An observational analysis of frailty in combination with loneliness or social isolation and their association with socioeconomic deprivation, hospitalisation and mortality among UK Biobank participants.

Frailty, social isolation, and loneliness have individually been associated with adverse health outcomes. This study examines how frailty in combination with loneliness or social isolation is associated with socioeconomic deprivation and with all-cause mortality and hospitalisation rate in a middle-aged and older population. Baseline data from 461,047 UK Biobank participants (aged 37-73) were used to assess frailty (frailty phenotype), social isolation, and loneliness. Weibull models assessed the association between frailty in combination with loneliness or social isolation and all-cause mortality adjusted for age/sex/smoking/alcohol/socioeconomic-status and number of long-term conditions. Negative binomial regression models assessed hospitalisation rate. Frailty prevalence was 3.38%, loneliness 4.75% and social isolation 9.04%. Frailty was present across all ages and increased with age. Loneliness and social isolation were more common in younger participants compared to older. Co-occurrence of frailty and loneliness or social isolation was most common in participants with high socioeconomic deprivation. Frailty was associated with increased mortality and hospitalisation regardless of social isolation/loneliness. Hazard ratios for mortality were 2.47 (2.27-2.69) with social isolation and 2.17 (2.05-2.29) without social isolation, 2.14 (1.92-2.38) with loneliness and 2.16 (2.05-2.27) without loneliness. Loneliness and social isolation were associated with mortality and hospitalisation in robust participants, but this was attenuated in the context of frailty. Frailty and loneliness/social isolation affect individuals across a wide age spectrum and disproportionately co-occur in areas of high deprivation. All were associated with adverse outcomes, but the association between loneliness and social isolation and adverse outcomes was attenuated in the context of frailty. Future interventions should target people living with frailty or loneliness/social isolation, regardless of age.

Multimorbidity and Blood Pressure Control in Patients Attending Primary Care in Canada.

BACKGROUND: There has been conflicting evidence on the association between multimorbidity and blood pressure (BP) control. This study aimed to investigate this associations in people with hypertension attending primary care in Canada, and to assess whether individual long-term conditions are associated with BP control. METHODS: This was a cross-sectional study in people with hypertension attending primary care in Toronto between January 1, 2017 and December 31, 2019. Uncontrolled BP was defined as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. A list of 11 a priori selected chronic conditions was used to define multimorbidity. Multimorbidity was defined as having ≥1 long-term condition in addition to hypertension. Logistic regression models were used to estimate the association between multimorbidity (or individual long-term conditions) with uncontrolled BP. RESULTS: A total of 67 385 patients with hypertension were included. They had a mean age of 70, 53.1% were female, 80.6% had multimorbidity, and 35.7% had uncontrolled BP. Patients with multimorbidity had lower odds of uncontrolled BP than those without multimorbidity (adjusted OR = 0.72, 95% CI 0.68-0.76). Among the long-term conditions, diabetes (aOR = 0.73, 95%CI 0.70-0.77), heart failure (aOR = 0.81, 95%CI 0.73-0.91), ischemic heart disease (aOR = 0.74, 95%CI 0.69-0.79), schizophrenia (aOR = 0.79, 95%CI 0.65-0.97), depression/anxiety (aOR = 0.91, 95%CI 0.86-0.95), dementia (aOR = 0.87, 95%CI 0.80-0.95), and osteoarthritis (aOR = 0.89, 95%CI 0.85-0.93) were associated with a lower likelihood of uncontrolled BP. CONCLUSION: We found that multimorbidity was associated with better BP control. Several conditions were associated with better control, including diabetes, heart failure, ischemic heart disease, schizophrenia, depression/anxiety, dementia, and osteoarthritis.

Social connection and mortality in UK Biobank: a prospective cohort analysis.

