Conflict of Interest Declaration
The following Task Force members and other authors were recused from the discussion as they declared a conflict of interest: none applicable. Conflict-of-interest declarations follow the ILCOR process and are held by ILCOR.
The following Task Force members and other authors declared an intellectual conflict of interest, and this was acknowledged and managed by the Task Force Chairs and Conflict of Interest Committee: no member of the review team is an author of an included study. Members of the review team co-authored the 2020 CoSTR, the 2022 meta-analysis (RG) and the ERC 2025 guideline (AL), with which this review’s findings are compared. Conflict-of-interest declarations follow the ILCOR process and are held by ILCOR.
CoSTR Citation
Yang C-W, Liu C-H, Greif R, Lockey A, Finn J, Lauridsen KG, Cheng A, on behalf of the International Liaison Committee on Resuscitation Education, Implementation, and Teams Task Force. First Responders Engaged by Mobile Technology, Consensus on Science with Treatment Recommendations [Internet] Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Education, Implementation, and Teams Task Force, [date of SAC approval pending]. Available from: http://ilcor.org ;
Background
The continuous evidence evaluation process used to produce this Consensus on Science with Treatment Recommendations (CoSTR) followed ILCOR guidance for methodology and conflict of interest management.1
This systematic review updates a prior ILCOR systematic review and its CoSTRs (EIT 878; 2015 CoSTR, 2019 systematic review, 2020 CoSTR),2–4 followed by Task Force evidence updates in 2021, 2022, 2024 and 2025. The current review uses a PICOST approved on 29 May 2026 and is registered with the Open Science Framework (doi:10.17605/OSF.IO/ANCQJ). The evidence is synthesized at two levels following Task Force agreement: the system level (with versus without a notification system) and the individual level (alert sent versus not, in the same period, within an operating system).
Studies whose only comparisons formed groups after the alert (for example, whether a responder arrived, or who started CPR or gave the first shock) were excluded at full text as the wrong comparator (no comparison of notification versus no notification).
All evidence identified was considered by the ILCOR Education, Implementation, and Teams (EIT) Task Force and is described below. Discussion points and highlights of the evidence-to-decision process are included. Data tables and the evidence-to-decision framework are provided as attachments.
Review Citation
Yang C-W, Liu C-H, Greif R, Lockey A, Finn J, Lauridsen KG, Cheng A. Mobile-technology activation of community responders for out-of-hospital cardiac arrest: a systematic review and structured evidence synthesis. In draft.
PICOST
The PICOST (Population, Intervention, Comparator, Outcome, Study Designs and Timeframe)
Population: All patients with out-of-hospital cardiac arrest (OHCA). (Adult and pediatric patients; neonatal OHCA is outside the scope of this review.)
Intervention: Activation of first responders with subsequent intervention (e.g., CPR, defibrillation) that is distinct from the primary EMS response via mobile communication technology or other strategy. (Mobile communication technology includes, but is not limited to, smartphone applications, SMS or text-message alert systems and GPS-enabled dispatch platforms.)
Comparator: No such notification. Head-to-head comparisons between notification strategies, a prespecified subgroup, were included and analyzed separately; they do not inform notification versus no notification.
Outcomes: Survival to hospital discharge, survival to hospital discharge with good neurological function, and 30-day survival were ranked as critical outcomes; return of spontaneous circulation (ROSC), bystander CPR rates, bystander defibrillation rates, and time to first compression or shock delivery as important outcomes. Five further outcomes (hospital admission, activation rate, system reliability, user satisfaction, cost-effectiveness) were prespecified for narrative synthesis only.
Study Designs: Randomized controlled trials (RCTs) and nonrandomized studies (nonrandomized controlled trials, interrupted time series, controlled before-and-after studies, cohort studies, and case series where n>5). Published conference abstracts reporting novel data not subsequently published as full-text articles were eligible for narrative synthesis only.
Timeframe: Publications from 2000 onward; search updated 4 September 2026 (PubMed/MEDLINE, Embase, CENTRAL, CINAHL). All languages are included as long as there is an English abstract.
Registration: Open Science Framework, doi:10.17605/OSF.IO/ANCQJ. The PICOST was approved by the SAC representative (J Finn) on 29 May 2026.
