Tag: Charity / nonprofit

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Digital Reassurance Seeking in OCD

– by Jordan Karr, PhD

We all need reassurance once in a while. Receiving reassurance from a friend, loved one, or expert can reduce short-term distress, foster a sense of connection, and help us move forward in a productive way. On the other hand, excessive reassurance seeking can lead to a painful cycle of stress and doubt. For individuals with obsessive-compulsive disorder (OCD), reassurance is fleeting and is quickly followed by a resurgence of perceived threat and a compelling drive to seek additional reassurance.

As an OCD and anxiety specialist, I am no stranger to reassurance traps. I see them in the patient who repeatedly consults doctors but never fully trusts their answers; the partner who keeps asking, “Do you still love me?” while scrutinizing every response; and the teenager who needs her homework checked repeatedly but does not learn to tolerate uncertainty. While the content of excessive reassurance seeking may vary, its function remains the same, relieving anxiety momentarily while allowing obsessions to rebound more strongly in the long run. In each example, we see an individual desperately trying to protect something they hold dear while being misled by one of OCD’s most convincing lines: “What if this time the reassurance sticks?”

Psychologists have long argued that excessive reassurance seeking helps maintain OCD, anxiety, and depression (Abramowitz et al., 2002; Burns et al., 2006). What is new is the landscape. Today, reassurance is available instantly and endlessly through search engines, medical websites, social media, and artificial intelligence tools. Emerging research suggests that online reassurance seeking may be as prevalent as interpersonal reassurance seeking (Parsons & Alden, 2022). These digital reassurance traps present in a variety of forms, including:

  • Excessive symptom-checking on WebMD
  • Checking a loved one’s location on Find My Friends to make sure they are still alive or where they promised they would be
  • Asking Google Gemini whether an intrusive thought makes you a monster, a pedophile, or a bad Christian, Muslim, or Jew
  • Analyzing the contents of a partner’s Instagram to see whether their feelings have changed
  • Endlessly searching Reddit for answers to existential questions
  • Obsessively checking likes and read receipts to monitor the durability of a relationship
  • Finding creative ways to ask ChatGPT whether you might be responsible for spreading a virus or causing someone harm
  • Compulsively checking the news to confirm whether a feared event has taken place

Why do some individuals seek reassurance primarily from others in face-to-face contexts while others turn to the internet? Findings from one study suggest that individuals may be more likely to seek interpersonal reassurance when they desire emotional support, whereas online reassurance seeking is more likely when individuals feel ashamed or fear judgment from others (Parsons et al., 2025). In the same study, shame was reported more frequently among individuals with OCD. The perceived anonymity of the internet creates a compelling environment for seeking reassurance about our most distressing fears and the fears we feel ashamed to have.

Wait, hold on… shouldn’t having access to all the information on the internet be empowering? After all, there’s that famous saying, “Knowledge is Power.” Well I’m not sure what Francis Bacon would say if he could scroll on TikTok, but in the age of the internet, more information does not always mean more power or knowledge. In fact, information overload seems to trigger excessive reassurance seeking online (Yang & Luo, 2024). Once digital algorithms detect health-related concerns, users may be exposed to increasingly frequent and targeted content, further amplifying health anxiety and driving additional reassurance seeking online (Zhang et al., 2024).

Despite these challenges, evidence-based treatments offer hope. Exposure and response prevention (ERP), the gold-standard treatment for OCD, helps individuals tolerate uncertainty and break free from compulsive behaviors. ERP therapists are prepared to respond to clients who compulsively seek reassurance while taking care to avoid reinforcing a client’s anxiety and setting boundaries when appropriate. On the other hand, AI chatbots are available 24/7 and will continue to reassure users when it would be clear to a skilled therapist that more reassurance is harmful. Some helpful activities I have encouraged clients to try include:

  • Going for a walk and leaving your phone behind
  • When you have the urge to seek reassurance, use a timer to delay. Start with 5 minutes and gradually increase the time as you go.
  • Imaginal exposure: Writing out OCD’s worst case scenario while refraining from seeking reassurance.
  • Embracing the uncertainty: When you have the urge to seek reassurance, lean into the uncertainty by responding “maybe that will happen, maybe not.”
  • Watch a movie that triggers obsessions but leave your phone in the other room
  • Try a fast from social media. Start with a short fast and increase the length of each fast as you gain confidence.
  • Agreeing with the obsessions: Whatever your OCD throws at you, respond by saying “Sure, that is true!”