BACKGROUND: Components of social connection are associated with mortality, but research examining their independent and combined effects in the same dataset is lacking. This study aimed to examine the independent and combined associations between functional and structural components of social connection and mortality. METHODS: Analysis of 458,146 participants with full data from the UK Biobank cohort linked to mortality registers. Social connection was assessed using two functional (frequency of ability to confide in someone close and often feeling lonely) and three structural (frequency of friends/family visits, weekly group activities, and living alone) component measures. Cox proportional hazard models were used to examine the associations with all-cause and cardiovascular disease (CVD) mortality. RESULTS: Over a median of 12.6 years (IQR 11.9-13.3) follow-up, 33,135 (7.2%) participants died, including 5112 (1.1%) CVD deaths. All social connection measures were independently associated with both outcomes. Friends/family visit frequencies < monthly were associated with a higher risk of mortality indicating a threshold effect. There were interactions between living alone and friends/family visits and between living alone and weekly group activity. For example, compared with daily friends/family visits-not living alone, there was higher all-cause mortality for daily visits-living alone (HR 1.19 [95% CI 1.12-1.26]), for never having visits-not living alone (1.33 [1.22-1.46]), and for never having visits-living alone (1.77 [1.61-1.95]). Never having friends/family visits whilst living alone potentially counteracted benefits from other components as mortality risks were highest for those reporting both never having visits and living alone regardless of weekly group activity or functional components. When all measures were combined into overall functional and structural components, there was an interaction between components: compared with participants defined as not isolated by both components, those considered isolated by both components had higher CVD mortality (HR 1.63 [1.51-1.76]) than each component alone (functional isolation 1.17 [1.06-1.29]; structural isolation 1.27 [1.18-1.36]). CONCLUSIONS: This work suggests (1) a potential threshold effect for friends/family visits, (2) that those who live alone with additional concurrent markers of structural isolation may represent a high-risk population, (3) that beneficial associations for some types of social connection might not be felt when other types of social connection are absent, and (4) considering both functional and structural components of social connection may help to identify the most isolated in society.

Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role?

BACKGROUND: In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility. METHODS: The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination. RESULTS: Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots). DISCUSSION: A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.

Do age, gender, and education modify the effectiveness of app-delivered and tailored self-management support among adults with low back pain?-Secondary analysis of the selfBACK randomised controlled trial.

selfBACK is an artificial intelligence based self-management app for low back pain (LBP) recently reported to reduce LBP-related disability. The aim of this study was to examine if age, gender, or education modify the effectiveness of the selfBACK intervention using secondary analysis of the selfBACK randomized controlled trial. Persons seeking care for LBP were recruited from primary care in Denmark and Norway and an outpatient clinic (Denmark). The intervention group (n = 232) received the selfBACK app adjunct to usual care. The control group (n = 229) received usual care only. Analyses were stratified by age (18-34, 35-64, ≥65 years), gender (male, female), and education (≤12, >12 years) to investigate differences in effect at three and nine months follow-up on LBP-related disability (Roland-Morris Disability Questionnaire [RMDQ]), LBP intensity and pain self-efficacy. Overall, there was no effect modification for any of the sociodemographic factors. However, data on LBP-related disability suggest that the effect of the intervention was somewhat more beneficial in older than in younger participants. The difference between the intervention and control group due to interaction was 2.6 (95% CI: 0.4 to 4.9) RMDQ points for those aged ≥65 years as compared to those aged 35-64 years. In conclusion, age, gender, or education did not influence the effect of the selfBACK intervention on LBP-related disability. However, older participants may have an additional long-term positive effect compared to younger participants. Trial registration: ClinicalTrials.gov Identifier: NCT03798288.

Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study.

Purpose: We aimed to classify individuals with RA and ≥2 additional long-term conditions (LTCs) and describe the association between different LTC classes, number of LTCs and adverse health outcomes. Methods: We used UK Biobank participants who reported RA (n=5,625) and employed latent class analysis (LCA) to create classes of LTC combinations for those with ≥2 additional LTCs. Cox-proportional hazard and negative binomial regression were used to compare the risk of all-cause mortality, major adverse cardiac events (MACE), and number of emergency hospitalisations over an 11-year follow-up across the different LTC classes and in those with RA plus one additional LTC. Persons with RA without LTCs were the reference group. Analyses were adjusted for demographic characteristics, smoking, BMI, alcohol consumption and physical activity. Results: A total of 2,566 (46%) participants reported ≥2 LTCs in addition to RA. This involved 1,138 distinct LTC combinations of which 86% were reported by ≤2 individuals. LCA identified 5 morbidity-classes. The distinctive condition in the class with the highest mortality was cancer (class 5; HR 2.66 95%CI (1.91-3.70)). The highest MACE (HR 2.95 95%CI (2.11-4.14)) and emergency hospitalisations (rate ratio 3.01 (2.56-3.54)) were observed in class 3 which comprised asthma, COPD & CHD. There was an increase in mortality, MACE and emergency hospital admissions within each class as the number of LTCs increased. Conclusions: The risk of adverse health outcomes in RA varied with different patterns of multimorbidity. The pattern of multimorbidity should be considered in risk assessment and formulating management plans in patients with RA.

