Perceptions of factors influencing engagement with health and wellbeing apps: a qualitative study using the COM-B model and Theoretical Domains Framework

Objectives. User engagement with health and wellbeing apps is typically poor. Understanding factors that influence engagement can inform the design of more engaging apps. This study explored users’ experiences of and reasons for engaging and not engaging with health and wellbeing apps. / Methods. UK-based adults (N=17) interested in using a health or wellbeing app took part in a semi-structured interview to explore experiences of engaging with these apps. Data were analysed with the framework approach, informed by the Capability, Opportunity, Motivation – Behaviour (COM-B) model and the Theoretical Domains Framework, two widely used frameworks that incorporate a comprehensive set of behavioural influences. / Results. Factors to influence capability included accessible information (e.g. user guidance, statistical and health information), reduced cognitive load, well-designed reminders, self-monitoring features, features that help to establish a routine, features that offer safety netting and stepping-stone app characteristics. Tailoring, peer support and embedded professional support were identified as important factors that enhance users’ opportunities for engagement. Feedback, rewards, encouragement, goal setting, action planning, self-confidence and commitment were judged to be motivation factors that affect engagement. / Conclusion. Multiple factors were identified across all components of the COM-B model that may be valuable for the development of more engaging health and wellbeing apps. Engagement appears to be influenced primarily by features that provide user guidance, promote minimal cognitive load and support self-monitoring (capability), provide embedded social support (opportunity), and goal setting with action planning (motivation)

Loss to Follow-Up Among Adults Attending Human Immunodeficiency Virus Services in England, Wales, and Northern Ireland

AIM: To assess the extent to which human immunodeficiency virus (HIV)-diagnosed adults attending HIV-services in England, Wales, and Northern Ireland are lost to follow-up or attend services intermittently. METHODS: A cohort of HIV-diagnosed adults was created by linking records across the 1998 to 2007 national annual Survey of Prevalent HIV Infections Diagnosed. The records were also linked to the national HIV and acquired immune deficiency syndrome New Diagnoses Database (n = 61,495) and to Office for National Statistics death records. Patterns of HIV-service attendance were analyzed. RESULTS: On average, 90% of adults attending HIV-services in any one year attended the following year. Nearly 5% of adults attending services in any one year were lost to follow-up, a further 4% subsequently attended services intermittently, whereas less than 2% died. Cumulatively, 19% of adults seen for HIV care between 1998 and 2006 were lost to follow-up by the end of 2007. Factors associated with loss to follow-up included being the following: female; aged 15 to 34 years; black-African or "other" ethnicity; not on antiretroviral therapy; recently diagnosed; and infected outside the United Kingdom. CONCLUSIONS: Although the majority of HIV-diagnosed adults in England, Wales, and Northern Ireland attended HIV-services regularly, cumulatively nearly 1 in 5 adults were lost to follow-up between 1998 and 2007. Innovative strategies focusing on those most likely to drop out of regular care should be developed to maintain regular service engagement and to ensure optimal care

Trends in HIV testing and recording of HIV status in the UK primary care setting: a retrospective cohort study 1995-2005

Objectives: To provide nationally representative data on trends in HIV testing in primary care and to estimate the proportion of diagnosed HIV positive individuals known to general practitioners (GPs). Methods: We undertook a retrospective cohort study between 1995 and 2005 of all general practices contributing data to the UK General Practice Research Database (GPRD), and data on persons accessing HIV care (Survey of Prevalent HIV Infections Diagnosed). We identified all practice-registered patients where an HIV test or HIV positive status is recorded in their general practice records. HIV testing in primary care and prevalence of recorded HIV positive status in primary care were estimated. Results: Despite 11-fold increases in male testing and 19-fold increases in non-pregnant female testing between 1995 and 2005, HIV testing rates remained low in 2005 at 71.3 and 61.2 tests per 100 000 person years for males and females, respectively, peaking at 162.5 and 173.8 per 100 000 person years at 25–34 years of age. Inclusion of antenatal tests yielded a 129-fold increase in women over the 10-year period. In 2005, 50.7% of HIV positive individuals had their diagnosis recorded with a lower proportion in London (41.8%) than outside the capital (60.1%). Conclusion: HIV testing rates in primary care remain low. Normalisation of HIV testing and recording in primary care in antenatal testing has not been accompanied by a step change in wider HIV testing practice. Recording of HIV positive status by GPs remains low and GPs may be unaware of HIV-related morbidity or potential drug interactions