Clinicians working with youth should also attend to parental accommodations that inadvertently facilitate online reassurance seeking. Supportive Parenting for Anxious Childhood Emotions (SPACE) is an evidence-based, parent-focused intervention that emphasizes increasing supportive statements while reducing unhelpful accommodations. Setting reasonable limits on smartphone use while responding with empathy and confidence can help youth build resilience and independence (i.e. “I know it’s tough to be away from your phone and I know you got this!”).

Finally, because shame plays a central role in digital reassurance traps, incorporating self-compassion practices may be beneficial. Compassion-focused exercises help individuals respond to their struggles with kindness rather than self-criticism, complementing ERP by fostering emotional resilience. I sometimes ask clients to imagine that a close friend or loved one is feeling ashamed because they are experiencing obsessions and are stuck in an OCD loop. Then, I invite them to write down how they would support this friend while paying special attention to how compassion feels in their body. By practicing compassion for others, we strengthen the same muscles in our brains that help us turn compassion inward.

If you are getting stuck in digital reassurance traps, you are not alone! Many of these technologies are brand new and we are learning how to integrate them into our lives in a healthy way. If you need help managing compulsive online habits, finding a therapist trained in ERP could be a useful step!

References

Abramowitz, J. S., Schwartz, S. A., & Whiteside, S. P. (2002). A contemporary conceptual model of hypochondriasis. Mayo Clinic Proceedings, 77(12), 1323–1330. https://doi.org/10.4065/77.12.1323

Burns, A. B., Brown, J. S., Plant, E. A., Sachs-Ericsson, N., & Joiner, T. E., Jr. (2006). On the specific depressotypic nature of excessive reassurance-seeking. Personality and Individual Differences, 40(1), 135–145. https://doi.org/10.1016/j.paid.2005.05.019

Parsons, C. A., & Alden, L. E. (2022). Online reassurance-seeking and relationships with obsessive-compulsive symptoms, shame, and fear of self. Journal of Obsessive-Compulsive and Related Disorders, 33, 100714. https://doi.org/10.1016/j.jocrd.2022.100714

Parsons, C. A., Kim, H. J., Singh, S., Lkhagva, T., Wang, J., & Alden, L. E. (2025). Covert or connected: Motivations for online and interpersonal reassurance-seeking in OCD. Journal of Anxiety Disorders, 115, 103057. https://doi.org/10.1016/j.janxdis.2025.103057

Yang, X., & Luo, X. (2024). Unpacking cyberchondria: The roles of online health information seeking, health information overload, and health misperceptions. Telematics and Informatics, 97, 102225.

Zhang, X., Zheng, H., Zeng, Y., Zou, J., & Zhao, L. (2024). Exploring how health-related advertising interference contributes to the development of cyberchondria: A stressor–strain–outcome approach. BMC Public Health, 24, 534.

Jordan Karr, PhD, is the owner of River Falls Therapy in Portland, OR. He is a licensed psychologist in Oregon and Virginia, and specializes in evidence-based therapies for OCD and anxiety disorders. Dr. Karr has experience working in outpatient, community-based, and school-based settings.

The post Digital Reassurance Seeking in OCD appeared first on International OCD Foundation.

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Revvity Creates Program to Equip Young Biotechs with Scalable Informatics Capabilities Sooner than Later

Revvity reports that its Signals Software business is launching Signals for Startups, a new program designed to help emerging biotech companies adopt scalable informatics capabilities earlier in their growth journey.