The selfBACK artificial intelligence-based smartphone app can improve low back pain outcome even in patients with high levels of depression or stress.

BACKGROUND: selfBACK provides individually tailored self-management support for low back pain (LBP) via an artificial intelligence-based smartphone app. We explore whether those with depressive/stress symptoms can benefit from this technology. METHODS: Secondary analysis of the selfBACK randomized controlled trial (n = 461). Participants with LBP were randomized to usual care (n = 229), or usual care plus selfBACK (n = 232). PRIMARY OUTCOME: LBP-related disability (Roland-Morris Disability Questionnaire, RMDQ) over 9 months. SECONDARY OUTCOMES: global perceived effect (GPE)/pain self-efficacy (PSEQ)/satisfaction/app engagement. Baseline depressive symptoms were measured using the patient health questionnaire (PHQ-8) and stress with the perceived stress scale (PSS). Outcomes stratified by baseline PHQ-8/PSS scores to assess associations across the whole cohort, and intervention versus control groups. RESULTS: Participants with higher levels of depressive/stress symptoms reported more baseline LBP-related disability (RMDQ 3.1; 1.6 points higher in most vs least depressed/stressed groups respectively); lower self-efficacy (PSEQ 8.1; 4.6 points lower in most vs least depressive/stressed groups respectively). LBP-related disability improved over time; relative risk of improvement in those with greatest depressive/stress symptoms versus nil symptom comparators at 9 months: 0.8 (95% CI: 0.6 to 1.0) and 0.8 (95% CI: 0.7 to 1.0) respectively. No evidence that different baseline levels of depressive/perceived stress symptoms are associated with different RMDQ/GPE/PSEQ outcomes. Whilst participants with higher PHQ-8/PSS were less likely to be satisfied or engage with the app, there was no consistent association among PHQ-8/PSS level, the intervention and outcomes. CONCLUSIONS: The selfBACK app can improve outcomes even in those with high levels of depressive/stress symptoms and could be recommended for patients with LBP. SIGNIFICANCE: We have demonstrated that an app supporting the self-management of LBP is helpful, even in those with higher levels of baseline depression and stress symptoms. selfBACK offers an opportunity to support people with LBP and provides clinicians with an additional tool for their patients, even those with depression or high levels of stress. This highlights the potential for digital health interventions for chronic pain.

Association between risk, duration and cause of hospitalisations in people with rheumatoid arthritis and multimorbidity in the UK Biobank and Scottish Early Rheumatoid Arthritis (SERA) cohorts: Longitudinal observational study.

OBJECTIVES: To investigate association between presence of multimorbidity in people with established and early rheumatoid arthritis (RA) and risk, duration and cause of hospitalisations. DESIGN: Longitudinal observational study. SETTING: UK Biobank, population-based cohort recruited between 2006 and 2010, and the Scottish Early Rheumatoid Arthritis (SERA), inception cohort recruited between 2011 and 2015. Both linked to mortality and hospitalisation data. PARTICIPANTS: 4757 UK Biobank participants self-reporting established RA; 825 SERA participants with early RA meeting the 2010 ACR/EULAR classification criteria. Participants stratified by number of long-term conditions (LTCs) in addition to RA (RA only, RA + 1 LTC and RA + ≥ 2 LTCs) and matched to five non-RA controls. MAIN OUTCOME MEASURES: Number and duration of hospitalisations and their causes. Incidence rate ratios (IRR) and 95% confidence intervals (CI) calculated using negative binomial regression models. RESULTS: Participants with RA + ≥ 2 LTCs experienced higher hospitalisation rates compared to those with RA alone (UK Biobank: IRR 2.10, 95% CI 1.91 to 2.30; SERA: IRR 1.74, 95% CI 1.23 to 2.48). Total duration of hospitalisation in RA + ≥ 2 LTCs was also higher (UK Biobank: IRR 2.48, 95% CI 2.17 to 2.84; SERA: IRR 1.90, 95% CI 1.07 to 3.38) than with RA alone. Rate and total duration of hospitalisations was higher in UK Biobank RA participants than non-RA controls with equivalent number of LTCs. Hospitalisations for respiratory infection were higher in early RA than established RA and were the commonest cause of hospital admission in early RA. CONCLUSIONS: Participants with established or early RA with multimorbidity experienced a higher rate and duration of hospitalisations than those with RA alone and with non-RA matched controls.

Chronic pain and COVID-19 hospitalisation and mortality: a UK Biobank cohort study.

