BackgroundNearly everyone in society uses the Internet in one form or another. The Internet is heralded as an efficient way of providing mental health treatments and services. However, some people are still excluded from using Internet-enabled technology through lack of resources, skills, and confidence.ObjectiveFive years ago, we showed that people with severe mental illness were at risk of digital exclusion, especially middle-aged patients with psychosis and/or people from black or minority ethnic groups with psychosis. An understanding of the breadth of potential digital exclusion is vital for the implementation of digital health services. The aim of this study is to understand the context of digital exclusion for people who experience mental illness.MethodsWe conducted a survey involving people with a primary diagnosis of psychosis or depression in London, United Kingdom. A total of 241 participants were recruited: 121 with psychosis and 120 with depression. The majority of surveys were collected face-to-face (psychosis: n=109; depression: n=71). Participants answered questions regarding familiarity, access, use, motivation, and confidence with Internet-enabled technologies (ie, computers and mobile phones). Variables predicting digital exclusion were identified in regression analyses. The results were compared with the survey conducted in 2011.ResultsDigital exclusion has declined since 2011. Online survey collection introduced biases into the sample, masking those who were likely to be excluded. Only 18.3% (20/109) of people with psychosis in our sample were digitally excluded, compared with 30% (28/93) in 2011 (χ21=3.8, P=.04). People with psychosis had less confidence in using the Internet than people with depression (χ21=7.4, P=.004). Only 9.9% (24/241) of participants in the total sample were digitally excluded, but the majority of these people had psychosis (n=20). Those with psychosis who were digitally excluded were significantly older than their included peers (t30=3.3, P=.002) and had used services for longer (t97=2.5, P=.02). Younger people were more likely to use mobile phones. Digitally excluded participants cited a lack of knowledge as a barrier to digital inclusion, and most wanted to use the Internet via computers (rather than mobile phones).ConclusionsDigital exclusion is lower, but some remain excluded. Facilitating inclusion among this population means helping them develop skills and confidence in using technology, and providing them with access. Providing mobile phones without basic information technology training may be counterproductive because excluded people may be excluded from mobile technology too. An evidence-based digital inclusion strategy is needed within the National Health Service to help digitally excluded populations access Internet-enabled services.

ObjectivesAssess the impact of text-based electronic notifications on improving clinic attendance, in relation to study quality (according to risk of bias), and to assess simple ways in which notifications can be optimised (ie, impact of multiple notifications).DesignSystematic review, study quality appraisal assessing risk of bias, data synthesised in meta-analyses.Data sourcesMEDLINE, EMBASE, PsycINFO, Web of Science and Cochrane Database of Systematic Reviews (01.01.05 until 25.4.15). A systematic search to discover all studies containing quantitative data for synthesis into meta-analyses.Eligibility criteriaStudies examining the effect of text-based electronic notifications on prescheduled appointment attendance in healthcare settings. Primary analysis included experimental studies where randomisation was used to define allocation to intervention and where a control group consisting of ‘no reminders’ was used. Secondary meta-analysis included studies comparing text reminders with voice reminders. Studies lacking sufficient information for inclusion (after attempting to contact study authors) were excluded.Outcome measuresPrimary outcomes were rate of attendance/non-attendance at healthcare appointments. Secondary outcome was rate of rescheduled and cancelled appointments.Results26 articles were included. 21 included in the primary meta-analysis (8345 patients receiving electronic text notifications, 7731 patients receiving no notifications). Studies were included from Europe (9), Asia (7), Africa (2), Australia (2) and America (1). Patients who received notifications were 23% more likely to attend clinic than those who received no notification (risk ratio=1.23, 67% vs 54%). Those receiving notifications were 25% less likely to ‘no show’ for appointments (risk ratio=.75, 15% vs 21%). Results were similar when accounting for risk of bias, region and publication year. Multiple notifications were significantly more effective at improving attendance than single notifications. Voice notifications appeared more effective than text notifications at improving attendance.ConclusionsElectronic text notifications improve attendance and reduce no shows across healthcare settings. Sending multiple notifications could improve attendance further.

Table of contentsS1 Using computerized adaptive testingTim CroudaceS2 Well-being: what is it, how does it compare to health and what are the implications of using it to inform health policyJohn BrazierO1 “Am I going to get better?”—Using PROMs to inform patients about the likely benefit of surgeryNils Gutacker, Andrew StreetO2 Identifying Patient Reported Outcome Measures for an electronic Personal Health RecordDan Robotham, Samantha Waterman, Diana Rose, Safarina Satkunanathan, Til WykesO3 Examining the change process over time qualitatively: transformative learning and response shiftNasrin Nasr, Pamela EnderbyO4 Developing a PROM to evaluate self-management in diabetes (HASMID): giving patients a voiceJill Carlton, Donna Rowen, Jackie Elliott, John Brazier, Katherine Stevens, Hasan Basarir, Alex LabeitO5 Development of the Primary Care Outcomes Questionnaire (PCOQ)Mairead Murphy, Sandra Hollinghurst, Chris SalisburyO6 Developing the PKEX score- a multimodal assessment tool for patients with shoulder problemsDominic Marley, James Wilson, Amy Barrat, Bibhas RoyO7 Applying multiple imputation to multi-item patient reported outcome measures: advantages and disadvantages of imputing at the item, sub-scale or score levelInes Rombach, Órlaith Burke, Crispin Jenkinson, Alastair Gray, Oliver Rivero-AriasO8 Integrating Patient Reported Outcome Measures (PROMs) into routine primary care for patients with multimorbidity: a feasibility studyIan Porter, Jaheeda Gangannagaripalli, Charlotte Bramwell, Jose M. ValderasO9 eRAPID: electronic self-report and management of adverse-events for pelvic radiotherapy (RT) patientsPatricia Holch, Susan Davidson, Jacki Routledge, Ann Henry, Kevin Franks, Alex Gilbert, Kate Absolom & Galina VelikovaO10 Patient reported outcomes (PROMs) based recommendation in clinical guidance for the management of chronic conditions in the United KingdomIan Porter, Jose M.ValderasO11 Cross-sectional and longitudinal parameter shifts in epidemiological data: measurement invariance and response shifts in cohort and survey data describing the UK’s Quality of LifeJan R. BoehnkeO12 Patient-reported outcomes within health technology decision making: current status and implications for future policyAndrew Trigg, Ruth HowellsO13 Can social care needs and well-being be explained by the EQ-5D? Analysis of Health Survey for England datasetJeshika Singh, Subhash Pokhrel, Louise LongworthO14 Where patients and policy meet: exploring individual-level use of the Long-Term Conditions Questionnaire (LTCQ)Caroline Potter, Cheryl Hunter, Laura Kelly, Elizabeth Gibbons, Julian Forder, Angela Coulter, Ray Fitzpatrick, Michele Peters