Consensus on Science
The search, updated on 4 September 2026, identified 8,716 records in four databases (PubMed/MEDLINE, Embase, CENTRAL and CINAHL) and 1 record from other methods. The search strategy was adapted by the review team from the strategy used for the 2020 ILCOR review and its later evidence updates, and reviewed by a medical librarian (Hsin-Ping Chiu, National Taiwan University Medical Library). Of 228 database reports assessed in full text, 207 were excluded, 13 of them as the wrong comparator; the record from other methods (a poster) was also excluded. Sixteen studies in 21 reports were included.5–25
Two included studies were randomized: Ringh 2015, the RCT of notification versus no notification,5 and the Swedish AED and Mobile Bystander Activation (SAMBA) trial (Berglund 2023), which compared dispatch strategies among notified responders.6 As the lone RCT evaluating the system level, Ringh 2015 was shown separately and not included in the pooled meta-analysis; the system-level before-after studies and the individual-level same-period comparisons were summarized separately. At the system level, four before-after studies were pooled (Stieglis 2022, Lee 2019, Oosterveer 2023, Wong 2021);7–10 Pek 2025 studied the same Singapore population as Wong 2021, so the two could not both be pooled. Because Pek 2025 evaluated residential AEDs, training and volunteer enrollment rolled out together, Wong 2021 was used in the main analysis and Pek 2025 replaced it only in a sensitivity analysis.11 At the individual level, five studies contributed same-period comparisons of arrests with and without an alert (Smida 2022, Smith 2022, Andelius 2020, Pijls 2016 through its companion report Pijls 2018a, and Takahashi 2025), with site data from the companion report Jonsson 2023.12–18 Head-to-head comparisons of dispatch strategies (SAMBA; Caputo 2017) formed a prespecified subgroup and were not used for notification versus no notification.6,19 Three further studies were not pooled at either level, for the following reasons: Derkenne 2020 (critical risk of bias),20 Andelius 2023 (its Swedish alert comparison was at critical risk of bias; its Danish arm was the Andelius 2020 population and was not used separately)21 and Pugnale 2025 (its alert-sent comparison concerned AED use, and the alert was withheld when an AED was already in use).22
Effects were expressed as odds ratios. Pooled analyses used random effects (restricted maximum likelihood [REML] with the Hartung-Knapp adjustment, the registered method) and required at least three studies; pooled values with I² above 75% were reported as exploratory. Absolute effects were calculated per 1,000 at the median control-group risk of the pooled studies.
For the critical outcome of survival with favorable neurological outcome, we identified very-low-certainty evidence at the system level (downgraded for risk of bias and inconsistency) from 2 before-after studies, which were not pooled (fewer than three studies): Stieglis 2022, OR 0.95 (95% CI 0.75–1.21),7 and Lee 2019, adjusted OR 2.13 (1.56–2.91).8 At the individual level, we identified very-low-certainty evidence (downgraded for risk of bias and imprecision) from 1 same-period comparison: Takahashi 2025, adjusted OR 1.54 (1.11–2.15), as reported by the authors (crude OR 0.97, 0.76–1.25).17 The RCT did not report this outcome.5
For the critical outcome of survival to hospital discharge or 30 days, we identified very-low-certainty evidence (downgraded for indirectness and very serious imprecision) from the RCT, which reported 30-day survival only (notification 32/286 [11.2%] vs control 28/326 [8.6%]; OR 1.34; 95% CI 0.79–2.29; 26 more survivors per 1,000 [from 17 fewer to 91 more]).5 At the system level, very-low-certainty evidence (downgraded for risk of bias, inconsistency and imprecision) from 3 pooled before-after studies (Stieglis 2022, Lee 2019, Wong 2021) gave OR 1.28 (0.72–2.28; I² 76%; exploratory), 22 more survivors per 1,000 (from 24 fewer to 94 more) at a control-group risk of 9.0%.7,8,10 At the individual level, very-low-certainty evidence (downgraded for risk of bias and imprecision) from 5 pooled same-period comparisons (Smith 2022, Takahashi 2025, and three sites of Jonsson 2023) gave OR 1.14 (0.89–1.45; I² 31%), 11 more survivors per 1,000 (from 9 fewer to 36 more) at a control-group risk of 9.0%.13,17,18
For the important outcome of ROSC, we identified very-low-certainty evidence (downgraded for indirectness and very serious imprecision) from the RCT (notification 90/306 [29.4%] vs control 105/361 [29.1%]; OR 1.02; 95% CI 0.73–1.42; 3 more patients with ROSC per 1,000 [from 61 fewer to 77 more]).5 At the system level, very-low-certainty evidence (downgraded for risk of bias, inconsistency and imprecision) from 3 pooled before-after studies (Oosterveer 2023, Lee 2019, Wong 2021) gave OR 1.43 (0.57–3.60; I² 92%; exploratory), 81 more patients with ROSC per 1,000 (from 108 fewer to 308 more) at a control-group risk of 31.3%.8–10 At the individual level, very-low-certainty evidence (downgraded for risk of bias and imprecision) from 3 pooled same-period comparisons (Smida 2022, Smith 2022, Andelius 2020) gave OR 1.05 (0.69–1.59; I² 0%), 9 more patients with ROSC per 1,000 (from 69 fewer to 102 more) at a control-group risk of 28.3%.12–14