Influences on the uptake of health and wellbeing apps and curated app portals: a think aloud and interview study

Background: Health and wellbeing smartphone apps can be identified through different routes, including via curated health app portals, but little is known about people’s experiences of this. Objective: This study explored how people select health apps online and their views on curated portals. Methods: Eighteen UK-based adults were recruited and asked to verbalise their thoughts whilst searching for a health or wellbeing app online, including on two curated health app portals. This was followed by semi-structured interviews. Data were analysed using Framework Analysis, informed by the COM-B model and the Theoretical Domains Framework. Results: Searching for health and wellbeing apps online was described as a ‘minefield’. App uptake appeared to be influenced by i) capabilities (e.g. app literacy skills, health and app awareness), ii) opportunities (e.g. app aesthetics, cost and social influences) and iii) motivation (e.g. the perceived utility and accuracy of the app, and transparency about data protection). Social influences and the percieved utility of an app, in particular, were important. People were not previously aware of curated portals but found the concept appealing and likely to engender trust and address data protection concerns. While apps listed on these were perceived as more trustworthy, their presentation was considered disappointing. Conclusions: The uptake of health and wellbeing apps appear primarily influenced by social influences and the perceived utility of the app. With curated health app portals perceived as credible, app uptake via such portals may mitigate concerns related to data protection and accuracy, but their implementation must better meet user needs. Clinical Trial: N

Perceptions of factors influencing engagement with health and wellbeing apps: a qualitative study using the COM-B model and Theoretical Domains Framework as an analytical framework

Background: Digital health media, such as health and wellbeing smartphone apps, could offer an accessible and cost-effective way to deliver health and wellbeing interventions. A key component of the effectiveness of these apps is user engagement. However, engagement with health and wellbeing apps is typically sub-optimal. Previous studies have identified multiple factors that influence engagement, however, most of these studies were conducted on specific populations or focused on apps targeting a particular behaviour. Understanding factors that influence engagement with a wide range of health and wellbeing apps can help inform the design and development of more engaging apps. Objective: The aim of this study was to explore users’ experiences of and reasons for engaging and not engaging with a wide range of health and wellbeing apps. Methods: A sample of adults in the UK (N=17) interested in using a health or wellbeing app took part in a semi-structured interview to explore experiences of engaging and reasons for not engaging with these apps. Participants were recruited via social media platforms. Data were analysed with the framework approach, informed by the Capability, Opportunity, Motivation – Behaviour (COM-B) model and the Theoretical Domains Framework, two widely used frameworks that incorporate a comprehensive set of behavioural influences. Results: Factors appearing to influence the capability of participants to engage with health and wellbeing apps included available user guidance, statistical and health information, reduced cognitive load, well-designed reminders, self-monitoring features, features that help to establish a routine, features that allow retaining the app for a potential precipitating event in the future (‘safety netting’) and features that offer a first step in the behaviour change process (‘stepping stone’). Tailoring, peer support and embedded professional support were identified as important factors that appeared to enhance users’ opportunity for engagement with health and wellbeing apps. Feedback, rewards, encouragement, goal setting, action planning, self-confidence and commitment were judged to be motivation factors affecting engagement with health and wellbeing apps. Conclusions: Multiple factors were identified across all components of the COM-B model that may be valuable for enhancing the engagement of health and wellbeing apps. Engagement appears to be influenced primarily by features that provide user guidance, promote minimal cognitive load and support self-monitoring (capability), provide embedded social support (opportunity), and goal setting with action planning (motivation). We provide recommendations for policy makers, industry, health care providers and app developers on how to increase engagement. Clinical Trial: Not applicable

Effectiveness of Digital Interventions for Reducing Behavioral Risks of Cardiovascular Disease in Nonclinical Adult Populations: Systematic Review of Reviews

