Pub Date : 2024-07-02DOI: 10.1038/s44184-024-00065-y
Maryam Golafshani, Daniel Z. Buchman, M. Ishrat Husain
Given the renewed interest in the use of psychedelics for the treatment of mental and substance use disorders in recent decades, there has also been renewed discussion and debate about whether it is necessary or beneficial for those who study and deliver psychedelic-assisted psychotherapy (PAP) to have had personal experience of using psychedelics. This paper provides a brief history of this debate and brings a disability-rights perspective to the discussion, given increasing efforts to dismantle ableism in medical training, practice, and research. Many psychiatric conditions and psychotropic medications, including ones as commonly prescribed as antidepressants, may preclude one from being able to safely and/or effectively use psychedelics. As such, we argue explicitly mandating or even implying the necessity of experiential training for psychedelic researchers and clinicians can perpetuate ableism in medicine by excluding those who cannot safely use psychedelics because of their personal medical histories. As PAP research and practice rapidly grow, we must ensure the field grows with disability inclusion amongst researchers and clinicians.
{"title":"Disability rights and experiential use of psychedelics in clinical research and practice","authors":"Maryam Golafshani, Daniel Z. Buchman, M. Ishrat Husain","doi":"10.1038/s44184-024-00065-y","DOIUrl":"10.1038/s44184-024-00065-y","url":null,"abstract":"Given the renewed interest in the use of psychedelics for the treatment of mental and substance use disorders in recent decades, there has also been renewed discussion and debate about whether it is necessary or beneficial for those who study and deliver psychedelic-assisted psychotherapy (PAP) to have had personal experience of using psychedelics. This paper provides a brief history of this debate and brings a disability-rights perspective to the discussion, given increasing efforts to dismantle ableism in medical training, practice, and research. Many psychiatric conditions and psychotropic medications, including ones as commonly prescribed as antidepressants, may preclude one from being able to safely and/or effectively use psychedelics. As such, we argue explicitly mandating or even implying the necessity of experiential training for psychedelic researchers and clinicians can perpetuate ableism in medicine by excluding those who cannot safely use psychedelics because of their personal medical histories. As PAP research and practice rapidly grow, we must ensure the field grows with disability inclusion amongst researchers and clinicians.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1038/s44184-024-00068-9
Ronit Kishon, Nadav Liam Modlin, Yael M. Cycowicz, Hania Mourtada, Tayler Wilson, Victoria Williamson, Anthony Cleare, James Rucker
Pre-prohibition psychedelic research with complex psychiatric patients generated a wealth of treatment methodologies and practices, providing invaluable clinical insights pertaining to the medical administration of psychedelics in various mental health diagnoses. Building upon these early studies, which lack the rigor and research tools available today, contemporary psychedelic research has focused on investigating the safety and efficacy of psychedelics in randomized controlled trials via psychometric measures and symptom assessments. Both then and now, the treatment context and the role of clinicians in psychedelic treatment has been recognized as an essential feature for positive patient outcomes. To broaden the knowledge base of modern psychedelic research and support the training of clinicians conducting medically supervised psychedelic research studies, this paper provides a review of pre-prohibition clinical research narratives pertaining to the phenomenology of psychedelic treatment and the role of the non-pharmacological treatment factors in the patient experience. Lastly, this paper explores a range of clinician perspectives and psychological interventions employed in pre-prohibition psychedelic research to inform future research directions and best practice guidelines.