ABSTRACT: The risk of COVID-19 in those with chronic pain is unknown. We investigated whether self-reported chronic pain was associated with COVID-19 hospitalisation or mortality. UK Biobank recruited 502,624 participants aged 37 to 73 years between 2006 and 2010. Baseline exposure data, including chronic pain (>3 months, in at least 1 of 7 prespecified body sites) and chronic widespread pain (>3 months, all over body), were linked to COVID-19 hospitalisations or mortality. Univariable or multivariable Poisson regression analyses were performed on the association between chronic pain and COVID-19 hospitalisation and Cox regression analyses of the associations with COVID-19 mortality. Multivariable analyses adjusted incrementally for sociodemographic confounders, then lifestyle risk factors, and finally long-term condition count. Of 441,403 UK Biobank participants with complete data, 3180 (0.7%) were hospitalised for COVID-19 and 1040 (0.2%) died from COVID-19. Chronic pain was associated with hospital admission for COVID-19 even after adjustment for all covariates (incidence rate ratio 1.16; 95% confidence interval [CI] 1.08-1.24; P < 0.001), as was chronic widespread pain (incidence rate ratio 1.33; 95% CI 1.06-1.66; P = 0.012). There was clear evidence of a dose-response relationship with number of pain sites (fully adjusted global P -value < 0.001). After adjustment for all covariates, there was no association between chronic pain (HR 1.01; 95% CI 0.89-1.15; P = 0.834) but attenuated association with chronic widespread pain (HR 1.50, 95% CI 1.04-2.16, P -value = 0.032) and COVID-19 mortality. Chronic pain is associated with higher risk of hospitalisation for COVID-19, but the association with mortality is unclear. Future research is required to investigate these findings further and determine whether pain is associated with long COVID.

One size does not fit all: Participants' experiences of the selfBACK app to support self-management of low back pain-a qualitative interview study.

BACKGROUND: Low back pain (LBP) is one of the most common reasons for disability globally. Digital interventions are a promising means of supporting people to self-manage LBP, but implementation of digital interventions has been suboptimal. An artificial intelligence-driven app, selfBACK, was developed to support self-management of LBP as an adjunct to usual care. To better understand the process of implementation from a participant perspective, we qualitatively explored factors influencing embedding, integrating, and sustaining engagement with the selfBACK app, and the self-perceived effects, acceptability, and satisfaction with the selfBACK app. METHODS: Using a qualitative interview study and an analytic framework approach underpinned by Normalization Process Theory (NPT), we investigated the experiences of patients who participated in the selfBACK randomized controlled trial (RCT). Interviews focused on the motivation to participate in the RCT, experiences of using the selfBACK app, and views about future intended use and potential of using digital health interventions for self-management of LBP. Participants were purposively sampled to represent diversity in age, sex, and implementation reflected by a proxy measure of number of app-generated self-management plans during the first three months of RCT participation. RESULTS: Twenty-six participants aged 21-78, eleven females and fifteen men, with two to fourteen self-management plans, were interviewed between August 2019 and April 2020. A broad range of factors influencing implementation of selfBACK within all constructs of NPT were identified. Key facilitating factors were preferences and beliefs favoring self-management, a friendly, motivational, and reassuring supporter, tailoring and personalization, convenience and ease of use, trustworthiness, perceiving benefits, and tracking achievements. Key impeding factors were preferences and beliefs not favoring self-management, functionality issues, suboptimal tailoring and personalization, insufficient time or conflicting life circumstances, not perceiving benefits, and insufficient involvement of health care practitioners. Self-perceived effects on pain and health, behavior/attitude, and gaining useful knowledge varied by participant. CONCLUSIONS: The high prevalence of LBP globally coupled with the advantages of providing help through an app offers opportunities to help countless people. A range of factors should be considered to facilitate implementation of self-management of LBP or similar pain conditions using digital health tools.

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank.

