scholarly journals Assessment of Mobile Health Apps Using Built-In Smartphone Sensors for Diagnosis and Treatment: Systematic Survey of Apps Listed in International Curated Health App Libraries

10.2196/16741 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e16741 ◽  
Author(s):  
Clarence Baxter ◽  
Julie-Anne Carroll ◽  
Brendan Keogh ◽  
Corneel Vandelanotte
Keyword(s):  
Mobile Phone ◽  
Mobile Health ◽  
Low Cost ◽  
Well Being ◽  
Third Party ◽  
Health Conditions ◽  
Systematic Survey ◽  
Smartphone Sensors ◽  
Cross Platform ◽  
Mhealth Apps

Background More than a million health and well-being apps are available from the Apple and Google app stores. Some apps use built-in mobile phone sensors to generate health data. Clinicians and patients can find information regarding safe and effective mobile health (mHealth) apps in third party–curated mHealth app libraries. Objective These independent Web-based repositories guide app selection from trusted lists, but do they offer apps using ubiquitous, low-cost smartphone sensors to improve health? This study aimed to identify the types of built-in mobile phone sensors used in apps listed on curated health app libraries, the range of health conditions these apps address, and the cross-platform availability of the apps. Methods This systematic survey reviewed three such repositories (National Health Service Apps Library, AppScript, and MyHealthApps), assessing the availability of apps using built-in mobile phone sensors for the diagnosis or treatment of health conditions. Results A total of 18 such apps were identified and included in this survey, representing 1.1% (8/699) to 3% (2/76) of all apps offered by the respective libraries examined. About one-third (7/18, 39%) of the identified apps offered cross-platform Apple and Android versions, with a further 50% (9/18) only dedicated to Apple and 11% (2/18), to Android. About one-fourth (4/18, 22%) of the identified apps offered dedicated diagnostic functions, with a majority featuring therapeutic (9/18, 50%) or combined functionality (5/18, 28%). Cameras, touch screens, and microphones were the most frequently used built-in sensors. Health concerns addressed by these apps included respiratory, dermatological, neurological, and anxiety conditions. Conclusions Diligent mHealth app library curation, medical device regulation constraints, and cross-platform differences in mobile phone sensor architectures may all contribute to the observed limited availability of mHealth apps using built-in phone sensors in curated mHealth app libraries. However, more efforts are needed to increase the number of such apps on curated lists, as they offer easily accessible low-cost options to assist people in managing clinical conditions.

Validity and Reliability of the Korean Version of the Health-ITUES Scale for Usability Assessments of Mobile Health Technology (Preprint)

2021 ◽  
Author(s):  
Jisan Lee ◽  
Rebecca Schnall
Keyword(s):  
Information Technology ◽  
Health Information Technology ◽  
Health Information ◽  
Mobile Health ◽  
Reliability And Validity ◽  
Usability Evaluation ◽  
World Health ◽  
Health Conditions ◽  
Korean Version ◽  
Mhealth Apps

BACKGROUND Rigorous development of mobile technologies requires the use of validated instruments to evaluate the usability of these tools, which has become more relevant with the expansion of these technologies. Although various usability evaluation tools have been developed, there are relatively few simple evaluation instruments which have been validated across diseases and languages in mobile health information technology validated for use for multiple diseases. OBJECTIVE The purpose of this study was to validate the Korean version of the Health Information Technology Usability Evaluation Scale (Korean Health-ITUES) and its applicability for different health conditions. METHODS To develop the Korean Health-ITUES, a validation process was composed of the following three steps: (1) customization of the Health-ITUES for menstrual symptoms, (2) translation to Korean Health-ITUES, and (3) reliability and validity examination. The translation process adhered to the World Health Organization (WHO) guidelines for translation and back translation, expert review, and reconciliation. After developing the Korean Health-ITUES draft, five female nursing science majors who used the menstrual app participated in a pilot test and provided feedback on the content of the instrument. Following this, 244 women were recruited for validation testing. RESULTS The Korean Health-ITUES showed reliable internal consistency with a Cronbach’s alpha of 0.951; meanwhile, factor loadings of the 20 items in the 4 subscales ranged from 0.416 to 0.892. CONCLUSIONS The Health-ITUES demonstrated reliability and validity for use in assessing mHealth apps’ usability in young Korean women living with menstrual discomfort. Given the strong psychometric properties of this tool in Korean and English and across two different health conditions, the Health-ITUES is a strong tool for mHealth apps’ usability evaluation. The Health-ITUES is a valid instrument for the evaluation of mHealth technology, which are widely used by patients to self-manage their health and by providers to improve healthcare delivery.

