A Co-Designed and Bilingual WhatsApp-Based Chatbot Intervention (PflegeBot) for Informal Caregivers in Germany: Single-Arm Nonrandomized Feasibility Study

Background: Informal caregivers play a central role in long-term care but experience elevated risks of stress, depressive symptoms, and caregiver burden, alongside barriers to accessing preventive support. Low-threshold digital public health interventions may offer scalable support; however, evidence on their feasibility, usability, and contextual fit remains limited, particularly for heterogeneous caregiver populations. Objective: This study describes the development and implementation of PflegeBot, an intersectionality-informed WhatsApp-based chatbot intervention, and evaluates its feasibility, usability, and acceptability among informal caregivers in Germany. Methods: A single-arm, nonrandomized feasibility study with pre-post assessments was conducted between December 2023 and March 2024. Informal caregivers aged 18 years and older were recruited via community-based, institutional, and online channels. PflegeBot was a co-designed, bilingual (German and Turkish) WhatsApp-based chatbot that delivered 84 scheduled messages over 12 weeks. Content included caregiving information, self-care, and cognitive behavioral therapy–informed self-help, plus optional frequently asked questions and question functions. Baseline and full follow-up assessments were administered online. Feasibility was assessed using indicators of recruitment, chatbot initiation and message delivery, opt-out, and provision of follow-up measures. Usability was assessed using the System Usability Scale (SUS), and acceptability was assessed using study-specific items. Exploratory outcomes included perceived stress (4-item Perceived Stress Scale [PSS-4]), caregiver burden (4-item Zarit Burden Interview [ZBI-4]), depressive symptoms (Center for Epidemiologic Studies Depression Scale [CES-D]), and loneliness (6-item De Jong Gierveld Loneliness Scale [DJGLS-6]). Analyses were descriptive; no hypothesis tests were conducted. Open-ended follow-up feedback was examined using a systematic inductive descriptive approach. Results: Of 102 participants included in the baseline analysis sample, 84 (82%) were women. According to chatbot log data, message delivery was initiated for 67 participants. Forty-nine participants provided at least one follow-up measure, and complete SUS data were available for 46 participants. The mean SUS score was 82.45 (SD 14.86, 95% CI 78.03‐86.86; range 47.5‐100), and 35 out of 46 (76%) participants scored at or above the benchmark of 68. Overall satisfaction with PflegeBot had a mean rating of 4.02 out of 5 (SD 0.98; n=46). Among participants responding to the relevant item, 1 out of 45 (2%) reported using the optional FAQ function. Mean changes were −0.57 (95% CI −1.24 to 0.10; n=35) for perceived stress, 0.81 (95% CI −0.02 to 1.63; n=36) for caregiver burden, −2.38 (95% CI −5.42 to 0.66; n=34) for depressive symptoms, and −0.39 (95% CI −0.90 to 0.12; n=36) for loneliness. Open-ended feedback highlighted needs for more flexible delivery, greater tailoring, and easier navigation. Conclusions: PflegeBot could be delivered through WhatsApp, and participants providing follow-up data reported high usability and generally favorable satisfaction. Controlled evaluations with objective engagement data and assessment of differential reach and outcomes are warranted.
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Depression Screening Positivity, Treatment-Seeking Intention, and Willingness to Take Antidepressants in a Self-Selected Online Sample of Korean Adults: Cross-Sectional Questionnaire Study

