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Panic Disorder

on Saturday, 14 December 2013. Posted in General

Part 1 of this 2-part podcast series, Stephen V. Sobel, MD, sheds some light on the pathogenesis.

Panic Disorder

Panic disorder is a tremendously vexing challenge: keys to its management include appropriate use of psychotropic medication and psychotherapy predicated on an understanding of the biopsychosocial underpinnings. In part 1 of this 2-part podcast series, Stephen V. Sobel, MD, sheds some light on the pathogenesis. (For Part 2, please click here).

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The Psychology Of Fight Club

on Saturday, 31 August 2013. Posted in General

Thanks to Brietta Mengel of Source:

The Psychology Of Fight Club


Fight Club’s narrator’s illness is the manifestation of trite and tedious modern life. Watch capitalism push fight club members to the edge in the following steps:

Board One(insomnia)

  • Side one
    • “We buy things we don’t need, with money we don’t have, to impress people we don’t like.”
    • 9-5 grind, obsession with trendy living
  • Side two
    • Neither sleep nor wakefulness
    • 24 hour fog
  • Side three
    • Doctor wont provide rx
    • Sent to support groups to see others in real pain
    • “feeling sorry for yourself plaza”
  • Side Four
    • Make yourself feel like a victim at prostate cancer support group
    • get a great rush
    • Cure your insomnia
    • Meet Tyler Durden: pull a card
    • Marla sees you as a phony, ruins the rush: find a new hobby
      • Card: ” Narrator: A new car built by my company leaves somewhere traveling at 60 mph. The rear differential locks up. The car crashes and burns with everyone trapped inside. Now, should we initiate a recall? Take the number of vehicles in the field, A, multiply by the probable rate of failure, B, multiply by the average out-of-court settlement, C. A times B times C equals X. If X is less than the cost of a recall, we don’t do one.
        Woman on plane: Are there a lot of these kinds of accidents?
        Narrator: You wouldn’t believe.
        Woman on plane: Which car company do you work for?
        Narrator: A major one.”
      • Experience Trauma, create imaginary friend, advance to board two
    • Go square: “pass go, do it again!”

Board Two(dissociative identity disorder)

  • Side one
    • Redemption through violence: get a rush
    • Who is Tyler Durden?
    • New Player Joins: Tyler Durden game piece placed at the same place
  • Side two
    • Apartment blown up, join Project Mayhem
    • What’s your hand? Draw a card
      • Card 1: Invisible hand of production. Capitalism moves towards the highest effiency for everyone.
      • Card 2: Tyler Durden pours lye on your hand. Experience another trial by fire.
  • Side three
    • Tyler sleeps with Marla, get jealous.
    • What direction are you headed, anyway?
      • Card 1:Homogenous capitalism: all experience can be reduced to a price. Everything can be bought and sold.
      • Card 2: Heterogeneous Capitalism: Some experiences are incompatible with normal buying and selling. Pick a sacred apple, buy/sell an orange.
  • Side four
    • Tyler kidnaps Marla, picks fight with you.
    • Realize you’re holding the gun and shoot Tyler in the mouth
    • Tyler disappears, you are hailed as Tyler
    • Watch credit card buildings blow up, holding Marla’s hand
    • “The first rule of fight club is: you don’t talk about fight club.”

Cultural Influence

  • Gentlemen’s Fight Club
    • Was founded in Menlo Park by tech workers in 2000
  • Princeton University Fight Club
    • Was founded in 2001, but broke the first rule of fight club by talking about it
  • Luke Helder
    • Planted pipe bombs in mailboxes across the U.S. trying to blow up a smiley face on the map
  • 17 y.o. founder of Manhatten fight club
    • Jailed for planting a bomb outside of capitalist standard-bearer Starbucks



Effective Personalized Strategies for Treating Bipolar Disorder

on Saturday, 17 August 2013. Posted in General

Bipolar disorder causes havoc in patients’ lives. Even in the best of circumstances, successful treatment is challenging

Effective Personalized Strategies for Treating Bipolar Disorder

By Stephen V. Sobel, MD

Bipolar disorder causes havoc in patients’ lives. Even in the best of circumstances, successful treatment is challenging. Treatment targets constantly shift; patients are frequently nonadherent; and comorbidity is the rule, not the exception. Diagnosis of bipolar disorder is often difficult. Comorbidities need to be identified and addressed if treatment is to be effective.

The importance of an accurate diagnosis

With apologies to Charles Dickens, bipolar disorder is often experienced as the “best of times and the worst of times.” This polarity often causes bipolar disorder to be undiagnosed, overdiagnosed, or misdiagnosed. Bipolar disorder is associated with a significantly elevated risk of suicide. Moreover, bipolar patients often use highly lethal means for suicide.1 Contributing factors include early age at disease onset, the high number of depressive episodes, comorbid alcohol abuse, a history of antidepressant-induced mania, and traits of hostility and impulsivity.

Bipolar I disorder, with episodes of full-blown mania, is usually easier to diagnose than bipolar II disorder, with episodes of subtler hypomania. Recognizing that the primary mood state may be irritability rather than euphoria increases the likelihood of diagnosis as does the recognition that symptoms often last fewer than the 4 days required for diagnosis by DSM-IV.2 Focusing more on overactivity than mood change further improves diagnostic accuracy, and the use of structured questionnaires is helpful.

Given the greater frequency of depression than manic episodes in bipolar disorder, what clues indicate bipolar disorder rather than unipolar depression? The Table lists factors that may help identify unipolar depression.

A moving target needs moving treatment

Effective personalized treatment recognizes bipolar disorder as a biopsychosocial disorder, but mood-stabilizing medications are the backbone of treatment. These medications fall into 3 categories: lithium, antikindling/antiepileptic agents, and second-generation antipsychotics. The mechanisms of actions by which these medications work are numer-ous and include increasing levels of serotonin, γ-aminobutyric acid, and brain-derived neurotrophic factor (BDNF) and decreasing glutamate levels; modifying dopamine pathways; stabilizing neuronal membranes; decreasing sodium channels; decreasing depolarization; decreasing apoptosis; and increasing neural cell growth/arborization.