BACKGROUND: Frailty, a state of reduced physiological reserve, is common in people with chronic obstructive pulmonary disease (COPD). Frailty can occur at any age; however, the implications in younger people (eg, aged <65 years) with COPD are unclear. We assessed the prevalence of frailty in UK Biobank participants with COPD; explored relationships between frailty and forced expiratory volume in 1 second (FEV1) and quantified the association between frailty and adverse outcomes. METHODS: UK Biobank participants (n=3132, recruited 2006-2010) with COPD aged 40-70 years were analysed comparing two frailty measures (frailty phenotype and frailty index) at baseline. Relationship with FEV1 was assessed for each measure. Outcomes were mortality, major adverse cardiovascular event (MACE), all-cause hospitalisation, hospitalisation with COPD exacerbation and community COPD exacerbation over 8 years of follow-up. RESULTS: Frailty was common by both definitions (17% frail using frailty phenotype, 28% moderate and 4% severely frail using frailty index). The frailty phenotype, but not the frailty index, was associated with lower FEV1. Frailty phenotype (frail vs robust) was associated with mortality (HR 2.33; 95% CI 1.84 to 2.96), MACE (2.73; 1.66 to 4.49), hospitalisation (incidence rate ratio 3.39; 2.77 to 4.14) hospitalised exacerbation (5.19; 3.80 to 7.09) and community exacerbation (2.15; 1.81 to 2.54), as was frailty index (severe vs robust) (mortality (2.65; 95% CI 1.75 to 4.02), MACE (6.76; 2.68 to 17.04), hospitalisation (3.69; 2.52 to 5.42), hospitalised exacerbation (4.26; 2.37 to 7.68) and community exacerbation (2.39; 1.74 to 3.28)). These relationships were similar before and after adjustment for FEV1. CONCLUSION: Frailty, regardless of age or measure, identifies people with COPD at risk of adverse clinical outcomes. Frailty assessment may aid risk stratification and guide-targeted intervention in COPD and should not be limited to people aged >65 years.

Frailty in rheumatoidrmdopen-2021-002111 arthritis and its relationship with disease activity, hospitalisation and mortality: a longitudinal analysis of the Scottish Early Rheumatoid Arthritis cohort and UK Biobank.

OBJECTIVE: To assess the prevalence of frailty in rheumatoid arthritis (RA) and its association with baseline and longitudinal disease activity, all-cause mortality and hospitalisation. PARTICIPANTS: People with RA identified from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort (newly diagnosed, mean age 58.2 years) and UK Biobank (established disease identified using diagnostic codes, mean age 59 years). Frailty was quantified using the frailty index (both datasets) and frailty phenotype (UK Biobank only). Disease activity was assessed using Disease Activity Score in 28 joints (DAS28) in SERA. Associations between baseline frailty and all-cause mortality and hospitalisation was estimated after adjusting for age, sex, socioeconomic status, smoking and alcohol, plus DAS28 in SERA. RESULTS: Based on the frailty index, frailty was common in SERA (12% moderate, 0.2% severe) and UK Biobank (20% moderate, 3% severe). In UK Biobank, 23% were frail using frailty phenotype. Frailty index was associated with DAS28 in SERA, as well as age and female sex in both cohorts. In SERA, as DAS28 lessened over time with treatment, mean frailty index also decreased. The frailty index was associated with all-cause mortality (HR moderate/severe frailty vs robust 4.14 (95% CI 1.49 to 11.51) SERA, 1.68 (95% CI 1.26 to 2.13) UK Biobank) and unscheduled hospitalisation (incidence rate ratio 2.27 (95% CI 1.45 to 3.57) SERA 2.74 (95% CI 2.29 to 3.29) UK Biobank). In UK Biobank, frailty phenotype also associated with mortality and hospitalisation. CONCLUSION: Frailty is common in early and established RA and associated with hospitalisation and mortality. Frailty in RA is dynamic and, for some, may be ameliorated through controlling disease activity in early disease.

The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort.

BACKGROUND: Infection with SARS-CoV-2 virus (COVID-19) impacts disadvantaged groups most. Lifestyle factors are also associated with adverse COVID-19 outcomes. To inform COVID-19 policy and interventions, we explored effect modification of socioeconomic-status (SES) on associations between lifestyle and COVID-19 outcomes. METHODS: Using data from UK-Biobank, a large prospective cohort of 502,536 participants aged 37-73 years recruited between 2006 and 2010, we assigned participants a lifestyle score comprising nine factors. Poisson regression models with penalised splines were used to analyse associations between lifestyle score, deprivation (Townsend), and COVID-19 mortality and severe COVID-19. Associations between each exposure and outcome were examined independently before participants were dichotomised by deprivation to examine exposures jointly. Models were adjusted for sociodemographic/health factors. RESULTS: Of 343,850 participants (mean age > 60 years) with complete data, 707 (0.21%) died from COVID-19 and 2506 (0.76%) had severe COVID-19. There was evidence of a nonlinear association between lifestyle score and COVID-19 mortality but limited evidence for nonlinearity between lifestyle score and severe COVID-19 and between deprivation and COVID-19 outcomes. Compared with low deprivation, participants in the high deprivation group had higher risk of COVID-19 outcomes across the lifestyle score. There was evidence for an additive interaction between lifestyle score and deprivation. Compared with participants with the healthiest lifestyle score in the low deprivation group, COVID-19 mortality risk ratios (95% CIs) for those with less healthy scores in low versus high deprivation groups were 5.09 (1.39-25.20) and 9.60 (4.70-21.44), respectively. Equivalent figures for severe COVID-19 were 5.17 (2.46-12.01) and 6.02 (4.72-7.71). Alternative SES measures produced similar results. CONCLUSIONS: Unhealthy lifestyles are associated with higher risk of adverse COVID-19, but risks are highest in the most disadvantaged, suggesting an additive influence between SES and lifestyle. COVID-19 policy and interventions should consider both lifestyle and SES. The greatest public health benefit from lifestyle focussed COVID-19 policy and interventions is likely to be seen when greatest support for healthy living is provided to the most disadvantaged groups.

Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability.

BACKGROUND: SELFBACK, an artificial intelligence (AI)-based app delivering evidence-based tailored self-management support to people with low back pain (LBP), has been shown to reduce LBP-related disability when added to usual care. LBP commonly co-occurs with multimorbidity (≥ 2 long-term conditions) or pain at other musculoskeletal sites, so this study explores if these factors modify the effect of the SELFBACK app or influence outcome trajectories over time. METHODS: Secondary analysis of a randomized controlled trial with 9-month follow-up. Primary outcome is as follows: LBP-related disability (Roland Morris Disability Questionnaire, RMDQ). Secondary outcomes are as follows: stress/depression/illness perception/self-efficacy/general health/quality of life/physical activity/global perceived effect. We used linear mixed models for continuous outcomes and logistic generalized estimating equation for binary outcomes. Analyses were stratified to assess effect modification, whereas control (n = 229) and intervention (n = 232) groups were pooled in analyses of outcome trajectories. RESULTS: Baseline multimorbidity and co-occurring musculoskeletal pain sites did not modify the effect of the SELFBACK app. The effect was somewhat stronger in people with multimorbidity than among those with LBP only (difference in RMDQ due to interaction, - 0.9[95 % CI - 2.5 to 0.6]). Participants with a greater number of long-term conditions and more co-occurring musculoskeletal pain had higher levels of baseline disability (RMDQ 11.3 for ≥ 2 long-term conditions vs 9.5 for LBP only; 11.3 for ≥ 4 musculoskeletal pain sites vs 10.2 for ≤ 1 additional musculoskeletal pain site); along with higher baseline scores for stress/depression/illness perception and poorer pain self-efficacy/general health ratings. In the pooled sample, LBP-related disability improved slightly less over time for people with ≥ 2 long-term conditions additional to LBP compared to no multimorbidity and for those with ≥4 co-occurring musculoskeletal pain sites compared to ≤ 1 additional musculoskeletal pain site (difference in mean change at 9 months = 1.5 and 2.2, respectively). All groups reported little improvement in secondary outcomes over time. CONCLUSIONS: Multimorbidity or co-occurring musculoskeletal pain does not modify the effect of the selfBACK app on LBP-related disability or other secondary outcomes. Although people with these health problems have worse scores both at baseline and 9 months, the AI-based selfBACK app appears to be helpful for those with multimorbidity or co-occurring musculoskeletal pain. TRIAL REGISTRATION: NCT03798288 . Date of registration: 9 January 2019.

Using Intervention Mapping to Develop a Decision Support System-Based Smartphone App (selfBACK) to Support Self-management of Nonspecific Low Back Pain: Development and Usability Study.

BACKGROUND: International guidelines consistently endorse the promotion of self-management for people with low back pain (LBP); however, implementation of these guidelines remains a challenge. Digital health interventions, such as those that can be provided by smartphone apps, have been proposed as a promising mode of supporting self-management in people with chronic conditions, including LBP. However, the evidence base for digital health interventions to support self-management of LBP is weak, and detailed descriptions and documentation of the interventions are lacking. Structured intervention mapping (IM) constitutes a 6-step process that can be used to guide the development of complex interventions. OBJECTIVE: The aim of this paper is to describe the IM process for designing and creating an app-based intervention designed to support self-management of nonspecific LBP to reduce pain-related disability. METHODS: The first 5 steps of the IM process were systematically applied. The core processes included literature reviews, brainstorming and group discussions, and the inclusion of stakeholders and representatives from the target population. Over a period of >2 years, the intervention content and the technical features of delivery were created, tested, and revised through user tests, feasibility studies, and a pilot study. RESULTS: A behavioral outcome was identified as a proxy for reaching the overall program goal, that is, increased use of evidence-based self-management strategies. Physical exercises, education, and physical activity were the main components of the self-management intervention and were designed and produced to be delivered via a smartphone app. All intervention content was theoretically underpinned by the behavior change theory and the normalization process theory. CONCLUSIONS: We describe a detailed example of the application of the IM approach for the development of a theory-driven, complex, and digital intervention designed to support self-management of LBP. This description provides transparency in the developmental process of the intervention and can be a possible blueprint for designing and creating future digital health interventions for self-management.

