The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes
BackgroundDespite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes). ObjectiveT...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/6/e35285 |
| _version_ | 1827858189019774976 |
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| author | Olivia De-Jongh González Claire N Tugault-Lafleur E Jean Buckler Jill Hamilton Josephine Ho Annick Buchholz Katherine M Morrison Geoff DC Ball Louise C Mâsse |
| author_facet | Olivia De-Jongh González Claire N Tugault-Lafleur E Jean Buckler Jill Hamilton Josephine Ho Annick Buchholz Katherine M Morrison Geoff DC Ball Louise C Mâsse |
| author_sort | Olivia De-Jongh González |
| collection | DOAJ |
| description |
BackgroundDespite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes).
ObjectiveThis study aims to identify digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explores whether participants’ characteristics and health outcomes differed across phenotypes.
MethodsLatent class analysis was used to identify distinct parent and child phenotypes based on their use of the app’s behavioral, gamified, and social features over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes.
ResultsAmong parents, 5 digital phenotypes were identified: socially engaged (35/214, 16.3%), independently engaged (18/214, 8.4%) (socially and independently engaged parents are those who used mainly the social or the behavioral features of the app, respectively), fully engaged (26/214, 12.1%), partially engaged (32/214, 15%), and unengaged (103/214, 48.1%) users. Married parents were more likely to be fully engaged than independently engaged (P=.02) or unengaged (P=.01) users. Socially engaged parents were older than fully engaged (P=.02) and unengaged (P=.01) parents. The latent class analysis revealed 4 phenotypes among children: fully engaged (32/214, 15%), partially engaged (61/214, 28.5%), dabblers (42/214, 19.6%), and unengaged (79/214, 36.9%) users. Fully engaged children were younger than dabblers (P=.04) and unengaged (P=.003) children. Dabblers lived in higher-income households than fully and partially engaged children (P=.03 and P=.047, respectively). Fully engaged children were more likely to have fully engaged (P<.001) and partially engaged (P<.001) parents than unengaged children. Compared with unengaged children, fully and partially engaged children had decreased total sugar (P=.006 and P=.004, respectively) and energy intake (P=.03 and P=.04, respectively) after 3 months of app use. Partially engaged children also had decreased sugary beverage intake compared with unengaged children (P=.03). Similarly, children with fully engaged parents had decreased zBMI, whereas children with unengaged parents had increased zBMI over time (P=.005). Finally, children with independently engaged parents had decreased caloric intake, whereas children with unengaged parents had increased caloric intake over time (P=.02).
ConclusionsFull parent-child engagement is critical for the success of mHealth interventions. Further research is needed to understand program design elements that can affect participants’ engagement in supporting behavior change.
Trial RegistrationClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/NCT03651284
International Registered Report Identifier (IRRID)RR2-10.1186/s13063-020-4080-2 |
| first_indexed | 2024-03-12T12:51:34Z |
| format | Article |
| id | doaj.art-1e56b26c988c424499da68993b5735c1 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:51:34Z |
| publishDate | 2022-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-1e56b26c988c424499da68993b5735c12023-08-28T22:21:22ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-06-01246e3528510.2196/35285The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital PhenotypesOlivia De-Jongh Gonzálezhttps://orcid.org/0000-0003-4782-9513Claire N Tugault-Lafleurhttps://orcid.org/0000-0002-4598-9372E Jean Bucklerhttps://orcid.org/0000-0002-4561-9419Jill Hamiltonhttps://orcid.org/0000-0002-1958-2800Josephine Hohttps://orcid.org/0000-0003-0379-4427Annick Buchholzhttps://orcid.org/0000-0003-2598-3368Katherine M Morrisonhttps://orcid.org/0000-0002-1737-256XGeoff DC Ballhttps://orcid.org/0000-0002-9432-3120Louise C Mâssehttps://orcid.org/0000-0003-2483-8791 BackgroundDespite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes). ObjectiveThis study aims to identify digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explores whether participants’ characteristics and health outcomes differed across phenotypes. MethodsLatent class analysis was used to identify distinct parent and child phenotypes based on their use of the app’s behavioral, gamified, and social features over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes. ResultsAmong parents, 5 digital phenotypes were identified: socially engaged (35/214, 16.3%), independently engaged (18/214, 8.4%) (socially and independently engaged parents are those who used mainly the social or the behavioral features of the app, respectively), fully engaged (26/214, 12.1%), partially engaged (32/214, 15%), and unengaged (103/214, 48.1%) users. Married parents were more likely to be fully engaged than independently engaged (P=.02) or unengaged (P=.01) users. Socially engaged parents were older than fully engaged (P=.02) and unengaged (P=.01) parents. The latent class analysis revealed 4 phenotypes among children: fully engaged (32/214, 15%), partially engaged (61/214, 28.5%), dabblers (42/214, 19.6%), and unengaged (79/214, 36.9%) users. Fully engaged children were younger than dabblers (P=.04) and unengaged (P=.003) children. Dabblers lived in higher-income households than fully and partially engaged children (P=.03 and P=.047, respectively). Fully engaged children were more likely to have fully engaged (P<.001) and partially engaged (P<.001) parents than unengaged children. Compared with unengaged children, fully and partially engaged children had decreased total sugar (P=.006 and P=.004, respectively) and energy intake (P=.03 and P=.04, respectively) after 3 months of app use. Partially engaged children also had decreased sugary beverage intake compared with unengaged children (P=.03). Similarly, children with fully engaged parents had decreased zBMI, whereas children with unengaged parents had increased zBMI over time (P=.005). Finally, children with independently engaged parents had decreased caloric intake, whereas children with unengaged parents had increased caloric intake over time (P=.02). ConclusionsFull parent-child engagement is critical for the success of mHealth interventions. Further research is needed to understand program design elements that can affect participants’ engagement in supporting behavior change. Trial RegistrationClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/NCT03651284 International Registered Report Identifier (IRRID)RR2-10.1186/s13063-020-4080-2https://www.jmir.org/2022/6/e35285 |
| spellingShingle | Olivia De-Jongh González Claire N Tugault-Lafleur E Jean Buckler Jill Hamilton Josephine Ho Annick Buchholz Katherine M Morrison Geoff DC Ball Louise C Mâsse The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes Journal of Medical Internet Research |
| title | The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes |
| title_full | The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes |
| title_fullStr | The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes |
| title_full_unstemmed | The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes |
| title_short | The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes |
| title_sort | aim2be mhealth intervention for children with overweight or obesity and their parents person centered analyses to uncover digital phenotypes |
| url | https://www.jmir.org/2022/6/e35285 |
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