Double-blind placebo-controlled studies of the medications—lithium, divalproex, carbamazepine, and atypical antipsychotics—used to treat symptoms of acute mania have demonstrated a response rate of approximately 50% to these drugs. Response was defined as a 50% decrease in symptoms using the Young Mania Rating Scale (YMRS) with onset of response within a few days.

An increasingly intriguing aspect of treatment with lithium and atypical antipsychotics involves their effect on BDNF. In a study of 10 manic patients treated with lithium for 28 days, most (87%) showed an increase in BDNF level (ie, from 406 pg/mL to 511 pg/mL). 

Factors that suggest bipolar depression rather than unipolar depression

In a typical 3-week study of acute mania, approximately half of the benefit was seen by day 4. A 3-week, double-blind, inpatient study of olanzapine and risperidone in 274 patients with acute mania found that of 117 patients who had a less than 50% decrease in the YMRS score at 1 week, only 39% responded and 19% had symptom remission at end point. Of 40 patients with a less than 25% decrease in the YMRS score at 1 week, only 25% responded and only 5% had symptom remission at 3 weeks. Of 157 patients who had at least a 50% decrease in the YMRS score at week 1, 84% responded and 64% had symptom remission at 3 weeks.4 Clinically, a medication change should be considered for patients who do not demonstrate substantial benefit by week 1.

A meta-analysis comprising 16,000 patients who had acute mania found that the most effective agents were haloperidol, risperidone, and olanzapine. The least effective were gabapentin, lamotrigine, and topiramate.5

A combination of medications—typically lithium or an antiepileptic with an atypical antipsychotic—is often necessary to successfully treat acute mania. A meta-analysis found the response rate increased from 42% to 62% when an antipsychotic was added.6

Bipolar depression has proved to be more resistant to medication treatment than mania. The same medications are used, with lamotrigine for maintenance treatment. The FDA has approved Seroquel, Seroquel XR, and Symbyax (the combination of olanzapine and fluoxetine), for the acute treatment of bipolar depression. Studies of acute bipolar depression have typically lasted 8 weeks. Approximately half of the benefit oc-curs by week 2, with statistical separation from placebo between weeks 1 and 3.7-9

The best treatment is prevention

Patients who have bipolar disorder almost always require lifelong maintenance treatment, frequently with 2 medications: one to prevent the upside (ie, hypomania/mania), and another to prevent the downside (ie, depression).

Findings from a registration trial showed that lamotrigine more effectively prevented depressions than lithium but lithium prevented mania/hypomania more effectively than lamotrigine.10

Another study added placebo or lamotrigine to lithium treatment for 124 patients. The median time to relapse/recurrence was 3.5 months for those taking lithium monotherapy but 10 months for those who received combination treatment.11

The effectiveness of a combination maintenance regimen was also seen in a study of 628 patients with bipolar I disorder treated for 2 years: 65% of those taking lithium or divalproex alone experienced a recurrence compared with 21% who received quetiapine added to lithium or divalproex.12 However, combination treatment may result in more adverse effects and increased risk of drug-drug interactions.

The best mood stabilizer

The best mood stabilizer for a patient is the one he or she will take. No matter how effective a medication is, it will not relieve symptoms if it is not being taken. The key to effective personalized treatment of bipolar disorder is a good patient-physician connection in which the patient is part of the treatment decision-making process.

Psychotherapy is an integral part of the effective treatment of bipolar disorder, not just an augmentation strategy. Psychotherapies that are helpful include cognitive-behavioral therapy and social rhythm therapy.13 Psychotherapy can focus on several areas, such as education, comorbidities, medication adherence, and interpersonal relationships. In addition, therapy can challenge the automatic, distorted, and dysfunctional thoughts and help the patient maintain social rhythms (eg, consistent sleep). The involvement of family members in treatment enhances success.

Patients may stop taking their medications because the adverse effects become intolerable; they may miss what they perceive as their more satisfying and productive hypomania; and they might believe that a period without symptoms means that they are cured and no longer need medications. One study of 3640 patients with bipolar disorder who made 48,000 physician visits found that 24% of patients were nonadherent (defined as missing at least 25% of doses) 20% of the time. Factors associated with nonadherence included rapid cycling, suicide attempts, earlier onset of illness, anxiety, and alcohol abuse.14

Patients who have bipolar II disorder spend far more time depressed than hypomanic. Lithium appears to be less effective than antikindling agents for rapid cycling as well as for mixed bipolar disorder states.15

Maintenance treatment is necessary for patients with acute mania or acute depression; therefore, choose medications that are more tolerable to the patient to facilitate long-term adherence. Recognize that medications may need to be adjusted or changed—in the acute phase of illness, rapid efficacy is often the priority, while medication adherence is the priority during the maintenance phase.

Other factors to consider when choosing the best medication for a particular patient include:

• A history of treatment response

• A family history of response

• Adverse effects of a particular drug

• Drug interactions

• Pregnancy

• Breast-feeding


The use of antidepressants in bipolar disorder is controversial because they may induce rapid cycling, especially in patients with episodes of rapid cycling.16 In a study by Altshuler and colleagues,17 patients who had breakthrough depression despite treatment with a mood stabilizer were treated with antidepressants for at least 60 days. Patients who had symptom remission for 6 weeks were followed up for 1 year: 36% of patients who continued antidepressants for longer than 6 months relapsed versus 70% who discontinued antidepressants before 6 months.

A randomized discontinuation study with antidepressants found no statistically significant symptomatic benefit in the long-term treatment of bipolar disorder.18 Trends toward mild benefits, however, were found in patients who continued antidepressants. This study also found, similar to studies of tricyclic antidepressants, that rapid-cycling patients had worsened outcomes with continuation of modern antidepressants, including SSRIs and SNRIs.

