Mental health check-ins to smartphones may monitor suicide risk better than weekly therapy sessions 66%

By Molly McVety46%

7/22/2026, 9:01:00 AM

BS Summary: This article contains 21 faulty reasoning types, including Ambiguity (Equivocation), Confirmation Bias, and Representativeness Heuristic, with Unattributed Quote as the most egregious example at 21% saturation with 88 hits. Analysis detected 787 faulty-reasoning hits from 419 analyzed words, generating a BS Score of 59.8% and a BS Rank of 66% (7,543 of 21,886 articles). This article is worse (more manipulative) than 65.50% of the article peer group.

Short, frequent mental health prompts sent directly to patients' cellphones may offer a more accurate picture into their risk for suicide, according to a newly published study from Penn. 
For roughly one month, 62 veterans and active-duty military personnel who were all considered at-risk for suicide were sent four check-ins every other day to evaluate their mental health. 
Researchers found that the spikes reported between therapy sessions gave medical providers valuable information about patients' well-being. 
MORE: How much coffee is safe to drink? 
American Heart Association weighs in 
EMA often uses smartphone notifications to prompt its users to describe their current mental state, rather than asking them to recall their general well-being as is standard in regular therapy sessions. 
Participants were recruited from Marine Corps Base Camp Lejeune in North Carolina. 
They were considered at risk of suicidal ideation and attended regular therapy sessions. 
Every other day, they received four messages at random times between 8 a.m. and 9 p.m. that asked them to evaluate suicidal urges. 
Among the whole sample, 54 of the 62 participants had reported a non-zero suicidal urge via smartphone during the study period. 
Researchers said these fluctuations taking place throughout the day are often not accurately reflected in regular therapy sessions that rely on patients’ imperfect memory. 
“A patient who experiences consistently moderate suicidal ideation between sessions and another patient who experiences minimal ideation for most days but had an intense spike may provide similar ratings at the session, despite markedly different risk trajectories,” the study said. 
Over the course of the research period, patients' peak levels of suicidal urges in between regular therapy sessions were shown to be an even stronger predictor of future suicidal thoughts than their average level of stress. 
Researchers concluded that EMA prompts, when disseminated frequently enough, could provide key insight for clinicians looking to predict future suicide risks for their patients. 
While the method has shown promise, safety and liability concerns prevent the practice from becoming more mainstream, Brown said. 
“Clinicians are overwhelmed and understandably concerned about changing their practice in ways that they perceive could increase liability —like assessing suicide risk between sessions, when they might not be available to respond to an emergency,” she said in a statement to Penn's Leonard Davis Institute of Health Economics. 
Researchers said the study is also limited by its relatively small sample size and suggest that future studies expand the research methods to a wider population. 
Article reasoning-pattern comparisonThis article: 12.6%Molly McVety: 3.4%PhillyVoice: 2.7%Confirmation Bias12.6%This article: 0.0%Molly McVety: 1.0%PhillyVoice: 1.1%Anchoring Bias0.0%This article: 9.1%Molly McVety: 2.5%PhillyVoice: 3.0%Availability Heuristic9.1%This article: 12.6%Molly McVety: 1.2%PhillyVoice: 1.4%Representativeness Heuristic12.6%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.5%Hindsight Bias0.0%This article: 8.6%Molly McVety: 2.3%PhillyVoice: 1.6%Overconfidence Bias8.6%This article: 6.9%Molly McVety: 13.5%PhillyVoice: 4.3%Framing Effect6.9%This article: 11.5%Molly McVety: 0.8%PhillyVoice: 0.6%Loss Aversion11.5%This article: 4.5%Molly McVety: 0.5%PhillyVoice: 0.4%Status Quo