Psychosocial interventions to improve mental health in adults with vision impairment: systematic review and meta-analysis

Purpose To systematically assess the literature on psychosocial interventions to improve mental health (i.e. depression, anxiety, mental fatigue, loneliness, psychological stress and psychological well-being) in visually impaired adults (≥18 years). Methods The databases Medline, Embase and Psychinfo were searched for relevant studies, which were categorised into randomised controlled trials (RCTs), non-RCTs and before and after comparisons (BA). The Cochrane Collaboration Risk of Bias Tool was used to assess study quality. Standardised mean differences (SMD) were calculated to quantitatively summarise the outcomes of the RCTs and non-RCTs in a meta-analysis. Meta-regression was used to explore sources of heterogeneity in the data. Results The search identified 27 papers (published between 1981 and 2015), describing the outcomes of 22 different studies (14 RCTs, four non-RCTs, and four BAs). Pooled analyses showed that interventions significantly reduced depressive symptoms (SMD −0.30, 95% confidence interval (CI) −0.60 to −0.01), while effects on anxiety symptoms, mental fatigue, psychological stress and psychological well-being were non-significant. Meta-regression analyses showed homogeneity in effect sizes across a range of intervention, population, and study characteristics. Only a higher age of participants was associated with less effective results on depressive symptoms (b = 0.03, 95% CI 0.01 to 0.05), psychological stress (b = 0.07, 95% CI 0.01 to 0.13) and psychological well-being (b = −0.03, 95% CI −0.05 to 0.01). However, after removing a clear outlier the overall effect on depressive symptoms and the influence of age on depressive symptoms and psychological stress were no longer significant, while the influence of age on psychological well-being remained. Conclusions There is currently only limited evidence for the effectiveness of psychosocial interventions in the field of low vision. More well-designed trials are needed with specific attention for interventions tailored to the needs of elderly patients

Clinical course of pain and disability following primary lumbar discectomy: systematic review and meta-analysis

© 2020, The Author(s). Purpose: To conduct a meta-analysis to describe clinical course of pain and disability in adult patients post-lumbar discectomy (PROSPERO: CRD42015020806). Methods: Sensitive topic-based search strategy designed for individual databases was conducted. Patients (> 16 years) following first-time lumbar discectomy for sciatica/radiculopathy with no complications, investigated in inception (point of surgery) prospective cohort studies, were included. Studies including revision surgery or not published in English were excluded. Two reviewers independently searched information sources, assessed eligibility at title/abstract and full-text stages, extracted data, assessed risk of bias (modified QUIPs) and assessed GRADE. Authors were contacted to request raw data where data/variance data were missing. Meta-analyses evaluated outcomes at all available time points using the variance-weighted mean in random-effect meta-analyses. Means and 95% CIs were plotted over time for measurements reported on outcomes of leg pain, back pain and disability. Results: A total of 87 studies (n = 31,034) at risk of bias (49 moderate, 38 high) were included. Clinically relevant improvements immediately following surgery (> MCID) for leg pain (0–10, mean before surgery 7.04, 50 studies, n = 14,910 participants) and disability were identified (0–100, mean before surgery 53.33, 48 studies, n = 15,037). Back pain also improved (0–10, mean before surgery 4.72, 53 studies, n = 14,877). Improvement in all outcomes was maintained (to 7 years). Meta-regression analyses to assess the relationship between outcome data and a priori potential covariates found preoperative back pain and disability predictive for outcome. Conclusion: Moderate-level evidence supports clinically relevant immediate improvement in leg pain and disability following lumbar discectomy with accompanying improvements in back pain. Graphic abstract: These slides can be retrieved under Electronic Supplementary Material. [Figure not available: see fulltext.]

Predicting chronic low-back pain based on pain trajectories in patients in an occupational setting: an exploratory analysis.

