Advertisement

Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial

Open AccessPublished:September 28, 2022DOI:https://doi.org/10.1016/j.focus.2022.100037

      HIGHLIGHTS

      • Dietary Approaches to Stop Hypertension is proven to manage hypertension.
      • Commercial dietary tracking apps offer a way to adhere to DASH.
      • Understanding how individuals engage with diet tracking apps is important.
      • There are 2 latent classes of engagement: consistent engagers and disengagers.
      • Consistent engagers were older, more educated, and married.

      Introduction

      The Dietary Approaches to Stop Hypertension dietary pattern is a proven way to manage hypertension, but adherence remains low. Dietary tracking applications offer a highly disseminable way to self-monitor intake on the pathway to reaching dietary goals but require consistent engagement to support behavior change. Few studies use longitudinal dietary self-monitoring data to assess trajectories and predictors of engagement. We used dietary self-monitoring data from participants in Dietary Approaches to Stop Hypertension Cloud (N=59), a feasibility trial to improve diet quality among women with hypertension, to identify trajectories of engagement and explore associations between participant characteristics.

      Methods

      We used latent class growth modeling to identify trajectories of engagement with a publicly available diet tracking application and used bivariate and regression analyses to assess the associations of classifications of engagement with participant characteristics.

      Results

      We identified 2 latent classes of engagement: consistent engagers and disengagers. Consistent engagers were more likely to be older, more educated, and married or living with a partner. Although consistent engagers exhibited slightly greater changes in Dietary Approaches to Stop Hypertension score, the difference was not significant.

      Conclusions

      This study highlights an important yet underutilized methodologic approach for uncovering dietary self-monitoring engagement patterns. Understanding how certain individuals engage with digital technologies is an important step toward designing cost-effective behavior change interventions.

      Trial registration

      This study is registered at www.clinicaltrials.gov NCT03215472.

      Keywords

      INTRODUCTION

      The Dietary Approaches to Stop Hypertension (DASH) dietary pattern is an important part of national blood pressure (BP) and dietary guidelines for Americans.
      • Lichtenstein AH
      • Appel LJ
      • Vadiveloo M
      • et al.
      2021 Dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association.
      It has shown proven results for a variety of disease risk factors, including hypertension, excess weight, and other cardiometabolic disorders.
      • Appel LJ
      • Moore TJ
      • Obarzanek E
      • et al.
      A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group.
      • Appel LJ
      • Sacks FM
      • Carey VJ
      • et al.
      Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial.
      • Gadgil MD
      • Appel LJ
      • Yeung E
      • Anderson CA
      • Sacks FM
      • Miller 3rd., ER
      The effects of carbohydrate, unsaturated fat, and protein intake on measures of insulin sensitivity: results from the OmniHeart trial.
      • Salehi-Abargouei A
      • Maghsoudi Z
      • Shirani F
      • Azadbakht L.
      Effects of Dietary Approaches to Stop Hypertension (DASH)-style diet on fatal or nonfatal cardiovascular diseases–incidence: a systematic review and meta-analysis on observational prospective studies.
      • Fung TT
      • Chiuve SE
      • McCullough ML
      • Rexrode KM
      • Logroscino G
      • Hu FB.
      Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women.
      However, adherence to the DASH dietary pattern remains low,
      • Powell-Wiley TM
      • Miller PE
      • Agyemang P
      • Agurs-Collins T
      • Reedy J.
      Perceived and objective diet quality in US adults: a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES).
      as evidenced by most Americans with hypertension exhibiting uncontrolled BP.
      • Egan BM
      • Li J
      • Sutherland SE
      • Rakotz MK
      • Wozniak GD.
      Hypertension control in the United States 2009 to 2018: factors underlying falling control rates during 2015 to 2018 across age- and race-ethnicity groups.
      Thus, intensified efforts are needed to support Americans’ adherence to the DASH dietary pattern. In response, we created DASH Cloud, a feasibility trial utilizing application (app)-based dietary self-monitoring to support adherence to the DASH dietary pattern.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      DASH Cloud focused on dietary self-monitoring because it is one of the most successful tools for managing dietary intake.
      • Michie S
      • Ashford S
      • Sniehotta FF
      • Dombrowski SU
      • Bishop A
      • French DP.
      A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy.
      Digital technologies, such as smartphone apps, can be used to support dietary self-monitoring and subsequent behavior change, particularly among people with chronic conditions.