Examining the Relationship Between Rheumatoid Arthritis, Multimorbidity, and Adverse Health-Related Outcomes: A Systematic Review.

OBJECTIVE: Multimorbidity (the coexistence of two or more long-term conditions) is highly prevalent in people who have rheumatoid arthritis (RA). The present work systematically reviewed the literature to determine the effect of multimorbidity on all-cause mortality, functional status, and quality of life in RA. METHODS: Six electronic databases were searched: CINAHL, The Cochrane Library, Embase, Medline, PsycINFO, and Scopus. Full-text longitudinal observational studies in English were selected. Quality appraisal of studies was undertaken using the Cochrane-developed QUIPS tool and a narrative synthesis of findings conducted. RESULTS: The search strategy identified 5,343 articles, with 19 studies meeting the inclusion criteria. Nine studies had mortality as an outcome, 9 reported functional status and/or quality of life, and 1 study reported both mortality and functional status. The number of participants ranged from 183 to 18,485, with studies conducted between 1985 and 2018. The mean age of participants ranged from 52.0 to 66.6 years, and 60.0-88.0% were female. Nine studies showed a significant association between multimorbidity and higher risk of mortality in people with RA. Ten studies reported significant associations between multimorbidity and reduced functional status in RA. Three studies also showed a further association with reduced quality of life. Only one study investigated the influence of mental health comorbidities on outcomes. CONCLUSION: Our review findings indicate that multimorbidity is a significant predictor for higher mortality and poorer functional status/quality of life in people with RA and should be considered in clinical management plans.

Associations between long-term conditions and upper gastrointestinal cancer incidence: A prospective population-based cohort of UK Biobank participants.

BACKGROUND/AIMS: Upper gastrointestinal cancers (oesophageal/stomach) have high mortality rates and are often diagnosed after the disease has progressed, making it important to identify populations at greater risk of upper gastrointestinal (UGI) cancer to promote earlier diagnosis. This study aims to determine if there is an association between a broad range of long-term conditions (LTCs) and incidence of UGI cancers. METHOD: A prospective-based cohort of 487,798 UK Biobank participants (age 37-73 years) after excluding previous UGI cancer. Least Absolute Shrinkage and Selection Operator (LASSO) regression used to identify candidate LTCs as predictors for UGI cancer. Strength of association was studied using Cox's regression adjusting for demographics and lifestyle factors. RESULTS: After median follow-up period of 86 months, 598 participants developed oesophageal cancer; 397 developed stomach cancer. In fully adjusted models, participants with alcohol addiction (Hazard Ratio-HR 4.11, 95% Confidence Interval-CI 2.01-8.43), Barrett's oesophagus (HR 5.68, 95% CI 3.36-9.58), bronchiectasis (HR 2.72, 95% CI 1.01-7.31), diabetes (HR 1.38, 95% CI 1.06-1.81), hiatus hernia (HR 1.69, 95% CI 1.16-2.45), Parkinson's disease (HR 3.86, 95% CI 1.60-9.37) and psoriasis/eczema (HR 1.53, 95% 1.08-2.17) were observed to have a higher risk of oesophageal cancer. Stomach cancer incidence was higher among participants with anorexia/bulimia (HR 8.86, 95% CI 1.20-65.14), Barrett's oesophagus (HR 3.37, 95% 1.39-8.14), chronic fatigue syndrome (HR 3.36, 95% CI 1.25-9.03), glaucoma (HR 2.06, 95% CI 1.16-3.67), multiple sclerosis (HR 4.60, 95% CI 1.71-12.34), oesophageal stricture (HR 1.04, 95% CI 1.46-74.46) and pernicious anaemia (HR 6.93, 95% CI 3.42-14.03). CONCLUSION: Previously unrecognised LTCs may have a role in symptom appraisal and risk assessment of UGI cancer in primary care. Further research should explore mechanisms underpinning these findings and determine whether they are replicable in other populations.