Background: Depression is a prevalent psychiatric condition with functional and socioeconomic consequences. Understanding the barriers to professional help-seeking and treatment acceptance among individuals with depressive symptoms is important. Objective: This study aimed to quantify depression screening positivity and examine treatment-seeking intention, willingness to use antidepressants, and perceived barriers among Korean adults, with attention to prior depression-related health care experiences. Methods: A cross-sectional online survey was conducted in South Korea (March 16-April 5, 2024) via paid social media advertising and messaging app invitations. A total of 1053 adults aged 20 years and older participated in this study. Depression screening was conducted using 2 Primary Care Evaluation of Mental Disorders–derived items, with the endorsement of either item defining a positive screen. History of depression diagnosis or treatment, intentions toward professional treatment, antidepressant use, and perceived barriers were assessed. Results: Overall, 65% (684/1053) of the participants had a positive depression screen. A positive depression screen was not associated with the intention to seek professional treatment for depression (adjusted odds ratio [aOR] 1.04, 95% CI 0.77‐1.41). Participants with a positive depression screen had lower odds of being willing to take antidepressants than those with a negative screen (aOR 0.73, 95% CI 0.54‐0.99). A within-participant analysis showed that the association of depression screening status differed between treatment-seeking intention and willingness to take antidepressants (ratio of odds ratios 0.70, 95% CI 0.53‐0.94; interaction =.02). The joint response of intending to seek treatment while being unwilling to take antidepressants was more common among participants with a positive than a negative depression screen (75/384, 11% vs 23/369, 6.2%; aOR 1.70, 95% CI 1.04‐2.77). A history of depression diagnosis or treatment was positively associated with treatment-seeking intention and willingness to use antidepressants. Among screen-positive adults, those without such a history more often reported that they would not seek treatment if diagnosed with depression (129/505, 25.5% vs 26/179, 14.5%; =.002) or would be unwilling to take antidepressants if recommended (154/505, 30.5% vs 29/179, 16.2%; <.001). Barrier profiles to treatment seeking differed by history of depression diagnosis or treatment (interaction <.001), whereas the patterns of reasons for unwillingness to take antidepressants did not differ according to history (interaction =.08). Conclusions: In this online survey sample, a positive depression screen was associated with a lower willingness to use antidepressants but not with treatment-seeking intention. Among screen-positive participants, those without prior depression-related health care experience had lower help-seeking intention and willingness to take antidepressants than those with prior contact, suggesting the need to reduce barriers to care and support earlier treatment initiation.

An Online Cross-Sectional Research Domain Criteria (RDoC) Positive Valence Assessment Study With Patients With a Severe Mental Illness: Methodological Evaluation

Background: Abnormal reward processing is considered a core feature of severe mental illness (SMI). Deficits in this domain can have severe implications for daily functioning. The Online Positive Valence Study aimed to assess reward processing in bipolar disorder and schizophrenia using the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) Positive Valence Systems (PVS) framework in an online task-based study. Recruitment and study adherence fell short of expectations. Objective: This paper critically evaluates the methodology used and shares the lessons learned from conducting an online task-based study of reward processing in a sample of patients with SMI. Methods: Using a cross-sectional design, the Online Positive Valence Study measured reward processing using 3 reward tasks, the monetary incentive delay task (MID), effort expenditure of rewards task (EEfRT), and a social and coin version of the Bandit task. Participants were eligible if they were 18 years or older. They were recruited mainly via email from 2 completed prospective cohorts. From 2023, the initial recruitment goal was no longer followed. Recruitment strategies and task performance are evaluated. Results: The sample consisted of 319 participants: 186 patients (58.3%), 81 first-degree relatives (25.4%), and 52 controls (16.3%). Multinomial logistic regression analyses revealed participants who were additionally called were more than 4 times more likely to respond “no” (odds ratio [OR] 4.57, 95% CI 3.12 to 6.70, <.001) and more than twice as likely to respond “yes” (OR 2.75, 95% CI 1.93 to 3.92, <.001) versus no response compared with those who received only emails. Personal salutations in invitation mails reduced the likelihood of participation compared to nonpersonal salutations (OR 0.31, 95% CI 0.20 to 0.47, <.001). Analyses of covariance (ANCOVAs) and linear mixed effects models (LLMs) revealed task performance was comparable between in-lab and online groups. Online participants showed longer reaction times on the MID task (=20.43, SE=2.55, =8.02, <.001) and lower win-stay rates on both Bandits (Bandit Coin: =–0.55, 95% CI –0.68 to –0.42, SE=0.06, =–8.73, <.001, η² =.14; Bandit Social: =–0.65, 95% CI –0.80 to –0.49, SE=0.08, =–8.54, <.001, η² =.14). Conclusions: For the first time, we performed a remote online task-based assessment of the PVS domain in patients with SMI, and we learned important lessons along the way. Future studies should carefully consider recruiting from existing cohorts; hire full-time dedicated personnel; and use personal approaches to recruitment, like phone calls, adjusting recruitment to the target population(s), and making participation technically as simple as possible. Attrition may be reduced by shortening the duration of participation and/or adding gamification. This study offers guidance for future research in online cognitive assessment in patients with SMI.