      Xu H, Long H. The effect of smartphone app–based interventions for patients with hypertension: systematic review and meta-analysis. JMIR MHealth UHealth. 2020/10/19;8(10):e21759. https://doi.org/10.2196/21759.

      ,
      • Cheikh-Moussa K
      • Mira JJ
      • Orozco-Beltran D.
      Improving engagement among patients with chronic cardiometabolic conditions using mhealth: critical review of reviews.
      Dietary self-monitoring apps allow people to report dietary intake in real time and with ease; they offer an accessible platform for dietary data entry and provide immediate feedback for evaluation of one's progress toward goals, another effective strategy for behavior change.
      • Han M
      • Lee E.
      Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials.
      ,
      • Ferrara G
      • Kim J
      • Lin S
      • Hua J
      • Seto E.
      A focused review of smartphone diet-tracking apps: usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates.
      However, for an app to be effective, one must use it
      • Yardley L
      • Spring BJ
      • Riper H
      • et al.
      Understanding and promoting effective engagement with digital behavior change interventions.
      ; meaning that to adopt the DASH dietary pattern, one must first self-monitor dietary intake. This successful utilization is often referred to as engagement.
      • Cole-Lewis H
      • Ezeanochie N
      • Turgiss J.
      Understanding health behavior technology engagement: pathway to measuring digital behavior change interventions.
      Engagement is defined in a variety of ways depending on the discipline.

      Nahum-Shani I, Shaw SD, Carpenter SM, Murphy SA, Yoon C. Engagement in digital interventions. Am Psychol. In press. Online March 17, 2022. https://doi.org/10.1037/amp0000983.

      For the purposes of this study, we refer to engagement on the basis of the frequency of usage and define engagement as the usage of an app to self-monitor one's daily dietary intake.
      • Short CE
      • DeSmet A
      • Woods C
      • et al.
      Measuring engagement in ehealth and mhealth behavior change interventions: viewpoint of methodologies.
      Poor engagement is commonly observed with smartphone apps, contributing to their insufficiency for sustaining behavior change, and evidence is lacking regarding the main factors contributing to this problem.
      • Perski O
      • Blandford A
      • West R
      • Michie S.
      Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis.
      ,
      • Michie S
      • Yardley L
      • West R
      • Patrick K
      • Greaves F
      Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop.
      In addition, studies assessing the relationship between user engagement and dietary self-care behaviors for the prevention of cardiovascular disease are limited.
      • Spaulding EM
      • Marvel FA
      • Piasecki RJ
      • Martin SS
      • Allen JK
      User engagement with smartphone apps and cardiovascular disease risk factor outcomes: systematic review.
      Furthermore, few studies identify distinct engagement trajectories, which may be helpful to characterize and predict engagement patterns and make digital behavior change interventions more effective.
      • Cole-Lewis H
      • Ezeanochie N
      • Turgiss J.
      Understanding health behavior technology engagement: pathway to measuring digital behavior change interventions.
      Latent class growth modeling offers a way to identify individuals with similar patterns of engagement when repeated measurements are available.
      • Andruff H
      • Carraro N
      • Thompson A
      • Gaudreau P
      • Louvet B.
      Latent class growth modelling: a tutorial.
      ,
      • van der Nest G
      • Lima Passos V
      • Candel MJJM
      • van Breukelen GJP
      An overview of mixture modelling for latent evolutions in longitudinal data: modelling approaches, fit statistics and software.
      Latent class growth modeling can be used for exploratory purposes to uncover trajectories within the population, which could help identify individuals who do not respond to behavior change interventions. Finally, given concerns around inequalities in app usage,
      • Mitchell UA
      • Chebli PG
      • Ruggiero L
      • Muramatsu N.
      The digital divide in health-related technology use: the significance of race/ethnicity.
      ,
      • Régnier F
      • Chauvel L.
      Digital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factors.
      it's important to identify patterns of engagement across sociodemographic characteristics that may be related to higher app usage.
      The objectives of this project were to (1) identify dietary self-monitoring engagement patterns using latent class growth modeling and (2) examine the associations between personal characteristics and dietary self-monitoring engagement patterns among those participating in DASH Cloud, a 3-month randomized controlled feasibility trial utilizing a commercial diet-tracking smartphone app. We also explored the relationship between engagement patterns and 3-month change in DASH adherence score.