Built for early-stage biotechs, the program combines access to Signals software with guided onboarding and best-practice configurations for smaller biotechs to help accelerate innovation, improve scientific productivity and shorten time-to-value, according to a company spokesperson.

Startup biotechs are often under pressure to move quickly with limited IT, informatics, and operational resources while managing increasingly complex discovery data. Signals for Startups addresses this challenge with a purpose-built, scalable Signals environment that helps teams focus on science while establishing a strong digital foundation from day one, explains Kevin Willoe, president of Revvity Signals Software, adding that out-of-the-box configurations for large and small molecules enable companies to accelerate adoption, standardize data, and enhance collaboration on a proven, scalable informatics infrastructure.

“Signals for Startups addresses a critical need for emerging biotech companies that want to move fast without creating data and workflow challenges that limit their ability to scale,” he continues. “By combining startup-friendly access with guided onboarding and scalable Signals workflows, we are helping early-stage teams build the digital foundation they need to advance discovery, support investor readiness and grow with confidence.”

Signals for Startups is expected to be available in the U.S., Europe, the Middle East, and Africa later this month.

 

The post Revvity Creates Program to Equip Young Biotechs with Scalable Informatics Capabilities Sooner than Later appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Bristol Myers Squibb becomes latest company to claim it’s building pharma’s largest NVIDIA AI supercomputer

It’s officially a trend. For the third time in nine months, a pharma company has announced that it is assembling the largest AI supercomputer in the life sciences industry. This time, it is Bristol Myers Squibb. 

When the company began its partnership with NVIDIA three years ago with a smaller computing cluster, it was focusing on simpler problems with individual AI tools, like protein structure prediction. But “we actually consumed all the space we had,” said Greg Meyers, chief digital & technology officer at BMS.

Adding extra computing power is necessary for the company now that it’s “become more convinced” that computationally hungry foundation models can give the company valuable insight into how its drug candidates interact with both the body and with disease, Meyers said in an interview with STAT. He mentioned oncology and neurodegeneration as examples of areas where BMS has developed such models.

Continue to STAT+ to read the full story…

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Transition Care for FEP

Conditions: Schizoaffective Disorders; Bipolar Disorder With Psychotic Features; Schizophrenia Disorders; Major Depressive Disorder With Psychotic Features; Psychosis NOS

Interventions: Behavioral: Transition Care Team

Sponsors: University of Chicago; Sidney R. Baer, Jr. Foundation

Recruiting
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Circuit logic of oxytocin and vasopressin complementary actions

Oxytocin (OT) and vasopressin (VP) are evolutionarily conserved neuropeptides that regulate social behavior, emotional processing and physiological homeostasis. Although traditionally studied as individual modulators of affiliation, stress and autonomic function, emerging evidence indicates that their actions are best understood at the level of neural circuits. Advances in optogenetics, cell-type-specific electrophysiology and systems neuroscience have revealed that OT and VP act within distributed networks through receptor-defined microcircuits composed of excitatory and inhibitory neuronal populations, astrocytes and long-range projections. Within these circuits, OT and VP can exert complementary, synergistic or opposing effects depending on receptor localization, cellular identity and network state. Here, we synthesize recent circuit-level and electrophysiological evidence to propose a framework in which OT and VP operate as a coordinated neuromodulatory axis. We argue that the functional consequences of OT/VP signaling emerge not from peptide identity alone, but from their engagement of recurrent circuit motifs that redistribute excitation and inhibition across neural networks. These motifs provide a mechanistic substrate for regulating transitions between competing behavioral and physiological states, including social safety vs. threat, affiliation vs. avoidance, and parasympathetic vs. sympathetic dominance. We further discuss how disruption of receptor topology, synaptic integration and circuit architecture may contribute to neurodevelopmental, psychiatric and stress-related disorders. By shifting the focus from peptide-centric models to circuit-level mechanisms, this framework reconciles seemingly contradictory findings across brain regions and behavioral paradigms, and provides a foundation for the development of next-generation circuit-based therapeutic strategies targeting the oxytocin-vasopressin axis.
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The risk of weather data sabotage is rising

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.

