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Suicidal thoughts are common among young people presenting to face-to-face and online mental health services. The early detection and rapid response to these suicidal thoughts and other suicidal behaviors is a priority for suicide prevention and early intervention efforts internationally. Establishing how best to use new and emerging technologies to facilitate person-centered systematic assessment and early intervention for suicidality is crucial to these efforts.
The aim of this study was to examine the use of a suicidality escalation protocol to respond to suicidality among help-seeking young people.
A total of 232 young people in the age range of 16-25 years were recruited from either a primary mental health care service or online in the community. Each young person used the Synergy Online System and completed an initial clinical assessment online before their face-to-face or online clinical appointment. A suicidality escalation protocol was used to identify and respond to current and previous suicidal thoughts and behaviors.
A total of 153 young people (66%, 153/232) reported some degree of suicidality and were provided with a real-time alert online. Further levels of escalation (email or phone contact and clinical review) were initiated for the 35 young people (15%, 35/232) reporting high suicidality. Higher levels of psychological distress (
This study demonstrates the use of new and emerging technologies to facilitate the systematic assessment and detection of help-seeking young people presenting with suicidality. This protocol empowered the young person by suggesting pathways to care that were based on their current needs. The protocol also enabled an appropriate and timely response from service providers for young people reporting high suicidality that was associated with additional comorbid issues, including psychosis-like symptoms, and a history of suicide plans and attempts.
Suicidal thoughts are common among young people presenting to traditional face-to-face mental health services and engaging with online mental health services [
This is a particularly pertinent issue given that almost half of those who have died by suicide had contact with a primary care provider within one month of the suicide [
New and emerging technologies (eg, mobile and Internet-based apps and e-tools) may be able to improve the systematic assessment and response to suicidality at a service and individual level so that those at risk can receive the appropriate care sooner [
The aim of this study was to examine the use of a suicidality escalation protocol embedded within the Synergy Online System (
The Synergy Online System is a personalized Internet-based resource designed to help people manage their physical, mental, and social wellbeing using a mixture of evidence-based apps, e-tools, and online and face-to-face services. One of the cornerstone principles of the Synergy Online System is a focus on the entire spectrum of health and well-being, from those who simply want to achieve goals to improve their daily habits, to those experiencing serious mental health problems. A key feature of the Synergy Online System is that it’s configurable (ie, can rearrange or turn on or off different components within the system as well as tailor content), which allows it to easily adapt and thus meet the needs of end users. The System aims to transform the provision of mental health services by delivering readily accessible, affordable, and equitable mental health care through an increased focus on prevention and early intervention and improving the management of mental disorders across settings.
Participants in this study included young people aged 16-25 years who had access to the Internet and were either seeking help through primary mental health care services (
Primary care sample 1: Participants were recruited from a group of young people presenting for the first time to
Primary care sample 2: Participants were recruited from a group of young people presenting for the first time to any
Community sample: Participants were recruited from three urban, regional, and rural communities in New South Wales that have a number of geographical, social, and economic vulnerabilities (ie, Central Coast, Western Sydney, and the Far West). Participants were recruited through targeted advertising in each of these communities (including posters and postcards in local businesses, paid Facebook advertisements, and advertisements on organizational social media channels) from March 2016 to June 2016. Young people were invited to participate in the study if they were currently living in one of these communities and had regular access to a mobile phone and the Internet.
The University of Sydney Human Research Ethics Committees approved these studies and all participants gave written or online informed consent when they first accessed the Synergy Online System and before completing the initial clinical assessment.
All participants were invited to complete an initial clinical assessment (accessed via the MHeC of the Synergy Online System). Participants from primary care sample 1 were provided with a URL to the alpha version of the MHeC and asked to complete the initial clinical assessment online before either a video visit or face-to-face appointment with a clinician. Participants from primary care sample 2 were provided with a URL to the beta version of the MHeC (with the video visit “turned off”) and asked to complete the initial assessment before their scheduled face-to-face appointment with a clinician. Participants from the community sample either navigated themselves to the MHeC or were automatically directed (via an e-tool embedded within the Synergy Online System) to the beta version of the MHeC (with the video visit “turned on”) if they were expressing psychological distress. For all participants using the MHeC, a “need help now” button was always displayed to provide the details of relevant emergency and helpline services for those who sought immediate help.
