Single-Subject Research in Psychiatry: Facts and Fictions

Zuidersma, Marij; Riese, Harriëtte; Snippe, Evelien; Booij, Sanne H.; Wichers, Marieke; Bos, Elisabeth H.

doi:10.3389/fpsyt.2020.539777

Cited by 36 publications

(44 citation statements)

References 106 publications

(150 reference statements)

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“…Notably, previous studies have only examined autocorrelation and standard deviation as static indicators of future shifts (21-23), whereas we have prospectively examined increases in these indicators to anticipate nearby mood shifts. Further, we used a personalized and idiographic approach that has been advocated to study within-person processes (25). Other strengths include the relatively large sample (N=20) for idiographic studies, in which each patient could be viewed as a replication of results in other patients, and the large number of observations per person.…”

Section: Discussionmentioning

confidence: 99%

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Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

Bos¹,

Schreuder²,

George³

et al. 2021

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Background. In bipolar disorder treatment, accurate prediction of manic and depressive episodes is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate mood shifts in individual patients with bipolar disorder. Methods. Twenty bipolar type I/II patients participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean=491 observations per person). Weekly completed symptom questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms were used to determine transitions. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA items. Positive and negative predictive values were calculated to determine clinical utility. Results. Eleven (of the twenty) patients reported 1-2 manic or depressive transitions. The presence of EWS increased the probability of impending transitions towards depression and mania from 32-36% to 46-48% (autocorrelation) and 29-41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65-100%) were cheerfulness, focusing ability, full of ideas, worry, racing thoughts, agitation, energy, and tiredness. Large individual differences in the utility of EWS were found.Conclusions. EWS may improve anticipating manic and depressive transitions in bipolar disorder, but await further empirical testing.

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Section: Discussionmentioning

confidence: 99%

“…Although a seemingly small sample, it is relatively large for idiographic studies. Here, power depends not on the number of individuals, but rather on the number of assessments per individual (25). As such, a sample of twenty patients allows us to examine the relative robustness of EWS in improving the detection of transitions.…”

Section: Participantsmentioning

confidence: 99%

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

Bos¹,

Schreuder²,

George³

et al. 2021

Preprint

Self Cite

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“…Indeed, some connections may occur only in certain individuals in the group while other connections occur only in other individuals. More idiographic research is thus needed (91), to verify whether presumed vicious cycles found at a group-level actually exist at an individual level. To conclude, there is still an important gap in the testing of assumptions of the network theory using individual models.…”

Section: The Importance Of Individual Modelsmentioning

confidence: 99%

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

et al. 2021

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The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

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“…Moreover, the foregoing scar and vulnerability theories posit that EF-symptom relations unfold within persons across long durations [46][47][48][49][50][51]. Awareness of withinperson prolonged trajectories of increased depression or anxiety, EF decrements, and their covariation may guide the design of personalized prevention, diagnostic, and treatment efforts that rely on idiographic (or within-person) more than between-person data, as part of precision psychiatry [52][53][54]. It is also important to note that observations of between-and within-person differences in EF and psychopathological symptoms do not always align with each other [55][56][57].…”

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confidence: 99%

Depression and executive functioning bidirectionally impair one another across 9 years: Evidence from within-person latent change and cross-lagged models

Zainal

Newman

2021

Eur. Psychiatr.

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Background: Scar and vulnerability models assert that increased psychopathology may predict subsequent executive functioning deficits (and vice versa) over protracted timescales, yet most prior work on this topic has been cross-sectional. Thus, we tested the within-and betweenperson relations between EF, depression and anxiety. Method: Older adult participants (n = 856) were assessed across 4 waves, approximately 2 years apart. Performance-based EF and caregiver-rated symptom measures were administered. Bivariate latent change score and random-intercept cross-lagged panel models were conducted. Results: Within persons, RI-CLPMs revealed that prior greater depression forecasted lower subsequent EF, and vice versa (d = -0.292 vs. -0.292). BCS models showed that within-person rise in depression predicted EF decreases, and vice versa (d = -0.245 vs. -0.245). No within-person, cross-lagged, EF-anxiety Accepted manuscript: Authors' Copy MENTAL HEALTH AND EXECUTIVE FUNCTIONING 2 relations emerged. Further, significant negative between-person EF-symptom relations were observed (d = -0.264--0.395). Discussion: Prospective, within-person findings offer some evidence for developmental scar and vulnerability models.

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Single-Subject Research in Psychiatry: Facts and Fictions

Cited by 36 publications

References 106 publications

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

Depression and executive functioning bidirectionally impair one another across 9 years: Evidence from within-person latent change and cross-lagged models

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