Examining the Feasibility, Acceptability, and Preliminary Efficacy of a Health App for Postacute Pandemic Years in Managing Physical and Psychological Distress Symptoms: Pilot Quasi-Experimental Study

<strong>Background:</strong> Physical and psychological distress symptoms can adversely affect daily functioning and quality of life, highlighting the need for accessible self-management support. Although mobile health (mHealth) interventions show promise in supporting physical symptom management, evidence regarding their feasibility and potential effects on psychological distress remains limited. <strong>Objective:</strong> This pilot study aimed to evaluate the feasibility, acceptability, and preliminary efficacy of the health app for postacute pandemic years (HAPPY) for managing physical and psychological distress symptoms. <strong>Methods:</strong> A 2-arm, single-blind, quasi-experimental trial was conducted from July 2023 to January 2024. Thirty-eight participants experiencing distress symptoms related to COVID-19 were assigned to the experimental group or a waitlist control group. The experimental group received a 24-week mHealth intervention through the HAPPY app, which included an 8-week supervised phase with weekly face-to-face sessions and a 16-week self-management phase. The control group received no active treatment. Feasibility was assessed through recruitment, adherence, and adverse event monitoring, whereas preliminary efficacy was evaluated at 8 weeks (T1) and 24 weeks (T2) and compared with baseline (T0) using quantitative measures of distress, fatigue, sleep quality, pain, emotional regulation, and posttraumatic stress disorder (PTSD). <strong>Results:</strong> The study demonstrated the feasibility of the HAPPY app, with a recruitment rate of 88.37% (38/43), a dropout rate of 10.5% (4/38), and an adherence rate of 93.33% (14/15) for the supervised sessions. No adverse events were reported. Significant within-group improvements were observed for distress (Kessler Psychological Distress Scale [K10]; <i>P</i>&lt;.001), fatigue (Brief Fatigue Inventory–Chinese [BFI-C]; <i>P</i>&lt;.001), sleep quality (Chinese Pittsburgh Sleep Quality Index [CPSQI]; <i>P</i>=.009), and PTSD symptoms (Chinese Impact of Event Scale–Revised [CIES-R]; <i>P</i>=.02). However, between-group differences were limited. <strong>Conclusions:</strong> The study demonstrated the HAPPY app’s feasibility, acceptability, and potential efficacy for managing physical and psychological distress symptoms. High recruitment, retention, and adherence rates, along with measurable improvements in distress, fatigue, sleep quality, and pain, suggest the app’s promise as a scalable intervention. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT05459896; https://clinicaltrials.gov/study/NCT05459896

Psychosocial Correlates of AI Chatbot Use for Emotional Support Among Young Adults: Cross-Sectional Study