For the important outcome of bystander CPR, we identified moderate-certainty evidence (downgraded for indirectness) from the RCT, in which mobile-phone dispatch of lay volunteers increased bystander-initiated CPR before the arrival of an ambulance or first responders, the trial’s primary outcome (notification 188/305 [61.6%] vs control 172/360 [47.8%]; OR 1.76; 95% CI 1.29–2.39; 139 more patients receiving bystander CPR per 1,000 [from 63 more to 209 more]). CPR given only with telephone instructions was not counted as bystander-initiated CPR.5 At the system level, very-low-certainty evidence (downgraded for risk of bias, inconsistency and imprecision) from 3 pooled before-after studies (Oosterveer 2023, Lee 2019, Wong 2021) gave OR 1.11 (0.75–1.65; I² 68%), 27 more patients receiving bystander CPR per 1,000 (from 72 fewer to 119 more) at a control-group risk of 54.9%; Stieglis 2022 reported bystander CPR for shockable rhythms only and was not pooled for this outcome, because rhythm is recorded after the alert (collider risk).7–10 At the individual level, very-low-certainty evidence (downgraded for risk of bias and inconsistency) from 7 pooled same-period comparisons (Andelius 2020, Smida 2022, Smith 2022, Pijls 2016 through Pijls 2018a, Takahashi 2025, and two sites of Jonsson 2023) gave OR 2.75 (1.64–4.60; I² 96%; exploratory), 210 more patients receiving bystander CPR per 1,000 (from 113 more to 281 more) at a control-group risk of 58.5%.12–18 Without the arrests witnessed by EMS in the comparison group of Andelius 2020, which cannot receive bystander CPR, the pooled estimate was 2.61 (1.54–4.42; I² 96%; exploratory).
For the important outcome of bystander defibrillation, defined in the included studies as an AED applied, attached or used before EMS arrival (not necessarily a shock; Lee 2019 did not define it), we identified very-low-certainty evidence at the system level (downgraded for risk of bias and imprecision) from 3 pooled before-after studies (Oosterveer 2023, Lee 2019, Wong 2021): OR 1.46 (0.70–3.01; I² 47%), 14 more patients with an AED applied before EMS arrival per 1,000 (from 10 fewer to 60 more) at a control-group risk of 3.3%.8–10 At the individual level, very-low-certainty evidence (downgraded for risk of bias) from 3 pooled same-period comparisons (Smida 2022, Smith 2022, Takahashi 2025) gave OR 2.14 (1.59–2.89; I² 0%), 78 more patients with an AED applied before EMS arrival per 1,000 (from 42 more to 122 more) at a control-group risk of 8.1%; with the crude rather than the adjusted estimate of Takahashi 2025, the pooled estimate was 1.22 (0.38–3.89; I² 80%; exploratory).12,13,17 Alert-sent comparisons for AED use in Pugnale 2025, Zijlstra 2014, Andelius 2020 and Jonsson 2023 were not pooled, because these systems send no alert when an AED is present or already in use.14,18,22,23 The RCT did not report this outcome.5
In the prespecified head-to-head subgroup (comparisons between notification strategies), the SAMBA trial compared directing notified responders to fetch an AED first with directing them straight to CPR: AED attachment RR 1.40 (0.97–2.01; low certainty, downgraded for indirectness and imprecision), shock delivered RR 0.94 (0.50–1.79; very low certainty, downgraded for indirectness and very serious imprecision), and any bystander CPR RR 0.96 (0.89–1.05).6 Caputo 2017 compared alerting responders through a smartphone application with alerting them by text message: the median time to responder arrival on scene was 3.5 versus 5.6 minutes, and lay responders were the first to arrive in 70% versus 15%.19
For the important outcome of time to first compression or shock, we identified very-low-certainty evidence (downgraded for risk of bias and indirectness; the outcome was measured only in shocked patients with a shockable initial rhythm) from 1 system-level before-after study. After the system was introduced, the interval from call to first shock was shorter, with the median falling from 11.7 to 9.3 minutes in residential arrests (mean difference -2.6 minutes; 95% CI -3.5 to -1.6) and a mean difference of -1.6 minutes (-2.7 to -0.5) in public arrests (Stieglis 2022).7 The RCT did not report this outcome.5
In sensitivity analyses, replacing Wong 2021 with Pek 2025 gave system-level survival OR 1.12 (0.53–2.40; I² 82%; exploratory),11 and with the DerSimonian-Laird estimator and a normal interval, system-level bystander defibrillation was OR 1.44 (1.07–1.94), which excludes 1, against OR 1.46 (0.70–3.01) with the registered method. Survival included 1 in every layer and every sensitivity analysis.