{"title":"A rapid narrative review of the clinical evolution of psychedelic treatment in clinical trials","authors":"Ronit Kishon, Nadav Liam Modlin, Yael M. Cycowicz, Hania Mourtada, Tayler Wilson, Victoria Williamson, Anthony Cleare, James Rucker","doi":"10.1038/s44184-024-00068-9","DOIUrl":"10.1038/s44184-024-00068-9","url":null,"abstract":"Pre-prohibition psychedelic research with complex psychiatric patients generated a wealth of treatment methodologies and practices, providing invaluable clinical insights pertaining to the medical administration of psychedelics in various mental health diagnoses. Building upon these early studies, which lack the rigor and research tools available today, contemporary psychedelic research has focused on investigating the safety and efficacy of psychedelics in randomized controlled trials via psychometric measures and symptom assessments. Both then and now, the treatment context and the role of clinicians in psychedelic treatment has been recognized as an essential feature for positive patient outcomes. To broaden the knowledge base of modern psychedelic research and support the training of clinicians conducting medically supervised psychedelic research studies, this paper provides a review of pre-prohibition clinical research narratives pertaining to the phenomenology of psychedelic treatment and the role of the non-pharmacological treatment factors in the patient experience. Lastly, this paper explores a range of clinician perspectives and psychological interventions employed in pre-prohibition psychedelic research to inform future research directions and best practice guidelines.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1038/s44184-024-00077-8
P. M. Briley, L. Webster, S. Lankappa, S. Pszczolkowski, R. H. McAllister-Williams, P. F. Liddle, D. P. Auer, R. Morriss
Repetitive transcranial magnetic stimulation (rTMS) is an established non-invasive brain stimulation treatment for major depressive disorder, but there is marked inter-individual variability in response. Using latent class growth analysis with session-by-session patient global impression ratings from the recently completed BRIGhTMIND trial, we identified five distinct classes of improvement trajectory during a 20-session treatment course. This included a substantial class of patients noticing delayed onset of improvement. Contrary to prior expectations, members of a class characterised by early and continued improvement showed greatest inter-session variability in stimulated location. By relating target locations and inter-session variability to a well-studied atlas, we estimated an average of 3.0 brain networks were stimulated across the treatment course in this group, compared to 1.1 in a group that reported symptom worsening (p < 0.001, d = 0.893). If confirmed, this would suggest that deliberate targeting of multiple brain networks could be beneficial to rTMS outcomes.
{"title":"Trajectories of improvement with repetitive transcranial magnetic stimulation for treatment-resistant major depression in the BRIGhTMIND trial","authors":"P. M. Briley, L. Webster, S. Lankappa, S. Pszczolkowski, R. H. McAllister-Williams, P. F. Liddle, D. P. Auer, R. Morriss","doi":"10.1038/s44184-024-00077-8","DOIUrl":"10.1038/s44184-024-00077-8","url":null,"abstract":"Repetitive transcranial magnetic stimulation (rTMS) is an established non-invasive brain stimulation treatment for major depressive disorder, but there is marked inter-individual variability in response. Using latent class growth analysis with session-by-session patient global impression ratings from the recently completed BRIGhTMIND trial, we identified five distinct classes of improvement trajectory during a 20-session treatment course. This included a substantial class of patients noticing delayed onset of improvement. Contrary to prior expectations, members of a class characterised by early and continued improvement showed greatest inter-session variability in stimulated location. By relating target locations and inter-session variability to a well-studied atlas, we estimated an average of 3.0 brain networks were stimulated across the treatment course in this group, compared to 1.1 in a group that reported symptom worsening (p < 0.001, d = 0.893). If confirmed, this would suggest that deliberate targeting of multiple brain networks could be beneficial to rTMS outcomes.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is increasing recognition of ‘higher preference for eveningness’ as a potential independent risk factor for poor mental health. To examine the chronotype-mental health relationship while also quantifying the potential roles of poor sleep quality, relevant personality traits, and childhood trauma, we assessed 282 young adults (18–40 years; 195 females) residing in North India, between January and March 2023 (to control for seasonal variation), using self-report measures of diurnal preference, sleep patterns, mental health (depression, anxiety, and stress), personality traits (extraversion, neuroticism, schizotypy, and impulsivity), and childhood trauma. The results showed a significant association between eveningness and poor mental health but this association was fully mediated by poor sleep quality. Neuroticism, emotional abuse and cognitive disorganisation were correlated with eveningness as well as with poor mental health and sleep quality. Neuroticism and emotional abuse, but not cognitive disorganisation, also had indirect effects on mental health via sleep quality. Our findings highlight the crucial role played by sleep quality in the chronotype-mental health relationship.