<strong>Background:</strong> Generative AI chatbots are increasingly used for mental health–related purposes, yet little is known about the factors associated with such use or AI chatbot dependence. <strong>Objective:</strong> This study examined factors associated with young adults’ use of AI chatbots for emotional self-disclosure and mental health–related support, as well as the associations between AI chatbot use and dependence. <strong>Methods:</strong> A cross-sectional online survey of individuals aged 18 to 29 years was conducted in Japan. Self-report measures assessed psychosocial factors (self-esteem and loneliness), mental health symptoms (anxiety and depression), AI chatbot use (emotional self-disclosure and mental health–related support), and AI chatbot dependence (emotional dependence and social anxiety–related dependence). <strong>Results:</strong> Of 820 participants, 381 (46.5%) reported using AI chatbots for emotional self-disclosure at least once in the past month, including 65 (7.9%) who reported doing so every day or almost every day. In addition, 336 (41%) reported using AI chatbots for mental health–related support, including 53 (6.5%) who reported doing so every day or almost every day. Anxiety and depressive symptoms were combined into a composite measure of mental health symptoms because they are highly correlated. Multivariable logistic regression showed that greater mental health symptom severity, but not self-esteem or loneliness, was associated with both emotional self-disclosure (odds ratio [OR] 2.23, 95% CI 1.85-2.69; <i>P</i>&lt;.001) and mental health–related support (OR 2.42, 95% CI 2.01-2.94; <i>P</i>&lt;.001). Among emotional self-disclosure users (n=381), the frequency of emotional self-disclosure was positively associated with emotional dependence (β=0.334; <i>P</i>&lt;.001) and social anxiety–related dependence (β=0.307; <i>P</i>&lt;.001). Path analyses indicated that a model specifying psychosocial factors and mental health symptoms as antecedent variables to AI chatbot use and dependence fit the data better than an alternative model specifying AI chatbot use and dependence as the antecedent variables (comparative fit index [CFI]=0.994 vs 0.583; root mean square error of approximation [RMSEA]=0.033 vs 0.293). In this model, lower self-esteem and greater loneliness were associated with more severe mental health symptoms, which, in turn, were associated with greater AI chatbot dependence, primarily through direct pathways and, to a lesser extent, through more frequent emotional self-disclosure. Among emotional self-disclosure users, loneliness was the only significant psychosocial correlate of emotional dependence (<i>F</i><sub>1,369</sub>=7.78; <i>P</i>=.006). <strong>Conclusions:</strong> Emotional engagement with AI chatbots was common in this sample of young adults in Japan. Findings suggest that young adults experiencing mental health symptoms may be more likely to use AI chatbots for emotional self-disclosure and mental health–related support, while loneliness may be particularly relevant to emotional dependence among users. These findings highlight the need to understand AI chatbot use not only as a technological behavior but also as part of young adults’ broader emotional and help-seeking landscape.

Efficacy of mindfulness-based interventions and biofeedback for migraine management: a systematic review and meta-analysis of randomized controlled trials

ObjectiveThis systematic review and meta-analysis of randomized controlled trials aims to analyze the efficacy of mindfulness-based interventions and biofeedback on headache frequency, pain intensity, headache-related disability, psychological distress, and quality of life in adult patients with migraine.MethodsThe review followed the PRISMA statement and was registered in PROSPERO (CRD420261381587). A structured search was conducted across PubMed, Scopus, and PsycInfo for RCTs published in English between May 2016 and May 2026. Seven clinical trials (n = 535 participants) met the inclusion criteria. Methodological quality and risk of bias were evaluated using the Cochrane Risk of Bias 2 (RoB 2) tool.ResultsThe pooled analysis for headache frequency (n = 6 studies) showed no statistically significant differences between groups (MD = −0.59; 95% CI: −1.81 to 0.62; p = 0.34). Conversely, significant improvements favoring the experimental groups were found for headache-related disability measured with the HIT-6 scale (n = 6 studies; MD = −2.28; 95% CI: −4.47 to −0.08; p = 0.04), pain intensity (n = 3 studies; SMD = −0.39; 95% CI: −0.64 to −0.14; p = 0.002), and health-related quality of life (n = 3 studies; MD = +5.21; 95% CI: 1.95 to 8.47; p = 0.002). No statistically significant differences were observed for anxiety (p = 0.30) or depression (p = 0.88).ConclusionInterventions incorporating Mindfulness-Based Interventions and Biofeedback do not significantly reduce objective migraine attack frequency compared to control conditions, nor do they yield distinct differences in affective outcomes (anxiety/depression)—which may partly reflect subclinical baseline depression scores in episodic cohorts, active-comparator effects, and instrument heterogeneity. However, they exhibit statistically significant efficacy in decreasing subjective pain intensity, mitigating headache-related functional disability, and restoring health-related quality of life, supporting their role as valuable complementary strategies for self-regulation and clinical coping in migraine management.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261381587, Identifier: CRD420261381587.

What Is Sextortion?

Andy, a 16-year-old high school junior in New York City, received a message from a girl he didn’t know on Snapchat. She was cute, blonde, a freshman at the University of Wisconsin, and her greeting was Midwestern nice. He barely paused before responding. They chatted on the platform for a few days before she asked him to send a nude photo of himself. Andy didn’t know what to do, so he texted his closest friends for advice. They told him to find a nude photo of a stranger online and send that, so he did. And that’s when everything changed.

