The Effects of Psychological Interventions on Diabetic ...
The Effects of Psychological Interventions on Diabetic Peripheral Neuropathy: A Systematic Review and Meta-AnalysisOriginal researchSimona Racaru1,2 Jackie Sturt1 Celik Aycan1Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King’s College London, UKSt. Mary’s Hospital, Imperial College Healthcare NHS Trust, London, UKCorresponding authorSimona Racaru, RN, MScSt. Mary’s Hospital, Zachary Cope Ward, 8th Floor, Imperial College Healthcare NHS TrustSouth Wharf Road, London, W2 1BL, UK. Telephone: 020 3312 6661; Email: simona.racaru@ AbstractBackground: Psychological interventions are effective at ameliorating the experience of pain in conditions such as rheumatoid arthritis and chronic back pain. However, their effect on diabetic peripheral neuropathy (DPN) pain has yet to be established.Aim: To assess the effectiveness of psychological interventions on pain and related outcomes in adults with DPN. Methods: Medline, Embase, PsychInfo, and CINAHL databases together with grey literature and trial registers were searched. A meta-analysis and narrative synthesis of included studies were undertaken.Results: Nine studies were selected from 1610 citations. At short-term follow-up psychological therapies showed a large effect on pain severity (SMD= -0.94, 95%CI[-1.50, -0.37], p=.001) a small effect on pain interference (SMD= -0.39, 95%CI[-0.73, -0.05], p=.02), and a moderate effect on depressive symptoms (SMD= -0.58, 95%CI[-0.95, -0.21], p=.002). Quality of life significantly improved in experimental subjects, (MD= -2.35, 95%CI[-3.99, -0.71], p=.005). At medium-term follow-up there was a large effect on pain severity (SMD= -1.26, 95%CI[-1.76, -0.77], p< .00001) and on pain interference (SMD= -0.91, 95%CI[-1.61, -0.21], p=.01) and a moderate effect on depressive symptoms (SMD= -0.76, 95%CI[-1.48, -0.05], p=.04). At long-term follow-up, improvements in pain interference, mood and self-care behaviors were reported.Conclusions. Psychological interventions can help to reduce pain levels and depressive symptoms and improve QoL in adults with DPN. These findings demonstrate that relationships between pain and perceived control among other groups who experience chronic pain may also be replicated in the DPN population. This is an important outcome that can guide further research and associated service developments.Key words Psychological interventions, diabetic peripheral neuropathy, diabetes, pain, quality of life, systematic reviewKey practice points Mindfulness-based stress reduction can decrease pain and improve quality of life for people who experience diabetic peripheral neuropathy.Cognitive behavioral therapy can also decrease pain, improve mood and promote self-care among this patient group.It is not possible to confidently determine the effect size of these interventions based on current evidence.Further research is needed with larger samples and more robust methodologies to confirm or refute these findings.Diabetic peripheral neuropathy (DPN) is a common microvascular complication of diabetes which is caused by nerve damage. On average, half of people with diabetes develop peripheral neuropathies (Tesfaye et al., 2011). DPN commonly affects the nerves of the feet and legs with symptoms varying between tingling and pain to numbness and complete loss of sensation (Funnell, 2014). Chronic DPN can lead to complications, the most common of which is diabetic foot disease which often leads to amputation (Hershey, 2017). Early diagnosis and treatment of DPN to lessen its impact on quality of life (QoL) may impact positively on self-care and therefore reduce the risk of diabetic foot ulceration and amputation (Diabetes UK, 2017).Chronic pain, experienced by 50% of individuals with DPN (Hershey, 2017), impairs physical ability and diminishes freedom of movement and mental health, leading to conditions such as depression, which affects social functioning (Breivik, Eisenberg, & O’Brien, 2013). The level (or intensity) of pain felt by one person is defined as pain severity while the degree to which DPN pain interferes or limits people’s daily functioning, such as disturbing sleep, walking or general activity, is defined as pain interference (Zelman, Gore, Dukes, Tai, & Brandenburg, 2005). Additionally, depression and diabetes specific distress are common complications of diabetes (Fisher et al., 2010). First line treatment for DPN is medication although sometimes pain is difficult to manage with drugs (Tesfaye et al., 2011). Other therapies include alpha-lipoic-acid supplements and spinal cord stimulation (Amato Nesbit et al., 2019), foot offloading (Mishra, Chhatbar, Kashikar, & Mehndiratta, 2017), acupuncture (Zhang, Tang, & Gao, 2019) and surgical decompression (Zhang, Li, & Zheng, 2013). In other conditions such as fibromyalgia or chronic back pain, research has demonstrated that psychological interventions can be effective for pain management (Sielski, Rief, & Glombiewski, 2017; Theadom, Cropley, Smith, Feigin, & McPherson, 2015).Psychological interventions are wide-ranging and aim to minimize emotional distress resulting from chronic pain and comorbid depression, and to enhance individuals’ adaptability and attitudes to diminish the pain experience (Benzon et al., 2013). They can therefore help people manage their condition in different ways to traditional medicine. For example, by increasing their motivation to improve self-management and coping skills, and by enhancing adherence to treatment (Tovote et al., 2017).Since chronic pain is a life-long condition that can never be entirely cured, evidence suggests that combining medication with psychological therapies might be a more sustainable and efficient method of pain management in the long-term (Turk, Wilson, & Cahana, 2011). Pain is an almost inextricable part of DPN which needs to be treated holistically (Turk, Audette, Levy, Mackey, & Stanos, 2010), and psychological therapies can help improve a person’s control of their bodies and their bodies’ response to both pain and treatment (Nijs et al., 2017).The