{"title":"Sleep quality mediates the association between chronotype and mental health in young Indian adults","authors":"Satyam Chauhan, Rakesh Pandey, Krupa Vakani, Ray Norbury, Ulrich Ettinger, Veena Kumari","doi":"10.1038/s44184-024-00076-9","DOIUrl":"10.1038/s44184-024-00076-9","url":null,"abstract":"There is increasing recognition of ‘higher preference for eveningness’ as a potential independent risk factor for poor mental health. To examine the chronotype-mental health relationship while also quantifying the potential roles of poor sleep quality, relevant personality traits, and childhood trauma, we assessed 282 young adults (18–40 years; 195 females) residing in North India, between January and March 2023 (to control for seasonal variation), using self-report measures of diurnal preference, sleep patterns, mental health (depression, anxiety, and stress), personality traits (extraversion, neuroticism, schizotypy, and impulsivity), and childhood trauma. The results showed a significant association between eveningness and poor mental health but this association was fully mediated by poor sleep quality. Neuroticism, emotional abuse and cognitive disorganisation were correlated with eveningness as well as with poor mental health and sleep quality. Neuroticism and emotional abuse, but not cognitive disorganisation, also had indirect effects on mental health via sleep quality. Our findings highlight the crucial role played by sleep quality in the chronotype-mental health relationship.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00076-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-19DOI: 10.1038/s44184-024-00075-w
Caitlin A. Stamatis, Deborah N. Farlow, Catherine Mercaldi, Minny Suh, Amanda Maple, Antonia Savarese, Ann Childress, Raun D. Melmed, Scott H. Kollins
Inattention symptoms represent a key driver of functional impairment in ADHD and often persist into adolescence and adulthood, underscoring a need for novel treatments targeting attentional control. We evaluated AKL-T01—a digital therapeutic that is FDA-cleared for children 8–12 y with ADHD—in adolescents and adults with ADHD in two independent single-arm trials: STARS-ADHD-Adolescent, a 4-week trial in adolescents 13–17 y (n = 162 enrolled), and STARS-ADHD-Adult, a 6-week trial in adults 18 and older (n = 221 enrolled). AKL-T01 was linked with improvements on the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS) of 2.6 (95% CI: 2.02, 3.26; p < 0.0001) in adolescents and 6.5 in adults (95% CI: 5.35, 7.57; p < 0.0001), along with improvements in secondary endpoints. 15 participants reported adverse device effects, all mild or moderate. Though limited by a single-arm design, results provide preliminary support for the safety and efficacy of AKL-T01 for adolescents and adults with ADHD.
{"title":"Two single arm trials of AKL-T01, a digital therapeutic for adolescents and adults with ADHD","authors":"Caitlin A. Stamatis, Deborah N. Farlow, Catherine Mercaldi, Minny Suh, Amanda Maple, Antonia Savarese, Ann Childress, Raun D. Melmed, Scott H. Kollins","doi":"10.1038/s44184-024-00075-w","DOIUrl":"10.1038/s44184-024-00075-w","url":null,"abstract":"Inattention symptoms represent a key driver of functional impairment in ADHD and often persist into adolescence and adulthood, underscoring a need for novel treatments targeting attentional control. We evaluated AKL-T01—a digital therapeutic that is FDA-cleared for children 8–12 y with ADHD—in adolescents and adults with ADHD in two independent single-arm trials: STARS-ADHD-Adolescent, a 4-week trial in adolescents 13–17 y (n = 162 enrolled), and STARS-ADHD-Adult, a 6-week trial in adults 18 and older (n = 221 enrolled). AKL-T01 was linked with improvements on the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS) of 2.6 (95% CI: 2.02, 3.26; p < 0.0001) in adolescents and 6.5 in adults (95% CI: 5.35, 7.57; p < 0.0001), along with improvements in secondary endpoints. 15 participants reported adverse device effects, all mild or moderate. Though limited by a single-arm design, results provide preliminary support for the safety and efficacy of AKL-T01 for adolescents and adults with ADHD.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1038/s44184-024-00074-x
Sumra Bari, Byoung-Woo Kim, Nicole L. Vike, Shamal Lalvani, Leandros Stefanopoulos, Nicos Maglaveras, Martin Block, Jeffrey Strawn, Aggelos K. Katsaggelos, Hans C. Breiter
Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2–3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29–31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.
{"title":"A novel approach to anxiety level prediction using small sets of judgment and survey variables","authors":"Sumra Bari, Byoung-Woo Kim, Nicole L. Vike, Shamal Lalvani, Leandros Stefanopoulos, Nicos Maglaveras, Martin Block, Jeffrey Strawn, Aggelos K. Katsaggelos, Hans C. Breiter","doi":"10.1038/s44184-024-00074-x","DOIUrl":"10.1038/s44184-024-00074-x","url":null,"abstract":"Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2–3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29–31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11189415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1038/s44184-024-00070-1
Nathaniel Z. Counts, Ashwin Vasan
Mental health and substance use parity provides a rhetorical device and policy strategy for achieving more equitable financing of mental health and substance use services, which the U.S. has pursued as a lead policy approach for improving access to mental healthcare. Parity implementation in the U.S. has improved access to care for children, but implementation challenges remain, leading to persistent treatment gaps and disparities, workforce shortages, and variable care quality. In the U.S., a recent policy change required health insurers to make available all of the data on their coverage and reimbursement practices for all health conditions. This new data enables a more detailed conceptualization of what parity means in children’s mental health and how it should be implemented and overseen. Researchers, clinicians, and advocates across the globe can use this data to build the case and the policy approach for parity, supporting more equitable financing of children’s mental health and substance use care and promoting families’ access to evidence-based care.
{"title":"Advancing mental health parity to ensure children’s access to care","authors":"Nathaniel Z. Counts, Ashwin Vasan","doi":"10.1038/s44184-024-00070-1","DOIUrl":"10.1038/s44184-024-00070-1","url":null,"abstract":"Mental health and substance use parity provides a rhetorical device and policy strategy for achieving more equitable financing of mental health and substance use services, which the U.S. has pursued as a lead policy approach for improving access to mental healthcare. Parity implementation in the U.S. has improved access to care for children, but implementation challenges remain, leading to persistent treatment gaps and disparities, workforce shortages, and variable care quality. In the U.S., a recent policy change required health insurers to make available all of the data on their coverage and reimbursement practices for all health conditions. This new data enables a more detailed conceptualization of what parity means in children’s mental health and how it should be implemented and overseen. Researchers, clinicians, and advocates across the globe can use this data to build the case and the policy approach for parity, supporting more equitable financing of children’s mental health and substance use care and promoting families’ access to evidence-based care.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00070-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1038/s44184-024-00072-z
Aada Ståhl, Milla Salonen, Emma Hakanen, Salla Mikkola, Sini Sulkama, Jari Lahti, Hannes Lohi
It has been described that many puppy owners experience a state called puppy blues involving stress, worry, anxiety, strain, frustration, or regret. While puppy blues is a commonly used term among dog owners, the term is nearly nonexistent in scientific literature. In turn, analogous phenomenon, postpartum affective disturbance of infant caregivers, is well described in the literature. This study aimed to develop and validate the first questionnaire to evaluate puppy blues. The methodology involved generating scale items based on a qualitative review of 135 pilot survey responses from people who had experienced distress during the puppy period, conducting exploratory factor analysis for the final scale items from a dataset of 1801 answers from Finnish dog owners (92% women), and collecting test-retest data from 265 individuals to assess the consistency of the measurement of items and factor structure across time. In addition, we collected an independent sample of 326 owners of 1–2-year-old dogs who answered the survey both regarding puppy period and current moment. The results indicate that the scale is a valid and reliable tool for measuring dog owners’ negative experiences and feelings related to puppyhood. We discovered three factors that describe different aspects of puppy blues: Frustration, Anxiety, and Weariness, which accounted for a significant proportion of the variance in puppy blues. The study demonstrated good internal consistency and consistency across two independent samples for the three identified factors. The test-retest reliability of the factors was good. Responses for the current timeframe compared to puppyhood experiences revealed significantly lower current scores across all factors for the current period, validating that the scale captures distress during puppyhood that diminishes over time. Interestingly, we found a fading affect bias where recollections of the experiences in the puppy period became more positive with time. Our findings shed light on the characteristics of puppy blues and provide a useful retrospective tool for measuring it.
{"title":"Development and validation of the puppy blues scale measuring temporary affective disturbance resembling baby blues","authors":"Aada Ståhl, Milla Salonen, Emma Hakanen, Salla Mikkola, Sini Sulkama, Jari Lahti, Hannes Lohi","doi":"10.1038/s44184-024-00072-z","DOIUrl":"10.1038/s44184-024-00072-z","url":null,"abstract":"It has been described that many puppy owners experience a state called puppy blues involving stress, worry, anxiety, strain, frustration, or regret. While puppy blues is a commonly used term among dog owners, the term is nearly nonexistent in scientific literature. In turn, analogous phenomenon, postpartum affective disturbance of infant caregivers, is well described in the literature. This study aimed to develop and validate the first questionnaire to evaluate puppy blues. The methodology involved generating scale items based on a qualitative review of 135 pilot survey responses from people who had experienced distress during the puppy period, conducting exploratory factor analysis for the final scale items from a dataset of 1801 answers from Finnish dog owners (92% women), and collecting test-retest data from 265 individuals to assess the consistency of the measurement of items and factor structure across time. In addition, we collected an independent sample of 326 owners of 1–2-year-old dogs who answered the survey both regarding puppy period and current moment. The results indicate that the scale is a valid and reliable tool for measuring dog owners’ negative experiences and feelings related to puppyhood. We discovered three factors that describe different aspects of puppy blues: Frustration, Anxiety, and Weariness, which accounted for a significant proportion of the variance in puppy blues. The study demonstrated good internal consistency and consistency across two independent samples for the three identified factors. The test-retest reliability of the factors was good. Responses for the current timeframe compared to puppyhood experiences revealed significantly lower current scores across all factors for the current period, validating that the scale captures distress during puppyhood that diminishes over time. Interestingly, we found a fading affect bias where recollections of the experiences in the puppy period became more positive with time. Our findings shed light on the characteristics of puppy blues and provide a useful retrospective tool for measuring it.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00072-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1038/s44184-024-00067-w
Emre Sezgin, Ian McKay
There have been considerable advancements in artificial intelligence (AI), specifically with generative AI (GAI) models. GAI is a class of algorithms designed to create new data, such as text, images, and audio, that resembles the data on which they have been trained. These models have been recently investigated in medicine, yet the opportunity and utility of GAI in behavioral health are relatively underexplored. In this commentary, we explore the potential uses of GAI in the field of behavioral health, specifically focusing on image generation. We propose the application of GAI for creating personalized and contextually relevant therapeutic interventions and emphasize the need to integrate human feedback into the AI-assisted therapeutics and decision-making process. We report the use of GAI with a case study of behavioral therapy on emotional recognition and management with a three-step process. We illustrate image generation-specific GAI to recognize, express, and manage emotions, featuring personalized content and interactive experiences. Furthermore, we highlighted limitations, challenges, and considerations, including the elements of human emotions, the need for human-AI collaboration, transparency and accountability, potential bias, security, privacy and ethical issues, and operational considerations. Our commentary serves as a guide for practitioners and developers to envision the future of behavioral therapies and consider the benefits and limitations of GAI in improving behavioral health practices and patient outcomes.
