Resilience Factors and Selective Learning in Patients With Fibromyalgia

Learning impairments (such as reduced selective learning or excessive generalization) in the context of pain can lead to disability. Learning deficits have been found in experimental studies in various pain populations. In current scientific discussions, the activation of resilience factors (in particular positive affect and optimism) is being considered to optimize learning experiences and to make therapeutic procedures more effective. Positive affect could promote selective learning since positive emotions broaden attention and focus and thus possibly improve inhibitory learning. There is first scientific evidence for improved safety learning through positive affect in non-clinical samples in the context of pain. In this research project, the influence of positive affect and optimism on selective learning will be investigated in a clinical sample of fibromyalgia patients. Data will be collected online and standardized questionnaires will be used. The authors expect that (1) There will be a larger increase in positive affect and positive future expectations in the Best Possible Self condition than in the Typical Day condition. (2) Patients in the Best Possible Self condition will show elevated positive affect and positive future expectations after the intervention compared to patients in the Typical Day condition. (3) And crucially, patients in the Best Possible Self condition will show better selective learning than patients in the Typical Day group. Thus the investigators hypothesize that the blocking effect will be higher for patients with higher degrees of positive affect and optimism.

Selective learning will be experimentally investigated by using the blocking procedure. The blocking effect is one mechanism that can provide information about selective learning. In this procedure, one event (A+) will be first paired with pain. Participants should learn the relation between this event and the occurrence of pain. After, another event (x), i.e. the blocking stimulus, is displayed with the first event A (AX+). The combination of both events will also be associated with pain. It will be tested, to which extent participants expect pain for the blocking stimulus alone. In case of "blocked", i.e. reduced pain expectation for X we can assume adaptive, selective learning. In the planned study a fictitious diary of a fibromyalgia patient will be used as "cover story". Different events and the words "pain" or "no pain" will be visually presented together, indicating in which situation pain occurs and in which not. Participants will be asked to imagine that they are the practitioner trying to learn which situations are associated with pain. This learning experiment is based on a former study by Meulders and colleagues, in which selective learning was investigated by presenting fictitious diary entries, such as "Kim was walking her dog" or "the weather was bad today" (CS) and pairing the sentences in terms of the blocking procedure (see above). In the central test phase, in which events were presented without pain report, participants were asked to give their pain expectations for the different situations. Deficits in selective learning were found in fibromyalgia patients compared to healthy control. For the planned study this learning paradigm will be used with minor adaptations (translated to German, minor instruction adaptations, renamed scale ends, higher scale resolution). The learning experiment will take about 15 minutes. Positive affect and optimism will be induced through the Best Possible Self (BPS) Intervention. Subjects will be randomly assigned to either the optimism condition or a control condition. The whole study will be conducted online. Participants will be asked to switch from the experimental platform to the questionnaire platform at various times. In order to recreate the laboratory situation and to keep participants engaged, we implemented several videos in which the virtual investigator guides participants throughout the experiment. The sample size should include 164 participants, assuming a small effect. A priori power analyses were calculated with the software program G*Power for ANOVA with repeated measures, within-between interaction with a small effect (f=0.1, Power 0.8, α=0.05). Patients with fibromyalgia will be recruited via social media platforms like Facebook and Instagram, but also through advertisement in magazines, self-help groups as well as via flyers in medical offices and clinics. Mainly ANOVA´s with repeated measures and t-tests are planned to be calculated. There will be four separate repeated measures (RM) ANOVA´s on positive and negative Affect and on positive and negative future expectations with Group (BPS/TD) as between-factor and Time (Pre/Post) as within-subjects factor. Planned contrasts will also be used to check whether the increase in a) positive affect and b) positive future expectations (and not a decrease in a) negative affect and b) negative future expectations) will be larger in the BPS group compared with the TD group. Further, we will calculate ANOVA´s to test acquisition of pain expectancy in the different phases. The central analysis will be a 2 x 3 RM ANOVA with Group (BPS/TD) as between-subjects variable and Stimulus Type (B/Z/X) as within-subjects variable on pain expectancy ratings in the test phase of the learning experiment. Further, following the literature, the investigators might do responder analyses and use the change in a) positive affect and b) positive future expectations as predictors for selective learning (in order to have more power in statistical analyses, if necessary). Transformations: Transformations of not normally distributed variables will be done. Inference criteria: We will use the standard p < .05 criteria. Data exclusion: Participants will be excluded from analyses, if they give Z>X ratings in the test phase or if they give the same rating throughout the whole experiment. Participants, who did not complete the BPS/TD writing exercise properly will be excluded from ANOVA´s with group factors.