purpose of this review is to bring together for the first time, evidence relating to the effectiveness of psychological interventions for DPN. It seeks to answer the following research question: what is the effectiveness of psychological interventions compared to usual care, active control or other interventions on DPN symptoms, management and quality of life in adults with diabetes?MethodsThis systematic review followed PRISMA guidelines (Moher et al., 2009), the Cochrane Handbook for Systematic Reviews of Interventions (Higgins & Green, 2011) and was registered on the PROSPERO database for systematic reviews; number CRD42019127274. Eligibility criteriaEligible studies were randomized controlled trials (RCTs) investigating the effect of psychological interventions compared to usual care, active control or other intervention. To be eligible the studies needed to capture outcome data relating to DPN symptoms, DPN management and QoL in adults. Psychological interventions in this review represent any treatment that is adapted for each participant, focusing on mental healing, relaxation and behavior changes e.g. cognitive behavioral therapy (CBT), mindfulness-based stress reduction (MBSR), motivational interviewing (MI), acceptance therapy and counselling.Studies that used mixed interventions (e.g. qigong, tai chi, yoga), or explored neuropathy of nondiabetic causation were excluded. Selection of eligible studies was performed by one reviewer (SR) under supervision of a second reviewer (JS).Search strategyTo retrieve relevant papers a search strategy was constructed on three facets of a Population, Intervention, Comparison, Outcome and Study design analysis: population, intervention and study design (PIS) (Sackett, Richardson, Rosenberg, & Haynes, 1997). The search strategy contained the key words: diabetes*, neuropath*, psycho* and the MeSH terms: diabetes mellitus, peripheral neuropathy and cognitive behavioral therapy. The search was conducted by one reviewer (SR) under supervision of a second reviewer (JS) without limitations to language, year, place, type or status of publication on four databases from inception to April 2019: Medline, Embase, PsychInfo, and CINAHL. Additionally, grey literature databases OpenGrey and PsychExtra, and the following trial registers were searched: World Health Organization International Clinical Trials Registry Platform, , European Union Clinical Trials Register. The reference lists of selected studies were also hand searched.Study selectionDatabase search results were combined in Endnote software and duplicates removed. Titles and abstracts for the remaining papers were examined and irrelevant reports were eliminated. Full texts were then retrieved and reviewed for eligibility criteria. Studies that did not meet the eligibility criteria were excluded at this stage.Papers retrieved for full text evaluation were read in full by two reviewers (SR, AC) and any disagreements were resolved by discussion. In cases where agreement was not reached, a third reviewer was consulted (JS). Data extractionData were extracted by one reviewer (SR) using a data extraction template that captured key study characteristics including sample, intervention, comparator, outcomes and results. Data were presented to a second reviewer (JS) and any uncertainties were discussed. Extractions were reviewed by a faculty staff member independently of the review team and any uncertainties were discussed.Risk of bias and GRADE assessmentThe internal and external validity of the included studies were assessed independently by two reviewers (SR, AC) using the GRADE system (Balshem et al., 2011) and the Cochrane Collaboration “Risk of bias” tool (Higgins & Green, 2011). Any uncertainties were resolved through discussion. Data analysisMeta-analysis was justified where at least two studies shared homogeneity in participants, interventions, control groups and outcome (Higgins & Green 2011). Meta-analysis was performed in Review Manager 5.3 (RevMan5.3), and the intervention effect estimate was expressed as standardized mean difference (SMD), when continuous outcomes were measured with various instruments. SMD effect estimate was calculated with a 95% confidence interval (CI) using the inverse variance technique with random effect model (Higgins & Green, 2011). SMD effect size was interpreted following Cohen (1988): 0.2 to 0.5= small effect, 0.5 to 0.8= moderate effect and over 0.8= large effect. When similar scales were used for continuous outcome measurements, meta-analysis was calculated as a difference in means between groups (MD) with a 95% CI using either a random or fixed effect model. The level of significance (alpha) for the overall effect estimate was defined by the p value of the test for overall effect (Z) (Higgins & Green, 2011). For a 95% CI, a p value of less than .05 (p< .05) was considered significant. Statistical heterogeneity across studies was quantified by computing I2 statistics. An I2 value between 30% to 60% was considered moderate and an I2 value greater than 60%, was considered substantial heterogeneity (Higgins & Green, 2011). A negative value for the effect size (e.g. SMD= -0.58) suggested that the intervention was more probably to be favorable for a continuous outcome. In trials where standard deviations (SD) were not provided these were calculated from the CI of the means in each group (Higgins & Green, 2011). For studies which could not be meta-analyzed, a narrative synthesis was undertaken. Follow-up times from pre-treatment were categorized in intervals of short-term (between 2 and 12 weeks), medium-term (between 12 and 24 weeks) and long-term (between 24 and 52 weeks).ResultsSelection of studiesThe search strategy retrieved 1610 citations. After duplicates were removed, 1227 titles and abstracts were screened and 29 were retrieved for full text assessment (Figure 1). Figure 1. Prisma flow diagram (Moher et al., 2009) Study characteristicsFrom 29 fully assessed papers, nine were included of which one was an unpublished RCT retrieved from , registration number NCT00829387 (Ghavami, Radfar, Soheily, Shamsi, & Khalkhali, 2018; Kerns, 2015; Keukenkamp, Merkx, Busch-Westbroek, & Bus, 2018; Nathan et al., 2017; Otis et al., 2013; Pfammatter, 2012; Skafjeld et al., 2015; Teixeira, 2010; Vedhara et al., 2012). The nine studies involved the randomization of 335 participants from which 298 datasets were analyzed. All studies were published in English and used randomization at the individual level. Study characteristics are presented in Table 1. Table 1 Characteristics of included studiesAuthor, country, designSample size (I/C), follow-up period, intention-to-treat (Y/N), primary outcomeMean age (years), % male, % T2DM, duration of condition (years)Intervention, therapist, control treatment Outcomes: MD [95%CI], (p value)Ghavami et al., (2018), Iran, RCTKerns, (2015), USA, RCTNathan et al., (2017), Canada, RCTOtis et al., (2013), USA, RCTTeixeira, (2010), USA, RCTPfammatter, (2012), USA, RCTKeukenkamp et al, (2018), Netherlands, RCTSkafjeld et al., (2015), Norway, RCTVedhara et al., (2012), UK, RCTI=40/C=40, 12 weeks, N, DPN severity assessed by mTCNSI=23/C=24, 36 weeks, N, pain severity assessed by NRSI=33/C=33, 12 weeks (20 weeks from baseline), N, pain interference assessed by BPII=11/C=8, 16 weeks, N/ NR, pain severity & interference assessed by MPI, depressive symptoms assessed by BDII=11/C=11, 4 weeks, N, quality of life assessed by NeuroQoLI=16/C=16, 12 weeks, N, pain severity assessed by MPII=6/C=7, 12 weeks, N, footwear adherence assessed by @monitorI=21/C=20, 1 year, Y, foot ulcer incidence assessed by Wagner classification systemI=10/C=5, 36 months, N/NR, SOS, BIPQ, POMS, SDSCAI=49/C=47, I=30%/C=19%, 100%, I=19/C=17I=64/C=61, I=91%/C=96%, 100%, NR60I=50%/C=62%, I=80%/C=75%, NR63, 100%, 100%, NR75, 25%, 100%, 12.6.I=61/C=57, I=50%/C=55%, NRI=57/C=62, I=100%/C=80%, I=80%/C=80%, I=29/C=17I=57/C=59, I=86%/C=75%, I=71% /C=70%, I=17/C=19.565, 77%, 77%, NRLifestyle counselling, one-on-one sessions, therapist details not specified, 12 weeks duration but frequency of sessions not stated, usual care control groupCognitive behavioural therapy, ten weekly sessions of 60 minutes, delivered one-on-one by a doctoral level psychologist, diabetes education control groupMindfulness based stress reduction delivered in group over 8 weeks with 2.5-hour sessions, plus one 6-hour session delivered at mid-course, practitioners with more than 5 years-experience in MBSR, usual care control group. Cognitive-behavioural pain management therapy delivered one-on-one, 60-minute weekly sessions over 11 weeks by PhD level psychologist or Master level therapist, usual treatment control groupMindfulness meditation, one group session of 60 minutes plus individual listening to a CD 5 days per week for 4 weeks plus weekly telephone calls and maintenance of a food diary, delivered by principal investigator with experience in MM and diabetes education, attention placebo control groupThermal Biofeedback Assisted Relaxation (TBAR), 15-minute weekly sessions delivered by the experimenter over 6-weeks, attention placebo control groupMotivational Interviewing (MI) delivered one-on-one by rehabilitation medicine specialist, standard education control.Theory based counselling for foot temperature monitoring, recording of daily steps and footwear adherence. Training on using digital infrared thermometer, study nurse, standard care controlCognitive behaviour therapy delivered in group by diabetes specialist nurse and diabetes specialist podiatrist, usual care control groupmTCNS: ST= -3.60[-4.70, -2.50], (p<.00001)NRS: ST= -0.38[-1.39, 0.63], (p=.46)MPI: ST= -0.34[-1.10, 0.42], (p=.38); LT= -0.93[-1.70, -0.16], (p=.03).BDI: ST -2.35[-6.73, 2.03], (p=.29); LT= -4.15[-9.37, 1.07], (p=.12).BPI interference ST= -1.41[-2.96, 0.14], (p=.07); MT= -2.18[-3.73, -0.63], (p=.006): BPI intensity ST= -1.60[-2.40, -0.80] (p<.0001), MT= -1.92[-2.71, -1.13] (p<.00001).BDI depressive symptoms: ST= -3.53[-5.83, -1.23] (p=.003); MT= -4.81[-7.11, -2.51], (p<.0001).NQoL ST= -2.44[-4.11, -0.77] (p=.004)pain catastrophising (p=.001) improved in the I group.MPI severity ST= -1.04[-2.00, -0.08], (p=.03); MT= -1.08[-1.73, -0.43], (p=.001)MPI interference ST= -0.88[-2.39, 0.63], (p=.025), MT= -1.57[-2.56, -0.58], (p=.002)BDI depressive symptoms ST= -7.40[-18.68, 3.88], (p=.20), MT= (p=.58)NQoL: insignificant improvement in QoLNRS: pain severity did not improve; insignificant improvement in pain interference.PSQI: Sleep did not improve following interventionMPI: pain severity did not decrease following interventionSOPA: pain perception and perceived control did not improve following intervention@monitor: footwear adherence improved at ST: I= 90% (30%–98%)* but not at MT: I=56% (28%–90%)*Wagner foot classification system: foot ulcer incidence at LT was better in the intervention group: I=33% /C=50%,SOS social support ST= 3.60 [-0.26, 7.46], p=.07; LT= 4.30[-1.46, 10.06], p=.14.BIPQ illness cognition ST= (p=.97); LT= (p=.26);POMS mood ST= -13.00[-24.27, -1.73], p=.02; LT= -10.20[-18.70, -1.70], p=.02SDSCA self-care behaviour ST= 12.80 [1.09, 24.51], (p=.03); LT= 14.80 [1.92, 27.68], (p=.02)Key: RCT=randomised controlled trial, I/C=intervention/control, T2DM= type 2 diabetes, mTCNS=modified Toronto Clinical Neuropathy Score, BPI=brief pain inventory, MPI=Multidimensional Pain Inventory, BDI=Beck Depression Inventory, QoL=quality of life, NQoL=Neuropathy Quality of Life, POMS=profile of mood state, SOS=Significant Others Scale, BIPQ=Brief Illness Perceptions Questionnaire, SDSCA=Summary of Diabetes Self-Care Activities, NRS= Numeric Rating Scale, SOPA=Survey of pain attitudes, DPN=diabetic peripheral neuropathy, NR= not reported, p≤.05=significant, MD= difference in means between groups, [95%CI] = 95% Confidence Interval, negative scores (-) = psychological intervention is favourable (e.g. reduction in pain symptoms), ST=short term, MT=medium term, LT=long term, [*]=data provided as Median (range). Intervention typeTwo studies used CBT strategies that focused on relaxation techniques, challenging negative thoughts, anger management, sleep hygiene and behavioral