The initial clinical assessment assesses a range of mental health outcomes, as well as comorbid and associated risk factors. Being administered online and using smart skips, the full assessment takes approximately 45 min to complete (median, 47.5 min) and includes 14 modules (in the following order): demographics; current education and employment participation; mental health concerns; self-harm and suicidal behaviors; tobacco, alcohol, and other substance use; physical activity; sleep-wake behaviors; lifetime disorders; physical and mental health history; cognition; eating behaviors and body image; social connectedness; and puberty. Participants completed all modules. For the purposes of this study, the following measures were specifically selected and included for analysis.
Participants’ age, gender, highest level of education, and current education, employment, and training status (used to determine not in education, employment or training [NEET] status).
Current psychological distress was assessed using the Kessler-10 (K10) questionnaire [
The Suicide Ideation Attributes Scale (SIDAS) is a 5-item scale assessing suicidal ideation over the past month [
An item from the Brief Disability Questionnaire (BDQ) was used to assess participant’s inability to carry out daily tasks over the previous month [
Two questions about alcohol and substance use were used to assess the presence of a current comorbid alcohol or substance use problem. Specifically, participants were asked “Have you recently thought that you should cut down on alcohol or other addictive drugs?” (derived from the CAGE questionnaire; [
All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS 22.0 for Windows). Group differences between the three sample groups (primary care sample 1, primary care sample 2, and community sample) were assessed using the Kruskal-Wallis H test for continuous variables and the chi-square test for categorical variables. The sample was then split by suicidality group (see Textbox 2; “no suicidality”, “low suicidality”, and “high suicidality”) to assess group differences using the Kruskal-Wallis H test for continuous variables and the chi-square test for categorical variables. To examine the independent predictors of suicidality, two separate logistic regressions were conducted. The first model compares the “no suicidality” group with the “any suicidality” group (low and high suicidality groups combined). The second model compares the “low suicidality” and “high suicidality” groups. For both models, variables were entered using a forward forced-entry method with demographic variables (age, gender, education, and NEET status) entered in the first block, current mental health variables (K10, hypomania-like symptoms, psychosis-like symptoms, and alcohol or substance use) entered in the second block, mental health history variables (previous mental health problem, suicide plans or attempts history) entered in the third block, and functioning (days out of role) entered in the final block. To control for sample groups in the analyses, “sample” was also entered in the final block. Only models with nonsignificant Hosmer-Lemeshow goodness of fit tests were included.
The suicidality escalation protocol involves multiple levels of action, dependent on the participants’ responses to the initial assessment (
Suicidality escalation protocol.
The demographic and behavioral characteristics for each sample are presented in
The first use of the suicidality escalation protocol in a primary mental health care setting occurred at
Demographic characteristics by sample group (N=232).
Characteristics | Primary care 1 |
Primary care 2 |
Community |
||
Age, mean (SDa) | 20.39 (2.56) | 20.41 (2.53) | 20.66 (2.90) | .88 | |
.76 | |||||
Female | 68 (72) | 71 (68) | 21 (66) | ||
Male | 27 (28) | 34 (32) | 11 (34) | ||
.70 | |||||
Secondary | 44 (46) | 49 (48) | 16 (55) | ||
Tertiary | 51 (54) | 54 (52) | 13 (45) | ||
.12 | |||||
Non-NEET | 62 (65) | 59 (56) | 24 (75) | ||
NEET | 33 (35) | 46 (44) | 8 (25) |
aSD: standard deviation.
b“no formal education” and “primary education” groups were left out due to insufficient cell counts (n=5 cases missing).
cNEET: not in education, employment or training.
The suicidality escalation protocol was scaled up and rolled out across all
Of the entire community sample, 37.5% (12/32) young people were identified as “no suicidality” and so no action was initiated, 37.5% (12/32) were identified as “low suicidality” and were presented with a real-time alert, and 25% (8/32) were identified as “high suicidality”, which initiated the real-time alert and an additional two escalation actions. All 8 individuals were contacted via email by the clinical research team, and had their data reviewed.
Behavioral characteristics by sample group (N=232).
