Network Analysis Supports Targeting Patients’ Strengths to Treat Chronic Pain

chronic pain
chronic pain
Depression is twice as common in patients with chronic pain compared to health subjects, and pain intensity, frequency, and number of locations are linked to depression and anxiety.

Psychosocial interventions for patients with chronic pain should target the interaction of pain, depression, sleep trouble, and anxiety, as well as positive affect, according to the results of a study published in the Clinical Journal of Pain. Depression is twice as common in patients with chronic pain compared to healthy subjects, and pain intensity, frequency, and location are linked to depression and anxiety.

In a model that accounts for co-occurring psychological symptoms across disorders, “symptoms are conceptualized as being interdependent and jointly forming a psychopathologic mental condition instead of assuming an underlying latent disease or disorder that explains the presence of a number of independent symptoms.”

Juan M. Gomez Penedo, PhD, of the department of clinical psychology and psychotherapy, University of Bern, Switzerland, and colleagues used network analysis, including the graphical least absolute shrinkage and selection operator, to explore associations between chronic pain, anxiety and depression. They suggested that identifying the most important symptom in a network could have clinical implications.

The investigators examined 454 patients with chronic pain disorder who underwent a 3 week program of interventions, including medical interventions, pharmacotherapy, physiotherapy, ergotherapy, individual psychotherapy, group psychotherapy, and relaxation training.  They assessed depression and anxiety symptoms using the Hospital Anxiety and Depression Scale (HADS), and pain symptoms were evaluated using the Brief Pain Inventory (BPI) at baseline and posttreatment.

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Depressive symptoms that demonstrated the strongest associations with pain interference, as well as the greatest centrality and expected influence in the network, were those that reflected the absence of positive psychological states, such as lack of cheerfulness and laughter. Sleep problems were correlated with anxiety and depressive symptoms and pain intensity symptoms.

Compared with baseline, symptom distress scores were significantly reduced after treatment but the network structure remained stable and the strength of the associations among the network nodes increased over the 3-week treatment period. Nonresponders’ networks demonstrated a greater nodal strength than responders’ networks (S-statistic, 2.65; P =.003). In addition, responders’ networks at posttreatment were significantly stronger than at baseline (S-statistic, 4.20; P <.001)

Limitations of the study included the failure to explore other psychopathologic phenomena associated with chronic pain, the lack of a direct measurement of positive affect or resilience related to chronic pain symptomatology, and the inclusion of only 2 time points—baseline and posttreatment. 

The investigators concluded that “trying to help the patient to increase the number of enjoyable experiences in his or her life with a greater potential for (genuine) laughter may be a promising target associated with greater changes in pain interferences.” They noted that a capitalization model may prove effective for chronic pain, as “activating the patient’s strengths as the main mechanism of change” could be more promising than a compensation model that targets “deficits or vulnerabilities.”


Gomez-Penedo JM, Rubel JA, Blattler L, et al. The complex interplay of pain, depression, and anxiety symptoms in patients with chronic pain. A network approach. Clin J Pain. 2020;36:249-259.

This article originally appeared on Psychiatry Advisor