goals (Otis et al., 2013; Vedhara et al., 2012). One study did not specify type of CBT intervention (Kerns, 2015). Three studies used variants of CBT: a mindfulness-based stress reduction (MBSR) intervention to allow the objective assessment of pain rather than catastrophizing it (Nathan et al., 2017); a mindfulness meditation intervention to increase awareness and relaxation (Teixeira, 2010); and thermal biofeedback assisted relaxation (TBAR), which used breathing techniques and imagery to aid relaxation (Pfammatter, 2012). Two studies investigated counselling techniques on lifestyle changes, goal setting and foot temperature monitoring (Ghavami et al., 2018; Skafjeld et al., 2015) and one study assessed a motivational interviewing intervention (Keukenkamp et al., 2018). Mode of deliveryTwo interventions were delivered in groups (Vedhara et al., 2012; Nathan et al., 2017), four were one-on-one interventions (Otis et al., 2013; Kerns, 2015; Ghavami et al., 2018; Keukenkamp et al., 2018), one was mixed (group and one-on-one) (Teixeira, 2010) and two did not specify the delivery method (Pfammatter, 2012; Skafjeld et al., 2015). Intervention duration varied from two weeks to one year. Delivery staffThe interventions were delivered by psychologists (Otis et al., 2013; Kerns, 2015), a diabetes specialist nurse and podiatrist (Vedhara et al., 2012), experienced practitioners trained in MBSR with more than 5 years of experience as workshop leaders (Nathan et al., 2017), a principal investigator (Teixeira, 2010; Pfammatter, 2012), care provider (details not specified) (Ghavami et al., 2018), study nurse (Skafjeld et al., 2015) and a rehabilitation medicine specialist (Keukenkamp et al., 2018).The included studies assessed clinical and psychosocial outcomes which are summarized in Table 2 together with the instruments used for their assessment. Table 2 Patient Reported Outcome Measurements (PROMs)OutcomeOutcome measureStudy IDDiabetes and DPN specificPain SeverityPain InterferenceQoLModified Toronto Clinical Neuropathy Score (mTCNS)Neuropathic Pain Scale (NPS)Neuropathic Pain ScaleNeuropathic Quality of Life scale (NQoL): NQoL/Pain, NQoL/Feeling, NQoL/Motor, NQoL/Restriction, NQoL/Disruption subscales, NQoL/Emotion, Overall Quality of Life, NQoL/SymptomGhavami et al., (2018)Teixeira, (2010)Teixeira, (2010)Nathan et al., (2017); Teixeira, (2010)GenericPain SeverityPain interferenceQoLPsychosocial functioningPsychosocial functioningFootwear adherenceBrief Pain Inventory (BPI)West Haven Yale Multidimensional Pain Inventory (WHYMPI)/MPINumeric Rating Scale (NRS)Brief Pain Inventory (BPI)West Haven Yale Multidimensional Pain Inventory (WHYMPI)/MPIMultidimensional Pain Inventory (MPI)The 12 item- Short Form Health Survey version 2 (SF-12v2): (Mental Composite Score (MCS), Role Emotion subscale (RE), Social Function subscale (SF), Vitality subscale (VT), Mental Health subscale (MH); Physical Composite Scale (PCS), General Health subscale (GH), Role Physical subscale (RP), Physical Function subscale (PF), Bodily Pain subscale (BP)Beck Depression Inventory (BDI) Patient Health Questionnaire-9 (PHQ-9)Pain Catastrophising Scale (PCS)Profile of mood state (POMS-2A)Perceived Stress Scale (PSS)State Measure of Affect, Pain, and ControlSurvey of Pain Attitudes (SOPA)Significant Other Scale (SOS)Brief Illness Perception Questionnaire (BIPQ)Summary of Diabetes Self-Care Activities (SDSCA)Self-reported questionnaireNathan et al., (2017)Otis et al., (2013); Pfammatter, (2012)Kerns, (2015)Nathan et al., (2017)Otis et al., (2013)Kerns, (2015)Nathan et al., (2017)Otis et al., (2013); Kerns, (2015)Nathan et al., (2017)Nathan et al., (2017)Nathan et al., (2017); Vedhara et al., (2012)Nathan et al., (2017)Pfammatter, (2012)Pfammatter, (2012)Vedhara et al., (2012)Vedhara et al., (2012)Vedhara et al., (2012)Skafjeld et al., (2015)Other measurements Non-PROMsFootwear adherenceIncidence of foot ulcerSkin temperature monitoring‘@monitorWagner classificationThermometerKeukenkamp et al., (2018)Skafjeld et al., (2015)Skafjeld et al., (2015)Risk of bias and GRADE assessmentInternal reliability assessments are presented in Figure 2 with full judgements for each study provided as online supplementary information (Appendix A). Among the nine studies five were pilot studies with sample sizes insufficient to detect the effectiveness of interventions. Four studies scored high risk of bias for not blinding participants, personnel or outcome assessors, two studies scored high risk of bias for high subject attrition rates, and five studies had inadequate quality reporting. Only one study had no risk of bias, however, the investigators stopped recruiting participants before attaining the sample size calculated to detect effectiveness (Nathan et al., 2017). GRADE rankings were performed for each outcome, all of which were graded as low quality due to serious imprecisions and serious limitations but no serious inconsistencies or serious indirectness. All outcomes in this study had very wide confidence intervals denoting imprecision in results. Publication bias could not be assessed due to the small number of studies. Judgements for rating and ranking evidence followed GRADE assessment criteria (Balshem et al., 2011). Figure 2 Risk of bias summary and graphEffect of interventions Meta-analyses were performed on short-term follow-up pain severity and pain interference scores in four studies, QoL in two studies and depressive symptoms in three studies. Additionally, two studies provided data for medium-term follow-up for pain severity, pain interference and depressive symptoms (Figure 3). A narrative synthesis is provided for seven studies. In cases where interventions did not reach statistical significance this does not mean that the intervention had 'a negligible' or 'no effect'. Indeed, positive but non-significant results were described by authors as 'clinically meaningful' and may, with more robust research designs on larger samples, reach statistical significance.Figure 3 Meta-analyses - Outcomes: Pain severity (1.1.1 & 1.1.2), pain interference (1.1.3 & 1.1.4), depressive symptoms (1.1.5 &1.1.6), quality of life (1.2.1); Sensitivity analysis (1.3.1). Pain severityOverall scores at short-term (Ghavami et al., 2018; Kerns, 2015; Nathan et al., 2017; Otis et al., 2013) and medium-term follow-up (Nathan et al., 2017; Otis et al., 2013) generated large effect sizes favoring psychological interventions (SMD= -0.94, 95%CI[-1.50, -0.37], Z=3.25, p=.001, I2=68% [analysis 1.1.1] and, SMD= -1.26, 95%CI[-1.76, -0.77], Z=5.03, p<.00001, I2=0% [analysis 1.1.2]) respectively. Pain severity significantly decreased with counselling (Ghavami et al., 2018) and MBSR (Nathan et al., 2017) at short-term while patients receiving MBSR (Nathan et al., 2017) or CBT (Otis et al., 2013) reported significantly less pain severity at medium-term follow-up compared to usual care. Additionally, two studies reported no reductions in pain severity following mindfulness meditation (Teixeira, 2010) or Thermal Biofeedback Assisted Relaxation (Pfammatter, 2012) at short-term compared with an attention placebo group. A further study of an unspecified CBT intervention reported no difference in pain severity levels between experimental and control group at long-term follow-up (Kerns, 2015). Pain interferenceOverall results generated a small effect size at short-term follow-up (Kerns, 2015; Nathan et al., 2017; Otis et al., 2013; Teixeira, 2010) (SMD= -0.39, 95%CI[-0.73, -0.05], Z=2.27, p=.02, I2=0% [analysis 1.1.3]) and a large effect size at medium-term follow-up (Nathan et al., 2017; Otis et al., 2013) (SMD= -0.91, 95%CI[-1.61, -0.21], Z=2.54, p=.01, I2 =35% [analysis 1.1.4]) favoring psychological interventions. MBSR (Nathan et al., 2017) and CBT (Otis et al., 2013) significantly decreased pain interference at medium-term follow-up and the unspecified CBT intervention resulted in less pain interference among experimental subjects compared to controls at long-term follow-up (MD= -0.93, 95%CI[-1.70, -0.16], Z=2.38, p=.02) (Kerns, 2015).Depressive symptomsExperimental subjects reported an overall significant improvement in depressive symptoms at short-term (Kerns, 2015; Nathan et al., 2017: Otis et al., 2013) and medium-term follow-up (Nathan et al., 2017: Otis et al., 2013), with moderate effect sizes (SMD= -0.58, 95%CI[-0.95, -0.21], Z=3.09, p=.002, I2 =0% [analysis 1.1.5], and SMD= -0.76, 95%CI[-1.48, -0.05], Z=2.10, p=.04, I2=44% [analysis 1.1.6]) respectively. In these two analyses, participants receiving MBSR (Nathan et al., 2017) reported a significant decrease in depressive symptoms at short-term and medium-term follow-up compared to control group. Additionally, the unspecified CBT intervention reported improvements in the experimental group compared to controls at long-term follow-up, but results did not reach statistical significance (MD= -4.15, 95%CI[-9.37, 1.07], Z=1.56, p=.12) (Kerns, 2015).Quality of lifeShort-term follow-up QoL scores (Nathan et al., 2017; Teixeira, 2010) showed an overall significant improvement following psychological interventions compared to controls (MD= -2.35, 95%CI[-3.99, -0.71], Z=2.82, p=.005, I2 =0% [analysis 1.2.1]). In this meta-analysis only experimental subjects receiving MBSR (Nathan et al., 2017) attained statistically significant improvements in QoL compared with control group. At medium-term follow-up only Nathan et al. (2017) reported QoL outcomes which also indicated statistically significant improvements for subjects receiving MBSR compared with controls (MD= -3.10, 95%CI[-4.77, -1.43], Z=3.64, p=.0003).Mood Participants receiving CBT reported significant improvements in mood at short-term (MD= -13.00, 95%CI[-24.27, -1.73], Z=2.26, p=.02) and long-term follow-up (MD= -10.20, 95%CI[-18.70, -1.70], Z=2.35, p=.02) (Vedhara et al., 2012). Participants receiving MBSR reported promising improvements in mood at short-term (MD= -12.38, 95%CI[-30.90, 6.14], Z=1.31, p=.19) and at medium-term follow-up (MD= -15.28, 95%CI[-33.80, 3.24], Z=1.62, p=.11) compared with usual care, but these scores didn’t reach statistical significance (Nathan et al., 2017).Psychosocial functioningSelf-care behaviors at short-term follow-up were significantly improved following CBT (MD= 12.80, 95%CI[1.09, 24.51], Z=2.14, p=.03), whilst social support improvements approached significance (p=.07) and no difference between groups was reported for illness cognition (p=.97) (Vedhara et al., 2012). Additionally, at long-term follow-up, CBT significantly improved self-care behaviors (MD= 14.80, 95%CI [1.92, 27.68], Z=2.25, p=.02), while social support and illness cognition showed promising improvements but scores did not reach significance (p=.14 and p=.26 respectively) (Vedhara et al., 2012). Perceived control did not significantly improve with Thermal Biofeedback Assisted Relaxation at short-term follow-up (Pfammatter, 2012). Pain catastrophizing Pain catastrophizing significantly decreased following MBSR at short-term (MD= -7.88, 95%CI[-12.56, -3.20], Z=3.30, p=.001) and at medium-term follow-up (MD= -12.36, 95%CI[-17.04, -7.68], Z=5.18, p< .00001) (Nathan et al., 2017). Perceived stressPerceived stress decreased in the MBSR group at short-term follow-up approaching significance (MD= -3.33, 95%CI[-6.94, 0.28], Z=1.81, p=.07), and significantly decreased at medium-term follow-up compared with usual care (MD= -6.39, 95%CI[-10.00, -2.78], Z=3.47, p=.0005) (Nathan et al., 2017).Footwear adherenceMotivational interviewing improved footwear adherence at short-term but this was not maintained at medium-term follow-up compared with an education group (Keukenkamp et al., 2018). Compared with usual care, footwear adherence did not improve with counselling at long-term follow-up (Skafjeld et al., 2015).Incidence of foot ulcersFoot ulcer incidence did not significantly decrease at long-term follow-up (Skafjeld et al., 2015). Sleep Sleep did not improve in a mindfulness meditation group compared with an attention placebo group at short-term follow-up (Teixeira, 2010).Sensitivity analysis A sensitivity analysis for pain severity outcomes was performed to determine if temporary exclusion from