Abstract
Background: Mobile health (mHealth) apps have great potential to support the management of chronic conditions. Despite widespread acceptance of mHealth apps by the public, healthcare providers (HCPs) are reluctant to prescribe or recommend such apps to their patients.
Objective: This study aimed to classify and evaluate interventions aimed at encouraging mHealth app prescription among HCPs.
Methods: A systematic literature search was conducted to identify studies published from January 1, 2008 to August 5, 2022 using four electronic databases: MEDLINE, Scopus, CINAHL and PsycInfo. We included studies that evaluated interventions encouraging HCPs to prescribe mHealth apps. Two review authors independently assessed the eligibility of the studies. The ‘National Institute of Health’s (NIH) quality assessment tool for before-and-after (pre-post) studies with no control group’ and ‘the mixed methods appraisal tool (MMAT)’ were used to assess the methodological quality. Due to high levels of heterogeneity between interventions, measures of practice change, specialties of HCPs and modes of delivery, we conducted a qualitative analysis. We adopted the behaviour change wheel (BCW) as a framework for classifying the included interventions according to intervention functions.
Results: Eleven studies were included in this review. Most of the studies reported positive findings, with improvements in a number of outcomes, including increased knowledge of mHealth apps among clinicians, improved self-efficacy or confidence in prescribing and increased number of mHealth apps prescriptions. Based on the BCW, 9 studies reported elements of environmental restructuring such as providing HCPs with lists of apps, technological systems, time and resources. Nine studies included elements of Education particularly workshops, class lectures, individual sessions with HCPs, videos or toolkits. Furthermore, training was incorporated in 8 studies using case studies/scenarios or app appraisal tools. Coercion and restriction were not reported in any of the interventions included. The quality of the studies was high in relation to the clarity of aims, interventions and outcomes, but weaker in terms of sample sizes, power calculations and duration of follow-up.
Conclusions: This study identified interventions to increase HCPs app prescriptions. Recommendations for future research should consider previously unexplored intervention functions such as restrictions and coercion. The findings of this review can help inform mHealth providers and policymakers regarding the key intervention strategies impacting mHealth prescriptions and assist them in making informed decisions to encourage this adoption.
Objective: This study aimed to classify and evaluate interventions aimed at encouraging mHealth app prescription among HCPs.
Methods: A systematic literature search was conducted to identify studies published from January 1, 2008 to August 5, 2022 using four electronic databases: MEDLINE, Scopus, CINAHL and PsycInfo. We included studies that evaluated interventions encouraging HCPs to prescribe mHealth apps. Two review authors independently assessed the eligibility of the studies. The ‘National Institute of Health’s (NIH) quality assessment tool for before-and-after (pre-post) studies with no control group’ and ‘the mixed methods appraisal tool (MMAT)’ were used to assess the methodological quality. Due to high levels of heterogeneity between interventions, measures of practice change, specialties of HCPs and modes of delivery, we conducted a qualitative analysis. We adopted the behaviour change wheel (BCW) as a framework for classifying the included interventions according to intervention functions.
Results: Eleven studies were included in this review. Most of the studies reported positive findings, with improvements in a number of outcomes, including increased knowledge of mHealth apps among clinicians, improved self-efficacy or confidence in prescribing and increased number of mHealth apps prescriptions. Based on the BCW, 9 studies reported elements of environmental restructuring such as providing HCPs with lists of apps, technological systems, time and resources. Nine studies included elements of Education particularly workshops, class lectures, individual sessions with HCPs, videos or toolkits. Furthermore, training was incorporated in 8 studies using case studies/scenarios or app appraisal tools. Coercion and restriction were not reported in any of the interventions included. The quality of the studies was high in relation to the clarity of aims, interventions and outcomes, but weaker in terms of sample sizes, power calculations and duration of follow-up.
Conclusions: This study identified interventions to increase HCPs app prescriptions. Recommendations for future research should consider previously unexplored intervention functions such as restrictions and coercion. The findings of this review can help inform mHealth providers and policymakers regarding the key intervention strategies impacting mHealth prescriptions and assist them in making informed decisions to encourage this adoption.
| Original language | English |
|---|---|
| Pages (from-to) | e43561 |
| Journal | JMIR mHealth and uHealth |
| Volume | 11 |
| Issue number | e43561 |
| DOIs | |
| Publication status | Published - 27 Feb 2023 |
Keywords
- behavioral change
- mHealth
- mobile apps
- mobile phone
- prescription