Sudden changes are common in clinical trajectories, yet a theoretical framework to understand the underlying mechanisms is still lacking. In this article, we aim to bridge this gap by introducing the mechanisms and features of four distinct types of transitions drawn from complex dynamic system research: bifurcation-induced tipping (B-tipping), noise-induced tipping (N-tipping), rate-induced tipping (R-tipping), and noise-induced diffusion (N-diffusion). Those types of transitions differ in the main cause of the change and data characteristics. To illustrate their application to clinical phenomena, we present two real-life scenarios using simulated time series. These examples demonstrate how theoretical types of change may connect to clinical phenomena and highlight how different types of transitions can co-occur in various subsystems. In the first example, we show that the mood system and the momentary affect system of a patient with sudden loss may show B-tipping and N-diffusion at the same time; in the second example, we show that increasing the stimulus strengthening speed in exposure therapy may lead to R-tipping, while the therapeutic decision in this context may be caused by N-tipping. Finally, we lay out possible pathways for determining the appropriate type of transition for future empirical research, highlighting methods both from dynamic system research and special opportunities for research in clinical psychology.
","journal":null,"year":null,"id":668485,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":20117,"name":"Fred Hasselman","orcid":"0000-0003-1384-8361","position":1,"is_corresponding":false},{"id":1745637,"name":"Merlijn Olthof","orcid":null,"position":2,"is_corresponding":false},{"id":1745638,"name":"Anna Lichtwarck-Aschoff","orcid":"0000-0002-4365-1538","position":3,"is_corresponding":false},{"id":1745636,"name":"Jingmeng Cui","orcid":"0000-0003-3421-8457","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Understanding Types of Transitions in Clinical Change: An Introduction from the Complex Dynamic Systems Perspective","abstract":"Sudden changes are common in clinical trajectories, yet a theoretical framework to understand the underlying mechanisms is still lacking. In this article, we aim to bridge this gap by introducing the mechanisms and features of four distinct types of transitions drawn from complex dynamic system research: bifurcation-induced tipping (B-tipping), noise-induced tipping (N-tipping), rate-induced tipping (R-tipping), and noise-induced diffusion (N-diffusion). Those types of transitions differ in the main cause of the change and data characteristics. To illustrate their application to clinical phenomena, we present two real-life scenarios using simulated time series. These examples demonstrate how theoretical types of change may connect to clinical phenomena and highlight how different types of transitions can co-occur in various subsystems. In the first example, we show that the mood system and the momentary affect system of a patient with sudden loss may show B-tipping and N-diffusion at the same time; in the second example, we show that increasing the stimulus strengthening speed in exposure therapy may lead to R-tipping, while the therapeutic decision in this context may be caused by N-tipping. Finally, we lay out possible pathways for determining the appropriate type of transition for future empirical research, highlighting methods both from dynamic system research and special opportunities for research in clinical psychology.
","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4390778977","authors":[],"funders":[{"funder_name":"Netherlands Organisation for Scientific Research (NWO)","grant_id":"VI.Vidi.191.178","title":"Tipping Points in Treatment Trajectories. A Small Data Approach to Empower Personalized Youth Care"}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://osf.io/b68wh/download","host_type":""},{"url":"https://osf.io/b68wh/download","host_type":""},{"url":"https://doi.org/10.31234/osf.io/b68wh","host_type":""},{"url":"https://doi.org/10.31234/osf.io/b68wh_v1","host_type":""},{"url":"https://doi.org/10.31234/osf.io/b68wh_v2","host_type":""},{"url":"https://doi.org/10.1037/abn0000991","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40111825","host_type":""},{"url":"https://hdl.handle.net/https://repository.ubn.ru.nl/handle/2066/303957","host_type":""},{"url":"https://research.rug.nl/en/publications/81d33056-a570-4159-ac56-414bf9032ec1","host_type":""},{"url":"https://hdl.handle.net/11370/81d33056-a570-4159-ac56-414bf9032ec1","host_type":""},{"url":"https://repository.ubn.ru.nl//bitstream/handle/2066/303957/303957.pdf","host_type":""},{"url":"https://hdl.handle.net/2066/303957","host_type":""}],"fields_of_study":["Ecosystem dynamics and resilience","Complex Systems and Decision Making","05 social sciences","0501 psychology and cognitive sciences"],"mesh_terms":[],"keywords":["Transition (genetics)","Tipping point (physics)","Bridge (graph theory)","Computer science","Statistical physics","Cognitive psychology","Psychology","Physics","Medicine","Engineering","Psychopathology","Mental Disorders","Learning and Plasticity","Humans","Mental Disorders/therapy","Psychopathology/methods"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-14T11:39:48.683245Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}