the meta-analysis of certain studies influenced the treatment effect (Polit & Beck, 2012). Pain severity scores were not reported separately by Ghavami et al. (2018) but were included in the overall DPN severity score. This study was included in the meta-analysis as it followed the same direction of effect. The study by Teixeira (2010) showed unfavorable results for experimental subjects and followed the opposite effect direction to other studies turning favorable results negative. This study was removed and only four studies were pooled for analysis (Figure 3 analysis 1.3.1). DiscussionFindings from this review suggest that psychological interventions can have a significant beneficial effect on pain severity, pain interference, depressive symptoms and QoL in adults with DPN. Pain severity and pain interference levels were significantly lower in the MBSR and CBT groups compared with usual care. MBSR also improved QoL and reduced depressive symptoms at short-term and medium-term follow-up. Additionally, MBSR was associated with decreased pain catastrophizing and perceived stress at both time points. CBT was associated with improvements in mood at short-term and long-term follow-up, while MBSR showed promising improvements in mood at short-term and medium-term follow-up. CBT also significantly improved self-care behaviors and showed promising results for social support and illness cognition at short-term and long-term follow-up. Although non-significant relationships were reported between some interventions and outcome measures, such as sleep patterns and mindfulness meditation at short-term follow-up, they may still be considered clinically meaningful. For example, the short-term benefits of MI on adherence to the use of custom-made footwear in the home environment. Furthermore, the psychological interventions were safe with no studies reporting worsening of symptoms or other detrimental effects on the progression of DPN. Low attrition rates were also reported among experimental group samples suggesting that the interventions were well received and that they were feasible treatment options.Findings compared with the wider literatureThe findings of this review are consistent with the wider psychological literature on chronic pain management. Meta-analyses have demonstrated the efficacy of CBT and MBSR to lessen painful symptoms across a range of chronic pain populations (Hoffman, Papas, Chatkoff, & Kerns, 2007; Grossman, Niemann, Schmidt, & Walach, 2004). More recent reviews appear to shore up these findings. For example, mindfulness (Veehof, Trompetter, Bohlmeijer, & Schreurs, 2016) and CBT interventions (Richmond et al., 2015) for chronic pain and lower back pain respectively resulted in significant beneficial effects on pain levels that echo the findings of this review. Collectively, these findings support a biopsychosocial model of pain, which posits that the experience of pain is moderated by affective and cognitive processes in the central nervous system and not just levels of nociceptor activation (Gatchel, Peng, Peters, Fuchs, & Turk, 2007). On that basis the positive findings from this review provide evidence for the mediating effects of MBSR and CBT on the experience of pain and on the physical and emotional consequences of living with chronic pain.LimitationsThe nine studies contained in this review had small sample sizes; the risk of a Type II error is therefore high and was acknowledged by a number of authors. Measurements were performed at various points in time and assessment instruments were highly variable. Additionally, the studies reported wide confidence intervals denoting imprecise results. Although difficult to generalize the effect of the psychological interventions, significant relationships commensurate with the wider psychological literature were reported. This provides some confidence in the results which are also supported by the biopsychosocial model of pain. Although publication bias was minimized by searching for unpublished studies no search strategy can retrieve 100% of relevant articles. This review may therefore be affected by a publication bias that could not be assessed on a funnel plot due to the limited number of included studies. Consequently, it is difficult to estimate whether the results of possible undetected studies might influence the review findings. The aforementioned methodological quality of papers is a limitation with more than 30% of domains scoring unclear risk of bias. When interpreting the review findings, it should be taken into consideration that more than 50% of studies did not blind participants, personnel or outcome assessors. Authors did not report the costs of the psychological interventions although some judged them to be low cost. However, without such data it is not possible to judge the benefit of these interventions in relation to each other and when compared with other treatments including the pharmacological management of pain.Implications for nursing practice and researchFirst line treatment for neuropathic pain is typically pharmacological interventions that carry the risk of adverse effects and the development of tolerance and dependence over time. The results of this review suggest that nursing staff might expect patients with DPN to benefit from psychological interventions either to augment medication or to be trialed as an initial alternative. Significant challenges will be the availability and integration of psychological approaches to pain management within traditional care. However, it should be noted that the MBSR and CBT programs used in the studies by Nathan et al. (2017) and Otis et al. (2013) respectively were established community programs, and in the latter case, study participants followed a protocol for chronic pain management that was not tailored specifically for patients with DPN. Nursing staff are beholden therefore to research local community resources, educate their patients on the potential benefits of psychological interventions for pain management, signpost patients to available resources when appropriate, and