An NIMH study of 159 patients who had breakthrough depression despite receiving a mood stabilizer were treated with sertraline (mean dosage, 192 mg/d), bupropion (mean dosage, 286 mg/d), or venlafaxine (mean dosage, 195 mg/d) for 10 weeks with a 1-year follow-up.19 At the end of 1 year, only 16% of the patients had continued remission while more than 55% had switched to mania/hypomania. The worst results were seen with venlafaxine and the best with bupropion.

In a study by Sachs and colleagues,20 patients who had breakthrough depression despite being treated with mood stabilizers were randomized to paroxetine (mean dosage, 30 mg/d), bupropion (mean dosage, 300 mg/d), or placebo. No significant differences on any effectiveness or safety outcome, including remission rates or affective switch frequency, were found.

Overall, these studies indicate that the role of antidepressants is limited and that, in fact, a trial of a mood stabilizer cannot be considered to have failed unless the failure occurs in the absence of an antidepressant. A meta-analysis of 18 studies with 4105 patients found that combination treatment including a mood stabilizer and an antidepressant was not statistically superior to monotherapy.21

When symptoms persist

Establish the context of each appointment by focusing on changes in occupational, social, family, and health status. Evaluate medication regimens, with a focus on effectiveness for carefully chosen target symptoms and adherence to treatment, as well as medication tolerability and patient attitudes. Be alert to the emergence of early symptoms of mood change, and adjust medications if necessary. Remember that treatment modalities often need to change over time.

Mood stabilizers should be optimized with combination therapy for sustained remission. Antidepressants may worsen the disease course, and a true trial of a mood stabilizer can-not occur within the setting of antidepressants. If symptoms persist, ask: Is the patient taking anything that is making symptoms worse, eg, drugs, alcohol, or antidepressants? Is the patient taking the medications? Is treatment adequate? Is another condition (including subclinical hypothyroidism) interfering with treatment? Is psychotherapy being ignored?

bStable Should Have Been Mentioned in Our Data, Ourselves - Discover Magazine 2011 Issue

on Friday, 02 August 2013. Posted in General

“Self-Tracking” enthusiasts collect 
data on every aspect of their lives. If digital navel-gazing goes mainstream, 
it could transform medicine.

bStable Should Have Been Mentioned in Our Data, Ourselves - Discover Magazine 2011 Issue

By Kate Greene|Thursday, December 08, 2011


Bob Evans has spent most of his life obsessing over how to track data. When the Google software engineer was a boy in Louisville, Kentucky, he collected star stickers to show that he had done his chores. In college, where he studied philosophy and classical guitar, Evans logged the hours he spent playing music. Later, as an engineer for a Silicon Valley software company, he defended his dog, Paco, against a neighbor’s noise complaints by logging barks on a spreadsheet (the numbers vindicated Paco, showing he was not the source of the public disturbance). For Evans, collecting data has always been a way to keep tabs on his habits, track his goals, and confirm or dispel hunches about his daily existence.

Last May, Evans reminisced about those early days in data collection as we sat in a large-windowed conference room in Building 47 of the Google campus, near San Jose, California. His personal fixation is shared by a growing number of self-trackers, a movement that is spreading far beyond data-obsessed engineers. Taking advantage of new wearable wireless devices that can measure things like sleep patterns, walking speeds, heart rates, and even calories consumed and expended, more and more people are signing up to download and analyze their personal data. Nearly 10 million such devices will be sold in North America in 2011, according to the market forecasting company ABI Research.

Most self-trackers are extreme fitness buffs or—like Evans—technology pioneers inherently interested in novel software applications. But Evans believes that personal data collecting could have stunning payoffs that go beyond just taking a better measure of everyday behavior. Already, some proponents claim personal benefits from logging their habits—eliminating foods that trigger migraines or upset stomachs, for instance, or saving certain tasks for their most productive time of day. Applied more broadly, data collected by self-trackers could help them find better treatments for diseases and even predict illness before symptoms become obvious.

Evans also sees the potential for individual citizens to pool nonmedical data collected through tracking experiments. Such data sets could have important social benefits. For instance, if members of a community tracked their feelings about safety in their neighborhood and shared their data regularly, crime trends could be detected earlier and addressed more effectively.

As Evans’s history with data collection shows, basic self-tracking is possible with nothing more than a pencil and paper. Still, people have been reluctant to sign on to an activity that has historically required inordinately high levels of self-curiosity and motivation. Now, with the wildfire spread of smartphones and tablet computers, that resistance could be melting away—and Evans plans to capitalize on the change. He has developed a tracking tool, conveniently contained in a mobile phone app, that he thinks can make self-tracking appealing to the masses.

Most self-tracking devices currently on the market measure only a few data points and have their own proprietary software and code limiting how users can analyze their own metrics. Evans’s app is different: It can be set up to track any kind of behavior or event and keeps data in one place, making it possible to analyze it all together. It is also designed to address another major objection to such detailed self-reporting, the fear that our personal data could too easily be leaked, stolen, or simply exposed to the public.

My visit to Google was a chance to understand Evans’s vision and to try out its practical application. I’m not a data obsessive by any means. If Evans could convert me, self-tracking just might be for real.

In 2009, while Evans was working for Google to help create new tools to increase programmers’ efficiency, he realized no one was working on the “soft science” side of the equation to help the programmers become more productive in their personal behavior. In his data-oriented way, he set out to understand everything that happens in a programmer’s work life. He wondered how attitudes toward food, distractions, and work environment—sampled throughout the day
—might affect creativity. If a programmer was stressed out or unhappy with a project, could a glance at her daily stats help set her right? Could immediate insight from a survey encourage her to make a change for the better? Evans had a hunch that by gathering the right data sets, he could help people improve their job performance in real time.

