Evidence map›Paper›PMID 30033853›Full record

ReviewHealth psychology review2019

Automatic processes and self-regulation of illness.

Sheina Orbell, L Alison Phillips

Abstract readReview
PubMed Publisher
In one paragraph

Review in Health psychology review, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Functional impairment representations and behavioral intentions in later life: a motivational perspective.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Sheina OrbellDepartment of Psychology, University of Essex, Colchester, UK.
L Alison PhillipsDepartment of Psychology, Iowa State University, Ames, IA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on the Commonsense Self-Regulation Model has emphasised reflective/conscious perceptual processes regarding illness threat (beliefs about symptoms, consequences, timeline, and curability) in predicting and changing coping behaviours. Understanding of illness self-regulation and avenues for intervention might be enriched by consideration of automatic processes that influence the recognition and identification of illness, response to illness, and ongoing management. This article adopts an integrative approach to (1) outline the theoretical importance of implicit processes in patients' self-regulation of illness and methods to study them; (2) review research evidence for these processes, including interventions tested to modify them; and (3) outline avenues for future research. A substantial body of research on implicit processes (cognitive bias and interpretational bias) in illness maintenance in chronic illness has recently been extended to detection and interpretation of acute illness and new perspectives relating to the self-system. There is encouraging evidence that cognitive accessibility of coping and implicit attitudes may impact upon coping behaviours. Procedures that strategically automatise coping responses and create habits have considerable promise. We outline an agenda for future research in which health psychology accepts the challenge posed by the interplay of the reflective and associative systems in promoting effective self-regulation of illness.

Indexed as

Adaptation, PsychologicalCognitionSelf-ControlAttentionBehavioral MedicineChronic DiseaseCuesDiagnostic Self EvaluationDiseaseHumansModels, Psychologicalaction planningautomatic processesCommonsense model of self-regulationhabitillness cognition

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.