For continuity with earlier reviews, the Summary of Evidence also reports an exploratory pool of all observational comparisons, including the studies excluded as the wrong comparator; it is post hoc, is not GRADE-rated and carries no effect judgement.4,26
Process, implementation and system outcomes were synthesized narratively from the included reports. In the RCT, dispatchers did not activate the system for 26% of eligible patients, and a volunteer reached the scene in 59% of intervention-arm cases and arrived before EMS in 23%.5 An alert was issued in 5,499 of 9,167 arrests (60.0%) in Singapore,17 and the system was not activated in 41.8% of EMS-treated arrests in two Dutch regions.23 Responders accepted 6.6% of 144,555 alerts in Singapore.11 System reliability depended on the trigger and on responder density (the number of registered responders close enough to be alerted): alerts often reached patients without cardiac arrest (362 of 654 patients assessed),12 and a responder was close enough to be alerted for 6.7% of confirmed arrests in London and 22% in East Midlands.13 Some systems were paused during the COVID-19 pandemic.6,17 User satisfaction was measured only in responders who answered the survey sent after an alert (response 75%): 99% (1,602/1,618) wished to continue their enrollment, and 1.4% (22/1,621) reported severe psychological impact.14 No included study contained an economic evaluation or supported a conclusion that notification increases hospital admission (Wong 2021: survival to admission 18.2% versus 18.3%, a period comparison).10
Treatment Recommendation
We recommend the use of mobile technology linked to emergency dispatch to notify nearby willing responders of a suspected out-of-hospital cardiac arrest (strong recommendation, very low-certainty evidence).
Remarks. This keeps the direction and the strength of the 2020 recommendation. Systems should activate notification for all eligible arrests, including those at home, and support responders after alerts. All evidence comes from high-income systems.
Previous recommendation (2020 CoSTR):3
“We recommend that citizen/individuals who are in close proximity to a suspected Out-Of-Hospital Cardiac Arrest (OHCA) event and willing to be engaged/notified by a smartphone app with mobile positioning system (MPS) or Text Message (TM)-alert system should be notified (strong recommendation, very low-certainty evidence).”
Justification and Evidence to Decision Framework Highlights
- Direction kept. In the only RCT of notification versus no notification, notification increased bystander CPR (moderate certainty).5 The point estimates of the main pooled analyses lie above 1, but survival includes 1 in every layer and every sensitivity analysis. Measured harms are small, fall mainly on responders and can potentially be mitigated. The intervention is probably acceptable and, where dispatch is integrated, probably feasible. The direction is consistent with the 2015 and 2020 ILCOR recommendations.2,4
- Strong recommendation on very low-certainty evidence. Out-of-hospital cardiac arrest is life-threatening, and survival depends on what happens before and when EMS arrives. The effect on survival, a critical outcome, could not be determined (Ringh 2015, 30-day survival OR 1.34, 0.79–2.29; before-after studies, OR 1.28, 0.72–2.28): no effect was detected in either direction, and the confidence intervals include fewer as well as more survivors (from 17 fewer to 91 more per 1,000 in the RCT, and from 24 fewer to 94 more in the before-after studies). The evidence that suggests benefit is the RCT's moderate-certainty increase in bystander CPR, an important outcome on the path to survival. This represents the first of the GRADE situations in which a strong recommendation can rest on low or very low certainty: a life-threatening condition, evidence that suggests benefit, and small harms.27 The new evidence does not justify downgrading the strong recommendation made in 2020.4 Costs vary, and are lower where dispatch software and responder networks already exist. The 2020 survival estimate drew partly on comparisons in which the notified responder arrived, started CPR or defibrillated; such comparisons form their groups after the alert, do not match the PICOST comparator and are excluded here.