Bias4.5%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.3%Sunk Cost Effect0.0%This article: 9.1%Molly McVety: 1.9%PhillyVoice: 4.7%Optimism Bias9.1%This article: 4.5%Molly McVety: 1.2%PhillyVoice: 1.4%Pessimism Bias4.5%This article: 11.5%Molly McVety: 8.5%PhillyVoice: 6.6%Negativity Bias11.5%This article: 0.0%Molly McVety: 1.5%PhillyVoice: 1.2%Self-Serving Bias0.0%This article: 0.0%Molly McVety: 1.2%PhillyVoice: 0.5%Fundamental Attribution Error0.0%This article: 0.0%Molly McVety: 0.3%PhillyVoice: 0.1%Actor-Observer Bias0.0%This article: 0.0%Molly McVety: 0.8%PhillyVoice: 0.5%In-Group Bias0.0%This article: 0.0%Molly McVety: 0.6%PhillyVoice: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Molly McVety: 2.1%PhillyVoice: 3.5%Halo Effect0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.1%Horn Effect0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Molly McVety: 1.0%PhillyVoice: 1.6%Recency Bias0.0%This article: 0.0%Molly McVety: 0.2%PhillyVoice: 0.5%Primacy Effect0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Blind-Spot Bias0.0%This article: 0.0%Molly McVety: 1.2%PhillyVoice: 0.1%Ad Hominem0.0%This article: 0.0%Molly McVety: 0.7%PhillyVoice: 0.1%Straw Man0.0%This article: 1.2%Molly McVety: 5.7%PhillyVoice: 3.0%Appeal to Authority1.2%This article: 11.5%Molly McVety: 1.6%PhillyVoice: 1.1%False Dilemma11.5%This article: 0.0%Molly McVety: 0.3%PhillyVoice: 0.4%Slippery Slope0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Circular Reasoning0.0%This article: 11.9%Molly McVety: 5.8%PhillyVoice: 3.8%Hasty Generalization11.9%This article: 3.1%Molly McVety: 0.7%PhillyVoice: 0.4%Red Herring3.1%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.3%Bandwagon0.0%This article: 11.5%Molly McVety: 8.7%PhillyVoice: 3.5%Appeal to Emotion11.5%This article: 0.0%Molly McVety: 2.2%PhillyVoice: 0.4%Begging the Question0.0%This article: 4.1%Molly McVety: 1.7%PhillyVoice: 1.8%Post Hoc (False Cause)4.1%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Tu Quoque0.0%This article: 0.0%Molly McVety: 1.0%PhillyVoice: 0.4%Burden of Proof0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.2%Appeal to Nature0.0%This article: 0.0%Molly McVety: 0.2%PhillyVoice: 0.3%Composition/Division0.0%This article: 0.0%Molly McVety: 0.3%PhillyVoice: 1.6%Anecdotal0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.1%No True Scotsman0.0%This article: 13.1%Molly McVety: 2.6%PhillyVoice: 1.4%Ambiguity (Equivocation)13.1%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Middle Ground0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Personal Incredulity0.0%This article: 0.0%Molly McVety: 0.4%PhillyVoice: 0.1%Special Pleading0.0%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.0%Genetic Fallacy0.0%This article: 21.0%Molly McVety: 2.0%PhillyVoice: 1.0%Unattributed Quote21.0%This article: 0.0%Molly McVety: 1.6%PhillyVoice: 0.8%Quote-first Misdirection0.0%This article: 10.3%Molly McVety: 1.5%PhillyVoice: 4.3%Biased Writer Voice10.3%This article: 7.4%Molly McVety: 4.7%PhillyVoice: 1.7%Indoctrination7.4%This article: 0.0%Molly McVety: 0.0%PhillyVoice: 0.3%Politically Left Leaning Bias0.0%This article: 0.0%Molly McVety: 0.4%PhillyVoice: 0.0%Politically Right Leaning Bias0.0%This article: 1.9%Molly McVety: 0.6%PhillyVoice: 2.6%Attempt to Sell a Product or S…1.9%

419 words analyzed.

Speakers

2speakers19%attributed speech340writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 14 words • 100.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageEMA • 31 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 40 words • 100.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageBrown • 48 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverage
Selected voice

Brown

100%flagged-word coverage
48 attributed words61% of attributed speech74% writer coverage
0%50.0%100.0%Unattributed Quote+88.2 ptsWriter: 11.8%Brown: 100.0%100.0%Biased Writer Voice-12.6 ptsWriter: 12.6%Brown: 0.0%0.0%Attempt to Sell a Product -2.4 ptsWriter: 2.4%Brown: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.