by Nicholas West
August 21, 2015
from ActivistPost Website

Spanish version






Updated research into predictive medicine combined with apps to achieve "mental health intervention."

Predictive technology is exploding.


The arrival of Big Data initiatives by government, as well as a massive industry of data brokers is not only putting privacy at risk, but is offering those with access to the information unprecedented ways to micromanage our lives.

Most people now seem resigned to the surveillance of our communications devices, which have become so intertwined with modern efficiency, economics and knowledge that there are real tradeoffs when choosing a fully opt-out lifestyle. Wearable gadgets add a new layer still, and are being bought into at record pace, thus donating the information that isn't already being stolen.

However, it might be our health information that is the most tempting, offering up potentially the most intrusive window yet into our everyday lives.

In July of last year I covered a development by researchers at Tel Aviv University with the announcement that a,

"Smartphone App May Revolutionize Mental Health Treatment."

The following excerpts from the press release were highlighted as some very stark writing on the wall.

  • There is a dire need for support services to assist clinicians in the evaluation and treatment of those suffering from mental illness.

  • A new smartphone-based system detects changes in patients' behavioral patterns, and then transmits them to professionals in real time.

  • By facilitating patient observation through smartphones, the technology also affords patients much-needed independence from hospitals, clinicians - and even family members.


  • Because most people own smartphones today, we thought, Why not harness the smartphone, a reservoir of daily activities, to monitor behavioral patterns?

"Bipolar disorder, for example, starts with a manic episode," said Dr. Uri Nevo.


"A patient who usually makes five or ten calls a day might suddenly start making dozens of calls a day. How much they talk, text, how many places they visit, when they go to bed and for how long - these are all indicators of mental health and provide important insights to clinicians who want to catch a disorder before it is full blown."







At the time, researchers noted that the concept already was well received by "psychiatrists, as well as U.S. federal policymakers in the field."


Indeed, it has been…

Northwestern University followed Tel Aviv's announcement by proclaiming that the standard smartphone can accurately detect general depression with nearly 90% accuracy, merely based on GPS location data and usage information.


Researchers seemed overjoyed from the results obtained from just 40 participants, but it is worth noting what their objective and conclusions state:

  • Objective:

    The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity.


  • Conclusions:

    Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.

There are a slew of technical details provided, but the upshot is that the complex movements, thoughts and desires of an individual human being are being replaced by an algorithmic overlay of sensor-feed results that transmit to centralized professionals who apparently know you better than yourself or your family and friends.

  • Did you know that the average smartphone user time for a depressed person is 68 minutes?

  • Did you know that 17 minutes makes you normal?



Is this your life?

Using terms like "location variance" "clustering" "circadian movement" and "transition time" is the standard operating procedure for reductionists and technocrats everywhere whose #1 trait is what Jon Rappoport has called OTO - The Obsession to Organize:

OTO speaks of a bottomless fear that somewhere, someone might be living free.

The presumption of a baseline, incontestable "normal" level of mental health speaks to the need for power structures and the medical establishment to seek as many diagnoses as possible to corral and monetize populations deemed to be wandering too far off the plantation.

  • Do you like to disconnect from the virtual matrix for a while?

  • Take a staycation?

  • Do you generally enjoy your home and family more than social carousing, mall strolling, and indiscriminate consumerism?

You've been deemed NOT AVERAGE - Red Flag in Sector 12.

And the political framework has been created. Embedded in the Patient Protection and Affordable Care Act (aka Obamacare), it states quite clearly the value of data obtained from gadgets and consumer behavior, and portends how government might mandate changes in the near future.

Health plans, integrated delivery systems, and other health care organizations (HCOs) increasingly channel their patients to interventions based in part on what they deduce from predictive models that have traditionally been run against databases of administrative claims.


In this arena, the Affordable Care Act (ACA) [Obamacare] is likely to exert a profound effect.

…a growing number of health care experts…see predictive modeling as an opportunity to prevent [disease] complications, control [hospital] readmissions, generate more precise diagnoses and treatments, predict risk, and control costs for a more diverse array of population segments than previously attempted…

New data streams will become available to providers, payers, and government as EHRs draw from a broader array of data to create more complete insight into patients and the care delivery process…


As HCOs gain access to data from more varied sources, such as health risk assessments, behavioral assessments, laboratory results, and pharmacy prescriptions (filled and unfilled), the impact of predictive modeling will increase.

