IMPORTANCE There is little evidence to guide management of depressive symptoms in older people. OBJECTIVE To evaluate whether a collaborative care intervention can reduce depressive symptoms and prevent more severe depression in older people. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial conducted from May 24, 2011, to November 14, 2014, in 32 primary care centers in the United Kingdom among 705 participants aged 65 years or older with Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) subthreshold depression; participants were followed up for 12 months. INTERVENTIONS Collaborative care (n=344) was coordinated by a case manager who assessed functional impairments relating to mood symptoms. Participants were offered behavioral activation and completed an average of 6 weekly sessions. The control group received usual primary care (n=361). MAIN OUTCOMES AND MEASURES The primary outcome was self-reported depression severity at 4-month follow-up on the 9-item Patient Health Questionnaire (PHQ-9; score range, 0-27). Included among 10 prespecified secondary outcomes were the PHQ-9 score at 12-month follow-up and the proportion meeting criteria for depressive disorder (PHQ-9 score Ն10) at 4-and 12-month follow-up. RESULTS The 705 participants were 58% female with a mean age of 77 (SD, 7.1) years. Four-month retention was 83%, with higher loss to follow-up in collaborative care (82/344 [24%]) vs usual care (37/361 [10%]). Collaborative care resulted in lower PHQ-9 scores vs usual care at 4-month follow-up. The proportions of participants meeting criteria for depression at 4-month follow-up were 17.2% (45/262) vs 23.5% (76/324), respectively (difference, −6.3% [95% CI, −12.8% to 0.2%]; relative risk, 0.83 [95% CI, 0.61-1.27]; P = .25) and at 12-month follow-up were 15.7% (37/235) vs 27.8% (79/284) (difference, −12.1% [95% CI, −19.1% to −5.1%]; relative risk, 0.65 [95% CI, 0.46-0.91]; P = .01). Collaborative Care Usual Care Difference (95% CI) P Value PHQ-9 score, mean At 4 mo (primary outcome) 5.36 6.67 −1.31 (−1.95 to −0.67) <.001 At 12 mo 5.93 7.25 −1.33 (−2.10 to −0.55) .001 CONCLUSIONS AND RELEVANCE Among older adults with subthreshold depression, collaborative care compared with usual care resulted in a statistically significant difference in depressive symptoms at 4-month follow-up, of uncertain clinical importance. Although differences persisted through 12 months, findings are limited by attrition, and further research is needed to assess longer-term efficacy.

Current interventions need to be adapted to address features other than binge eating. Further research is required to help us understand the effectiveness of GSH in children and young people, invariably high dropout rates and how technology can enhance interventions. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

Background Older adults, including those with long-term conditions (LTCs), are vulnerable to social isolation. They are likely to have become more socially isolated during the Coronavirus Disease 2019 (COVID-19) pandemic, often due to advice to “shield” to protect them from infection. This places them at particular risk of depression and loneliness. There is a need for brief scalable psychosocial interventions to mitigate the psychological impacts of social isolation. Behavioural activation (BA) is a credible candidate intervention, but a trial is needed. Methods and findings We undertook an external pilot parallel randomised trial (ISRCTN94091479) designed to test recruitment, retention and engagement with, and the acceptability and preliminary effects of the intervention. Participants aged ≥65 years with 2 or more LTCs were recruited in primary care and randomised by computer and with concealed allocation between June and October 2020. BA was offered to intervention participants (n = 47), and control participants received usual primary care (n = 49). Assessment of outcome was made blind to treatment allocation. The primary outcome was depression severity (measured using the Patient Health Questionnaire 9 (PHQ-9)). We also measured health-related quality of life (measured by the Short Form (SF)-12v2 mental component scale (MCS) and physical component scale (PCS)), anxiety (measured by the Generalised Anxiety Disorder 7 (GAD-7)), perceived social and emotional loneliness (measured by the De Jong Gierveld Scale: 11-item loneliness scale). Outcome was measured at 1 and 3 months. The mean age of participants was aged 74 years (standard deviation (SD) 5.5) and they were mostly White (n = 92, 95.8%), and approximately two-thirds of the sample were female (n = 59, 61.5%). Remote recruitment was possible, and 45/47 (95.7%) randomised to the intervention completed 1 or more sessions (median 6 sessions) out of 8. A total of 90 (93.8%) completed the 1-month follow-up, and 86 (89.6%) completed the 3-month follow-up, with similar rates for control (1 month: 45/49 and 3 months 44/49) and intervention (1 month: 45/47and 3 months: 42/47) follow-up. Between-group comparisons were made using a confidence interval (CI) approach, and by adjusting for the covariate of interest at baseline. At 1 month (the primary clinical outcome point), the median number of completed sessions for people receiving the BA intervention was 3, and almost all participants were still receiving the BA intervention. The between-group comparison for the primary clinical outcome at 1 month was an adjusted between-group mean difference of −0.50 PHQ-9 points (95% CI −2.01 to 1.01), but only a small number of participants had completed the intervention at this point. At 3 months, the PHQ-9 adjusted mean difference (AMD) was 0.19 (95% CI −1.36 to 1.75). When we examined loneliness, the adjusted between-group difference in the De Jong Gierveld Loneliness Scale at 1 month was 0.28 (95% CI −0.51 to 1.06) and at 3 months −0.87 (95% CI −1.56 to −0.18), suggesting evidence of benefit of the intervention at this time point. For anxiety, the GAD adjusted between-group difference at 1 month was 0.20 (−1.33, 1.73) and at 3 months 0.31 (−1.08, 1.70). For the SF-12 (physical component score), the adjusted between-group difference at 1 month was 0.34 (−4.17, 4.85) and at 3 months 0.11 (−4.46, 4.67). For the SF-12 (mental component score), the adjusted between-group difference at 1 month was 1.91 (−2.64, 5.15) and at 3 months 1.26 (−2.64, 5.15). Participants who withdrew had minimal depressive symptoms at entry. There were no adverse events. The Behavioural Activation in Social Isolation (BASIL) study had 2 main limitations. First, we found that the intervention was still being delivered at the prespecified primary outcome point, and this fed into the design of the main trial where a primary outcome of 3 months is now collected. Second, this was a pilot trial and was not designed to test between-group differences with high levels of statistical power. Type 2 errors are likely to have occurred, and a larger trial is now underway to test for robust effects and replicate signals of effectiveness in important secondary outcomes such as loneliness. Conclusions In this study, we observed that BA is a credible intervention to mitigate the psychological impacts of COVID-19 isolation for older adults. We demonstrated that it is feasible to undertake a trial of BA. The intervention can be delivered remotely and at scale, but should be reserved for older adults with evidence of depressive symptoms. The significant reduction in loneliness is unlikely to be a chance finding, and replication will be explored in a fully powered randomised controlled trial (RCT). Trial registration ISRCTN94091479.