advocate for the integration of these treatment options within existing care pathways.DPN is painful, highly prevalent and challenging to manage pharmacologically. Further investigation of psychological interventions is therefore warranted. This review represents an important step in the systematic appraisal of available evidence and points to the value of further work. However, given the low quality of current evidence, larger trials with sufficient statistical power are needed to confirm or refute the effectiveness of these interventions on the experience of pain and their impact on the quality of life of people with DPN. In particular, CBT and MBSR have demonstrated the potential to improve DPN management and, if researched with more robust methodologies, might provide increased confidence for the interventions’ effectiveness.ConclusionsDiabetic peripheral neuropathy (DPN) impacts on individuals’ physical and psychosocial wellbeing, and is the cause of all diabetic foot disease in people with diabetes. Managing DPN at an earlier stage in its development to lessen its impact on quality of life may impact on positive self-care and therefore prevent the development of diabetic foot disease. This review suggests that by augmenting pharmacological treatment with psychological interventions, adults with diabetes can benefit from better DPN management. However, there was insufficient evidence to confidently interpret the effect size of the psychological interventions. Nevertheless, this systematic review demonstrates that the known relationships between pain and perceived control among other groups who experience chronic pain may also be replicated in the DPN population. This is an important finding that can guide further research and associated service developments. AcknowledgementsThe authors acknowledge the contribution to Iain Ryrie for his reading and advice on the manuscript. Declarations of interestNoneReferencesAmato Nesbit, S., Sharma, R., Waldfogel, J. M., Zhang, A., Bennett, W. L., Yeh, H. C., ... & Dy, S. M. (2019). Non-pharmacologic treatments for symptoms of diabetic peripheral neuropathy: a systematic review. Current medical research and opinion, 35(1), 15-25. Balshem, H., Helfand, M., Schünemann, H. J., Oxman, A. D., Kunz, R., Brozek, J., ... & Guyatt, G. H. (2011). GRADE guidelines: 3. 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Current medical research and opinion, 35(7), 1319-1320. Zhang, W., Li, S. & Zheng, X. (2013). Evaluation of the clinical efficacy of multiple lower extremity nerve decompression in diabetic peripheral neuropathy. Journal of Neurological Surgery Part A: Central European Neurosurgery, 74(02), 096-100. DOI:10.1055/s-0032-1320029 Appendix ARisk of bias assessment for each studyGhavami 2018 ?CriteriaAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote: "Eligible participants were systematically randomized by computer into the control or lifestyle intervention group"Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)Unclear Not reportedBlinding of outcome assessment (detection bias)UnclearNot reportedIncomplete outcome data (attrition bias)LowAttrition clearly described 3 (7.5%) participants from each arm discontinued participation. Complete outcome dataSelective reporting (reporting bias)UnclearProtocol number not provided. Insuf?cient information to permit a judgementOther biasUnclearBaseline characteristics for both groups were similar. Possible risk of bias: Participants enrolled from one hospital only & relatively short intervention time. Source of funding not stated.Kerns 2015BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)UnclearQuote: "Participants will be randomized in equal numbers to the two conditions."Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)LowSingle masked-participants. Personnel not blinded.Blinding of outcome assessment (detection bias)HighNot reported. Probably not done. (single masked – participants)Incomplete outcome data (attrition bias)HighComment: Attrition clearly described. High rate of drop out at F/U 36 weeks assessment: 6 (26%) IG and 10(42%) CG. Intention to treat analysis not stated.Selective reporting (reporting bias)LowComment: Study Protocol available (NCT01269502) and all outcomes are reported.Other biasUnclearQuote: "There were no significant differences in demographics characteristics between the intervention and control groups at baseline." Conflicts of interest not stated. Keukenkamp 2018BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote:" Patients were randomly assigned to either standard education (control group) or standard education plus two sessions of motivational interviewing (intervention group). Block randomization with variable block sizes was used."Allocation concealment (selection bias)LowQuote: "A sealed envelope randomization sequence was created and managed by an independent investigator."Blinding of participants and personnel (performance bias)LowQuote: "Participants were not blinded to treatment allocation, but we blinded them at baseline to the goal of monitoring treatment adherence. The participants’ rehabilitation medicine specialist was blinded to treatment allocation. Patients were asked not to disclose their study allocation to their rehabilitation medicine specialist"Blinding of outcome assessment (detection bias)UnclearNot reported/ maybe yes? “The participants’ rehabilitation medicine specialist was blinded to treatment allocation”Incomplete outcome data (attrition bias)Low Quote: “One participant in the intervention group withdrew. Two patients in the standard education group dropped out because of a fractured foot (n = 1) and death (n = 1). These events were not related to the study intervention”. Comment: high dropout rate but attrition clearly described: 1 (17%) IG and 2 (29%) CG lost to follow up. Selective reporting (reporting bias)UnclearComment: Protocol number not provided. Insuf?cient information provided to permit a judgementOther biasLowComment: No significant differences between groups at baseline. The study appears to be free of other sources of bias.Nathan 