, meaning your goals.
A good template to use is the Hero’s Journey. Start the narrative with where you are, and what caused you to seek therapy. Write about the obstacles you will need to overcome, and the kind of help from a therapist that you’ll need in the process. Then, describe the final end state: how will you be better off after this journey, including what you will have learned.
Especially in online therapy, you need to be on top of things. Too many people let the therapist manage the treatment plan. As you pursue your hero’s journey, another way to organize for success is to take notes on your progress, and reevaluate how you’re doing every month with your therapist.
Identify your ideal mentor
Since it’s more difficult to be confident about the quality of service providers in an online setting, you should identify in advance the traits of your desired therapist. Every Hero’s Journey involves a mentor figure who guides the protagonist through this journey. So who’s your ideal mentor? Write out their top 10 characteristics, from most to least important.
For example, you might want someone who is:
- Empathetic
- Caring
- Good listener
- Logical
- Direct
- Questioning
- Non-judgmental
- Organized
- Curious
- Flexible
That’s my list. Depending on what challenge you’re facing and your personality and preferences, you should make your own. Then, when you are matched with a therapist, evaluate how well they fit your ideal list.
Fail fast
When you first match with a therapist, try to fail fast. That means, instead of focusing on getting treatment, focus on figuring out if the therapist is a good match based on the traits you identified above. That will enable you to move on quickly if they’re not, and it’s very much worth it to figure that out early.
Tell them your goals, your story, and your vision of your ideal mentor. Ask them whether they think they are a match, and what kind of a treatment plan they would suggest based on the information you provided. And observe them yourself in your initial interactions, focusing on whether they’re a good match. Often, you’ll find that your initial vision of your ideal mentor is incomplete, and you’ll learn through doing therapy what kind of a therapist is the best fit for you.
Choose a small but meaningful subgoal to work on first
This small subgoal should be sufficient to be meaningful and impactful for improving your mental health, but not a big stretch for you to achieve. This subgoal should be a tool for you to use to evaluate whether the therapist is indeed a good fit for you. It will also help you evaluate whether the treatment plan makes sense, or whether it needs to be revised.
Know when to wrap things up
As you approach the end of your planned work and you see you’re reaching your goals, talk to the therapist about how to wrap up rather than letting things drag on for too long. You don’t want to become dependent on therapy: it’s meant to be a temporary intervention. Some less scrupulous therapists will insist that therapy should never end and we should all stay in therapy forever, and you want to avoid falling for this line. When you reach your goals, end your therapy, unless you discover a serious new reason to continue it. Still, it may be wise to set up occasional check-ins once every three to six months to make sure you’re staying on the right track.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.