Moderate-to-vigorous physical activity (MVPA) is beneficial for health, and reducing sedentary behavior (SB) is recommended in international guidelines. People with mental illnesses are at higher risk of preventable diseases than the general population, partly attributable to lower MVPA and higher SB. Self-determination theory provides a framework for understanding how motivation regulates behavior. This study aimed to evaluate the contribution of different forms of motivation for physical activity (amotivation, controlled, autonomous) to MVPA and SB in people with mental illnesses.

Cross-sectional self-reported and accelerometer-derived MVPA and SB in people with a range of mental illnesses across four countries were pooled for analysis (Australia, Belgium, England, Uganda). Motivation for physical activity was measured using the Behavioural Regulation in Exercise Questionnaire (BREQ). Regression analyses were used to investigate the association of MVPA and SB with amotivation, controlled, autonomous motivations, controlling for mental health and demographic variables.

Autonomous motivation was associated with 31% higher self-reported MVPA, and amotivation and controlled motivation were associated with 18% and 11% lower self-reported MVPA, respectively (n = 654). In contrast, controlled motivation was positively associated with SB (n = 189). Having physical comorbidities or an alcohol use disorder was associated with lower MVPA (n = 318). Sub-analyses with accelerometer-derived MVPA and SB (n = 139 and n = 145) did not reveal any associations with motivational forms.

Findings with an international sample support the universal relevance of motivation in promoting health-related behavior. Strategies for facilitating autonomous motivation should be utilized by health professionals seeking to support people with mental illnesses to become physically active.

Certain contextual factors, including non-restorative sleep (Niermeyer & Suchy, 2020), sleep deprivation (Lim & Dinges, 2010), burdensome emotion regulation (Franchow & Suchy, 2017), and pain interference (Boselie, Vancleef, & Peters, 2016) have been shown to contribute to temporary declines in executive functioning (EF). Contextually-induced decrements in EF in turn have been associated with temporary decrements in performance of instrumental activities of daily living (IADLs) among healthy older adults (Brothers & Suchy, 2021; Suchy et al., 2020; Niermeyer & Suchy 2020). Furthermore, some evidence suggests that higher variability in levels of contextual factors across days (i.e., deviations from routine) may contribute to IADL lapses above and beyond average, albeit high, levels of these contextual burdens (Bielak, Mogle, & Sliwinksi, 2019; Brothers & Suchy, 2021). Taken together, these findings highlight the importance of accounting for transient contextual burdens when assessing EF and IADL abilities in older adults.

Poor sleep quality has been associated with poor IADL performance (Fung et al., 2012; Holfeld & Ruthing, 2012) when assessed in a single visit. However, the potential contributions of variable sleep quantity and quality on IADL performance have not been assessed in healthy older adults using longitudinal methods. Accordingly, the aim of this study was to examine the impact of fluctuations in sleep quantity and quality, assessed daily, above and beyond average levels, on at-home IADL performance across 18 days in a group of community-dwelling older adults.

Fifty-two non-demented community-dwelling older adults (M age = 69 years, 65% female) completed 18 days of at-home IADL tasks, as well as daily ecological momentary assessment (EMA) measures of EF, sleep hours, and restfulness questions. An 18-day mean EMA EF score was computed controlling for practice effects. Mean levels of and variability in EMA sleep hours and EMA restfulness ratings were computed. IADL scores were computed for timeliness and accuracy across the 18 days.

A series of hierarchical linear regressions were run using separate IADL timeliness and accuracy as the dependent variable. In the first step, demographics (age, sex, education) were entered. Then, EMA EF was entered, followed by mean EMA sleep hours and EMA mean restfulness, and lastly, variability in EMA sleep hours and EMA restfulness. EMA EF was found to significantly predict both IADL accuracy (B = .46, p = .001) and timeliness (B = .45, p = .005). Variability in EMA sleep hours (B = .40, p = .008) and restfulness (B = -.29, p = .043) both predicted IADL accuracy beyond other variables, while mean levels did not. Additionally, variability in sleep hours and restfulness substantially improved the prediction of IADL accuracy above and beyond other variables in the model, accounting for an additional 16% of variance (F (2) = 3.80, ∆ R2 = .16, p = .006). Neither mean levels of or variability in sleep hours or restfulness predicted IADL timeliness.

Results suggest that greater fluctuations in the amount and quality of sleep across days may render healthy older adults more susceptible to lapses in daily functioning abilities, particularly the accuracy with which IADL tasks are completed.

To determine patient-specific risk factors and clinical outcomes associated with contaminated blood cultures.

A single-center, retrospective case-control risk factor and clinical outcome analysis performed on inpatients with blood cultures collected in the emergency department, 2014–2018. Patients with contaminated blood cultures (cases) were compared to patients with negative blood cultures (controls).

A 509-bed tertiary-care university hospital.

Risk factors independently associated with blood-culture contamination were determined using multivariable logistic regression. The impacts of contamination on clinical outcomes were assessed using linear regression, logistic regression, and generalized linear model with γ log link.

Of 13,782 blood cultures, 1,504 (10.9%) true positives were excluded, leaving 1,012 (7.3%) cases and 11,266 (81.7%) controls. The following factors were independently associated with blood-culture contamination: increasing age (adjusted odds ratio [aOR], 1.01; 95% confidence interval [CI], 1.01–1.01), black race (aOR, 1.32; 95% CI, 1.15–1.51), increased body mass index (BMI; aOR, 1.01; 95% CI, 1.00–1.02), chronic obstructive pulmonary disease (aOR, 1.16; 95% CI, 1.02–1.33), paralysis (aOR 1.64; 95% CI, 1.26–2.14) and sepsis plus shock (aOR, 1.26; 95% CI, 1.07–1.49). After controlling for age, race, BMI, and sepsis, blood-culture contamination increased length of stay (LOS; β = 1.24 ± 0.24; P < .0001), length of antibiotic treatment (LOT; β = 1.01 ± 0.20; P < .001), hospital charges (β = 0.22 ± 0.03; P < .0001), acute kidney injury (AKI; aOR, 1.60; 95% CI, 1.40–1.83), echocardiogram orders (aOR, 1.51; 95% CI, 1.30–1.75) and in-hospital mortality (aOR, 1.69; 95% CI, 1.31–2.16).

These unique risk factors identify high-risk individuals for blood-culture contamination. After controlling for confounders, contamination significantly increased LOS, LOT, hospital charges, AKI, echocardiograms, and in-hospital mortality.

Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors.

Three tools were developed and tested: (1) a PRO Planning Guide summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a Decision Log allows decision tracking; and (3) an Implementation Plan Template simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders.

Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.