OBJECTIVE: This study aimed to (i) identify subpopulations of patients in an occupational setting who will still have or develop chronic low-back pain (LBP) and (ii) evaluate a previously developed prediction model based on the determined subpopulations. METHOD: In this prospective cohort, study data were analyzed from three merged randomized controlled trials, conducted in an occupational setting (N=622). Latent class growth analysis (LCGA) was used to distinguish patients with a different course of pain intensity measured over 12 months. The determined subpopulations were used to derive a definition for chronic LBP and evaluate an existing model to predict chronic LBP. RESULTS: The LCGA model identified three subpopulations of LBP patients. These were used to define recovering (353) and chronic (269) patients. None of the interventions showed a relevant treatment effect over another but the rate of decline in symptoms during the first months of the intervention seems to predict recovery. The prediction model, based on this dichotomous outcome, with the variables pain intensity, kinesiophobia and a clinically relevant change in pain intensity and functional status in the first three months, showed a bootstrap-corrected performance with an area under the operating characteristic curve (AUC) of 0.75 and explained variance of 0.26. CONCLUSION: In an occupational setting, different subpopulations of chronic LBP patients could be identified using LCGA. The prediction model based on these subpopulations showed a promising predictive performance

External validation of prognostic models for recovery in patients with neck pain

BackgroundNeck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice.ObjectiveThe purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care.MethodsThis validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated.ResultsThe Dmodel and Amodel discriminative performance (AUC ConclusionsExternal validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care

Development and internal validation of prognostic models for recovery in patients with non-specific neck pain presenting in primary care

Objectives: Development and internal validation of prognostic models for post-treatment and 1-year recovery in patients with neck pain in primary care. Design: Prospective cohort study. Setting: Primary care manual therapy practices. Participants: Patients with non-specific neck pain of any duration (n = 1193). Intervention: Usual care manual therapy. Outcome measures: Recovery defined in terms of pain intensity, disability, and global perceived improvement directly post-treatment and at 1-year follow-up. Results: All post-treatment models exhibited acceptable discriminative performance after derivation (AUC ≥ 0.7). The developed post-treatment disability model exhibited the best overall performance (R2 = 0.24; IQR, 0.22–0.26), discrimination (AUC = 0.75; 95% CI, 0.63–0.84), and calibration (slope 0.92; IQR, 0.91–0.93). After internal validation and penalization, this model retained acceptable discriminative performance (AUC = 0.74). The five other models, including those predicting 1-year recovery, did not reach acceptable discriminative performance after internal validation. Baseline pain duration, disability, and pain intensity were consistent predictors across models. Conclusion: A post-treatment prognostic model for disability was successfully developed and internally validated. This model has potential to inform primary care clinicians about a patient’s individual prognosis after treatment, but external validation is required before clinical use can be recommended

Behavioral determinants as predictors of return to work after long-term sickness absence: an application of the theory of planned behavior

Background The aim of this prospective, longitudinal cohort study was to analyze the association between the three behavioral determinants of the theory of planned behavior (TPB) model-attitude, subjective norm and self-efficacy-and the time to return-to-work (RTW) in employees on long-term sick leave. Methods The study was based on a sample of 926 employees on sickness absence (maximum duration of 12 weeks). The employees filled out a baseline questionnaire and were subsequently followed until the tenth month after listing sick. The TPB-determinants were measured at baseline. Work attitude was measured with a Dutch language version of the Work Involvement Scale. Subjective norm was measured with a self-structured scale reflecting a person's perception of social support and social pressure. Self-efficacy was measured with the three subscales of a standardised Dutch version of the general self-efficacy scale (ALCOS): willingness to expend effort in completing the behavior, persistence in the face of adversity, and willingness to initiate behavior. Cox proportional hazards regression analyses were used to identify behavioral determinants of the time to RTW. Results Median time to RTW was 160 days. In the univariate analysis, all potential prognostic factors were significantly associated (P < 0.15) with time to RTW: work attitude, social support, and the three subscales of self-efficacy. The final multivariate model with time to RTW as the predicted outcome included work attitude, social support and willingness to expend effort in completing the behavior as significant predictive factors. Conclusions This prospective, longitudinal cohort-study showed that work attitude, social support and willingness to expend effort in completing the behavior are significantly associated with a shorter time to RTW in employees on long-term sickness absence. This provides suggestive evidence for the relevance of behavioral characteristics in the prediction of duration of sickness absence. It may be a promising approach to address the behavioral determinants in the development of interventions focusing on RTW in employees on long-term sick leave

Variable selection under multiple imputation using the bootstrap in a prognostic study

Background: Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method: In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results: We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion: We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values

A systematic review and meta-synthesis of the impact of low back pain on people's lives