      METHODS

      Study Sample

      The design and primary results of DASH Cloud have previously been published.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      In brief, DASH Cloud was a parallel-group randomized controlled feasibility trial comparing app-based diet tracking (active comparator) with app-based diet tracking plus a digital behavior change intervention, including tailored feedback through text messages about adherence to DASH and motivational messages designed to support behavior change. Women were eligible to participate if they were aged between 21 and 70 years, had a BMI >18.5 kg/m2, self-reported being diagnosed with hypertension, were on medication for BP, or had a recent systolic BP measurement of 120‒159 mmHg or a diastolic measurement of 80‒99 mmHg. Participants had to own a smartphone and be willing to receive daily text messages. Eligible participants were randomized to 1 of 2 study arms using a permuted block randomization scheme. All study protocols were in accordance with the ethical standards of Duke University and were approved by the Duke University Health Center IRB. The study was registered on ClinicalTrials.gov on July 12, 2017 (NCT03215472).
      Participants in both study arms of DASH Cloud were asked to self-monitor their daily dietary intake using the commercially available diet-tracking smartphone app, Nutritionix (Syndigo LLC). Nutritionix is the largest verified database of nutrition information and is maintained by a team of registered dietitians. DASH Cloud integrated with Nutritionix using an app-programming interface. Through sophisticated algorithms, DASH Cloud used the dietary-tracking data collected from Nutritionix to calculate a DASH score on the basis of daily consumption. This DASH adherence score was calculated using the Mellen et al.
      • Mellen PB
      • Gao SK
      • Vitolins MZ
      • Goff Jr., DC
      Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
      index. The Mellen et al.
      • Mellen PB
      • Gao SK
      • Vitolins MZ
      • Goff Jr., DC
      Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
      index uses a 9-point scale on the basis of the previous day's intake for potassium, sodium, magnesium, calcium, saturated fat, total fat, total protein, cholesterol, and fiber. Our algorithm compared the total reported intake in Nutritionix with the recommended targets for these nutrients in the DASH dietary pattern as reported in Mellen et al.
      • Mellen PB
      • Gao SK
      • Vitolins MZ
      • Goff Jr., DC
      Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
      Using our software platform, each nutrient was assigned a score on the basis of its difference from the recommended target (1=met target, 0.5=met intermediate target, 0=did not meet target) (Table 1). Scores for each nutrient were summed for a total DASH adherence score. Participants in the intervention group received (up to) daily text messages providing feedback and tips on the basis of their DASH adherence score from the previous day. At 3 months, both study arms saw a small increase in DASH adherence; adding a digital behavioral change intervention to a diet tracking app did not increase DASH adherence compared with diet tracking alone.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      Table 1Scoring Methodology for Adherence to the DASH Dietary Pattern by Mellen et al.
      • Mellen PB
      • Gao SK
      • Vitolins MZ
      • Goff Jr., DC
      Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
      ComponentDASH score targetIntermediate target
      Dietary components for which greater intakes receive higher scores
       Protein, g18% of total daily kcal16.5% of total daily kcal
       Fiber, g14.8 g/1,000 kcal per day9.5 g/1,000 kcal per day
       Magnesium, mg238 mg/1,000 kcal per day158 mg/1,000 kcal per day
       Calcium, mg590 mg/1,000 kcal per day402 mg/1,000 kcal per day
       Potassium, mg2,238 mg/1,000 kcal per day1,534 mg/1,000 kcal per day
      Dietary components for which lower intakes receive higher scores
       Total fat, g27% of total daily kcal32% of total daily kcal
       Saturated fat, g6% of total daily kcal11% of total daily kcal
       Cholesterol, mg71.4 mg/1,000 kcal per day107.1 mg/1,000 kcal per day
       Sodium, mg2,400 mg per day3,000 mg per day
      DASH, Dietary Approaches to Stop Hypertension.