The person posing as a college girl threatened to share the photo everywhere online and claim it was him — unless Andy sent a $100 gift card. Scared, he went to the drug store and sent it right away. But the threats didn’t stop, and the messages got more frequent and intense. The person texting revealed that they were an adult overseas, not a college student, and would be tortured if they didn’t succeed in getting Andy to send more money. The amounts increased to $300 at a time, and he scrambled to do it, using his parents’ credit card. Over the course of a week, he racked up a bill of nearly $2,000.

This is just one example of sextortion, the use of explicit content to threaten someone with exposure if they don’t do what is demanded. One large U.S. survey found that approximately 5 percent of middle and high school students have been victims of sextortion, while about 3 percent admitted to targeting others. The National Center for Missing and Exploited Children (NCMEC) notes that they have seen a dramatic increase of cases reported to their CyberTipline, in particular from teenage boys.

Definition of sextortion

Sextortion is a form of exploitation where a child is threatened or blackmailed, most commonly with the possible public sharing of nude or sexual images of the child, by someone who demands additional sexual images or money.

A teen can be sextorted by a someone they know — often someone they are dating or used to date. But the perpetrator also can be an adult stranger pretending to be a teen to lure kids.   

With strangers, the interaction starts off innocuously enough: a friendly text or message online, often from someone who appears to be a similar age as the child and usually of another gender. That might be on video games such as Roblox or Minecraft for younger kids or Discord servers for teens. They may share connections in common on the platform because they’ve reached out to the child’s friends as well, so even though they’re a stranger, they don’t seem like one. “What’s tricky is who kids will describe as a friend versus a stranger is different than for adults,” says Megan Ice, PhD, a psychologist at the Child Mind Institute. “A kid may feel like they know this person” who they just met online and texted a few times.

The predator will establish a friendship or romantic relationship, then they may propose “I’ll show you mine if you show me yours” to ask for a nude photo. Or they will secretly record a video or live chat to get explicit content. In the era of generative AI and “nudify” apps, a kid doesn’t even have to provide a nude — they can use regular photos of a child to create sexual images for blackmailing purposes.

There is also sadistic sextortion, a newer but growing form of sextortion where the child is forced to do dangerous and disturbing things like torture pets or hurt themselves and write their name in blood on the wall, all performed on camera. A global network called 764 has targeted children and adolescents with sadistic sextortion, and the FBI has posted warnings about the group.

The pressure to comply is intense — the texts might come every few seconds, dozens of messages an hour and progressively faster, to create a sense of urgency. The blackmailer might have a package delivered or send the police to a child’s house on a false report (known as “swatting”) to show they know where they live, so the threat of retribution feels very real. The fear, shame, and hopelessness have led some kids to die by suicide after being sextorted.

Why teens are at risk for sextortion

While all age groups are at risk for sextortion, teens are often targeted because they are particularly vulnerable. During adolescence, changes in the brain can lead teens to feel social rewards intensely, so they care a lot about what others think of them, Dr. Ice says.  And that makes them more likely to underestimate the risks of doing something that feels good in the moment.

“Adolescents are exploring their identity, they’re exploring their sexuality,” says Justin Patchin, PhD, a professor of criminal justice at the University of Wisconsin–Eau Claire, who studies sextortion. Their prefrontal cortex is still developing, so short-term rewards are prioritized over long-term consequences. That means “they’re more vulnerable to acting impulsively and sharing explicit images with somebody that they’re interested in.”

“The other reason high school students make a particularly good target is because if a 16-year-old girl sends a topless picture to somebody online, they’ve now created and distributed child pornography, which is a crime. Whereas if an 18-year-old girl does that, it’s not a crime,” Dr. Patchin says. Blackmailers may tell teens who send sexual images or other explicit content that they’ve broken the law and could be prosecuted, which makes the extortion even more effective.

“I teach criminal justice, and sexting is the only behavior that I can think of where teens are treated more harshly by the system than adults,” Dr. Patchin says.

Teen groups at higher risk for sextortion

Research shows that LGBTQ+ kids are more at risk for sextortion. They often find community online, and it’s common for them to have friendships with people that they haven’t met in person.

Kids with autism, ADHD, or mental health issues such as social anxiety may also be particularly at risk. “Neurodiverse kids may develop strong friendships in online communities related to niche interests, making them vulnerable to predators who take advantage of the lack of supervision in these spaces,” Dr. Ice says. She is especially concerned about autistic kids, who may not question the intentions behind other members’ communications, and kids with ADHD, whose struggles with impulsivity may mean they act quickly when responding to a request.