To make this process as simple as possible, Evans decided to collect the data through the smart cell phones that Google employees already kept close at hand. He set up an app so a programmer’s phone would chime or buzz a few times throughout the day at random times, as if a text message had arrived. When the employee clicked the message open, the app would ask her if she felt passionate and productive about her project. If not, it asked what she could do to change it.

In addition to gathering data about work habits, Evans set up another survey that asked programmers to outline their work goals. When the app checked in later, it listed those goals and asked which one the programmer was engaged in—the idea being that if a programmer had been distracted, a reminder of what she wanted to accomplish might improve her focus. “I thought it would be cool to build a platform that was not just for collecting data,” Evans says. “It could have the tools and interventions so people could do their own self-improvement.”

The survey was rolled out two years ago to a small number of programmers at the Google campus. Although Evans worried that the app would be too intrusive, he was heartened to see that most programmers continued to use it even after the pilot program officially ended. Since each programmer had different goals, measuring the overall effectiveness of the app was difficult, Evans says, but subjectively, he and his colleagues felt the simple act of observing their behavior through the app led them to change in ways that helped them meet their work goals.

Evans’s daily productivity surveys soon inspired him to create a broader, more flexible mobile platform for self-experimentation that he dubbed PACO—an acronym for Personal Analytics Companion, but also a tribute to the dog that helped inspire his data-tracking ideas. Now PACO is used by thousands of Google employees, and not just for productivity. The app is fully customizable, which means it can track any data point a user dreams up. Some Googlers employ it to log exercise or participation in volunteer programs. Evans tailored his version of PACO to monitor his work tasks and exercise and as a reminder to eat fewer sweets. A colleague uses it to track carbohydrate intake and weight fluctuations and to compare trends across PACO experiments. “I look at the information I track every couple of months and remind myself of the progress I’ve made, or where I need to change my behavior,” Evans says.

After hearing him describe all the ways PACO has subtly changed the lives of his colleagues, I was ready for my own plunge into the world of self-tracking.

Logging personal data is probably as old as writing itself, but some modern self-trackers trace its origin to that godfather of American ingenuity, Benjamin Franklin. He was interested in how well he adhered to his famous 13 virtues, including frugality, sincerity, and moderation. Each day for several years he noted the ones he’d violated in a book he kept especially for the purpose.

More recently, Gordon Bell, a computer pioneer and researcher at Microsoft, introduced the concept of “life logging.” From 1998 to 2007, Bell collected his emails and scanned documents, photographs, and even continuous audio and video recordings of his day-to-day life into a searchable online database—an attempt to create a digital record of every thought and experience he’d had for a decade.

Within the past three years, though, self-tracking has grown into a veritable grassroots movement, embodied by an organization called Quantified Self, a community of data-driven types founded in the San Francisco Bay Area by journalists Kevin Kelly and Gary Wolf. Most Quantified Selfers have technology backgrounds, or at the very least a penchant for numbers. They gather in online forums and at face-to-face events to talk about their self-experimental methods, analyses, and conclusions. How does coffee correlate with productivity? What physical activity leads to the best sleep? How does food affect bowel movements? Mood? Headaches? No detail, it seems, is too intimate or banal to share.

The current explosion in 
self-tracking would not be possible without the mass digitization of personal data. Websites for tracking, graphing, and sharing data about health, exercise, and diet—many of which are linked to phone apps—are on the rise. RunKeeper, a popular data collection app for runners, reports 6 million users, up from 2 million in November 2010. The new small, affordable sensors, like the $100 Fitbit, can wirelessly log all sorts of human metrics: brainwave patterns during sleep, heart rates during exercise, leg power exerted on bike rides, number of steps taken, places visited, sounds heard. And a number of these sensors, such as microphones, GPS locators, and accelerometers, come inside smartphones, making some types of tracking effortless. Research firm eMarketer projects that by the end of 2012, 84.4 million people will use smartphones in the United States, up from 40.4 million in 2009.

2011 study by Pew Internet, a project at the Pew Research Center that investigates the impact of the Internet on American society, estimates that 27 percent 
of Internet users have kept track of their weight, diet, or exercise or monitored health indicators or symptoms online. Still, the Pew report also hints at a limitation inherent in the current self-tracking paradigm. It is still done mainly by conscientious people who are highly motivated to collect specific types of data about specific cases. Of the adults surveyed who own a cell phone, only 9 percent have mobile apps for tracking or managing their health.

“It’s still a relatively new idea that phones are windows into your behavior,” says computer scientist Alex Pentland, director of the Human Dynamics Laboratory at MIT. Most people, he adds, think that “health is the responsibility of your doctor, not you.” But self-tracking tools that give both patient and physician a snapshot of symptoms and lifestyle could become increasingly important to personal health.

Health is exactly what was on the mind of Alberto Savoia, a Google software engineer who supervises Evans, when he joined us in the conference room to discuss which PACO experiments had worked best for his team.

Savoia himself had created an experiment to track the effects of his allergy shots. He’d never had allergies until he moved to America from Italy. “I made fun of Americans,” he says, for sneezing at everything from cats to dust. “But lo and behold, I started to sniffle.” He suspected that his shots were helping, but as an engineer, Savoia knew to be skeptical of his own perceptions. He wanted quantitative proof. “Our brains construct fabulous stories,” he says. The daily reports he logged into PACO indicated that his shots for cat dander and pollen were working well: His symptoms were less severe and less frequent than they had been before the shots.

During the same test period, Evans created an experiment called Food Rules, based on the book of that name by Michael Pollan, a journalist who advocates eating simply and avoiding processed food. After each meal, PACO would ask: Did you eat real food? Was it mostly plants? Evans found that the very act of responding to these questions made him more aware of his eating habits. He started choosing his food in the Google cafeteria more carefully, knowing he would have to answer for it after lunch. Within weeks he stopped running the experiment because every answer was “yes.”