Background: Digital health interventions are increasingly being used as a supplement or replacement for face-to-face services as a part of predictive prevention. They may be offered to those who are at high risk of cardiovascular disease and need to improve their diet, increase physical activity, stop smoking, or reduce alcohol consumption. Despite the popularity of these interventions, there is no overall summary and comparison of the effectiveness of different modes of delivery of a digital intervention to inform policy. Objective: This review aims to summarize the effectiveness of digital interventions in improving behavioral and health outcomes related to physical activity, smoking, alcohol consumption, or diet in nonclinical adult populations and to identify the effectiveness of different modes of delivery of digital interventions. Methods: We reviewed articles published in the English language between January 1, 2009, and February 25, 2019, that presented a systematic review with a narrative synthesis or meta-analysis of any study design examining digital intervention effectiveness; data related to adults (≥18 years) in high-income countries; and data on behavioral or health outcomes related to diet, physical activity, smoking, or alcohol, alone or in any combination. Any time frame or comparator was considered eligible. We searched MEDLINE, Embase, PsycINFO, Cochrane Reviews, and gray literature. The AMSTAR-2 tool was used to assess review confidence ratings. Results: We found 92 reviews from the academic literature (47 with meta-analyses) and 2 gray literature items (1 with a meta-analysis). Digital interventions were typically more effective than no intervention, but the effect sizes were small. Evidence on the effectiveness of digital interventions compared with face-to-face interventions was mixed. Most trials reported that intent-to-treat analysis and attrition rates were often high. Studies with long follow-up periods were scarce. However, we found that digital interventions may be effective for up to 6 months after the end of the intervention but that the effects dissipated by 12 months. There were small positive effects of digital interventions on smoking cessation and alcohol reduction; possible effectiveness in combined diet and physical activity interventions; no effectiveness for interventions targeting physical activity alone, except for when interventions were delivered by mobile phone, which had medium-sized effects; and no effectiveness observed for interventions targeting diet alone. Mobile interventions were particularly effective. Internet-based interventions were generally effective. Conclusions: Digital interventions have small positive effects on smoking, alcohol consumption, and in interventions that target a combination of diet and physical activity. Small effects may have been due to the low efficacy of treatment or due to nonadherence. In addition, our ability to make inferences from the literature we reviewed was limited as those interventions were heterogeneous, many reviews had critically low AMSTAR-2 ratings, analysis was typically intent-to-treat, and follow-up times were relatively short

Primary care consultations and costs among HIV-positive individulas in UK primary care 1995-2005: a cohort study

Objectives: To investigate the role of primary care in the management of HIV and estimate primary care-associated costs at a time of rising prevalence. Methods: Retrospective cohort study between 1995 and 2005, using data from general practices contributing data to the UK General Practice Research Database. Patterns of consultation and morbidity and associated consultation costs were analysed among all practice-registered patients for whom HIV-positive status was recorded in the general practice record. Results: 348 practices yielded 5504 person-years (py) of follow-up for known HIV-positive patients, who consult in general practice frequently (4.2 consultations/py by men, 5.2 consultations/py by women, in 2005) for a range of conditions. Consultation rates declined in the late 1990s from 5.0 and 7.3 consultations/py in 1995 in men and women, respectively, converging to rates similar to the wider population. Costs of consultation (general practitioner and nurse, combined) reflect these changes, at £100.27 for male patients and £117.08 for female patients in 2005. Approximately one in six medications prescribed in primary care for HIV-positive individuals has the potential for major interaction with antiretroviral medications. Conclusion: HIV-positive individuals known in general practice now consult on a similar scale to the wider population. Further research should be undertaken to explore how primary care can best contribute to improving the health outcomes of this group with chronic illness. Their substantial use of primary care suggests there may be potential to develop effective integrated care pathways

Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial

SummaryBackgroundUnnecessary antibiotic prescribing contributes to antimicrobial resistance. In this trial, we aimed to reduce unnecessary prescriptions of antibiotics by general practitioners (GPs) in England.MethodsIn this randomised, 2 × 2 factorial trial, publicly available databases were used to identify GP practices whose prescribing rate for antibiotics was in the top 20% for their National Health Service (NHS) Local Area Team. Eligible practices were randomly assigned (1:1) into two groups by computer-generated allocation sequence, stratified by NHS Local Area Team. Participants, but not investigators, were blinded to group assignment. On Sept 29, 2014, every GP in the feedback intervention group was sent a letter from England's Chief Medical Officer and a leaflet on antibiotics for use with patients. The letter stated that the practice was prescribing antibiotics at a higher rate than 80% of practices in its NHS Local Area Team. GPs in the control group received no communication. The sample was re-randomised into two groups, and in December, 2014, GP practices were either sent patient-focused information that promoted reduced use of antibiotics or received no communication. The primary outcome measure was the rate of antibiotic items dispensed per 1000 weighted population, controlling for past prescribing. Analysis was by intention to treat. This trial is registered with the ISRCTN registry, number ISRCTN32349954, and has been completed.FindingsBetween Sept 8 and Sept 26, 2014, we recruited and assigned 1581 GP practices to feedback intervention (n=791) or control (n=790) groups. Letters were sent to 3227 GPs in the intervention group. Between October, 2014, and March, 2015, the rate of antibiotic items dispensed per 1000 population was 126·98 (95% CI 125·68–128·27) in the feedback intervention group and 131·25 (130·33–132·16) in the control group, a difference of 4·27 (3·3%; incidence rate ratio [IRR] 0·967 [95% CI 0·957–0·977]; p<0·0001), representing an estimated 73 406 fewer antibiotic items dispensed. In December, 2014, GP practices were re-assigned to patient-focused intervention (n=777) or control (n=804) groups. The patient-focused intervention did not significantly affect the primary outcome measure between December, 2014, and March, 2015 (antibiotic items dispensed per 1000 population: 135·00 [95% CI 133·77–136·22] in the patient-focused intervention group and 133·98 [133·06–134·90] in the control group; IRR for difference between groups 1·01, 95% CI 1·00–1·02; p=0·105).InterpretationSocial norm feedback from a high-profile messenger can substantially reduce antibiotic prescribing at low cost and at national scale; this outcome makes it a worthwhile addition to antimicrobial stewardship programmes.FundingPublic Health England

Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study

Background: There remains uncertainty about Covid-19 risk factors. We examined UK adults’ risk perceptions for severe Covid-19 symptoms and whether engaging in concurrent health behaviours is associated with risk perceptions. / Methods: Cross-sectional analysis of data from the HEBECO study where 2206 UK adults classified potential factors (age 70+, ethnic minority, medical comorbidities, vaping, smoking cigarettes, alcohol drinking, regular physical activity, being overweight, eating unhealthy foods, using nicotine replacement therapy – NRT, lower income, poor housing, being a keyworker) as either increasing, decreasing, or having no impact on severe Covid-19 symptoms. Logistic regressions examined whether engaging in health behaviours was associated with risk perceptions after adjusting for socio-demographic characteristics, health conditions and other behaviours. / Results: The great majority (89-99%) of adults classified age 70+, having comorbidities, being a key worker, overweight, and from an ethnic minority as increasing the risk. People were less sure about alcohol drinking, vaping, and nicotine replacement therapy use (17.4-29.5% responding ‘don’t know’). Relative to those who did not, those who smoked tobacco, vaped and consumed alcohol had significantly (all p<0.015) higher odds (aORs=1.58 to 5.80) for classifying these behaviours as ‘no impact’ or ‘decreasing risk’, and lower odds (aORs=.25 to .72) for classifying as ‘increasing risk’. Similarly, eating more fruit and vegetables was associated with classifying unhealthy diet as ‘increasing risk’ (aOR=1.37,1.12-1.69), and exercising more with classifying regular physical activity as ‘decreasing risk’ (aOR=2.42,1.75-3.34). / Conclusions: Risk perceptions for severe Covid-19 symptoms were lower for adults’ own health behaviours, evidencing optimism bias. / Implications: These risk perceptions may form barriers to changing one’s own unhealthy behaviours or make one less responsive to interventions that refer to the risk of Covid-19 as a motivating factor. Thus, in some cases risk perceptions could help sustain unhealthy behaviours and exacerbate inequalities in health behaviours and outcomes