While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.

However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems. 

To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns. 

Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.

Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.  

Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.

But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.

The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms. 

Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.

At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.  

As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.

1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.

2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially  make us more resilient to adversarial attacks. 

3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.

It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.

This op-ed was written by:

  • Monique Kuglitsch — Innovation Manager at Fraunhofer Heinrich Hertz Institute and Chair of the UN Global Initiative on Resilience to Natural Hazards through AI Solutions
  • Jesper Dramsch — Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF), where they work on AIFS (Artificial Intelligence Forecasting System), ECMWF’s data-driven weather prediction model
  • Franz G. Kuglitsch — Climate Scientist and Executive Secretary of the International Union of Geodesy and Geophysics (IUGG) at the GFZ Helmholtz Centre for Geosciences in Potsdam
  • Andrea Toreti — Senior Scientist at the European Commission’s Joint Research Centre (JRC), where he coordinates the European and Global Drought Observatory under the Copernicus Emergency Management Service
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Comparing Two Moral Injury Treatments

Conditions: Moral Injury

Interventions: Behavioral: Acceptance and Commitment Therapy for Moral Injury (ACT-MI); Behavioral: Present Centered Therapy for Moral Injury (PCT-MI)

Sponsors: VA Eastern Colorado Health Care System; Denver Research Institute; Northern California Institute of Research and Education; Henry M. Jackson Foundation for the Advancement of Military Medicine; Palo Alto Veterans Institute for Research

Not yet recruiting
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HelloType1 Digital Education Platform for Individuals With Type 1 Diabetes in Southeast Asia: Data Analytics Study

Background: Type 1 diabetes remains an underrecognized and challenging condition across Southeast Asia, where many countries face limited health care infrastructure, shortages of trained health care professionals, inconsistent access to diabetes education, and a lack of culturally appropriate resources in local languages. These barriers contribute to delayed diagnosis, suboptimal self-management, high rates of diabetic ketoacidosis, and inequities in care. During the COVID-19 pandemic, Action4Diabetes, a nonprofit organization working with local health care professionals and diabetes associations across Southeast Asia, developed HelloType1, a multilingual digital educational platform designed to improve awareness, education, and access to credible type 1 diabetes information. The platform was launched sequentially in Cambodia in 2021, Vietnam and Thailand in 2022, and Malaysia in 2023 through formal memorandums of understanding with local partners. Objective: This study aimed to evaluate the reach, platform usage, and online engagement of the HelloType1 digital educational platform across Southeast Asia between 2021 and 2024. Methods: Website analytics from Google Analytics 4 and Meta Business Suite metrics were descriptively analyzed to assess digital reach and engagement across countries. Metrics were compared over time and by country to examine patterns of platform uptake and user engagement. Results: HelloType1 demonstrated substantial growth between 2021 and 2024. Total unique website users increased from 1178 in 2021 to 40,361 in 2024, representing a marked expansion in regional reach. Pageviews rose from 4644 in 2021 to 83,689 in 2024, suggesting increasing content use and user platform engagement. By 2024, most website visits originated from organic search engines. Platform use was predominantly mobile-based, particularly in Vietnam, Thailand, and Malaysia, with the strongest engagement among adults aged 25 to 54 years. Facebook followers increased from 940 to 4553, and average engagement rates rose between 2022 and 2024. Cambodia achieved the highest number of Facebook interactions, whereas Thailand demonstrated the highest engagement rate. Content-level analysis showed that practical self-management topics, including blood glucose monitoring, insulin treatment, nutrition and exercise, complications, and emotional support, generated high levels of reach and interaction. Conclusions: HelloType1 demonstrates strong growth, mobile-first use, search-driven visibility, and engagement with practical self-management content. These findings support the feasibility and potential utility of a low-cost, multilingual digital education model, while future studies should evaluate its effects on knowledge, behavior, and clinical outcomes.
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BFRBs vs. OCD: Similarities and Differences

This blog was originally posted by the TLC Foundation for BFRBs

Body-focused repetitive behaviors (BFRBs) and obsessive-compulsive disorder (OCD) are two distinct mental health conditions that share some similarities but also have significant differences. BFRBs involve repetitive, self-grooming behaviors that can cause physical damage, such as hair pulling or skin picking. On the other hand, OCD is a condition characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions) performed to alleviate anxiety. 

While both conditions involve repetitive behaviors and can impact daily life, their underlying mechanisms, triggers, and treatment approaches differ. This article explores the key similarities and differences between BFRBs and OCD to better understand these complex conditions.

Similarities Between BFRBs and OCD

Most professionals view BFRBs and OCD as similar conditions due to the similarity in symptoms, such as compulsivity and repetitive behaviors. These two conditions share several similar systems and are usually a reaction to triggering factors such as stress and anxiety. Below are some of their similarities.

Repetitive Behaviors

Individuals dealing with BFRBs often engage in various repetitive behaviors such as hair pulling, lip biting, or skin picking. These actions are usually challenging to control and are frequently triggered by stress or anxiety. One may indulge in the habit subconsciously to find instant relief from the trigger. 

Individuals with OCD often experience intrusive thoughts that result in repetitive behaviors known as compulsions. Some common compulsions include washing hands and repetitively checking or counting to alleviate the stress caused by obsessive thoughts. In both conditions, the repetitive behaviors are often exacerbated by stress and anxiety, and individuals may adapt these behaviors as a coping mechanism.

Impulse Control

Closely related to repetitive behaviors is the concept of impulse control. Both BFRBs and OCD involve challenges in this area, albeit in different ways. Individuals with BFRBs and OCD may find it hard to control the urge to perform repetitive behaviors. This is because these repetitive behaviors often relieve tension. Despite knowing the consequences of these behaviors, the desire to indulge in them is usually irresistible. 

For example, individuals with BFRBs understand that hair pulling may affect their appearance, but they cannot refrain from doing it. OCD occurs as a result of intrusive thoughts whereby one believes that if they do not perform a specific action, the stressor won’t go away. These intrusive thoughts often cause anxiety, which can be eased by engaging in the said repetitive behavior.

Onset and Course

Having examined the behavioral aspects, let’s now consider how these conditions develop over time. The onset of these two conditions shares several similarities regarding age, triggers, and psychological mechanisms. 

The onset of both conditions is usually during childhood or adolescence and often coincides with various developmental changes and stressors. For individuals with BFRBs, the repetitive behaviors alleviate stress and anxiety instantly. At the same time, for those with OCD, performing the compulsions temporarily relieves them from the stress caused by their intrusive thoughts. The cognitive patterns involve repetitive actions, intrusive thoughts, and a lack of impulse control. In BFRBs, the urge to engage in these repetitive behaviors can be intrusive and persistent, while in OCD, one’s obsessions create a sense of urgency, which leads to the adoption of compulsive actions.

Neurobiological Factors

To fully understand the similarities between BFRBs and OCD, we must delve deeper into their biological underpinnings. Both conditions have a genetic origin and are associated with neurobiological factors. Neurobiological studies indicate that the impulse control and emotional regulation difficulties for people with BFRBs and OCD are often caused by abnormalities in brain regions that are responsible for impulse control and habit formation. Therefore, the underlying brain mechanism may result in the onset and development of both conditions. It is not uncommon for individuals to have both BFRBs and OCD or for both conditions to coincide with other mental health conditions, usually depression and anxiety. The overlap is generally because they typically share common underlying factors that play a part in their severity and development.

Differences Between OCD and BFRBs

While BFRBs and OCD share several commonalities, it’s equally important to understand their distinct characteristics, from the symptoms to the underlying mechanisms. Let’s explore the key differences that distinguish these two conditions.