I considered their examples. It occurred to me that I sometimes sneeze fairly aggressively after meals. When I was a teenager, I ribbed my mother for her after-dinner sneezes, but in my early twenties I started sneezing too, with no obvious connection to specific foods. My mother had a hunch that the trigger was sugar, but I had my doubts: Who ever heard of a sugar allergy? I never kept a food log to find the actual culprit, but the question seemed perfect for PACO. In just a couple of minutes, the Google engineers walked me through the steps of creating my own experiment, which I called Sneezy, to track the problem.

I constructed a handful of 
other experiments as well, including one I dubbed Good Morning, Sunshine! in which PACO was programmed to ask me how well I had slept and what I’d dreamed about; Flossy, in which PACO asked me if I had flossed the day before; and the self-explanatory Call Your Mother, 
which had PACO pestering me on Sunday evenings to see if I had talked to my mother lately—and if so, what we’d discussed.

I chose to keep these experiments private: No one else could sign up to use them, and my data would be stored, encrypted, on a PACO server. The issue of privacy looms large over discussions of personal data collection. “It’s your daily ebb and flow,” Evans says of PACO-
collected data. “That’s something you need to control.” As PACO is currently built, a user can keep everything private, or she can share data by joining an experiment created by someone else. The information is stored in the cloud, on servers rented from Google. But unlike search terms, data from PACO are not mined by the company for patterns.

Self-tracking tools will probably never catch on with the wider public unless people are confident that their data are safe. “The key is giving individuals more control over their data, yet the flexibility to share it when they need to,” says MIT’s Pentland. To do this, he suggests, data should be protected by a “trust network” that is not a company or government agency. People might then establish their own personal data vaults for which they define the rules of sharing.

Pentland participates in a group called id3, which brings together government officials, academics, and industry representatives to establish guidelines for such networks. He expects the details to be worked out within the next two years. The stakes are high. If secure methods for sharing data anonymously can be developed, it won’t be just individuals taking advantage of the information they gather through self-tracking. Society as a whole could benefit.

in 2009 Matt Killingsworth, a psychology doctoral student at Harvard University, put a call out for people to join a study he called Track Your Happiness. An iPhone app queried participants—ranging in age from 18 to 88, living in 83 countries, and working in 86 job categories—throughout the day about their state of mind, their current activity, and their environment, among other things. At the end of the study, participants were given a happiness report, with graphs illustrating how happy they were and the activities and environment that affected their mood.

In 2010 Killingsworth analyzed responses from more than 2,200 people to see if what they were thinking about affected their happiness. The most striking result was that overall, people’s minds were wandering in almost half the survey responses, 
and people were less happy when their minds were wandering than when they were not. The findings were unexpected because previous studies, done with small numbers of people in the lab, concluded that people’s minds wander less often.

“The project illustrates that the promise and ability to track things in real time on a mobile phone in the course of your daily life is incredibly powerful,” Killingsworth says. Most previous studies would have been limited to questions asking a small number of people, after the fact, how they had felt at a certain time. Using mobile phones for this sort of study is “incredibly exciting,” Killings­worth says. “It allows us to collect more accurate data from many thousands of people.”

In the same vein as the health-oriented PACO experiments, Ian Eslick, a Ph.D. candidate in the New Media Medicine group at MIT’s Media Lab, is helping online patient communities convert anecdotes about treatments, such as how certain diets affect symptoms, into structured self-experiments. He is building an automated recommendation system that can suggest experiments to people based on their previous symptoms and responses to interventions.

For instance, no studies have uncovered a solid connection between diet and the symptoms of psoriasis, an inflammatory skin condition from which Eslick suffers. Some people find that cutting out sugar alleviates symptoms, while others 
do not. Eslick hopes that by collecting information on people’s self-experiments over a long period of time, he’ll have enough useful data to warrant the deployment of a traditional clinical trial to investigate the most successful interventions for psoriasis. “It’s a very different model than traditional medical research,” Eslick says. “Trials are expensive and hard to administer. They’re short. They run once and have to get your answer.” Self-experimentation, on the other hand, has the luxury of time. Experiments can run longer and produce more data because they are cheap to administer.

Customizable data collection systems like PACO make it easy to run those experiments, Eslick says. “PACO is cool not so much because it does data collection, but because it’s trying to make it easier to collect just the data you want, and just the stuff that’s relevant.”

Today’s smartphones can collect data such as location, speech patterns, and motion without any active input from the user. This sort of passive sensing of a person’s daily life makes them powerful tools for personal medical and psychological diagnostics.

Data sets of a person’s speech and movement could provide insight into conditions such as depression and Alzheimer’s disease. Some people’s speech and movements slow when they experience severe depression. If phone sensors could effectively measure change in speech or movement over time, then an app could suggest a doctor’s visit when a person’s state of mind declines.

A 2010 study by William Jarrold, a cognitive scientist at the University of California, Davis, suggests that an automated system that analyzes speech patterns on phone calls can potentially pick up on cognitive impairment and clinical depression or determine if someone is in the very early stages of Alzheimer’s. “Machine learning is getting better, the prevalence of cell phones and cloud computing is increasing, and we’re getting more data and doing more studies,” Jarrold says. “When data are collected over the course of years, they can provide relevant information about a person’s cognitive functions, diagnosing a decline before obvious symptoms arise.”

Data tracking could even help monitor infectious disease. Pentland has shown that certain patterns picked up by a person’s phone—such as a decrease in calls and text messages—correspond to onset of the common cold and influenza. If outfitted with software that can intervene when data analysis suggests the early stages of an illness, your next phone could help you figure out you’re sick before you are even aware of a problem.

My PACO experiments ran for about a month. Initially I wasn’t sure I’d like the distraction of a self-tracking app, let alone one that insisted I respond seven to nine times a day. Unexpectedly, I came to appreciate the way the app made me mindful of what I ate and how well I slept.