Positive affect and optimism will be induced through the Best Possible Self (BPS) Intervention. Subjects will be randomly assigned to either the optimism or the control condition. In the optimism intervention, participants will be asked to imagine a future in which everything went well and in which all their wishes are fulfilled. After, it will be tested if selective learning can be increased through higher degrees of positive affect/optimism.

Positive affect and optimism will be induced through the Best Possible Self (BPS) Intervention. In the optimism intervention, participants will be asked to imagine a future in which everything went well and in which all their wishes are fulfilled. This procedure is known to reliably generate positive affect and positive future expectations (Carrillo et al., 2019). Orientated at Flink et al. (2015) the BPS condition was adapted for a pain population.

In the control condition, participants are asked to describe and visualize a typical day (TD). We adapted the TD condition in order to take possible changes in participants´ TD due to the COVID-19 pandemic into account.

Participants are asked to think about their Best Possible Self for one minute, then describe it for 15 minutes (in writing) and, subsequently, imagine it for another 5 minutes as vividly as possible.

Participants are asked to think about their Typical Day for one minute, then describe it for 15 minutes (in writing) and, subsequently, imagine it for another 5 minutes as vividly as possible.

Positive Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if participants of the BPS group score higher in Positive Affect after the intervention than at baseline. Further, we will test if participants in the BPS condition score higher in Positive Affect than participants in the TD group.

State Optimism/Positive Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016). It will be tested if participants of the BPS group score higher in Optimism after the intervention than at baseline. Further, we will test if participants in the BPS condition score higher in Optimism than participants in the TD group.

Numerical Rating Scale (NRS; 0-100; 0 = expect not all, 100 = expect very much) It will be tested to which extent participants expect pain for the different stimuli which are used in the experiment (A, B, X, Z).

Numerical Rating Scale (NRS; 0-100; 0 = expect not all, 100 = expect very much) It will be tested to which extent participants expect pain for the stimuli A+ and Z-.

Numerical Rating Scale (NRS; 0-100; 0 = expect not all, 100 = expect very much) It will be tested to which extent participants expect pain for the stimuli A+ and Z-.

Numerical Rating Scale (NRS; 0-100; 0 = expect not all, 100 = expect very much) It will be tested to which extent participants expect pain for the stimuli B+, AX+ and Z-.

Numerical Rating Scale (NRS; 0-100; 0 = expect not all, 100 = expect very much) It will be tested to which extent participants expect pain for X, B, and Z. In case of "blocked", i.e. reduced pain expectation for X in the test phase we can assume adaptive, selective learning.

Positive Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if affect is stable from immediate postvisualization to end of the experiment.

State Optimism/Positive Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016). It will be tested if state optimism is stable from immediate postvisualization to end of the experiment.

Trait Optimism will be measured with the revised version of the life-orientation-test (LOT-R; Scheier & Carver 1985).

Depression will be measured with the Patient Health Questionnaire (PHQ-9, Spitzer, Kroenke & Williams, 1999).

Pain Catastrophizing will be measured with the Pain Catastrophizing Scale (PCS; Sullivan, Bishop, Pivik 1995). Answers are scored on a 5-point rating scale (0 = not at all; 4 = all the time).

Negative Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if participants´ scores in Negative Affect are influenced by the intervention.

Negative Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if Negative Affect is stable from immediate postvisualization to end of the experiment.

Negative Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016).It will be tested if participants´ score in Negative Future Expectancies are influenced by the intervention.

Negative Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016).It will be tested if participants´ score in Negative Future Expectancies are influenced by the intervention.

Participants can train to give pain expectancy ratings for the stimuli C+ und D-, which are only used in the practice phase.

Positive Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if there are baseline differences between the BPS and TD group. Further, this outcome might be used as covariate.

State Optimism/Positive Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016). It will be tested if there are baseline differences between the BPS and TD group. Further, this outcome might be used as a covariate.

Negative Future Expectancies will be measured with the future expectancies scale (FEX; Hanssen et al., 2013; Peters, Vieler, & Lautenbacher, 2016).It will be tested if participants´ score in Negative Future Expectancies are influenced by the intervention.

Negative Affect will be measured with the Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016). It will be tested if there are baseline differences between the BPS and TD group.

Inclusion Criteria: self-reported fibromyalgia diagnosis access to a computer or laptop with audio output Exclusion Criteria: dyslexia other impairments that affect cognitive performance (e.g. stroke or brain damages)

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