Evidence mapPaperPMID 41364913Full record

ArticleJMIR mHealth and uHealth2025

Unraveling the Factors Associated With Digital Health Intervention Uptake: Cross-Sectional Study.

Ilona Ruotsalainen, Mikko Valtanen, Riikka Kärsämä, Adil Umer, Hilkka Liedes, Suvi Parikka, Annamari Lundqvist, Kirsikka Aittola, Suvi Koivunen, Jussi Pihlajamäki and 2 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Trial
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

12 authors.

Ilona RuotsalainenVTT Technical Research Centre of Finland Ltd, Kuopio, Finland.ORCID https://orcid.org/0000-0001-9493-0070
Mikko ValtanenPopulation Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID https://orcid.org/0009-0008-4132-7065
Riikka KärsämäPopulation Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID https://orcid.org/0009-0003-4224-2104
Adil UmerVTT Technical Research Centre of Finland Ltd., Espoo, Finland.ORCID https://orcid.org/0000-0002-4681-4486
Hilkka LiedesVTT Technical Research Centre of Finland Ltd., Espoo, Finland.ORCID https://orcid.org/0000-0003-4655-7675
Suvi ParikkaPopulation Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID https://orcid.org/0000-0001-5767-6915
Annamari LundqvistPopulation Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID https://orcid.org/0000-0002-0262-8585
Kirsikka AittolaInstitute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, Kuopio, Finland.ORCID https://orcid.org/0000-0002-6283-7805
Suvi KoivunenInstitute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, Kuopio, Finland.ORCID https://orcid.org/0000-0002-6548-4734
Jussi PihlajamäkiInstitute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, Kuopio, Finland.ORCID https://orcid.org/0000-0002-6241-6859
Anna-Leena VuorinenUnit of Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland.ORCID https://orcid.org/0000-0002-5658-1305
Jaana LindströmPopulation Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID https://orcid.org/0000-0001-9255-020X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic noncommunicable diseases (NCDs) remain a leading health challenge worldwide, and reducing modifiable lifestyle risk factors is a key prevention strategy. Digital health interventions (DHIs) offer scalable, cost-effective tools to support healthy behaviors, but concerns persist about their equitable reach and uptake across population groups.

objectiveThis study aimed to examine how socioeconomic factors, health status, lifestyle behaviors, and attitudes and experiences related to the use of electronic services (e-services) are associated with the uptake of a DHI.

methodsIn this cross-sectional study, we invited (through mail or SMS) a subgroup of 6978 participants aged 20-74 years from the population-based Healthy Finland survey to take part in a DHI. The DHI, delivered via the web-based BitHabit app, aimed to support the adoption of healthy lifestyle habits. Uptake was defined as successful registration, agreeing to the terms of use, and accepting the invitation to participate. Predictor variables were drawn from national registry and self-reported survey data and included socioeconomic status, health indicators, lifestyle behaviors, and attitudes and experiences related to the use of e-services. Adjusted logistic regression models were used to identify significant predictors of DHI uptake.

resultsOf the final sample of 6975 participants, 1287 (18.5%) started using the DHI. Uptake was significantly higher among women (adjusted odds ratio [aOR] 1.69, 95% CI 1.49-1.93), middle-aged individuals (aOR 1.47, 95% CI 1.21-1.79), and those with higher income (aORs 1.76-1.97, 95% CIs 1.37-2.59) and more years of education (aOR 1.10, 95% CI 1.08-1.12). Healthier lifestyle indicators, including better diet quality (aOR 1.07, 95% CI 1.04-1.10), less frequent smoking or nonsmoking (aORs 1.59-2.29, 95% CIs 1.08-3.12), sleep (aOR 0.58, 95% CI 0.37-0.86), higher functional capacity (aOR 1.06, 95% CI 1.02-1.11), and good overall current health (aOR 1.46, 95% CI 1.15-1.89), were associated with increased likelihood of DHI uptake. The strongest predictors were related to the use of e-services: Individuals who used e-services (aORs 2.48-6.08, 95% CIs 1.19-11.92) reported higher competence to use e-services (aORs 2.00-4.10, 95% CIs 1.44-5.92), had low concerns about data security (aORs 1.37-1.76, 95% CIs 1.03-2.33), believed in the benefits of digital services (aOR 1.04, 95% CI 1.02-1.05), and had better internet connections had higher odds of uptake.

conclusionsOur findings show that DHI uptake is associated with socioeconomic status, health and lifestyle factors, and, especially, individuals' experience and attitudes toward e-services. Individuals with lower education levels, lower income, and poorer health and lifestyle habits are less likely to adopt DHIs, raising concerns about potential digital health inequities. These results underscore the need for targeted strategies to reduce barriers and ensure more equitable reach and engagement in future DHI implementations.

Indexed as

Patient Acceptance of Health CareAdultAgedCross-Sectional StudiesDigital HealthFemaleFinlandHumansMaleMiddle AgedSocioeconomic FactorsSurveys and QuestionnairesTelemedicineadoptioncross-sectionalcross-sectional studyDHIdigital health interventiondigital literacye-servicesFinlandhabitshealth equityhealthy lifestylelifestyleliteracylogistic regression modelquestionnaire surveyregression modelsocioeconomicuptake

Identifiers

PMID41364913
PMCPMC12728403

What Socratic holds

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Registered trials

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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 Socratic graph.