“Kids who are hiding behavior from adults” — such as kids with eating disorders or who self-harm — tend to be very active in online communities like Reddit, Dr. Ice says. Predators will commonly target those communities to find the kids who are most vulnerable and then work to build relationships with them.

How to prevent sextortion

Parents can’t prevent sextortion from happening, but they can talk to their kids to make them aware of it — and advise them to speak up immediately if something happens. “I tell parents if there’s an incident in the news, if you see an article that you’re reading online, that’s a good prompt to have that conversation with your kid and say, ‘Hey, look at this story,’” Dr. Patchin says. “You can ask them, ‘Has anything like this happened to you? If it does, please come talk to me. We’ll deal with it together.’”

You can discuss the risks of sexting, Dr. Patchin says: “If that image does get online somewhere and a bunch of people see it, what kind of potential consequences there might be. And in an extreme, terrible situation, maybe it could be a crime if a prosecutor or a police officer wants to pursue it.”

It’s also important to be aware of common tactics and tell kids about them. For example, predators often move the conversation to messaging apps disguised to look like a clock or calculator app, but when you click on it, you have to enter a password. That can help them avoid detection, because mainstream apps often have content filters. Private messaging apps also make it harder for law enforcement to track and prosecute them.    

“Kids need to be educated, because there are people online who will take advantage of them not knowing what is safe,” Dr. Ice says. “Just like you would take certain safety precautions when you’re walking down the street, you need to take safety precautions online.”

While you want kids to understand the risks of engaging in these behaviors, you want to emphasize they should come to you for help no matter what. “You want to make clear that if they tell you, they will not lose access to their phone or all the things that keep them connected to their friends,” Dr. Ice says. “I think that fear is what keeps it secret a lot of the time.”

NCMEC has useful resources about sextortion, including an interactive activity for teens, noescaperoom.org.  

Signs of sextortion

Many signs of sextortion are similar to those of depression or even addiction, particularly online gambling. For example, a child might show sudden changes in eating or sleeping habits, uncharacteristic changes in behavior or routine, or engage in more secretive online behavior. Kids might become either distant or clingy because they are stressed out and afraid to tell you what is going on. According to NCMEC, other signs might include:

  • Receipt of anonymous gifts (physical or digital)
  • Law enforcement being called to your home under false pretenses
  • Random occurrences at your address, such as unexpected deliveries

Signs of sadistic sextortion might include:

  • Scarring on the thighs or forearms, especially if the scars form a pattern
  • Sudden interest in collecting tools including drills, screwdrivers, hammers, blow torches, or large knives with no clear purpose
  • Family pets or other animals being harmed or dying under suspicious circumstances

What to do if someone is blackmailing your child

If your child tells you they are being sextorted, “you want to avoid shaming and blaming — of yourself or the child,” Dr. Ice says,  because these are professional scammers who know what works. “Let them know they did the right thing to tell you, and you can help them.” Don’t pay the blackmailer more money because it rarely makes them stop. Here are some steps you can take:

  • Report the account on the platform where the interaction occurred.
  • Block the person, but don’t delete your child’s profile or messages — they can be helpful to track the person and stop the blackmailing.
  • Go to the police station to report the sextortion. You can also report the incident to the CyberTipline, run by NCMEC, which can connect you with legal, emotional, and other supports.
  • Use NCMEC’s Take It Down, which helps youth remove online nude, partially nude, or sexually explicit photos and videos taken before they were 18.

When Andy finally told his mom Jackie about the sextortion — about a week after that first message — it was nearly 11pm, but his dad took him to the police station to report it. The police said that they were the fourth family to come in that day about sextortion and reassured them that the perpetrators would not come after them. “Hearing that was the biggest relief,” Jackie said. “Once we wiped his phone and computer, it was over.”  

Frequently Asked Questions

What is sextortion?

Sextortion is a form of exploitation in which someone threatens to share a nude or sexual images unless the person comply with demands, typically for money, more explicit content, or other harmful actions. Kids are often targeted, and the perpetrator may be someone the child knows or a stranger who has built trust online by pretending to be a peer.

How can you get rid of blackmailers?