One thing I learned was that my mother was wrong: It wasn’t sugar that caused my sneezes. The Sneezy experiment told me that my morning meal was the main offender, especially when I drank coffee with cream. Beer also seemed to give me sniffles, though not every time. Thanks to PACO, I have narrowed down the possible culinary culprits. The experiment Happy Work Day was less surprising but also instructive. Twice a day it asked if I was working at my desk, and it often caught me doing something other than work (16 counts for not working to 25 counts for working). It made me more aware of the non-work tasks, like household chores, I spend time on during the day. I’ve since left many of these tasks for after conventional work hours.

The two experiments I hoped would influence my behavior were telling. According to Call Your Mother, I spoke with my mother only three times over the course of the experiment. I can’t say I have radically changed that behavior yet. But Flossy was a complete success. Having PACO ask me every day if I had flossed the day before seemed to do the psychological trick. I’m flossing every day. It’s a small miracle.

My thoroughly nonscientific experiences also suggest that PACO will have widespread appeal. When I explained it to my nontechnical friends, most instantly grasped the possibilities. A social worker imagined using the app to help find the triggers for negative feelings or actions in clients. A teacher wanted to use it to measure how exercise and food affect student engagement in class. A college professor I met thought he could use PACO to get a sense of how students are handling their workload.

It is still early days for the self-tracking movement, and future versions of applications like PACO will, no doubt, be much more powerful. Even if PACO itself doesn’t catch on, the idea of a program that allows people to adjust their behavior and monitor their well-being is too enticing to ignore; someone will make it work. The Bill and Melinda Gates Foundation and the mHealth Alliance, a group that includes representatives from the United Nations and the Rockefeller Foundation, are already encouraging the development of health-related phone apps. They are acting on the premise that a world in which it is easy for anyone anywhere to collect and securely share data with medical researchers could be a healthier place for all of us.

As any self-tracker knows, there is strength in numbers.

Celebrities with mental disorders

on Saturday, 15 June 2013. Posted in General

God love them for stepping forward and spreading awareness!!!

Celebrities with mental disorders

Stress Sucks but You Can Fight Back

on Saturday, 15 June 2013. Posted in General

How it kicks you in the ass and how you can boot it back

Stress Sucks but You Can Fight Back

Getting an In-Depth Look at Depression

on Saturday, 15 June 2013. Posted in General

The mental health screening site Help Yourself Help Others provides this infographic showing that 17-20 million Americans develop depression each year. Common symptons and an explanation of the different forms of depression are listed below

Getting an In-Depth Look at Depression

Awesome Bipolar Disorder Infographic!!

on Friday, 14 June 2013. Posted in General

Created by Deyanara Riddix of - thanks!

Awesome Bipolar Disorder Infographic!!

What a great way to summarize bipolar disorder.

Great job Deyanara - all the way from West Bengal, India!


6 Keys To Building Resilience

on Wednesday, 12 June 2013. Posted in General

Resilience key to dealing with depression or bipolar disorder

6 Keys To Building Resilience

Tactics for building resiliance include:

1. Learn how to regulate your emotions

2. Adopt a positive but realistic outlook

3. Become physically fit

4. Accept challenges

5. Maintain a close and supportive social network

6. Observe and imitate resilient role models

bStable in NAMI North Carolina 2013 Spring Clippings Newsletter

on Saturday, 25 May 2013. Posted in General

McGraw Systems Proud to Support NAMI

bStable in NAMI North Carolina 2013 Spring Clippings Newsletter

bStable Presented to Alzheimer's Association!!

on Thursday, 09 May 2013. Posted in General

bStable Presented to the Western North Carolina Alzheimer's Association Chapter's Caregiver Education Forum

bStable Presented to Alzheimer's Association!!

Parents With Bipolar Disorder - WAKE UP!

on Saturday, 27 April 2013. Posted in General


Parents With Bipolar Disorder - WAKE UP!





Offspring of Parents With Bipolar Disorder

By Karen Dineen Wagner, MD, PhD | February 8, 2010

Dr Wagner is the Marie B. Gale Centennial Professor and vice chair of the department of psychiatry and behavioral sciences and director of child and adolescent psychiatry at the University of Texas Medical Branch at Galveston.

It is generally held that the offspring of parents with bipolar disorder (BD) are at risk for BD. The degree of risk is an important question for both clinicians and parents. A

recent study of bipolar offspring by Birmaher and colleagues1 sheds light on this issue.

These authors compared the lifetime prevalence of bipolar and other psychiatric disorders in children whose parents had–or did not have–BD. The study involved 233 parents with BD and their 388 offspring and a control group of 143 parents without BD and their 251 offspring.

Parents with BD were recruited from outpatient clinics and advertisements for participation in the study. On the basis of diagnostic interviews, 158 parents had bipolar I disorder and 75 had bipolar II disorder. The majority (80%) of the parents interviewed were female. The mean age of parents with BD was 40 years. Sixty-four percent of parents reported that the onset of their mood disorder occurred before they

were 20 years old. Parents with BD were less likely to be married at the time of intake and had a slightly lower socioeconomic status than parents without BD.

The offspring of parents with BD did not have to be symptomatic to participate in the study. The mean age of these children was 12 years; 49% were female; and 88% were white. Fewer than half (42%) were living with both biological parents.

The rate of bipolar spectrum disorder in the offspring of parents with BD was 10.6% versus 0.8% in the offspring of control parents. The rate of bipolar I disorder was 2.1%; bipolar II disorder, 1.3%; and bipolar not otherwise specified (NOS), 7.2%. The rate of BD increased substantially–to 29%–when both parents had BD.

Overall, the offspring of parents with BD were at significantly greater risk (52%) for any Axis I disorder than those in the control group (29%).

The majority (76%) of these offspring experienced childhood-onset bipolar disorder before age 12 years. Bipolar NOS was the most common first episode of illness. Rates of comorbidity in these youths were high: 51% had anxiety disorder, 53% had disruptive behavior disorder, and 39% had attention-deficit/hyperactivity disorder (ADHD).