Characteristics | Primary care 1 |
Primary care 2 |
Community |
||
K10a score, mean (SDb) | 29.28 (8.16) | 29.75 (8.28) | 25.59 (11.76) | .11 | |
.08 | |||||
No | 13 (14) | 15 (14) | 12 (38) | ||
Mild | 15 (16) | 13 (13) | 3 (9) | ||
Moderate | 18 (19) | 20 (19) | 5 (16) | ||
Severe | 49 (51) | 57 (54) | 12 (37) | ||
SIDASc score, mean (SD) | 7.93 (11.50) | 7.52 (9.71) | 11.59 (16.05) | .87 | |
.34 | |||||
No ideation | 37 (39) | 40 (38) | 13 (41) | ||
Low ideation | 43 (45) | 53 (51) | 11 (34) | ||
High ideation | 15 (16) | 12 (11) | 8 (25) | ||
.06 | |||||
No | 68 (72) | 88 (84) | 22 (69) | ||
Yes | 27 (28) | 17 (16) | 10 (31) | ||
.51 | |||||
No | 65 (68) | 71 (68) | 25 (78) | ||
Yes | 30 (32) | 34 (32) | 7 (22) | ||
Days out of role in past month, mean (SD) | 7.53 (7.22) | 8.04 (8.47) | 2.34 (2.66) | <.001 | |
.22 | |||||
No problem | 60 (73) | 87 (83) | 25 (78) | ||
Likely problem | 26 (27) | 18 (17) | 7 (22) | ||
.24 | |||||
No | 27 (28) | 31 (29) | 14 (44) | ||
Yes | 68 (72) | 74 (71) | 18 (56) | ||
.18 | |||||
No | 67 (71) | 66 (63) | 17 (53) | ||
Yes | 28 (29) | 39 (37) | 15 (47) |
aK10: Kessler-10.
bSD: standard deviation.
cSIDAS: Suicide Ideation Attributes Scale.
The overall sample was split according to “no suicidality”, “low suicidality”, and “high suicidality” to examine the demographic and behavioral differences between these groups (
Demographic characteristics by suicidality group (N=232).
Characteristics | Suicidality | ||||
No |
Low |
High |
|||
.33 | |||||
Primary care 1 | 31 (39) | 49 (42) | 15 (43) | ||
Primary care 2 | 36 (46) | 57 (48) | 12 (34) | ||
Community | 12 (15) | 12 (10) | 8 (23) | ||
Age, mean (SD)a | 20.32 (2.66) | 20.75 (2.52) | 19.66 (2.53) | .08 | |
.74 | |||||
Female | 57 (72) | 79 (67) | 24 (69) | ||
Male | 22 (28) | 39 (33) | 11 (31) | ||
.01 | |||||
Secondary | 32 (41) | 53 (46) | 24 (71) | ||
Tertiary | 46 (59) | 62 (54) | 10 (29) | ||
.29 | |||||
Non-NEET | 48 (61) | 71 (60) | 26 (74) | ||
NEET | 31 (39) | 47 (40) | 9 (26) |
aSD: standard deviation.
b“no formal education” and “primary education” groups were left out due to insufficient cell counts (n=5 cases missing).
CNEET: not in education, employment or training.
Behavioral characteristics by suicidality group (N=232).
Characteristics | Suicidality | ||||
No |
Low |
High |
|||
.33 | |||||
Primary care 1 | 31 (39) | 49 (42) | 15 (43) | ||
Primary care 2 | 36 (46) | 57 (48) | 12 (34) | ||
Community | 12 (15) | 12 (10) | 8 (23) | ||
K10a score, mean (SDb) | 24.30 (8.04) | 29.92 (8.22) | 36.43 (6.41) | <.001 | |
<.001 | |||||
No | 26 (33) | 14 (12) | 0 (0) | ||
Mild | 14 (18) | 16 (14) | 1 (3) | ||
Moderate | 17 (21) | 22 (18) | 4 (11) | ||
Severe | 22 (28) | 66 (56) | 30 (86) | ||
.002 | |||||
No | 67 (85) | 92 (78) | 19 (54) | ||
Yes | 12 (15) | 26 (22) | 16 (46) | ||
<.001 | |||||
No | 67 (85) | 82 (70) | 12 (34) | ||
Yes | 12 (15) | 36 (30) | 23 (66) | ||
Days out of role in past month, mean (SD) | 6.22 (7.63) | 7.18 (7.69) | 8.46 (7.35) | .09 | |
.02 | |||||
No problem | 70 (89) | 87 (74) | 24 (69) | ||
Likely problem | 9 (11) | 31 (26) | 11 (31) | ||
<.001 | |||||
No | 34 (43) | 36 (30) | 2 (6) | ||
Yes | 45 (57) | 82 (70) | 33 (94) | ||
<.001c | |||||
No | 79 (100) | 67 (57) | 4 (11) | ||
Yes | 0 (0) | 51 (43) | 31 (89) |
aK10: Kessler-10.
bSD: standard deviation.
cThis
Further analyses using logistic regression were conducted to (1) identify predictors of “no suicidality” compared with “any suicidality” (low and high suicidality groups combined) (Model 1,
Logistic regression models showing predictors of suicidality (N=232).