scholarly journals Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review (Preprint)

2019 ◽  
Author(s):  
Meghan Bradway ◽  
Elia Gabarron ◽  
Monika Johansen ◽  
Paolo Zanaboni ◽  
Patricia Jardim ◽  
...  
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Self Efficacy ◽  
Health Intervention ◽  
Noncommunicable Disease ◽  
Health Conditions ◽  
Self Management ◽  
Qualitative And Quantitative ◽  
Mental Health Conditions ◽  
Mhealth Apps

BACKGROUND Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. OBJECTIVE This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. METHODS A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. RESULTS A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). CONCLUSIONS This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

scholarly journals What Drives Young Vietnamese to Use Mobile Health Innovations? Implications for Health Communication and Behavioral Interventions (Preprint)

2016 ◽  
Author(s):  
Bach Xuan Tran ◽  
Melvyn WB Zhang ◽  
Huong Thi Le ◽  
Hinh Duc Nguyen ◽  
Long Hoang Nguyen ◽  
...  
Keyword(s):  
Disease Prevention ◽  
Mobile Phone ◽  
Mobile Health ◽  
Health Management ◽  
Sampling Technique ◽  
Online Questionnaire ◽  
Cross Sectional ◽  
Web Based ◽  
Health Related ◽  
Mhealth Apps

BACKGROUND Mobile phone use in Vietnam has become increasingly popular in recent years, with youth (people aged 15-24 years) being one of the groups with the heaviest use. Health-related apps on mobile phones (mobile health [mHealth] apps) appear to be a feasible approach for disease and health management, especially for self-management. However, there has been a scarcity of research on mobile phone usage for health care among youth and adolescents. OBJECTIVE This study aims to identify the patterns of usage of mobile phone apps and the preferences for functionalities of mobile phone-based health-related apps among Vietnamese youth. METHODS An online cross-sectional study was conducted in Vietnam in August to October 2015. Web-based respondent-driven sampling technique was adopted to recruit participants. The online questionnaire was developed and distributed using Google Forms. Chi square and Mann-Whitney tests were used to investigate the difference in attitude and preference for mobile phone apps between the two genders. RESULTS Among 356 youths (age from 15 to 25 years) sampled, low prevalence was found of using mHealth apps such as beauty counseling (6.5%, 23/356), nutrition counseling (7.9%, 28/356), disease prevention (9.8%, 35/356), and disease treatment (7.6%, 27/356). The majority of users found the app(s) they used to be useful (72.7%, 48/356) and reported satisfaction with these apps (61.9%, 39/356). No significant differences were found between the genders in their perception of the usefulness of apps and their satisfaction with mobile health apps. Most of the participants (68.2%, 238/356) preferred apps which are conceptualized and designed to run on a mobile phone compared to Web-based apps, and 50% (176/356) preferred visual materials. Approximately 53.9% (188/356) reported that it was integral for the mobile phone apps to have a sharing/social network functionality. Participants with a higher perceived stress score and EuroQol-5 Dimensions (EQ-5D) index were significantly less likely to use mHealth apps. CONCLUSIONS This study found a low proportion using mHealth-related mobile phone apps, but a high level of receptiveness and satisfaction among Vietnamese youth. Acceptance level and preferences toward mHealth apps as well as specifically preferred functionalities discovered in this study are essential not only in conceptualizing and developing appropriate mobile phone interventions targeting youth and adolescents, but also in the application of technically advanced solutions in disease prevention and health management.

scholarly journals Effects of Social Media and Mobile Health Apps on Pregnancy Care: Meta-Analysis (Preprint)

2018 ◽  
Author(s):  
Ko Ling Chan ◽  
Mengtong Chen
Keyword(s):  
Social Media ◽  
Health Outcomes ◽  
Mobile Health ◽  
Effect Size ◽  
Program Effectiveness ◽  
Maternal Mental Health ◽  
Meta Analysis ◽  
Well Being ◽  
Pregnancy Care ◽  
Mhealth Apps