2017BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote: "Patients were randomly allocated to a waiting list or MBSR using computer-generated random numbers in a permuted block design with randomly varying block lengths. Allocations were stratified by type 1 or type 2 diabetes and by pain severity as indicated by an average of the four pain-severity numeric rating scales of the Brief Pain Inventory (BPI)".Allocation concealment (selection bias)LowComment: Concealed by opaque envelope.Blinding of participants and personnel (performance bias)LowQuote: "Allocations were generated by an independent statistician and concealed from investigators and treating physicians. Treatment allocation occurred as close as possible to the start of the MBSR course when the next consecutively numbered opaque envelope for the appropriate strata was opened". Blinding of outcome assessment (detection bias)LowQuote: “Allocations were generated by an independent statistician and concealed from investigators and treating physicians.”Incomplete outcome data (attrition bias)LowComment: Attrition clearly described: 3 (9%) participants withdrew from IG and 1 (3%) participant withdrew from CG.Selective reporting (reporting bias)LowComment: Protocol available (NCT02127762) and all outcomes reported.Other biasLowComment: No significant differences between groups at baseline. The study appears to be free of other sources of bias.Otis 2013BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote: "participants were randomized via flip of a coin to 1 of the treatment conditions: CBT versus TAU."Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)HighComment: participants and investigators not blindedBlinding of outcome assessment (detection bias)HighNot reported. Probably not done.Incomplete outcome data (attrition bias)LowHigh attrition rate in IG 3 (27%) discontinued participation but this was clearly described and attrition was not related to the intervention.Selective reporting (reporting bias)UnclearComment: Protocol not provided (the study followed the CONSORT of reporting trials). Insuf?cient information provided to permit a judgement Other biasLowComment: Study participants similar at baseline characteristics. Incentives to participants reported.Pfammatter 2012BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)UnclearQuote: "Participants were randomly assigned to one of the groups"Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)Low Single - participantsBlinding of outcome assessment (detection bias)HighNot reported. Probably not done.Incomplete outcome data (attrition bias)HighQuote: “The current study suffered from low recruitment as well as many participants withdrawing from the study.”Comment: Very high attrition rate and although this was described no reasons were given. 32 randomized/ 21 analysed, 6 (37.5%) dropped out from IG, and 5 (31%) dropped out from CG. Selective reporting (reporting bias)LowComment: study protocol available NCT00858351 and all outcomes are reported.Other biasUnclearQuote: there were no significant baseline differences between groups on Haemoglobin A1C, age, measures of pain, measures of control, or temperature in any limb.” Conflicts of interest not stated.Skafjeld 2015BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote: "Block randomization was used to assign each four subjects to blocks with two in each group. Randomization was stratified for patients with a history of Charcot foot, who have an extra high risk of recurrence."Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)HighComment: clearly stated - single blinded- assessors (hence participants and personnel not blinded)Blinding of outcome assessment (detection bias)LowComment: single blinded- assessorsIncomplete outcome data (attrition bias)LowComment: Attrition reported: 3(7.3%) CG, dropped out. Analysis performed for all participants randomized.Selective reporting (reporting bias)LowComment: Study Protocol provided (NCT01269502) and all outcomes are reported.Other biasLowQuote: "There were no significant differences in demographics characteristics between the intervention and control groups at baseline." Teixeira 2010BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)LowQuote: "A convenience sample of 22 adults with diabetes who described symptoms of PDPN participated in this pilotstudy. Participants were assigned by random number assignment to either the intervention group (Group A) or attention-placebo group (Group B)".Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)UnclearNot reportedBlinding of outcome assessment (detection bias)UnclearNot reportedIncomplete outcome data (attrition bias)LowComment: Attrition clearly described. One participant in each group dropped out. Selective reporting (reporting bias)UnclearProtocol not provided. Insuf?cient information provided to permit a judgement. Baseline data, not provided, for the outcomes. Other biasUnclearBaseline data did not differ between groups. Incentives to participants reported. Source of funding and conflicts of interest not stated.Vedhara 2012BiasAuthors' judgementSupport for judgementRandom sequence generation (selection bias)UnclearQuote: "eligible individuals were randomized 2:1 to the intervention or control groups" Comment: Randomization method not described.Allocation concealment (selection bias)UnclearNot reportedBlinding of participants and personnel (performance bias)UnclearNot reportedBlinding of outcome assessment (detection bias)LowComment: Interviews transcripts anonymised and coded; assessor did not participate in intervention. Incomplete outcome data (attrition bias)LowComment: high attrition in the intervention group (20%) but this was clearly described: 2 participants randomized to IG withdrew before the intervention commencement. Selective reporting (reporting bias)highComment: study protocol available ISRCTN17915181 available at . Not all outcomes reported: (SF-12, HADS), intervention duration = 10 weeks instead of 13 weeks.Other biasLowNothing detected ................
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