No suicidality versus any suicidalitya | Low suicidality versus high suicidalityb | |||||||
Beta (SEc) | OR (95% CI) | Beta (SE) | OR (95% CI) | |||||
Age | .05 (0.07) | 1.05 (0.91-1.22) | .47 | −.17 (0.15) | 0.84 (0.63-1.13) | .26 | ||
Female | 1.00 | 1.00 | ||||||
Male | .65 (0.38) | 1.92 (0.92-4.02) | .08 | .66 (0.64) | 1.94 (0.55-6.81) | .30 | ||
Secondary | 1.00 | 1.00 | ||||||
Tertiary | −.22 (0.39) | 0.81 (0.37-1.75) | .58 | −.91 (0.69) | 0.40 (0.10-1.57) | .19 | ||
NEET | 1.00 | 1.00 | ||||||
Non-NEET | .30 (0.35) | 1.35 (0.68-2.67) | .39 | .92 (0.68) | 2.50 (0.66-9.51) | .18 | ||
K10f score | .11 (0.02) | 1.12 (1.07-1.17) | <.001 | .11 (0.04) | 1.12 (1.03-1.21) | .01 | ||
No | 1.00 | 1.00 | ||||||
Yes | .14 (0.45) | 1.16 (0.48-2.76) | .75 | .41 (0.60) | 1.50 (0.47-4.84) | .50 | ||
No | 1.00 | 1.00 | ||||||
Yes | .80 (0.41) | 2.22 (1.00-4.95) | .05 | 1.54 (0.58) | 4.68 (1.51-14.53) | .01 | ||
No problem | 1.00 | 1.00 | ||||||
Likely problem | 1.04 (0.46) | 2.84 (1.15-7.05) | .02 | −.12 (0.63) | 0.89 (0.26-3.04) | .85 | ||
No | 1.00 | 1.00 | ||||||
Yes | .42 (0.35) | 1.52 (0.77-3.03) | .23 | 2.43 (0.99) | 11.34 (1.64-78.30) | .01 | ||
No | 1.00 | |||||||
Yes | N/Ag | N/A | N/A | 2.34 (0.70) | 10.41 (2.65-40.83) | .001 | ||
Days out of role, past month | −.02 (0.02) | 0.98 (0.94-1.03) | .42 | .01 (0.04) | 1.00 (0.92-1.09) | .93 | ||
Community | 1.00 | 1.00 | ||||||
Primary care 1 | −.22 (.57) | 0.80 (0.26-2.43) | .69 | .25 (0.86) | 1.28 (0.24-6.83) | .77 | ||
Primary care 2 | −.27 (.57) | 0.76 (0.25-2.30) | .63 | −.51 (0.92) | 0.60 (0.10-3.67) | .58 |
aModel 1
bModel 2
cSE: standard error.
d“no formal education” and “primary education” groups were left out due to insufficient cell counts (n=5 cases missing)
eNEET: not in education, employment or training.
fK10: Kessler-10.
gN/A: Not applicable, this comparison is invalid since the “no suicidality” group, by definition, has no history of suicide plans or attempts and therefore was left out of the model.
We identified that two-thirds of help-seeking young people reported some degree of suicidality, and the protocol provided these young people with a real-time alert online. Further levels of escalation (email or phone contact and clinical review) were initiated for the 15% (35/232) of young people who reported high suicidality. Higher levels of psychological distress and a current alcohol or substance use problem predicted any level of suicidality (compared with no suicidality). In addition to higher levels of psychological distress, psychosis-like symptoms in the last 12 months, a previous mental health problem, and a history of suicide plans or attempts were specific predictors of high suicidality (compared with low suicidality). These results support the use of new and emerging technologies to facilitate the systematic assessment and detection of young people experiencing suicidal thoughts with additional comorbidities and enable an appropriate and timely response from service providers.