Copyright @ 2014 Froud et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background - Low back pain (LBP) is a common and costly problem that many interpret within a biopsychosocial model. There is renewed concern that core-sets of outcome measures do not capture what is important. To inform debate about the coverage of back pain outcome measure core-sets, and to suggest areas worthy of exploration within healthcare consultations, we have synthesised the qualitative literature on the impact of low back pain on people’s lives. Methods - Two reviewers searched CINAHL, Embase, PsycINFO, PEDro, and Medline, identifying qualitative studies of people’s experiences of non-specific LBP. Abstracted data were thematic coded and synthesised using a meta-ethnographic, and a meta-narrative approach. Results - We included 49 papers describing 42 studies. Patients are concerned with engagement in meaningful activities; but they also want to be believed and have their experiences and identity, as someone ‘doing battle’ with pain, validated. Patients seek diagnosis, treatment, and cure, but also reassurance of the absence of pathology. Some struggle to meet social expectations and obligations. When these are achieved, the credibility of their pain/disability claims can be jeopardised. Others withdraw, fearful of disapproval, or unable or unwilling to accommodate social demands. Patients generally seek to regain their pre-pain levels of health, and physical and emotional stability. After time, this can be perceived to become unrealistic and some adjust their expectations accordingly. Conclusions - The social component of the biopsychosocial model is not well represented in current core-sets of outcome measures. Clinicians should appreciate that the broader impact of low back pain includes social factors; this may be crucial to improving patients’ experiences of health care. Researchers should consider social factors to help develop a portfolio of more relevant outcome measures.Arthritis Research U

Galaxy and mass assembly: the G02 field, Herschel–ATLAS target selection and data release 3

We describe data release 3 (DR3) of the Galaxy And Mass Assembly (GAMA) survey. The GAMA survey is a spectroscopic redshift and multiwavelength photometric survey in three equatorial regions each of 60.0 deg2 (G09, G12, and G15), and two southern regions of 55.7 deg2 (G02) and 50.6 deg2 (G23). DR3 consists of: the first release of data covering the G02 region and of data on H-ATLAS (Herschel – Astrophysical Terahertz Large Area Survey) sources in the equatorial regions; and updates to data on sources released in DR2. DR3 includes 154 809 sources with secure redshifts across four regions. A subset of the G02 region is 95.5 per cent redshift complete to r < 19.8 mag over an area of 19.5 deg2, with 20 086 galaxy redshifts, that overlaps substantially with the XXL survey (X-ray) and VIPERS (redshift survey). In the equatorial regions, the main survey has even higher completeness (98.5 per cent), and spectra for about 75 per cent of H-ATLAS filler targets were also obtained. This filler sample extends spectroscopic redshifts, for probable optical counterparts to HATLAS submillimetre sources, to 0.8 mag deeper (r < 20.6 mag) than the GAMA main survey. There are 25 814 galaxy redshifts for H-ATLAS sources from the GAMA main or filler surveys. GAMA DR3 is available at the survey website (www.gama-survey.org/dr3/)

Development and Validation of Diagnostic Models for Cervical Nerve Root Involvement Based on Items From the Patient Interview and Clinical Examination

OBJECTIVE To develop and internally validate diagnostic models for cervical nerve root involvement based on patient interview and clinical examination items. DESIGN Diagnostic predictive modelling study. METHODS People with a suspicion of cervical nerve root involvement (i.e., radicular pain and/or radiculopathy) (N 134) were included. Three diagnostic models (i.e., patient interview items alone, clinical examination alone, and combined patient interview plus clinical examination) were developed using multivariable logistic regression analyses. For internal validation, we performed bootstrapping techniques (250 repetitions). The diagnostic accuracy (Area Under the Curve (AUC)) and explained variance (Nagelkerke s r-squared) of the models were assessed. An AUC of 0.7 or higher was considered adequate. RESULTS The patient interview model consisted of two items and showed an explained variance of 0.23 and an AUC of 0.74 (95 CI 0.66-0.81) after bootstrapping. The clinical examination model consisted of 2 items and had an explained variance of 0.29, and an AUC of 0.77 (95 CI 0.69-0.85) after internal validation. The combined model had an explained variance of 0.38 and an AUC of 0.82 (95 CI 0.75-0.89) after bootstrapping and consisted of the Spurling test (odds ratio (OR) 8.0 (95 CI 3.1-20.4)), Arm pain worse than neck pain (OR 4.8 (95 CI 1.9-11.8)) and the patient-reported Presence of paraesthesia and/or numbness (OR 2.8 (95 CI 1.0-7.8)). CONCLUSIONS The combined model showed the best diagnostic accuracy to determine the likelihood of cervical nerve root involvement. External validation is required before implementing any diagnostic model