This journal is a member of and subscribes to the principles of the Committee on Publication Ethics (COPE) (www.publicationethics.org/).Editorial contact: journals.library@nihr.ac.ukThe full HTA archive is freely available to view online at www.journalslibrary.nihr.ac.uk/hta. Print-on-demand copies can be purchased from the report pages of the NIHR Journals Library website: www.journalslibrary.nihr.ac.uk Criteria for inclusion in the Health Technology Assessment journalReports are published in Health Technology Assessment (HTA) if (1) they have resulted from work for the HTA programme, and (2) they are of a sufficiently high scientific quality as assessed by the reviewers and editors.Reviews in Health Technology Assessment are termed 'systematic' when the account of the search appraisal and synthesis methods (to minimise biases and random errors) would, in theory, permit the replication of the review by others. HTA programmeThe HTA programme, part of the National Institute for Health Research (NIHR), was set up in 1993. It produces high-quality research information on the effectiveness, costs and broader impact of health technologies for those who use, manage and provide care in the NHS. 'Health technologies' are broadly defined as all interventions used to promote health, prevent and treat disease, and improve rehabilitation and long-term care.The journal is indexed in NHS Evidence via its abstracts included in MEDLINE and its Technology Assessment Reports inform National Institute for Health and Care Excellence (NICE) guidance. HTA research is also an important source of evidence for National Screening Committee (NSC) policy decisions.For more information about the HTA programme please visit the website: http://www.nets.nihr.ac.uk/programmes/hta This reportThe research reported in this issue of the journal was funded by the HTA programme as project number 10/57/43. The contractual start date was in September 2012. The draft report began editorial review in July 2016 and was accepted for publication in February 2017. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors' report and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. Published by ...

Aims To explore the association between the use of glycaemic technologies and person‐reported outcomes (PROs) in adults with type 1 diabetes (T1D). Methods We included T1D and technology publications reporting on PROs since 2014. Only randomised controlled trials and cohort studies that used validated PRO measures (PROMs) were considered. Results T1D studies reported on a broad range of validated PROMs, mainly as secondary outcome measures. Most studies examined continuous glucose monitoring (CGM), intermittently scanned CGM (isCGM), and the role of continuous subcutaneous insulin infusion (CSII), including sensor‐augmented CSII and closed loop systems. Generally, studies demonstrated a positive impact of technology on hypoglycaemia‐specific and diabetes‐specific PROs, including reduced fear of hypoglycaemia and diabetes distress, and greater satisfaction with diabetes treatment. In contrast, generic PROMs (including measures of health/functional status, emotional well‐being, depressive symptoms, and sleep quality) were less likely to demonstrate improvements associated with the use of glycaemic technologies. Several studies showed contradictory findings, which may relate to study design, population and length of follow‐up. Differences in PRO findings were apparent between randomised controlled trials and cohort studies, which may be due to different populations studied and/or disparity between trial and real‐world conditions. Conclusions PROs are usually assessed as secondary outcomes in glycaemic technology studies. Hypoglycaemia‐specific and diabetes‐specific, but not generic, PROs show the benefits of glycaemic technologies, and deserve a more central role in future studies as well as routine clinical care.