BACKGROUND The use of social media and mobile health (mHealth) apps has been increasing in pregnancy care. However, the effectiveness of these interventions is still unclear. OBJECTIVES We conducted a meta-analysis to examine the effectiveness of these interventions with regard to different health outcomes in pregnant and postpartum women and investigate the characteristics and components of interventions that may affect program effectiveness. METHOD We performed a comprehensive literature search of major electronic databases and reference sections of related reviews and eligible studies. A random effects model was used to calculate the effect size. RESULTS Fifteen randomized controlled trial studies published in and before June 2018 that met the inclusion criteria were included in the meta-analysis. The interventions were effective in promoting maternal physical health including weight management, gestational diabetes mellitus control, and asthma control with a moderate to large effect size (d=0.72). Large effect sizes were also found for improving maternal mental health (d=0.84) and knowledge about pregnancy (d=0.80). Weight control interventions using wearable devices were more effective. CONCLUSION Social media and mHealth apps have the potential to be widely used in improving maternal well-being. More large-scale clinical trials focusing on different health outcomes are suggested for future studies.

scholarly journals Beyond clicks and downloads: a call for a more comprehensive approach to measuring mobile-health app engagement

BJPsych Open ◽  
2020 ◽  
Vol 6 (5) ◽  
Author(s):  
Heather L. O'Brien ◽  
Emma Morton ◽  
Andrea Kampen ◽  
Steven J. Barnes ◽  
Erin E. Michalak
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Online Intervention ◽  
Well Being ◽  
Mental Health Research ◽  
Comprehensive Approach ◽  
Health Conditions ◽  
Health Apps ◽  
Manage Mental ◽  
Manage Mental Health

Downloading a mobile health (m-health) app on your smartphone does not mean you will ever use it. Telling another person about an app does not mean you like it. Using an online intervention does not mean it has had an impact on your well-being. Yet we consistently rely on downloads, clicks, ‘likes’ and other usage and popularity metrics to measure m-health app engagement. Doing so misses the complexity of how people perceive and use m-health apps in everyday life to manage mental health conditions. This article questions commonly used behavioural metrics of engagement in mental health research and care, and proposes a more comprehensive approach to measuring in-app engagement.

scholarly journals Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review

10.2196/16814 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e16814 ◽  
Author(s):  
Meghan Bradway ◽  
Elia Gabarron ◽  
Monika Johansen ◽  
Paolo Zanaboni ◽  
Patricia Jardim ◽  
...  
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Self Efficacy ◽  
Health Intervention ◽  
Noncommunicable Disease ◽  
Health Conditions ◽  
Self Management ◽  
Qualitative And Quantitative ◽  
Mental Health Conditions ◽  
Mhealth Apps

Background Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

scholarly journals A Review and Comparative Analysis of Security Risks and Safety Measures of Mobile Health Apps

2015 ◽  
Vol 19 ◽  
Author(s):  
Karen Scott ◽  
Deborah Richards ◽  
Rajindra Adhikari
Keyword(s):  
Comparative Analysis ◽  
Mobile Health ◽  
Well Being ◽  
Privacy And Security ◽  
Health Related ◽  
Mobile Health Applications ◽  
Personal Fitness ◽  
Safety Features ◽  
And Control ◽  
Mhealth Apps

In line with a patient-centred model of healthcare, Mobile Health applications (mhealth apps) provide convenient and equitable access to health and well-being resources and programs that can enable consumers to monitor their health related problems, understand specific medical conditions and attain personal fitness goals. This increase in access and control comes with an increase in risk and responsibility to identify and manage the associated risks, such as the privacy and security of consumers’ personal and health information. Based on a review of the literature, this paper identifies a set of risk and safety features for evaluating mHealth apps and uses those features to conduct a comparative analysis of the 20 most popular mHealth apps. The comparative analysis reveals that current mHealth apps do pose a risk to consumers. To address the safety and privacy concerns, recommendations to consumers and app developers are offered together with consideration of mHealth app future trends.

scholarly journals Mobile Health Divide Between Clinicians and Patients in Cancer Care: Results From a Cross-Sectional International Survey (Preprint)

