Evidence map›Paper›PMID 30282621›Full record

ArticleJMIR mHealth and uHealth2018

Behavior Change Techniques in mHealth Apps for the Mental and Physical Health of Employees: Systematic Assessment.

Elsbeth de Korte, Noortje Wiezer, Maartje Bakhuys Roozeboom, Peter Vink, Wessel Kraaij

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 8 pooled it
–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

25 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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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

5 authors.

Elsbeth de KorteNetherlands Organization for Applied Scientific Research, Leiden, Netherlands.ORCID http://orcid.org/0000-0002-8491-623X
Noortje WiezerNetherlands Organization for Applied Scientific Research, Leiden, Netherlands.ORCID http://orcid.org/0000-0002-4898-4040
Maartje Bakhuys RoozeboomNetherlands Organization for Applied Scientific Research, Leiden, Netherlands.ORCID http://orcid.org/0000-0001-7796-7808
Peter VinkFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID http://orcid.org/0000-0001-9985-3369
Wessel KraaijNetherlands Organization for Applied Scientific Research, Leiden, Netherlands.ORCID http://orcid.org/0000-0001-7797-619X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmployees remain at risk of developing physical and mental health problems. To improve the lifestyle, health, and productivity many workplace interventions have been developed. However, not all of these interventions are effective. Mobile and wireless technology to support health behavior change (mobile health [mHealth] apps) is a promising, but relatively new domain for the occupational setting. Research on mHealth apps for the mental and physical health of employees is scarce. Interventions are more likely to be useful if they are rooted in health behavior change theory. Evaluating the presence of specific combinations of behavior change techniques (BCTs) in mHealth apps might be used as an indicator of potential quality and effectiveness.

objectiveThe aim of this study was to assess whether mHealth apps for the mental and physical health of employees incorporate BCTs and, if so, which BCTs can be identified and which combinations of BCTs are present.

methodsAn assessment was made of apps aiming to reduce the risk of physical and psychosocial work demands and to promote a healthy lifestyle for employees. A systematic search was performed in iTunes and Google Play. Forty-five apps were screened and downloaded. BCTs were identified using a taxonomy applied in similar reviews. The mean and ranges were calculated.

resultsOn average, the apps included 7 of the 26 BCTs (range 2-18). Techniques such as "provide feedback on performance," "provide information about behavior-health link," and "provide instruction" were used most frequently. Techniques that were used least were "relapse prevention," "prompt self-talk," "use follow-up prompts," and "provide information about others' approval." "Stress management," "prompt identification as a role model," and "agree on behavioral contract" were not used by any of the apps. The combination "provide information about behavior-health link" with "prompt intention formation" was found in 7/45 (16%) apps. The combination "provide information about behavior-health link" with "provide information on consequences," and "use follow-up prompts" was found in 2 (4%) apps. These combinations indicated potential effectiveness. The least potentially effective combination "provide feedback on performance" without "provide instruction" was found in 13 (29%) apps.

conclusionsApps for the occupational setting might be substantially improved to increase potential since results showed a limited presence of BCTs in general, limited use of potentially successful combinations of BCTs in apps, and use of potentially unsuccessful combinations of BCTs. Increasing knowledge on the effectiveness of BCTs in apps might be used to develop guidelines for app developers and selection criteria for companies and individuals. Also, this might contribute to decreasing the burden of work-related diseases. To achieve this, app developers, health behavior change professionals, experts on physical and mental health, and end-users should collaborate when developing apps for the working context.

Indexed as

appbehavior change techniquesemployeelifestylemental healthmHealthphysical healthworkworkplace

Identifiers

PMID30282621
PMCPMC6231882

What OpenQuestion holds

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LicenceCC BY
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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.