Evidence map›Paper›PMID 40728569›Full record

ArticleJournal of public health (Oxford, England)2025

The active cohort: a population-based smartphone intervention for health outcomes.

Adi Berliner Senderey, Tom Mushkat, Ofer Hadass, Daphna Carmeli, Eyal Jacobson, Aiden Doherty, Derrick A Bennett, Ran D Balicer, Samah Hayek

Abstract read
In one paragraph

Article in Journal of public health (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Adi Berliner SendereyClalit Research Institute, Innovation Division, Clalit Health Services, Ramat Gan, 6578898, Israel.
Tom MushkatClalit Research Institute, Innovation Division, Clalit Health Services, Ramat Gan, 6578898, Israel.
Ofer HadassClalit Supplementary Health Services, Clalit Health Services, 6116201, Bnei Brak, Israel.
Daphna CarmeliClalit Supplementary Health Services, Clalit Health Services, 6116201, Bnei Brak, Israel.
Eyal JacobsonClalit Supplementary Health Services, Clalit Health Services, 6116201, Bnei Brak, Israel.
Aiden DohertyNuffield Department of Population Health, University of Oxford, 30054, Oxford, UK.
Derrick A BennettNuffield Department of Population Health, University of Oxford, 30054, Oxford, UK.
Ran D BalicerClalit Research Institute, Innovation Division, Clalit Health Services, Ramat Gan, 6578898, Israel.
Samah HayekClalit Research Institute, Innovation Division, Clalit Health Services, Ramat Gan, 6578898, Israel.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundThe Clalit Active Cohort Study (CACS) assess the impact of lifestyle factors, particularly physical activity, on short- and long-term health outcomes using real-world data. Launched in January 2021, CACS focuses on Clalit Health Services members with supplemental health insurance who use the Clalit Active smartphone app.

methodsThe study integrates data from the Clalit Active app with electronic health records from CHS, covering primary and secondary care, hospitalizations, medications, laboratory results, and imaging. The cohort currently includes 622 584 participants and continues to grow.

resultsThe app monitors various health-related behaviors, including physical activity and sleep. Preliminary findings show significant variations in daily step counts based on sociodemographic and clinical factors. Substantial differences were found between app users and non-users app users and non-users. On average, males recorded higher daily step counts compared to females, and individuals under the age of 40 demonstrated greater activity levels than older participants. Participants with pre-existing comorbidities demonstrated lower activity levels.

conclusionsCACS is a powerful resource for researchers and policymakers, providing insights into the relationship between lifestyle factors and health outcomes within a diverse population. Findings can inform public health policies and guide the lifestyle interventions, highlighting the potential of integrating smartphone data with electronic health records to improve health outcomes.

Indexed as

ExerciseMobile ApplicationsSmartphoneAdultAgedCohort StudiesElectronic Health RecordsFemaleHealth BehaviorHumansLife StyleMaleMiddle Agedbehaviorepidemiologypublic health

Identifiers

PMID40728569
PMCPMC12670004

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.