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Animal Assisted Activity (AAA) is a non-medical intervention that has been shown to reduce anxiety among nursing home patients in various settings. However, AAA has not been tested among acute care hospitalized older adult patients ages 65 and older. This pilot study explored if a visit from a trained dog and its handler would decrease anxiety among hospitalized, older adult patients ages 65 and greater. The participants were recruited from medical surgical/oncology units, and the Speilberger State-Trait Anxiety Inventory (STAI) 6-item short form was used to measure anxiety both pre- and post-interactions with the AAA-team. The data revealed that a one-time, 12–20-min visit, allowing the patients to pet and to interact with the dog, reduced (p = .000) the participants’ self-reported anxiety.

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors., Digital health technologies can effectively reach individuals within the context of their daily lives to improve health behaviors.

Objectives The objective of the study was to examine whether clinical trials that have been included in systematic reviews have been registered in clinical trial registers and, when they have, whether results of the trials were included in the clinical trial register. Study Design and Setting This study used a sample of 100 systematic reviews published by the Cochrane Musculoskeletal, Oral, Skin and Sensory Network between 2014 and 2019. Results We identified 2,000 trials (369,778 participants) from a sample of 100 systematic reviews. The median year of trial publication was 2007. Of 1,177 trials published in 2005 or later, a clinical trial registration record was identified for 368 (31%). Of these registered trials, 135 (37%) were registered prospectively and results were posted for 114 (31%); most registered trials evaluated pharmaceutical interventions (62%). Of trials published in the last 10 years, the proportion of registered trials increased to 38% (261 of 682). Conclusion Although some improvement in clinical trial registration has been observed in recent years, the proportion of registered clinical trials included in recently published systematic reviews remains less than desirable. Prospective clinical trial registration provides an essential role in assessing the risk of bias and judging the quality of evidence in systematic reviews of intervention safety and effectiveness.

Objective: We examined nonsystematic adverse events (AEs) in Part 2 (of 2) of a study describing the assessment and reporting AEs in clinical trials. Study Design and Setting: We examined 21 trials of gabapentin for neuropathic pain (52 sources) and seven trials of quetiapine for bipolar depression (80 sources) using data from the Multiple Data Sources study. We extracted and compared information about nonsystematic AEs (i.e., AEs that were not assessed for every participant), including AEs categorized as “serious.” We recorded whether AEs were grouped by anatomic or physiological system. Results: Trials of the same drug reported information about different AEs. Information in public sources was inadequate for decision-making. No public source reported all AEs, or all serious AEs, identified in nonpublic sources about the same trial. Of trials with only public sources, 2/15 (13%) gabapentin and 0/3 (0%) quetiapine trials grouped AEs by anatomic or physiological system. Conclusion: Public sources contained little information about nonsystematic AEs, including serious AEs. Grouping might make nonsystematic AEs easier to detect; however, most public sources did not report grouped AEs. Standards are needed to improve the collection and reporting of nonsystematic AEs so that stakeholders can use trials to assess the balance of potential benefits and harms.

Objectives The objective of this study was to determine whether disagreements among multiple data sources affect systematic reviews of randomized clinical trials (RCTs). Study Design and Setting Eligible RCTs examined gabapentin for neuropathic pain and quetiapine for bipolar depression, reported in public (e.g., journal articles) and nonpublic sources (clinical study reports [CSRs] and individual participant data [IPD]). Results We found 21 gabapentin RCTs (74 reports, 6 IPD) and 7 quetiapine RCTs (50 reports, 1 IPD); most were reported in journal articles (18/21 [86%] and 6/7 [86%], respectively). When available, CSRs contained the most trial design and risk of bias information. CSRs and IPD contained the most results. For the outcome domains “pain intensity” (gabapentin) and “depression” (quetiapine), we found single trials with 68 and 98 different meta-analyzable results, respectively; by purposefully selecting one meta-analyzable result for each RCT, we could change the overall result for pain intensity from effective (standardized mean difference [SMD] = −0.45; 95% confidence interval [CI]: −0.63 to −0.27) to ineffective (SMD = −0.06; 95% CI: −0.24 to 0.12). We could change the effect for depression from a medium effect (SMD = −0.55; 95% CI: −0.85 to −0.25) to a small effect (SMD = −0.26; 95% CI: −0.41 to −0.1). Conclusions Disagreements across data sources affect the effect size, statistical significance, and interpretation of trials and meta-analyses.

Background: Adverse events (AEs) in randomized clinical trials may be reported in multiple sources. Different methods for reporting adverse events across trials, or across sources for a single trial, may produce inconsistent and confusing information about the adverse events associated with interventions Methods: We sought to compare the methods authors use to decide which AEs to include in a particular source (i.e., “selection criteria”) and to determine how selection criteria could impact the AEs reported. We compared sources (e.g., journal articles, clinical study reports [CSRs]) of trials for two drug-indications: gabapentin for neuropathic pain and quetiapine for bipolar depression. We identified selection criteria and assessed how criteria affected AE reporting. Results: We identified 21 gabapentin trials and 7 quetiapine trials. All CSRs (6 gabapentin, 2 quetiapine) reported all AEs without applying selection criteria; by comparison, no other source reported all AEs, and 15/68 (22%) gabapentin sources and 19/48 (40%) quetiapine sources reported using selection criteria. Selection criteria greatly affected the number of AEs that would be reported. For example, 67/316 (21%) AEs in one quetiapine trial met the criterion “occurring in ≥2% of participants in any treatment group,” while only 5/316 (2%) AEs met the criterion, “occurring in ≥10% of quetiapine-treated patients and twice as frequent in the quetiapine group as the placebo group.” Conclusions: Selection criteria for reporting AEs vary across trials and across sources for individual trials. If investigators do not pre-specify selection criteria, they might “cherry-pick” AEs based on study results. Even if investigators pre-specify selection criteria, selective reporting of AEs will produce biased meta-analyses and clinical practice guidelines. Data about all AEs identified in clinical trials should be publicly available; however, sharing data will not solve all the problems we identified in this study. Keywords: Harms, adverse events, clinical trials, reporting bias, selective outcome reporting, data sharing, trial registration

Clinical trials and systematic reviews of clinical trials inform healthcare decisions. There is growing concern, however, about results from clinical trials that cannot be reproduced. Reasons for nonreproducibility include that outcomes are defined in multiple ways, results can be obtained using multiple methods of analysis, and trial findings are reported in multiple sources (“multiplicity”). Multiplicity combined with selective reporting can influence dissemination of trial findings and decision-making. In particular, users of evidence might be misled by exposure to selected sources and overly optimistic representations of intervention effects. In this commentary, drawing from our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study and evidence from previous research, we offer practical recommendations to enhance the reproducibility of clinical trials and systematic reviews.

Background We used various methods for identifying and prioritizing patient-centered outcomes (PCOs) for comparative effectiveness research (CER). Methods We considered potential PCOs (“benefits” and “harms”) related to (1) gabapentin for neuropathic pain and (2) quetiapine for bipolar depression. Part 1 (April 2014 to March 2015): we searched for PCO research and core outcome sets (COSs). We conducted electronic searches of bibliographic databases and key websites and examined FDA prescribing information and reports of clinical trials and systematic reviews. We asked patient and clinician co-investigators to identify PCOs. Part 2 (not part of our original study protocol): in 2015, we surveyed members of The TMJ Association, Ltd., a patient group associated with temporomandibular disorders (4130 invitations sent). Participants prioritized (1) the importance of six potential benefits and (2) 21 potential harms selected by the investigators in part 1, using stated preference methods. We calculated descriptive statistics. Results In part 1, we identified a COS for pain, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations. The COS identified several important benefits, but it lacked specific recommendations about which potential harms to include in CER. We did not identify a COS for bipolar depression. Research reports, prescribing information, and patient co-investigators helped identify but not prioritize outcomes. We abandoned our electronic search for PCO research because we found it would be resource-intensive and yield few relevant reports. In part 2, surveying patients was useful for prioritizing PCOs. Members of The TMJ Association, Ltd., completed the survey (N = 746) and successfully prioritized both benefits and harms. Participants did not identify many benefits other than those we identified in part 1; several participants identified additional harms. Conclusions These exploratory results could inform future research about identifying and prioritizing PCOs. We found that stakeholder co-investigators and research reports contributed to identifying PCOs; surveying a patient group contributed to prioritizing PCOs. Prioritizing potential harms was particularly challenging because there are many more potential harms than potential benefits. Methods for identifying and prioritizing potential benefits for CER might not be appropriate for harms. Further research is needed to determine the generalizability of these results. Electronic supplementary material The online version of this article (10.1186/s40814-018-0284-6) contains supplementary material, which is available to authorized users.

Data for individual trials included in systematic reviews may be available in multiple sources. For example, a single trial might be reported in 2 journal articles and 3 conference abstracts. Because of differences across sources, source selection can influence the results of systematic reviews. We used our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study, and evidence from previous studies, to develop practical guidance for using multiple data sources in systematic reviews. We recommend the following: (1) Specify which sources you will use. Before beginning a systematic review, consider which sources are likely to contain the most useful data. Try to identify all relevant reports and to extract information from the most reliable sources. (2) Link individual trials with multiple sources. Write to authors to determine which sources are likely related to the same trials. Use a modified Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flowchart to document both the selection of trials and the selection of sources. (3) Follow a prespecified protocol for extracting trial characteristics from multiple sources. Identify differences among sources, and contact study authors to resolve differences if possible. (4) Prespecify outcomes and results to examine in the review and meta-analysis. In your protocol, describe how you will handle multiple outcomes within each domain of interest. Look for outcomes using all eligible sources. (5) Identify which data sources were included in the review. Consider whether the results might have been influenced by data sources used. (6) To reduce bias, and to reduce research waste, share the data used in your review.

Objective To identify variations in outcomes and results across reports of randomized clinical trials (RCTs). Study Design and Setting Eligible RCTs examined gabapentin for neuropathic pain and quetiapine for bipolar depression, reported in public (e.g., journal articles) and nonpublic (e.g., clinical study reports) sources by 2015. We prespecified outcome domains. From each source, we collected “outcomes” (i.e., domain, measure, metric, method of aggregation, and time point); “treatment effect” (i.e., outcome plus the methods of analysis [e.g., how missing data were handled]); and results (i.e., numerical contrasts of treatment and comparison groups). We assessed whether results included sufficient information for meta-analysis. Results We found 21 gabapentin (68 public, 6 nonpublic reports) and seven quetiapine RCTs (46 public, 4 nonpublic reports). For four (gabapentin) and seven (quetiapine) prespecified outcome domains, RCTs reported 214 and 81 outcomes by varying four elements. RCTs assessed 605 and 188 treatment effects by varying the analysis of those outcomes. RCTs reported 1,230 and 661 meta-analyzable results, 305 (25%) and 109 (16%) in public reports. Conclusion RCTs included hundreds of outcomes and results; a small proportion were in public reports. Trialists and meta-analysts may cherry-pick what they report from multiple sources of RCT information.

The correct title of the article [1] should be “Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol”.