2019 ◽  
Author(s):  
Rosanna Tarricone ◽  
Maria Cucciniello ◽  
Patrizio Armeni ◽  
Francesco Petracca ◽  
Kevin C Desouza ◽  
...  
Keyword(s):  
Mobile Health ◽  
Cancer Care ◽  
Mobile Apps ◽  
Digital Health ◽  
Low Cost ◽  
International Workshop ◽  
The United States ◽  
Mobile Technologies ◽  
Cross Sectional ◽  
Mhealth Apps

BACKGROUND Mobile technologies are increasingly being used to manage chronic diseases, including cancer, with the promise of improving the efficiency and effectiveness of care. Among the myriad of mobile technologies in health care, we have seen an explosion of mobile apps. The rapid increase in digital health apps is not paralleled by a similar trend in usage statistics by clinicians and patients. Little is known about how much and in what ways mobile health (mHealth) apps are used by clinicians and patients for cancer care, what variables affect their use of mHealth, and what patients’ and clinicians’ expectations of mHealth apps are. OBJECTIVE This study aimed to describe the patient and clinician population that uses mHealth in cancer care and to provide recommendations to app developers and regulators to generally increase the use and efficacy of mHealth apps. METHODS Through a cross-sectional Web-based survey, we explored the current utilization rates of mHealth in cancer care and factors that explain the differences in utilization by patients and clinicians across the United States and 5 different countries in Europe. In addition, we conducted an international workshop with more than 100 stakeholders and a roundtable with key representatives of international organizations of clinicians and patients to solicit feedback on the survey results and develop insights into mHealth app development practices. RESULTS A total of 1033 patients and 1116 clinicians participated in the survey. The proportion of cancer patients using mHealth (294/1033, 28.46%) was far lower than that of clinicians (859/1116, 76.97%). Accounting for age and salary level, the marginal probabilities of use at means are still significantly different between the 2 groups and were 69.8% for clinicians and 38.7% for patients using the propensity score–based regression adjustment with weighting technique. Moreover, our analysis identified a gap between basic and advanced users, with a prevalent use for activities related to the automation of processes and the interaction with other individuals and a limited adoption for side-effect management and compliance monitoring in both groups. CONCLUSIONS mHealth apps can provide access to clinical and economic data that are low cost, easy to access, and personalized. The benefits can go as far as increasing patients’ chances of overall survival. However, despite its potential, evidence on the actual use of mobile technologies in cancer care is not promising. If the promise of mHealth is to be fulfilled, clinician and patient usage rates will need to converge. Ideally, cancer apps should be designed in ways that strengthen the patient-physician relationship, ease physicians’ workload, be tested for validity and effectiveness, and fit the criteria for reimbursement.

scholarly journals Mobile Health Divide Between Clinicians and Patients in Cancer Care: Results From a Cross-Sectional International Survey

10.2196/13584 ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. e13584 ◽  
Author(s):  
Rosanna Tarricone ◽  
Maria Cucciniello ◽  
Patrizio Armeni ◽  
Francesco Petracca ◽  
Kevin C Desouza ◽  
...  
Keyword(s):  
Mobile Health ◽  
Cancer Care ◽  
Mobile Apps ◽  
Digital Health ◽  
Low Cost ◽  
International Workshop ◽  
The United States ◽  
Mobile Technologies ◽  
Cross Sectional ◽  
Mhealth Apps