Hospitalisation events in people with chronic kidney disease as a component of multimorbidity: parallel cohort studies in research and routine care settings.

BACKGROUND: Chronic kidney disease (CKD) typically co-exists with multimorbidity (presence of 2 or more long-term conditions: LTCs). The associations between CKD, multimorbidity and hospitalisation rates are not known. The aim of this study was to examine hospitalisation rates in people with multimorbidity with and without CKD. Amongst people with CKD, the aim was to identify risk factors for hospitalisation. METHODS: Two cohorts were studied in parallel: UK Biobank (a prospective research study: 2006-2020) and Secure Anonymised Information Linkage Databank (SAIL: a routine care database, Wales, UK: 2011-2018). Adults were included if their kidney function was measured at baseline. Nine categories of participants were used: zero LTCs; one, two, three and four or more LTCs excluding CKD; and one, two, three and four or more LTCs including CKD. Emergency hospitalisation events were obtained from linked hospital records. RESULTS: Amongst 469,339 UK Biobank participants, those without CKD had a median of 1 LTC and those with CKD had a median of 3 LTCs. Amongst 1,620,490 SAIL participants, those without CKD had a median of 1 LTC and those with CKD had a median of 5 LTCs. Compared to those with zero LTCs, participants with four or more LTCs (excluding CKD) had high event rates (rate ratios UK Biobank 4.95 (95% confidence interval 4.82-5.08)/SAIL 3.77 (3.71-3.82)) with higher rates if CKD was one of the LTCs (rate ratios UK Biobank 7.83 (7.42-8.25)/SAIL 9.92 (9.75-10.09)). Amongst people with CKD, risk factors for hospitalisation were advanced CKD, age over 60, multiple cardiometabolic LTCs, combined physical and mental LTCs and complex patterns of multimorbidity (LTCs in three or more body systems). CONCLUSIONS: People with multimorbidity have high rates of hospitalisation. Importantly, the rates are two to three times higher when CKD is one of the multimorbid conditions. Further research is needed into the mechanism underpinning this to inform strategies to prevent hospitalisation in this very high-risk group.

Multimorbidity and the risk of major adverse kidney events: findings from the UK Biobank cohort.

BACKGROUND: Multimorbidity [the presence of two or more long-term conditions (LTCs)] is associated with a heightened risk of mortality, but little is known about its relationship with the risk of kidney events. METHODS: Associations between multimorbidity and major adverse kidney events [MAKE: the need for long-term kidney replacement therapy, doubling of serum creatinine, fall of estimated glomerular filtration rate (eGFR) to <15 mL/min/1.73 m2 or 30% decline in eGFR] were studied in 68 505 participants from the UK Biobank cohort. Participants were enrolled in the study between 2006 and 2010. Associations between LTC counts and MAKE were tested using survival analyses accounting for the competing risk of death. RESULTS: Over a median follow-up period of 12.0 years, 2963 participants had MAKE. There were associations between LTC count categories and the risk of MAKE [one LTC adjusted subhazard ratio (sHR) = 1.29, 95% confidence interval (CI) 1.15-1.45; two LTCs sHR = 1.74 (95% CI 1.55-1.96); and three or more LTCs sHR = 2.41 (95% CI 2.14-2.71)]. This finding was more pronounced when only cardiometabolic LTCs were considered [one LTC sHR = 1.58 (95% CI 1.45-1.73); two LTCs sHR = 3.17 (95% CI 2.80-3.59); and three or more LTCs sHR = 5.24 (95% CI 4.34-6.33)]. Combinations of LTCs associated with MAKE were identified. Diabetes, hypertension and coronary heart disease featured most commonly in high-risk combinations. CONCLUSIONS: Multimorbidity, and in particular cardiometabolic multimorbidity, is a risk factor for MAKE. Future research should study groups of patients who are at high risk of progressive kidney disease based on the number and type of LTCs.

Family history of diabetes and risk of SARS-COV-2 in UK Biobank: A prospective cohort study.

INTRODUCTION: The aim of this study was to determine risk of being SARS-CoV-2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. METHODS: We used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS-CoV-2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without. RESULTS: Of 401,268 participants in total, 13,331 tested positive for SARS-CoV-2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS-CoV-2 positive (risk ratio-RR 1.35; 95% confidence interval-CI 1.24-1.47) and severe infection (RR 1.30; 95% CI 1.04-1.59), compared to those without. The excess risk of being tested positive for SARS-CoV-2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes. CONCLUSION: The totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes.