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Abstract Background No algorithms exist to identify important osteoarthritis (OA) patient subgroups (i.e., moderate-to-severe disease, inadequate response to pain treatments) in electronic healthcare data, possibly due to the complexity in defining these characteristics as well as the lack of relevant measures in these data sources. We developed and validated algorithms intended for use with claims and/or electronic medical records (EMR) to identify these patient subgroups. Methods We obtained claims, EMR, and chart data from two integrated delivery networks. Chart data were used to identify the presence or absence of the three relevant OA-related characteristics (OA of the hip and/or knee, moderate-to-severe disease, inadequate/intolerable response to at least two pain-related medications); the resulting classification served as the benchmark for algorithm validation. We developed two sets of case-identification algorithms: one based on a literature review and clinical input (predefined algorithms), and another using machine learning (ML) methods (logistic regression, classification and regression tree, random forest). Patient classifications based on these algorithms were compared and validated against the chart data. Results We sampled and analyzed 571 adult patients, of whom 519 had OA of hip and/or knee, 489 had moderate-to-severe OA, and 431 had inadequate response to at least two pain medications. Individual predefined algorithms had high positive predictive values (all PPVs ≥ 0.83) for identifying each of these OA characteristics, but low negative predictive values (all NPVs between 0.16–0.54) and sometimes low sensitivity; their sensitivity and specificity for identifying patients with all three characteristics was 0.95 and 0.26, respectively (NPV 0.65, PPV 0.78, accuracy 0.77). ML-derived algorithms performed better in identifying this patient subgroup (range: sensitivity 0.77–0.86, specificity 0.66–0.75, PPV 0.88–0.92, NPV 0.47–0.62, accuracy 0.75–0.83). Conclusions Predefined algorithms adequately identified OA characteristics of interest, but more sophisticated ML-based methods better differentiated between levels of disease severity and identified patients with inadequate response to analgesics. The ML methods performed well, yielding high PPV, NPV, sensitivity, specificity, and accuracy using either claims or EMR data. Use of these algorithms may expand the ability of real-world data to address questions of interest in this underserved patient population.

Abstract Background Consuming a balanced diet and regular activity have health benefits. However, many adults have a difficult time adhering to diet and activity recommendations, especially in lifestyle interventions. Adherence to recommendations could be improved if common facilitators and barriers are accounted for in intervention design. The aim of this systematic review was to understand perceived barriers and facilitators to lifestyle (diet and/or activity) intervention guidelines. Methods This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Studies included relied on qualitative methods to explore the barriers and facilitators healthy adults ( $$\ge$$ ≥ 18 years) experienced in lifestyle interventions. Google Scholar, Cochrane Reviews, Medline, PubMed, and Web of Science were searched from January 2005 to October 2021. Main themes from each paper were thematically analyzed and reported as a barrier or facilitator to adherence at the individual, environment or intervention level using inductively derived themes. Study quality was assessed using the Critical Appraisal Skills Programme. Results Thirty-five papers were included. Of these, 46% were conducted in North America and the majority had more female participants (86% in mixed-sex studies, 26% females only). Similar themes emerged across all three levels as facilitators and barriers. At the individual level, attitudes, concern for health and physical changes. At the environmental level, social support, social accountability, changeable and unchangeable aspects of the community. Finally, delivery and design and content at the intervention level. An additional facilitator at the intervention level included fostering self-regulation through Behavior Change Taxonomies (BCT). Conclusions Lifestyle interventions that foster self-regulatory skills, opportunities for social engagement and personalization of goals may improve behaviour adherence. This can be achieved through inclusion of BCT, tapering off of intervention supports, identification of meaningful goals and anticipated barriers with participants.

Abstract Background Weight loss increases survivorship following breast cancer diagnosis. However, most breast cancer survivors (BCS) do not meet diet and exercise recommendations. Aim The purpose of this study was to explore the barriers and facilitators of BCS who had lymphedema and who participated in a 22‐week weight loss lifestyle intervention. Methods and results Participants completed semi‐structured interviews about barriers and facilitators to intervention adherence. Interviews were transcribed verbatim and a thematic analysis was conducted. Participants (n = 17) were 62 ± 8.0 years of age with a mean body mass index of 34.0 ± 7.1 kg/m2. Four themes emerged: (1) facilitators of intervention adherence, (2) barriers of intervention adherence, (3) continuation of healthy habits post intervention, and (4) recommendations for intervention improvements. Facilitators of intervention adherence were education, social support, routine, motivation, goal‐setting, meal‐provisioning, self‐awareness, and supervised exercise. Barriers to intervention adherence were personal life, health, meal dissatisfaction, seasonality, unchallenging exercises, and exercising alone. All women planned to continue the acquired healthy habits post intervention. Recommendations to improve the study included addressing the exercise regime, meal‐provisioning, and dietary intake monitoring methods. Conclusion Future strategies to engage BCS in weight loss interventions should promote group exercise, offer individualized meal‐provisioning and exercise regimes, provide transition tools, and allow participants to choose their self‐monitoring method.