The use of the suicidality escalation protocol of the Synergy Online System as an adjunct to traditional primary mental health care services assisted clinical decision-making about suicide risk and the need for care among those young people reporting higher levels of suicidality. Of the young people in primary care, 13.5% (27/200) had their case escalated to clinical review by a clinician or clinical team before their entry into care. Importantly, none of these young people were referred to crisis services but instead had their entry into care facilitated due to a clinically perceived higher need for immediate care. This escalation process ensured that individuals presenting to primary care services with increased suicidality were not delayed by a service waitlist, which commonly arises from a mismatch between service demand and capacity [
Importantly, the results here also highlight the benefits of offering online services to young people by allowing mental health care and the service to be brought to the young person when they need it, wherever they live, rather than relying on young people to present initially to a face-to-face service which has many barriers to overcome [
Psychological distress differentiated between each level of suicidality identified, which is consistent with the established relationship between distress and suicidality [
The ongoing development of the Synergy Online System would benefit from employing methodologies that utilize longitudinal outcomes to improve the existing algorithms accuracy for identifying individual cases of suicidality that should be escalated and followed up immediately by a clinician and service. Machine learning methodologies are increasingly used in psychiatric research as they facilitate individual-level prediction of unseen observations, which makes them suitable for the development of clinically useful digital tools [
For the future development of the protocol, some limitations need to be addressed. First, the initiation of the suicidality escalation protocol is dependent on when the young person completes the online assessment. So young people at-risk who don’t complete the online assessment immediately cannot be identified and spend a longer period under distress and not in care. Second, the outcome for those who had their entry into care escalated is unclear, so it is difficult to determine the impact of the suicidality escalation protocol on their clinical outcome. This was beyond the scope of this particular study, but it is an important focus for future research to establish the long term impact of this protocol on engagement with services and clinical trajectory. Another key focus for this work would be to determine whether the protocol missed individuals who would become high risk or later engage in suicidal behaviors. Third, the relatively small sample size of the community sample, compared with the two primary care sample groups, means that the sample characteristics were somewhat biased toward the primary care groups and limits the generalizability of these results to young people in the community who seek help online. Finally, the use of the K10 as a measure of general psychological distress may be limited primarily to depression and anxiety symptoms and less useful for other mental health problems common in adolescence.
This study contributes to the research and knowledge about the use of new and emerging technologies to identify and respond to increased suicidality among help-seeking young people. Young people with increased suicidality were more likely to present with a number of comorbid issues including psychosis-like symptoms and a history of plans or attempts, which emphasizes the need for these young people to receive appropriate and timely care.
Brief Disability Questionnaire
Kessler-10 Questionnaire
Mental health eClinic
Not in Educational, Employment or Training
Suicide Ideation Attributes Scale
Statistical Package for the Social Sciences
Frank Iorfino is supported by an Australian Postgraduate Award (APA). Data collected for primary care sample 1 was supported by the Young and Well CRC (2011-16), and data collected for primary care sample 2 and the community sample was supported by a Commonwealth Government of Australia investment in Project Synergy — the design, build, and evaluation of an innovative system of care that embeds new and emerging technology into Australia’s youth mental health services (2014-16). This project was carried out through a partnership between the Young and Well CRC and The University of Sydney’s Brain and Mind Centre. When Young and Well CRC closed on June 30, 2016, Project Synergy was novated to The University of Sydney. Thanks to the entire Youth Mental Health and Technology Team at the Brain and Mind Centre.
Professor Ian Hickie has been a commissioner in Australia’s National Mental Health Commission since 2012. He is the co-director, Health and Policy at The University of Sydney’s Brain and Mind Centre. The Brain and Mind Centre operates an early-intervention youth services at Camperdown under contract to headspace. Professor Hickie has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca) projects focused on the identification and better management of anxiety and depression. He is a member of the Medical Advisory Panel for Medibank Private, a board member of Psychosis Australia Trust, and a member of Veterans Mental Health Clinical Reference group. He is the chief scientific advisor to, and an equity shareholder in, Innowell. Innowell has been formed by The University of Sydney and PricewaterhouseCoopers (PwC) to deliver the $30m Australian Government-funded “Project Synergy.” Project Synergy is a 3- year program for the transformation of mental health services through the use of new and innovative technologies. Professor Jane Burns is the CEO of, and an equity shareholder in, Innowell.