Nature of the Behavior

First and foremost, let’s examine how the behaviors associated with each condition differ in their fundamental nature. Individuals dealing with these two conditions adopt diverse behaviors as coping mechanisms for their triggers. In BFRBs, the behaviors adopted, such as trichotillomania (hair-pulling) or cheek-biting, usually result in physical harm. However, regardless of the consequences, one always feels relieved when picking their skin or pulling their hair. 

OCD, on the other hand, involves a wide range of compulsions, from washing to organizing, checking, and counting. Compulsive behaviors are performed due to intrusive thoughts that make one think that if they fail to indulge in a specific behavior, they might get hurt, or there might be other negative consequences.

Presence of Obsessions

Another crucial distinction lies in the cognitive processes behind these behaviors. Generally, BFRBs do not involve obsessive thoughts. The primary focus on BFRBs is usually more on the physical behavior and not the fear of specific consequences. 

However, the major characteristic of OCD is intrusive thoughts, which increase the urge to indulge in particular behaviors for relief. The thoughts are usually persistent with unwanted images that result in distress. 

People with BFRBs DO NOT report that if they do not pick on their skin, something terrible will happen. Instead, they report that picking or pulling their hair helps relieve them from intense and negative emotions. These behaviors, therefore, serve a self-regulatory function, unlike in OCD, where the repetitive behavior calms them from their intrusive thoughts.

Triggers

The nature of triggers for each condition is closely related to the presence or absence of obsessions. The primary trigger in BFRBs is stress and anxiety, but for OCD, the main trigger is intrusive thoughts, which then result in anxiety. OCD and BFRBs triggers differ in several ways, often resulting in different outcomes. OCD triggers often result in one taking measures to prevent harm, while for BFRBs, one uses the adopted behaviors to regulate and manage intense emotions. The nature of thoughts is an essential distinguishing factor, seeing as OCD involves intrusive and obsessive thoughts that trigger specific behaviors adopted to prevent harm. The purpose of compulsions in OCD is to reduce the anxiety caused by the obsessive thoughts, while in BFRBs, the behaviors are for emotional relief.

Awareness

Beyond triggers, the level of conscious awareness also differentiates these two conditions. Those dealing with BFRBs usually find themselves biting their nails or even pulling their hair subconsciously. Individuals with OCD are generally aware of their intrusive thoughts and are compelled to adopt specific behaviors as a response to these thoughts. Individuals with OCD are often aware of their compulsions and understand when they are being irrational, but they are unable to control themselves. Compared to people with OCD, those with BFRBs often find their behaviors more rewarding than distressing.

Treatment

Finally, while both conditions may benefit from cognitive behavioral therapy, the specific approach to treatment varies significantly. For individuals with BFRBs, the focus is on behavior modification and awareness, achieved through habit reversal training. For OCD, the emphasis is often placed on exposure to anxiety-provoking thoughts to help an individual tolerate anxiety, which prevents compulsive behavior.

Bottom Line 

While BFRBs and OCD can coexist, they are distinct disorders with unique manifestations despite sharing some similarities. The key distinctions between these conditions are evident in their underlying mechanisms and treatment approaches.

Both involve compulsive behaviors, but their purposes differ. BFRBs primarily serve as subconscious tools for emotional regulation. OCD compulsions are conscious attempts to alleviate anxiety and prevent perceived harmful consequences. BFRB behaviors often occur with limited conscious awareness, while OCD sufferers are typically more aware of their compulsive actions.

Both conditions can significantly affect daily functioning and social interactions.BFRBs may lead to physical injuries and lowered self-esteem due to visible effects. OCD can cause severe anxiety and time-consuming rituals that interfere with daily activities.BFRB treatment emphasizes behavior modification and awareness techniques, while OCD treatment often involves exposure therapy to reduce anxiety responses.

Understanding these distinctions is crucial for accurate diagnosis and effective treatment. While both conditions present challenges, with proper support and intervention, individuals with BFRBs or OCD can learn to manage their symptoms and improve their overall quality of life.

The post BFRBs vs. OCD: Similarities and Differences appeared first on International OCD Foundation.

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Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas

ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.