Impact of cortisol on lymphocyte cell division following 12 days in low folic acid (FA) (25 nM) or high FA (100 nM) culture conditions.

<p>(A) Cell growth. (B-D) frequencies (per 500 total cells) of mononucleated cells (monos); (C) binucleated cells (BN); and (D) multinucleated cells (mulits). (E) Nuclear division index (NDI); (duplicate measures for N = 6 participants. Mean ± SEM. *p ≤ 0.05).</p

Lymphocyte telomere length.

<p>Lymphocyte telomere length.</p

Biomarkers of DNA damage and chromosomal instability.

<p>Frequency of binucleated (BN) lymphocytes displaying one or more DNA damage biomarker (per 500 BN), following 12 days in low (25 nM) or high folic acid (FA) (100 nM) culture medium containing 0, 400, 1000 or 3500 nM cortisol: BN with (A) fused nuclei (FUS), (B) ≥1 NPB, (C) circular nuclei (CIR), (D) ≥1 MN, (E) ≥1 NBud, and (F) total frequency of BN cells containing one or more DNA damage biomarker. (mean ± SEM; 500 BN scored per duplicate slide per treatment, N = 6 participants; *represents p ≤ 0.05; VC, vehicle control).</p

A Systematic Assessment of Smartphone Tools for Suicide Prevention

<div><p>Background</p><p>Suicide is a leading cause of death globally, and there has been a rapid growth in the use of new technologies such as mobile health applications (apps) to help identify and support those at risk. However, it is not known whether these apps are evidence-based, or indeed contain potentially harmful content. This review examines the concordance of features in publicly available apps with current scientific evidence of effective suicide prevention strategies.</p><p>Methods</p><p>Apps referring to suicide or deliberate self-harm (DSH) were identified on the Android and iOS app stores. Systematic review methodology was employed to screen and review app content. App features were labelled using a coding scheme that reflected the broad range of evidence-based medical and population-based suicide prevention interventions. Best-practice for suicide prevention was based upon a World Health Organization report and supplemented by other reviews of the literature.</p><p>Results</p><p>One hundred and twenty-three apps referring to suicide were identified and downloaded for full review, 49 of which were found to contain at least one interactive suicide prevention feature. Most apps focused on obtaining support from friends and family (n = 27) and safety planning (n = 14). Of the different suicide prevention strategies contained within the apps, the strongest evidence in the literature was found for facilitating access to crisis support (n = 13). All reviewed apps contained at least one strategy that was broadly consistent with the evidence base or best-practice guidelines. Apps tended to focus on a single suicide prevention strategy (mean = 1.1), although safety plan apps provided the opportunity to provide a greater number of techniques (mean = 3.9). Potentially harmful content, such as listing lethal access to means or encouraging risky behaviour in a crisis, was also identified.</p><p>Discussion</p><p>Many suicide prevention apps are available, some of which provide elements of best practice, but none that provide comprehensive evidence-based support. Apps with potentially harmful content were also identified. Despite the number of apps available, and their varied purposes, there is a clear need to develop useful, pragmatic, and multifaceted mobile resources for this population. Clinicians should be wary in recommending apps, especially as potentially harmful content can be presented as helpful. Currently safety plan apps are the most comprehensive and evidence-informed, for example, “Safety Net” and “MoodTools—Depression Aid”.</p></div

PRISMA flowchart showing the app search, screening, and review.

<p>PRISMA flowchart showing the app search, screening, and review.</p

sj-docx-1-gqn-10.1177_23333936241242915 – Supplemental material for Suicidal Emotions, Motivations and Rationales in Australian Men: A Qualitative Exploration

Supplemental material, sj-docx-1-gqn-10.1177_23333936241242915 for Suicidal Emotions, Motivations and Rationales in Australian Men: A Qualitative Exploration by Diane Macdonald, Ally Nicolopoulos, Stephanie Habak, Helen Christensen and Katherine Boydell in Global Qualitative Nursing Research</p

Correlation between resampled networks.

<p><b>A) T</b>he weighted adjacency matrix of the app network was constructed using a fixed number of random samples for each participant to investigate potential biases resulting from unequal scanning rates. The number of required samples was varied from 10 to 500 samples. The Mantel test was again used to estimate the correlation with the networks constructed using badge and survey data. Colour patches show the 99% confidence interval estimated by resampling the network 1000 times. <b>B)</b> The size of the network that was compared decreased with increasing number of required samples, as participants with insufficient number of scans were excluded.</p

Weighted adjacency matrix of the social network mapped using the smartphone app and the sociometric badges.

<p>Both axes reflect the 21 participants and each element reflects the percentage of time the two participants were in close proximity. <b>A)</b> Office hours, <b>B)</b> When both devices were active.</p

App scanning statistics.

<p><b>A)</b> Percentage of scheduled Bluetooth scans that were made by each smartphone in the 4-week period. <b>B)</b> Scanning rates for each edge of the network. The scanning rate between node A and B is determined by the number of scans made by smartphone A and B combined, as the edges are undirected (symmetric). The horizontal solid line reflects a scanning rate of 1 scan every 15 min; the dashed line 1 scan every hour.</p

Contingency table when the app and badge are both active.

<p>Table shows the number of times a particular edge of the network was detected (hit) or not (miss) by the smartphone app and the sociometric badges. Only time intervals when both the app and badge were active were considered.</p