人工智能（AI），特别是生成式人工智能（GAI）模型已经取得了长足的进步。GAI 是一类旨在创建新数据（如文本、图像和音频）的算法，这些数据与经过训练的数据非常相似。最近，医学界对这些模型进行了研究，但对 GAI 在行为健康领域的应用机会和效用的探索相对较少。在这篇评论中，我们探讨了 GAI 在行为健康领域的潜在用途，尤其侧重于图像生成。我们建议将 GAI 应用于创建个性化和与上下文相关的治疗干预，并强调需要将人类反馈整合到人工智能辅助治疗和决策过程中。我们通过一个关于情绪识别和管理的行为疗法案例研究，报告了 GAI 在三步流程中的应用。我们展示了针对图像生成的 GAI，用于识别、表达和管理情绪，具有个性化的内容和互动体验。此外，我们还强调了局限性、挑战和注意事项，包括人类情感要素、人类与人工智能合作的必要性、透明度和问责制、潜在偏见、安全性、隐私和伦理问题以及操作注意事项。我们的评论可作为从业人员和开发人员的指南，帮助他们展望行为疗法的未来，并考虑 GAI 在改善行为健康实践和患者预后方面的益处和局限性。
{"title":"Behavioral health and generative AI: a perspective on future of therapies and patient care","authors":"Emre Sezgin, Ian McKay","doi":"10.1038/s44184-024-00067-w","DOIUrl":"10.1038/s44184-024-00067-w","url":null,"abstract":"There have been considerable advancements in artificial intelligence (AI), specifically with generative AI (GAI) models. GAI is a class of algorithms designed to create new data, such as text, images, and audio, that resembles the data on which they have been trained. These models have been recently investigated in medicine, yet the opportunity and utility of GAI in behavioral health are relatively underexplored. In this commentary, we explore the potential uses of GAI in the field of behavioral health, specifically focusing on image generation. We propose the application of GAI for creating personalized and contextually relevant therapeutic interventions and emphasize the need to integrate human feedback into the AI-assisted therapeutics and decision-making process. We report the use of GAI with a case study of behavioral therapy on emotional recognition and management with a three-step process. We illustrate image generation-specific GAI to recognize, express, and manage emotions, featuring personalized content and interactive experiences. Furthermore, we highlighted limitations, challenges, and considerations, including the elements of human emotions, the need for human-AI collaboration, transparency and accountability, potential bias, security, privacy and ethical issues, and operational considerations. Our commentary serves as a guide for practitioners and developers to envision the future of behavioral therapies and consider the benefits and limitations of GAI in improving behavioral health practices and patient outcomes.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00067-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1038/s44184-024-00071-0
Mathew Varidel, Ian B. Hickie, Ante Prodan, Adam Skinner, Roman Marchant, Sally Cripps, Rafael Oliveria, Min K. Chong, Elizabeth Scott, Jan Scott, Frank Iorfino
There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual’s level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.
{"title":"Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care","authors":"Mathew Varidel, Ian B. Hickie, Ante Prodan, Adam Skinner, Roman Marchant, Sally Cripps, Rafael Oliveria, Min K. Chong, Elizabeth Scott, Jan Scott, Frank Iorfino","doi":"10.1038/s44184-024-00071-0","DOIUrl":"10.1038/s44184-024-00071-0","url":null,"abstract":"There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual’s level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00071-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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