      Measures

      Participant demographics, psychosocial characteristics, and other health-related behaviors were assessed through an online survey before their in-person assessment using the Research Electronic Data Capture Project (REDCap) web application. Table 2
      • Voils CI
      • Maciejewski ML
      • Hoyle RH
      • et al.
      Initial validation of a self-report measure of the extent of and reasons for medication nonadherence.
      • Waxman A
      • Health Assembly World
      WHO global strategy on diet, physical activity and health.
      • Smith A.
      The smartphone difference.
      Pew Research Center
      Internet/broadband fact sheet.
      • Kroenke K
      • Strine TW
      • Spitzer RL
      • Williams JB
      • Berry JT
      • Mokdad AH.
      The PHQ-8 as a measure of current depression in the general population.
      briefly summarizes the survey measures used in the current analysis.
      Table 2Survey Measures and Descriptions Administered in the DASH Cloud Feasibility Trial
      ConstructMeasure description
      Medication adherenceTwo items from the measure developed by Voils et al.
      • Voils CI
      • Maciejewski ML
      • Hoyle RH
      • et al.
      Initial validation of a self-report measure of the extent of and reasons for medication nonadherence.
      were used to determine medication adherence over the past 7 days: “I missed or skipped at least one dose of my blood pressure medication,” and “I was not able to take all of my blood pressure medication.” Participants who reported never for both items were classified as adherent.
      Physical activityThe GPAQ, an 18-item measure developed by the WHO, estimates time spent doing moderate‒vigorous physical activity in a typical week and classifies respondents by adherence to WHO's recommendations for physical activity.
      • Waxman A
      • Health Assembly World
      WHO global strategy on diet, physical activity and health.
      Text messaging frequencyParticipants were asked several questions related to their use of smartphones and the internet previously used by the Pew Research Center.
      • Smith A.
      The smartphone difference.
      ,
      Pew Research Center
      Internet/broadband fact sheet.
      Depressive symptomsThe PHQ-8 is an 8-item measure used to diagnose depressive symptoms in general populations.
      • Kroenke K
      • Strine TW
      • Spitzer RL
      • Williams JB
      • Berry JT
      • Mokdad AH.
      The PHQ-8 as a measure of current depression in the general population.
      A threshold of 5 indicates mild symptoms, 10 suggests moderate symptoms, and 15–24 indicates severe depressive symptoms.
      DASH, Dietary Approaches to Stop Hypertension; GPAQ, Global Physical Activity Questionnaire; PHQ-8, Patient Health Questionnaire-8.
      Height, weight, and BP were measured by our study staff at baseline and the 3-month in-person assessment. Full details of the data collection protocol have been previously published.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      To assess change in adherence to the DASH dietary pattern, participants were asked to complete two 24-hour dietary recalls at baseline and 3 months using the Automated Self-Administered 24-hour (ASA24) recall tool from the National Cancer Institute.
      • Subar AF
      • Kirkpatrick SI
      • Mittl B
      • et al.
      The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.
      The ASA24 is an automated tool that uses the U.S. Department of Agriculture's validated multiple pass method to assess dietary intake for a 24-hour period.
      • Subar AF
      • Kirkpatrick SI
      • Mittl B
      • et al.
      The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.
      Participants completed 1 weekend day and 1 weekday of dietary intake within a 2-week period. The data were used to calculate adherence to the DASH dietary pattern using the scoring methodology defined by Mellon and colleagues.
      • Mellen PB
      • Gao SK
      • Vitolins MZ
      • Goff Jr., DC
      Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
      Nutrient targets can be found in Table 1. Individual nutrient scores were summed to calculate a total DASH adherence score. The score range is 0‒9, with higher scores indicative of greater adherence and a score of 9 indicating full adherence to the DASH dietary pattern.
      The main outcome of this analysis was engagement with the dietary tracking app (i.e., self-monitoring), which was operationally defined as the percentage of days each week a participant tracked their dietary intake in Nutritionix. Days in which a participant logged <600 or >3,500 calories were considered invalid tracking completions.
      • Subar AF
      • Thompson FE
      • Kipnis V
      • et al.
      Comparative validation of the block, Willett, and National Cancer Institute food frequency questionnaires: the eating at America's table study.

