Introducing Perceived Causal Networks in Sleep Medicine DOI Creative Commons
Christophe Gauld, Tessa F. Blanken, Lars Klintwall

et al.

Journal of Sleep Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Since the publication of Diagnostic Classification Sleep and Arousal Disorders (DCSAD) by 'Association Centers' for Psychophysiological Study Sleep' in 1979 (Association Centers 1979), sleep medicine has been largely structured around classifications, themselves built upon a categorical diagnosis-based framework. The successive versions International (ICSD), promoted American Academy Medicine (American 2023, 2014, 2005, 1990), Statistical Manual Mental (DSM), developed Psychiatric Association 2022, 1980), have continued to rely on such categorizations disorders based standardised diagnostic criteria, support communication, decision-making, evidence-based interventions discipline (Buysse et al. 1997; Hauri 2011). However, it is important distinguish this framework (which follows 'nomothetic' approach, focused identifying categories) from case formulation adheres an 'idiographic' highlighting unique clinical characteristics context individual; Bringmann 2021; Kuper 2024). In practice, diagnosis represents merely first step toward formulation. For example, patient with insomnia disorder criteria provides label, but developing comprehensive requires understanding individual's patterns, psychosocial stressors, comorbidities, environmental factors contributing their condition. no formalised tools currently exist systematically operationalise formulation, particularly medicine. To address these limitations, reflecting trends other medical specialties as oncology (Hanahan Weinberg 2011) psychiatry (van Dellen 2024; Fusar-Poli 2022), ambition develop formulation-based (Gauld 2021) significantly shaped past two decades. This effort personalised precise cases also faced challenges, especially due difficulty formally modelling analytical reasoning clinicians (Burger Among that enable reasoning, ecological momentary assessments (EMA) time-series analyses regularly used 2007; Shen 2022). while models offer temporally fine-grained insights reduce recall bias (Kapur 2017; Loddo 2019; Myin-Germeys 2018; Shiffman 2008), they are limited statistical assumptions, representing interactions at varying timescales, very short timescales (e.g., worry → trouble falling asleep) long insufficient over several days anhedonia), requirement specific stationarity conditions, or complexity implementation practice (Bringmann 2022; Burger 2023; Mansueto 2023). An alternative method formulations construct directed networks causal relationships between symptoms, perceived patients (Deserno 2020; Frewen 2012; Klintwall approach anchored symptom network theory, originally psychopathology (Borsboom Borsboom 2017). Symptom theory conceptualises broadly defined any manifestation, 'variable' identifiable nonrestorative sleep, time, risk factor drinking alcohol), mutually connected potentially self-reinforcing through feedback loops. These interconnected symptoms form dynamical system may stabilise into pathological state. Applied medicine, framework, maintain asleep lead worries, which increase arousal, which, turn, asleep, etc.). (PECAN) can be viewed one within explore patients' clinicians' beliefs about causes experience observe (Andreasson By asking clinician quantify influence another, PECAN generates individualised network. short, collects subjectively influences quantified shareable reflects patient/clinician's global understanding. might ask select relevant sleep-related predefined list, frequent awakenings, daytime sleepiness, then rate extent each others how often occur 'How does sleepiness?'). Alternatively, own observations identify without using list. instance, could assess relationship fragmented irritability asking: certain you irritability?' use certainty ratings map patient's Figure 1 example PECANs hypothetical patients, presenting sleepiness core complaint complaint. Table summarises different key steps options PECANs, providing conceptual designing reported self-perceived causality contribute least four reasons. First, mentioned earlier, serves second refining established DSM ICSD, illustrates interrelations (personalised) 2003; Ohayon Reynolds 2009). Second, capacity interpretative logic (i.e., process establishes clinical, contextual, mechanistic elements guide decision-making), useful practice. Third, structures refine profiles improve phenotyping data comparisons generalisability. Finally, pathways (Arnardottir Vogel 2024), visualisation tool, supporting personalisation interventions. It thus constitutes shared model (Figure 1). Guidelines proposed assist creation open-access code), its limitations reliance individual introspection bias, applicability vary across populations), standardise practices studies discussing challenges metrics aggregating data, well validity reliability (Vogel 2024)). must acknowledged some those disorder) biased (Harvey Tang Trimmel 2021). still serve starting point interest taking account subjective disturbances, helping treatments, cognitive behavioural therapy (CBTi). way, benefit rigorous integrating building initial classical nosography. indeed enables dynamic, subjective, heterogeneous nature interrelations. Christophe Gauld: conceptualization, writing – original draft, methodology, review editing, visualization. Tessa F. Blanken: validation. Lars Klintwall: supervision, project administration, methodology. Jean-Arthur Micoulaud-Franchi: authors nothing report. declare conflicts interest. Data sharing not applicable article new were created analysed study.