- Time before EMS arrival. As EMS systems improve, the remaining gap is the minutes before EMS arrives, especially for arrests at home; community responder systems are designed to shorten this interval. Responders arrived before EMS in 23%, 47% and 71% of cases when the EMS response time was under 5, 5 to 10, and over 10 minutes.14 Where EMS or dispatch is not yet in place, a community responder system is not the first priority.
- Effects and certainty. The EIT Taskforce proposes that desirable effects are small: the RCT’s moderate-certainty increase in bystander CPR is an important outcome, while no effect on survival or neurological outcome can be determined at either level. Undesirable effects are proposed as small: no major adverse events in Ringh 2015;5 no safety issues in SAMBA;6 severe psychological impact in 1.4% (22/1,621) and one injury needing hospital care among 1,630 responders who answered the survey;14 and alerts that reach patients without cardiac arrest.12 The balance is proposed as favoring the intervention; the benefit shown is a process outcome, not a demonstrated survival benefit. The overall certainty follows the critical outcomes: very low.
- Values, resources, equity, acceptability and feasibility. No included study measured how patients, families or the public value the outcomes; in a public survey cited by included reports, acceptance of help from alerted strangers was lower in the home (85–89%) than in public places (92–95%).28 No included study costed the intervention or evaluated its cost-effectiveness, and economic models need a survival effect that this review could not determine. No study measured equity; it is judged to vary with system design (dispatch to private homes, coverage at night, responder recruitment where EMS is slow), and all evidence comes from high-income systems; no study reported estimates for rural or remote areas. Of alerted responders who answered the survey, 99% (1,602/1,618) wished to continue.14 Feasibility is probably yes: the systems run at national scale, with activation, responder density and false alerts as the limits. Feasibility in low- and middle-income countries was not assessed in this review.
- Subgroups. Subgroup analyses by notification technology or setting were not feasible with the included studies. Head-to-head comparisons of dispatch strategies (SAMBA; Caputo 2017), a prespecified subgroup, do not inform notification versus no notification.6,19 No pediatric comparative evidence and no evidence from low- or middle-income settings were identified.
- Guidelines. The direction is consistent with ERC 2025 Adult Basic Life Support29 and System Saving Lives30 and with AHA 2025, which rates mobile technologies COR 2a, LOE B-NR and limits its claim to timely bystander CPR and AED use;31 this review could not determine an effect on survival.
- Emerging evidence. HeartRunner (NCT03835403), a Danish randomized trial of smartphone-activated community first responders versus standard care with 30-day survival as its primary outcome, has been reported only by press release at ESC 2026; its design has been published (Folke 2026).32 It is not appraised; publication of its results should trigger re-evaluation.
Knowledge Gaps
1. There is insufficient evidence to determine whether mobile-technology notification of community responders (volunteers registered with a smartphone application or text-message alert linked to emergency dispatch, distinct from the EMS response), compared with no notification, improves survival to hospital discharge or 30 days and survival with favorable neurological outcome in adults and children with out-of-hospital cardiac arrest.
2. There is insufficient evidence to determine whether mobile-technology notification of community responders, compared with no notification, is cost-effective for out-of-hospital cardiac arrest from a health-system perspective.
3. There is insufficient evidence to determine how survivors of out-of-hospital cardiac arrest, their families and members of the public weigh a possible survival benefit of notification of community responders, compared with an EMS response alone, against privacy and a stranger entering a private home.
4. There is insufficient evidence to determine whether mobile-technology notification of community responders, compared with no notification, improves bystander defibrillation and survival after out-of-hospital cardiac arrest in rural and remote areas, where EMS response times are longer.
5. There is insufficient evidence to determine whether mobile-technology notification of community responders, compared with no notification, increases bystander CPR, bystander defibrillation and survival after out-of-hospital cardiac arrest in low- and middle-income countries.
6. There is insufficient evidence to determine which dispatch strategy for notified community responders, such as the alert radius, sending responders to an AED first or straight to the patient, or alerting by app or text message, compared with an alternative strategy, increases defibrillation before EMS arrival in out-of-hospital cardiac arrest, an outcome on the path to survival.
7. There is insufficient evidence to determine whether structured follow-up of community responders after an alert (for example a debriefing call and an offer of professional support), compared with no or usual follow-up, reduces psychological harm beyond six weeks, measured with validated instruments, and keeps responders enrolled, on which the coverage of the system depends.
Evidence to Decision (EtD) Table
EIT 6302 Evidence to Decision (EtD)Table_10October2026
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