Finally, if any doubts remain about how slippery this slope has become, listen to the following statement from David Mohr - director of the Orwellian Center for Behavioral Intervention Technologies at Northwestern University Feinberg School of Medicine:

The significance of this is we can detect if a person has depressive symptoms and the severity of those symptoms without asking them any questions…


We now have an objective measure of behavior related to depression. And we're detecting it passively. Phones can provide data unobtrusively and with no effort on the part of the user.

Naturally, following from diagnoses of depression would be the next step: stopping actual suicides. This is where the ethical road is probably murkiest, so let's wade through and see what is being discussed as a solution.

Indiana University is looking to use biomarkers from blood samples taken from those being treated for biopolar disorder and other mental "illnesses" who they say are at a maximum risk of committing suicide, combined with apps.


I've highlighted some sections that have a familiar echo to what you have read above.

From the Press Release

Researchers at Indiana University School of Medicine reported Tuesday in the Nature Publishing Group's leading journal in psychiatry, Molecular Psychiatry, that they have developed blood tests and questionnaire instruments that can predict with more than 90 percent accuracy which of those patients will begin thinking of suicide, or attempt it.

"We believe that widespread adoption of risk prediction tests based on these findings during healthcare assessments will enable clinicians to intervene with lifestyle changes or treatments that can save lives," said Alexander B. Niculescu III, M.D., Ph.D., professor of psychiatry and medical neuroscience at the IU School of Medicine and attending psychiatrist and research and development investigator at the Richard L. Roudebush Veterans Affairs Medical Center.

Using RNA biomarkers from blood samples along with a newly developed questionnaires in the form of an app, the researchers were able to predict which individuals in a group of patients being seen for a variety of psychiatric illnesses would experience significant suicidal ideation with approximately 92 percent accuracy.


Among patients with bipolar disorder, the accuracy reached 98 percent, Dr. Alexander B. Niculescu said.


The combination of biomarkers and app was also accurate in predicting which of the patients would be hospitalized for suicidality in the year following testing (71 percent across all diagnoses, 94 percent for bipolar disorder).

The questionnaires by themselves, implemented as apps on tablets, were able to predict the onset of significant suicidal thoughts with more than 80 percent accuracy.

The research expands upon work reported by Dr. Niculescu and colleagues in 2013 in which they identified a panel of biomarkers that were significantly elevated in bipolar disorder patients with suicidal thoughts or who were hospitalized as a result of suicide attempts.

"We now have developed a better panel of biomarkers that are predictive across several psychiatric diagnoses. Combined with the apps, we have a broader spectrum predictor for suicidality," Dr. Niculescu said.


"In additional to reproducing and expanding our own previous work, we reproduce and expand other groups' results in this burgeoning field."

The current study began with a group of 217 male psychiatric participants, followed by Dr. Niculescu and colleagues for several years with diagnoses of bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia.


The researchers identified 37 participants who switched from no suicidal ideation to high suicidal ideation at different testing visits.


The scientists were able to identify RNAs that were present at different levels in blood samples taken at those different testing visits, in common across these 37 individuals. Those candidate biomarkers were then evaluated using the Niculescu group's Convergent Functional Genomics approach, to prioritize the best markers.

Next, working with the Marion County (Indianapolis, Ind.) Coroner's Office, the researchers validated those prioritized biomarkers using blood samples from 26 men who had committed suicide.

Finally, the researchers used blood samples and medical records from a different group of patients with the same psychiatric diagnoses to confirm that the biomarkers and apps predicted suicidal ideation, and also examined their ability to predict future hospitalizations for suicidality in the first year following testing.

The app-based questionnaires were developed separately, said Dr. Niculescu, director of the Laboratory of Neurophenomics at the Institute of Psychiatric Research at the IU School of Medicine.

One of the apps assesses measures of mood and anxiety; the other asks questions related to life issues including physical and mental health, addictions, cultural factors and environmental stress.


Neither app, he emphasized, asks whether the individual is thinking of committing suicide.

Dr. Niculescu said he believes the apps are ready to be deployed and tested by medical professionals, particularly in emergency department settings. The biomarkers could also be more widely tested for in the near future he said.

However, he noted two limitations that require additional research.

  • First, all of the participants in this study were men.

  • Studies in women are currently being conducted and are showing promising preliminary results. In addition, the research was based on work with people with psychiatric diagnoses.

How well the biomarkers would work among people who have not been diagnosed with a psychiatric disease is not known.