Experts advise not paying the blackmailer, because paying rarely makes the demands stop and often leads to requests for more money. Instead, report the account on the platform, block the person, save the messages as evidence, and contact law enforcement. If your child is being targeted, the National Center for Missing and Exploited Children has useful resources.

What should you do if someone is blackmailing you with nudes?

If someone is threatening you with intimate images, tell a trusted adult right away and remember that you are not alone. Report and block the offender, keep copies of messages and other evidence, and make a police report. If the images or videos were taken before you turned 18, the National Center for Missing and Exploited Children has a service called Take It Down to help you remove them from the internet.

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AI-Derived Emotional States and Trajectories in Real-World Mental Health Sessions: Large-Scale Observational Study

<strong>Background:</strong> AI-powered digital mental health interventions (DMHIs) are increasingly deployed to support employee mental health, yet evaluations have focused predominantly on pretest-posttest symptom outcomes. The emotional states that unfold within real-world AI-assisted sessions remain largely uncharacterized, limiting understanding of how and when these tools may support users in distress. <strong>Objective:</strong> This study aimed to (1) characterize AI-derived emotional states and patterns that emerge during AI-supported mental health sessions, (2) characterize and compare reduced distress and persistent distress sessions among sessions beginning in AI-derived distress states, and (3) identify conversational features associated with reduced distress trajectories. <strong>Methods:</strong> This retrospective study analyzed message-level AI-derived emotional state data from 28,870 sessions contributed by 4463 unique users of Yuna, an AI-powered DMHI. Yuna’s emotion detection system, grounded in semantic space theory, generated continuous scores across 36 fine-grained emotional states at the message level. Fine-grained states were aggregated into 4 categories: distress associated, cognitive processing, regulated positive, and social-interpersonal. Session-level trajectory types were operationalized from start-to-end emotional category transitions. Linear mixed-effects models and mixed-effects logistic regression were used to identify characteristics and conversational predictors of reduced distress trajectories among sessions beginning in distress-associated states. <strong>Results:</strong> Five trajectory types were identified: stable (7679/28,870, 26.6%), reduced distress (7042/28,870, 24.4%), transitional processing (6195/28,870, 21.5%), persistent distress (4568/28,870, 15.8%), and escalating distress (3386/28,870, 11.7%). Cognitive processing states were the most frequently observed at the message level (130,611/365,088, 35.8%), followed by distress-associated states (117,485/365,088, 32.2%). Among sessions beginning in distress-associated states with available session duration data (n=1597), 73.3% (1170/1597) demonstrated reduced distress trajectories by session end. Reduced distress sessions were characterized by more messages (<i>B</i>=1.67; <i>P</i>=.02), longer duration (<i>B</i>=3.61; <i>P</i>=.001), AI-derived emotional switches (<i>B</i>=1.57; <i>P</i>=.01), unique AI-derived emotional states (<i>B</i>=0.94; <i>P</i>&lt;.001), and higher proportions of cognitive processing (<i>B</i>=0.09; <i>P</i>&lt;.001) and regulated positive (<i>B</i>=0.10; <i>P</i>&lt;.001) states relative to persistent distress sessions. In the mixed-effects logistic regression, a greater number of unique AI-derived emotional states (odds ratio [OR] 1.16, 95% CI 1.09-1.24; <i>P</i>&lt;.001) and longer session duration (OR 1.01, 95% CI 1.00-1.02; <i>P</i>=.04) were positively associated with reduced distress trajectories, while more AI-derived emotional switches were negatively associated with reduced distress trajectories (OR 0.97, 95% CI 0.95-0.99; <i>P</i>=.003). <strong>Conclusions:</strong> AI-derived emotional trajectory data may offer a scalable, low-burden signal for understanding engagement quality in real-world mental health sessions, laying an empirical foundation for process-level evaluation of AI-powered DMHIs. Future research incorporating self-report validation, longitudinal designs, and experimental methods is needed to determine whether these trajectory patterns carry a meaningful signal for user outcomes.