Language: Английский

Introducing Perceived Causal Networks in Sleep Medicine DOI Creative Commons
Christophe Gauld, Tessa F. Blanken, Lars Klintwall

et al.

Journal of Sleep Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Since the publication of Diagnostic Classification Sleep and Arousal Disorders (DCSAD) by 'Association Centers' for Psychophysiological Study Sleep' in 1979 (Association Centers 1979), sleep medicine has been largely structured around classifications, themselves built upon a categorical diagnosis-based framework. The successive versions International (ICSD), promoted American Academy Medicine (American 2023, 2014, 2005, 1990), Statistical Manual Mental (DSM), developed Psychiatric Association 2022, 1980), have continued to rely on such categorizations disorders based standardised diagnostic criteria, support communication, decision-making, evidence-based interventions discipline (Buysse et al. 1997; Hauri 2011). However, it is important distinguish this framework (which follows 'nomothetic' approach, focused identifying categories) from case formulation adheres an 'idiographic' highlighting unique clinical characteristics context individual; Bringmann 2021; Kuper 2024). In practice, diagnosis represents merely first step toward formulation. For example, patient with insomnia disorder criteria provides label, but developing comprehensive requires understanding individual's patterns, psychosocial stressors, comorbidities, environmental factors contributing their condition. no formalised tools currently exist systematically operationalise formulation, particularly medicine. To address these limitations, reflecting trends other medical specialties as oncology (Hanahan Weinberg 2011) psychiatry (van Dellen 2024; Fusar-Poli 2022), ambition develop formulation-based (Gauld 2021) significantly shaped past two decades. This effort personalised precise cases also faced challenges, especially due difficulty formally modelling analytical reasoning clinicians (Burger Among that enable reasoning, ecological momentary assessments (EMA) time-series analyses regularly used 2007; Shen 2022). while models offer temporally fine-grained insights reduce recall bias (Kapur 2017; Loddo 2019; Myin-Germeys 2018; Shiffman 2008), they are limited statistical assumptions, representing interactions at varying timescales, very short timescales (e.g., worry → trouble falling asleep) long insufficient over several days anhedonia), requirement specific stationarity conditions, or complexity implementation practice (Bringmann 2022; Burger 2023; Mansueto 2023). An alternative method formulations construct directed networks causal relationships between symptoms, perceived patients (Deserno 2020; Frewen 2012; Klintwall approach anchored symptom network theory, originally psychopathology (Borsboom Borsboom 2017). Symptom theory conceptualises broadly defined any manifestation, 'variable' identifiable nonrestorative sleep, time, risk factor drinking alcohol), mutually connected potentially self-reinforcing through feedback loops. These interconnected symptoms form dynamical system may stabilise into pathological state. Applied medicine, framework, maintain asleep lead worries, which increase arousal, which, turn, asleep, etc.). (PECAN) can be viewed one within explore patients' clinicians' beliefs about causes experience observe (Andreasson By asking clinician quantify influence another, PECAN generates individualised network. short, collects subjectively influences quantified shareable reflects patient/clinician's global understanding. might ask select relevant sleep-related predefined list, frequent awakenings, daytime sleepiness, then rate extent each others how often occur 'How does sleepiness?'). Alternatively, own observations identify without using list. instance, could assess relationship fragmented irritability asking: certain you irritability?' use certainty ratings map patient's Figure 1 example PECANs hypothetical patients, presenting sleepiness core complaint complaint. Table summarises different key steps options PECANs, providing conceptual designing reported self-perceived causality contribute least four reasons. First, mentioned earlier, serves second refining established DSM ICSD, illustrates interrelations (personalised) 2003; Ohayon Reynolds 2009). Second, capacity interpretative logic (i.e., process establishes clinical, contextual, mechanistic elements guide decision-making), useful practice. Third, structures refine profiles improve phenotyping data comparisons generalisability. Finally, pathways (Arnardottir Vogel 2024), visualisation tool, supporting personalisation interventions. It thus constitutes shared model (Figure 1). Guidelines proposed assist creation open-access code), its limitations reliance individual introspection bias, applicability vary across populations), standardise practices studies discussing challenges metrics aggregating data, well validity reliability (Vogel 2024)). must acknowledged some those disorder) biased (Harvey Tang Trimmel 2021). still serve starting point interest taking account subjective disturbances, helping treatments, cognitive behavioural therapy (CBTi). way, benefit rigorous integrating building initial classical nosography. indeed enables dynamic, subjective, heterogeneous nature interrelations. Christophe Gauld: conceptualization, writing – original draft, methodology, review editing, visualization. Tessa F. Blanken: validation. Lars Klintwall: supervision, project administration, methodology. Jean-Arthur Micoulaud-Franchi: authors nothing report. declare conflicts interest. Data sharing not applicable article new were created analysed study.

Language: Английский

Citations

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