ArticleTrials2026
Cost-effectiveness of a smartphone-based personalized lifestyle intervention for symptoms of depression in primary care: protocol for a cluster-randomized controlled trial (LIDIAS).
Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07022184 (A Personalized Lifestyle Intervention for Depression Symptomatology in General Practice - a Cluster Randomized Multicenter Trial), which is not on this map. Not yet cited in PubMed.
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.
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.
A Personalized Lifestyle Intervention for Depression Symptomatology in General Practice - a Cluster Randomized Multicenter Trial
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression is common and mostly managed in primary care. In the Netherlands, general practitioners (GPs) and mental health nurses (GP-MHNs) generally start with education and lifestyle advice, yet the latter remains underused despite evidence of its effectiveness. Smartphone-based eHealth tools may help integrate lifestyle interventions into routine care by offering personalized support and monitoring. This study evaluates the (cost-)effectiveness of a Personalized eHealth Lifestyle Intervention (PLI) delivered via a smartphone app for patients with symptoms of depression in primary care.
methodsThis multicenter, cluster-randomized controlled trial will be conducted in Dutch general practices. Practices will be randomized (1:1), stratified by region and area-level socioeconomic status, into two groups: (1) intervention group including care as usual plus the PLI, or (2) control group including care as usual plus a monitoring app. Within the PLI, patients will use an app for 6 months to set lifestyle goals, monitor progress in lifestyle behavior, and track symptoms of depression and anxiety. GPs and GP-MHNs will have access to patient progress through a web-based version. Control group patients will use a similar app for symptom tracking only. The primary outcome is the change in severity of symptoms of depression assessed with the Patient Health Questionnaire-9 (PHQ-9). Secondary outcomes include lifestyle behaviors, satisfaction with lifestyle, anxiety symptoms, patient satisfaction with treatment by GP and GP-MHNs, mental health-related quality of life, social support, use of antidepressants, use of anxiolytics, and utilization of medical and mental health services. Cost-effectiveness will also be evaluated. A total of 105 patients per arm (PHQ-9 ≥ 5) will be recruited. DISCUSSION: The results of this study will provide valuable insights into the clinical and economic impact of a digital lifestyle support tool. This trial aims to contribute to more accessible, personalized, sustainable, and cost-effective care for people with symptoms of depression in primary care.
trial registrationClinicalTrials.gov NCT07022184. Registered on June 22, 2025.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.