Renewing a Commitment to Collaboration

Ottawa ON, November 10, 2026 — The Canadian Centre on Substance Use and Addiction and the Mental Health Commission of Canada   signed a renewed memorandum of understanding on November 9, 2026, issuing the following joint statement:

“The Canadian Centre on Substance Use and Addiction (CCSA) and the Mental Health Commission of Canada (the Commission) have renewed their memorandum of understanding, reaffirming a long-standing commitment to working together to improve mental health and substance use health outcomes for people in Canada. Through this collaboration, both organizations can leverage expertise and networks to bring about meaningful change and improve wellness for individuals and their communities.

While our organizations have collaborated for many years, both with and without a formal agreement, this renewed MOU reflects our shared belief that the challenges facing people in Canada are best addressed through partnership.

Mental health and substance use health are deeply interconnected. Many people experience challenges in these areas together, and they require coordinated, person-centred approaches that recognize the complexity of people’s lives. By bringing together our respective expertise, CCSA and the Commission can strengthen knowledge exchange, inform policy discussions, support innovation, and advance practical solutions that better serve people and communities across Canada.

Our collaboration already extends across a range of initiatives. Most recently, the CCSA and the Commission partnered to develop Canada’s first guidance for the responsible use of artificial intelligence in mental health and substance use health. This work is helping establish practical criteria grounded in trust and explainability, human-centred care, equity, and data governance, demonstrating how our organizations can work together to address emerging opportunities and challenges in health care.

This renewed agreement is not simply about formalizing a partnership. It is about affirming our continued commitment to collaboration, recognizing that meaningful progress happens when organizations work together toward shared goals.

We look forward to building on our partnership in the years ahead.”

About CCSA
CCSA was created by Parliament to provide national leadership to address substance use in Canada. A trusted counsel, we provide national guidance to decision makers by harnessing the power of research, curating knowledge and bringing together diverse perspectives.

CCSA activities and products are made possible through a financial contribution from Health Canada. The views of CCSA do not necessarily represent the views of Health Canada.

About the Commission
The Mental Health Commission of Canada is a pan-Canadian, not-for-profit organization that connects evidence, knowledge, and lived experience to provide actionable insights and solutions that advance mental health and improve lives. Guided by our vision of “Strong Evidence. Strong Solutions. Strong Mental Health.” We deliver on our mission by enabling collective action, informing solutions and decision-making, transforming knowledge and building capacity.  

Media contacts
Mélissa Joseph, Strategic Communications Advisor 
Canadian Centre on Substance Use and Addiction
613‑235‑4048 x 364|mjoseph@ccsa.ca

and

Debra Yearwood, Director, Marketing and Communications
Mental Health Commission of Canada
613-402-9080
mediaphone@mentalhealthcommission.ca

The post Renewing a Commitment to Collaboration appeared first on Mental Health Commission of Canada.

Maladaptive state allocation: an orexin-based framework linking sleep disturbance, chronic pain, and substance use disorder

Sleep disturbance, chronic pain, and substance use disorders are highly prevalent conditions that frequently co-occur and exacerbate one another. Although traditionally studied as distinct clinical entities, growing evidence suggests they share common neurobiological mechanisms. Among these, the orexin (OX; also known as hypocretin) system has emerged as a critical regulator of arousal, motivation, stress responsiveness, and nociception, positioning it as a potential contributor to interactions among sleep, pain, and addiction. In this review, we propose that OX may function as a state-integration network that coordinates behavioral responses to competing physiological and environmental demands. Through widespread projections to brainstem, thalamic, limbic, and mesolimbic circuits, OX transmission dynamically regulates vigilance, salience attribution, pain modulation, and reward-seeking behavior. Under physiological conditions, these actions promote adaptive behavioral prioritization. However, chronic dysregulation of OX transmission may result in maladaptive state allocation characterized by persistent hyperarousal, amplified pain-related salience, and excessive motivational value assigned to drug-associated cues. We synthesize experimental evidence implicating OX-dependent circuits in the pathophysiology of sleep disturbance, chronic pain, and substance use disorder and introduce a conceptual triad model linking these conditions. Within this framework, we propose that sleep loss may increase pain sensitivity and craving, chronic pain may disrupt sleep and promote substance use, and repeated drug exposure may further destabilize sleep and pain regulation, creating a self-reinforcing cycle. Finally, we discuss individual factors that may influence vulnerability to triadic comorbidity and highlight emerging therapeutic approaches targeting the OX system.