Background Mobile technologies are increasingly being used to manage chronic diseases, including cancer, with the promise of improving the efficiency and effectiveness of care. Among the myriad of mobile technologies in health care, we have seen an explosion of mobile apps. The rapid increase in digital health apps is not paralleled by a similar trend in usage statistics by clinicians and patients. Little is known about how much and in what ways mobile health (mHealth) apps are used by clinicians and patients for cancer care, what variables affect their use of mHealth, and what patients’ and clinicians’ expectations of mHealth apps are. Objective This study aimed to describe the patient and clinician population that uses mHealth in cancer care and to provide recommendations to app developers and regulators to generally increase the use and efficacy of mHealth apps. Methods Through a cross-sectional Web-based survey, we explored the current utilization rates of mHealth in cancer care and factors that explain the differences in utilization by patients and clinicians across the United States and 5 different countries in Europe. In addition, we conducted an international workshop with more than 100 stakeholders and a roundtable with key representatives of international organizations of clinicians and patients to solicit feedback on the survey results and develop insights into mHealth app development practices. Results A total of 1033 patients and 1116 clinicians participated in the survey. The proportion of cancer patients using mHealth (294/1033, 28.46%) was far lower than that of clinicians (859/1116, 76.97%). Accounting for age and salary level, the marginal probabilities of use at means are still significantly different between the 2 groups and were 69.8% for clinicians and 38.7% for patients using the propensity score–based regression adjustment with weighting technique. Moreover, our analysis identified a gap between basic and advanced users, with a prevalent use for activities related to the automation of processes and the interaction with other individuals and a limited adoption for side-effect management and compliance monitoring in both groups. Conclusions mHealth apps can provide access to clinical and economic data that are low cost, easy to access, and personalized. The benefits can go as far as increasing patients’ chances of overall survival. However, despite its potential, evidence on the actual use of mobile technologies in cancer care is not promising. If the promise of mHealth is to be fulfilled, clinician and patient usage rates will need to converge. Ideally, cancer apps should be designed in ways that strengthen the patient-physician relationship, ease physicians’ workload, be tested for validity and effectiveness, and fit the criteria for reimbursement.

Feasibility study of technology-enabled prevention intervention for children and families (Preprint)

2019 ◽  
Author(s):  
Nikki Theofanopoulou ◽  
Katherine Isbister ◽  
Julian Edbrooke-Childs ◽  
Petr Slovák
Keyword(s):  
Emotion Regulation ◽  
Pilot Study ◽  
Low Cost ◽  
Digital Camera ◽  
Well Being ◽  
Structured Interview ◽  
Object A ◽  
Intervention Delivery ◽  
Parents And Children

BACKGROUND A common challenge within psychiatry and prevention science more broadly is the lack of effective, engaging, and scale-able mechanisms to deliver psycho-social interventions for children, especially beyond in-person therapeutic or school-based contexts. Although digital technology has the potential to address these issues, existing research on technology-enabled interventions for families remains limited. OBJECTIVE The aim of this pilot study was to examine the feasibility of in-situ deployments of a low-cost, bespoke prototype, which has been designed to support children’s in-the-moment emotion regulation efforts. This prototype instantiates a novel intervention model that aims to address the existing limitations by delivering the intervention through an interactive object (a ‘smart toy’) sent home with the child, without any prior training necessary for either the child or their carer. This pilot study examined (i) engagement and acceptability of the device in the homes during 1 week deployments; and (ii) qualitative indicators of emotion regulation effects, as reported by parents and children. METHODS In this qualitative study, ten families (altogether 11 children aged 6-10 years) were recruited from three under-privileged communities in the UK. The RA visited participants in their homes to give children the ‘smart toy’ and conduct a semi-structured interview with at least one parent from each family. Children were given the prototype, a discovery book, and a simple digital camera to keep at home for 7-8 days, after which we interviewed each child and their parent about their experience. Thematic analysis guided the identification and organisation of common themes and patterns across the dataset. In addition, the prototypes automatically logged every interaction with the toy throughout the week-long deployments. RESULTS Across all 10 families, parents and children reported that the ‘smart toy’ was incorporated into children’s emotion regulation practices and engaged with naturally in moments children wanted to relax or calm down. Data suggests that children interacted with the toy throughout the duration of the deployment, found the experience enjoyable, and all requested to keep the toy longer. Child emotional connection to the toy—caring for its ‘well-being’—appears to have driven this strong engagement. Parents reported satisfaction with and acceptability of the toy. CONCLUSIONS This is the first known study investigation of the use of object-enabled intervention delivery to support emotion regulation in-situ. The strong engagement and qualitative indications of effects are promising – children were able to use the prototype without any training and incorporated it into their emotion regulation practices during daily challenges. Future work is needed to extend this indicative data with efficacy studies examining the psychological efficacy of the proposed intervention. More broadly, our findings suggest the potential of a technology-enabled shift in how prevention interventions are designed and delivered: empowering children and parents through ‘child-led, situated interventions’, where participants learn through actionable support directly within family life, as opposed to didactic in-person workshops and a subsequent skills application.

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