Psychiatric Times. Vol. No. February 8, 2010Psychiatric Times. Vol. No. February 8, 2010

The authors concluded that there is a 14-fold increase in the rate of bipolar spectrum disorder in youths who have a biological parent with BD. If both parents have BD, then the offspring are 3 times more likely to have BD.

The mean age of youths in this study was 12 years. Prevalence rates may therefore be an underestimate because some children with depression may become bipolar in adolescence. It is recommended that clinicians who treat adults with BD inquire about the functioning of their children to provide appropriate early intervention.

Posttraumatic stress disorder and substance abuse

In a family study of BD in youths, Steinbuchel and colleagues2 investigated the relationships among adolescent BD, posttraumatic stress disorder (PTSD), and substance use disorder (SUD). Because adults with BD who were severely abused as children are at high risk for SUD, these investigators sought to determine whether there is a similar association in adolescents.

A total of 105 adolescent offspring of parents with BD and a control group of 98 youths without mood disorders participated in this study. The diagnosis of BD was based on structured psychiatric interviews. SUDs included any alcohol(Drug information on alcohol) or drug abuse or dependence.

Rates of PTSD were significantly higher in adolescents with BD than in the control group. Sixteen percent of youths with BD had full or subthreshold PTSD compared with 3% in the control group. These youths had experienced trauma in the form of physical abuse, sexual abuse, witnessing of death, or family violence. Rates of SUDs were higher among youths with BD than in those in the control group (32% vs 4%, respectively). Alcohol was the most frequently used substance (86%) followed by marijuana (71%) and tobacco (29%).

What was the temporal order of these disorders? In half of the cases, BD preceded PTSD. In the other half of cases, PTSD was diagnosed before BD. For those youths in whom SUD developed, the majority had BD followed by PTSD and then SUD.

This study confirms an association between PTSD in adolescents with BD and subsequent development of SUD. Rates of SUD were higher in those youths who met full criteria for PTSD than for those with subthreshold symptoms. The findings reveal that BD increases the risk for PTSD, which in turn increases the risk for SUDs. The investigators suggest that treatment of adolescents with BD may prevent trauma related to the development of PTSD and subsequent SUD. It is recommended that clinicians who treat adolescents with BD evaluate for the presence of PTSD and SUD.


1. Birmaher B, Axelson D, Monk K, et al. Lifetime psychiatric disorders in school-aged offspring of parents with bipolar disorder: the Pittsburgh Bipolar Offspring study. Arch Gen Psychiatry. 2009;66:287-296.

2. Steinbuchel PH, Wilens TE, Adamson JJ, Sgambati S. Posttraumatic stress disorder and substance use disorder in adolescent bipolar disorder. Bipolar Disord. 2009;11:198-204.



Overdiagnosis: Examine the Assumptions, Anticipate New Bipolar Criteria

on Saturday, 20 April 2013. Posted in General

Bipolar Disorder

Overdiagnosis: Examine the Assumptions, Anticipate New Bipolar Criteria
By James Phelps, MD | March 13, 2013
Dr Phelps is Director of the Mood Disorders Program at Samaritan Mental Health in Corvallis, Ore. His Web site gathers no information on visitors and produces no income for him or others. He is the author of Why Am I Still Depressed? Recognizing and Managing the Ups and Downs of Bipolar II and Soft Bipolar Disorder (New York: McGraw-Hill; 2006), from which he receives royalties. He stopped taking honoraria from pharmaceutical companies in 2008.
Overdiagnosis of bipolar disorder is an increasing concern, particularly since the widely cited study by Zimmerman and colleagues.1 Findings from that study indicate that there is a problem with overdiagnosis (positive predictive value of only 43%) as well as with the much less publicized parallel finding of 30% underdiagnosis (sensitivity of 70%).

A recent review noted a much lower underdiagnosis rate of 4.8%, which is an inaccurate interpretation of the original data.Zimmerman and colleagues themselves allude to the higher figure.3

Will the new criteria in DSM-5 address these varying claims of overdiagnosis and underdiagnosis? After all, concern about overdiagnosis is one of the driving forces behind these debated changes.4 I’ll take up that question in the next essay in this series, suggesting that the new criteria will not significantly improve positive predictive value—the most debated aspect of diagnostic accuracy. But an important step should precede that review of predictive value and specificity, namely, a careful examination of the very concept of overdiagnosis.

Consider the implicit assumptions.

Bipolar disorder is like bacterial sepsis or mononucleosis: a patient either has it or he does not. One of the origins of dichotomous diagnosis in psychiatry is bacterial. The discovery that many debilitating illnesses were caused by invasive bacteria was a tremendous medical advance. An illness was present if the offending agent was present and absent if it was not—the first of Koch’s 4 postulates. But this perspective has been carried forward into the realm of mental health, where emerging understanding of phenomenology is not consistent with this black-and-white, yes or no way of thinking.5,6

The DSM’s dichotomous system—mental illnesses are either present or absent—is an accurate model for bipolar disorders. Consider the sheer number of genes and consider the role of environmental variation in modifying gene impact, as seen in the short/long variation of the serotonin transporter gene and depression vulnerability, where an otherwise substantial gene effect is completely overridden by benign up-bringing.7Imagine the number of combinations of genes and environments possible and imagine the array of phenotypes that would emerge from them?

A DSM-5 committee considered all of these factors in their 2006 discussion of whether to introduce a spectrum approach to diagnosis in the upcoming edition. Virtually everyone involved was in favor of incorporating a “dimensional” approach (as opposed to the current “categorical” approach). Michael First8 wrote a masterful summary of those proceedings. Ironically, at this meeting, the mood disorders subgroup chose to work on the spectrum of depression severity, not the unipolar-bipolar spectrum. That side step leaves the entire “overdiagnosis” debate open, in spite of a new DSM.

The Structured Clinical interview for Diagnosis (SCID) is a valid gold standard. Even if one presumes that bipolar disorder can be regarded as present or absent and that a diagnostic system should operate accordingly, another major assumption remains: the SCID is a realistic gold standard against which to judge clinicians’ diagnoses. Obviously, the only way to judge diagnostic accuracy is to have some means of recognizing whether the illness is truly present. The SCID is accepted in this role, because psychiatry lamentably has little else to replace it. Is it adequate?

Administering the SCID consists of asking questions in a semi-structured fashion. All the SCID does is ensure that all relevant diagnostic questions are asked in a systematic fashion. The trick in using it is to keep the instrument from interfering too much with the patient’s account of his symptoms. At best, interference can be kept to a minimum.

So, why would we uncritically accept the idea that an SCID user who does not know the patient and whose relationship with the patient can only be hampered, not enhanced, by the instrument he is using, generates a more definitive diagnostic impression than a clinician who actually knows the patient? The advantage of the SCID is in its completeness. It does not otherwise enhance the accuracy of data. Those who accept that the study by Zimmerman and colleagues1 demonstrates overdiagnosis are tacitly accepting that a clinician who does not know the patient, wielding an instrument that does not enhance the clinical relationship, is the authority. If the SCID says bipolar disorder is absent while the clinician says it is present, the clinician is wrong.

While I deeply respect the importance of this kind of research, the underlying logic is necessarily simplistic. Therefore, any conclusion of overdiagnosis based on this study is likewise an oversimplification.

Consider a recent study of bipolar screening tests in which the gold standard was instead a 1-year confirmation of the initial diagnosis.9 While not ideal (eg, clinicians were not blind to their initial diagnosis), it has longitudinal validity regarding what the patient “truly has.” Or, consider a study of pediatric mood and attention-deficit diagnoses by Chilakamarri and colleagues10 in which underdiagnosis of bipolar disorder was a far greater problem than overdiagnosis, but which is cited far less frequently than the Zimmerman study.1 Perhaps because there was no SCID for the gold standard—only experienced clinicians?

Risks of overdiagnosis

None of the above considerations diminish the negative impact of an inappropriate diagnosis.8 The effect of potential “grief for the lost healthy self,” akin to the impact of a diagnosis of diabetes, should give pause. Stigma risks are broad, from the impact on the patient’s sense of self, to friendships and intimate relationships, to serious unintended consequences in divorce proceedings or employment. Treatment risks are also broad—certainly beyond those of serotonin reuptake inhibitors. The risk of diluting true bipolar disorders with a fundamentally different disorder is likewise significant, as is the impact through this dilution on our ability to identify appropriate treatments when psychiatry has more targeted options in the future.

In the next essay in this series, I will examine whether the new DSM criteria will significantly address this diagnostic dilemma: can they improve accuracy? That essay will focus on specificity. Can tightening DSM criteria (as DSM-5 attempts to do in 2 important ways) improve on specificity? How much of an improvement in positive predictive value can thus be produced? Will it raise the value of a bipolar diagnosis beyond a coin toss?

Effects of Pharmacokinetic and Pharmacodynamic Changes in the Elderly

on Saturday, 20 April 2013. Posted in General

PK & PD Changes

Effects of Pharmacokinetic and Pharmacodynamic Changes in the Elderly

This interesting article explains and demonstrates the need for monitoring and altering psychotropic medications and dosages in older patients.

Resources on dementia for health care providers and caregivers

on Saturday, 20 April 2013. Posted in General

Some great resources to supplement the use of bStable for dementia monitoring

Resources on dementia for health care providers and caregivers


Mace NL, Rabins PV. The 36-Hour Day: A Family Guide to Caring for Persons With Alzheimer’s Disease, Related Dementia Illness, and Memory Loss in Later Life.Baltimore: Johns Hopkins University Press; 1999

Mayo Clinic Guide to Alzheimer’s Disease: The Essential Resource for Treatment, Coping, and Caregiving. bookstore; 2006.

Rabins PV, Lyketsos CG, Steele CD. Practical Dementia Care. New York: Oxford University Press; 1999.

Warner M, Warner E, Warner ML. The Complete Guide to Alzheimer’s-Proofing Your Home. West Lafayette, IN: Purdue University Press; 2000.

Radin L, Radin G. What If It’s Not Alzheimer’s? A Caregiver’s Guide to Dementia. Amherst, NY: Prometheus Books; 2008.

Web Sites – Disease-Related

NINDS—Dementia: Hope Through Research

AlzGene—Database of genetic association studies on Alzheimer disease

National Mental Health Association—Multi-Infarct Dementia

NINDS—Multi-Infarct Dementia

NINDS—Dementia With Lewy Bodies

NINDS—Frontotemporal Dementia

NINDS—Parkinson’s Disease

NINDS—Huntington Disease

NIH Senior Health—Parkinson’s Disease

Web Sites – Practice Guidelines

American Psychiatric Association—Practice Guideline for the Treatment of Patient’s With Alzheimer’s Disease and Other Dementias

American Association for Geriatric Psychiatry—Position Statements

AMA—Physician’s Guide to Assessing and Counseling Older Adult Drivers

American Academy of Neurology—Guideline Summary for Clinicians – Detection, Diagnosis, and Management of Dementia

American Academy of Neurology—Dementia Encounter Kit

American Geriatrics Society—Clinical Practice Guidelines – Dementia

Web Sites – Associations

Alzheimer’s Association

Lewy Body Dementia Association

The Association for Frontotemporal Dementias

American Parkinson Disease Association

Huntington’s Disease Society of America

Web Sites – Family and Caregiver Support

NIH Senior Health—Caring for Someone With Alzheimer’s

Alzheimer’s Disease Education and Referral Center

Family Caregiver Alliance


MedicAlert and Safe Return

NINDS, National Institute of Neurological Disorders and Stroke.

7. American Psychiatric Association, Work Group on Alzheimer’s Disease and Other Dementias. Practice Guideline for the Treatment of Patients With Alzheimer’s Disease and Other Dementias. 2nd ed. Washington, DC: American Psychiatric Association; 2007. Accessed December 25, 2009.

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