      Statistical Analysis

      Participants’ baseline demographic, clinical, health behavior, and psychological characteristics were summarized using descriptive statistics. We used latent class growth analysis to identify distinct engagement trends within our participant sample. To facilitate the model selection process and explore overall trends in engagement, we produced empirical summary plots of weekly engagement over the 12-week intervention. We also produced panels of individual profile plots (spaghetti plots) to examine the underlying heterogeneity in patterns of changes in engagement. On the basis of the continuous operationalization of engagement in dietary tracking, we used a censored normal (CNORM) distribution option to model the outcome.
      • Jones BL
      • Nagin DS
      • Roeder K.
      A SAS procedure based on mixture models for estimating developmental trajectories.
      On the basis of the different types of engagement observed in individual profile plots, we built latent class models with 2 and 3 classes. We also included possible polynomial order in time (e.g., linear and quadratic) to account for the different time trends in the classes. We compared these models for fit on the basis of visual inspection, Akaike Information Criteria, and Bayesian Information Criteria. We also calculated relative entropy, an averaged posterior probability of group membership to evaluate classification quality. On the basis of the interpretation of the subgroups, the percentage of the sample in each subgroup, the classification quality of the model, and the model fit indices, we determined the optimal number of latent classes for our analysis was 2.
      • Nagin DS
      Group-Based Modeling of Development.
      ,
      • van de Schoot R
      • Sijbrandij M
      • Winter SD
      • Depaoli S
      • Vermunt JK.
      The GRoLTS-checklist: guidelines for reporting on latent trajectory studies.
      We explored the associations between participant characteristics and engagement class membership (engagement groups) using bivariate analysis. Pearson chi-square tests were conducted for most categorical variables, but if the expected cell size was <5, we alternatively used Fisher's exact tests. We conducted t-tests to compare group means for continuous variables. We also calculated the OR for categorical variables and least square means for continuous variables to show effect sizes.
      Change in DASH score adherence between baseline and 3 months was compared between engagement groups using repeated-measures ANOVA. Participants who failed to complete 3-month ASA24 surveys or who reported invalid caloric intakes <600 kcal or >3,500 kcal on the dietary assessment had missing DASH adherence scores at 3 months; these missing values were addressed in the analysis of change using maximum likelihood methods.
      All data management, descriptive analysis, bivariate analysis, and regression models were conducted in SAS 9.4 (SAS Institute, Cary, NC). Proc Traj, a stand-alone SAS macro was used for constructing the latent class growth analysis.
      • Jones BL
      • Nagin DS
      • Roeder K.
      A SAS procedure based on mixture models for estimating developmental trajectories.
      Because our analysis was exploratory in nature, all tests used an α level of 0.05, and we did not adjust for multiple testing. All p-values were 2 sided.

      RESULTS

      Our sample of women was on average aged 49.9 (±11.9) years. The majority were non-Hispanic White (69.5%), were married/living with a partner (66.1%), and had a 4-year college degree or higher education (83.1%) (Table 3). Over two thirds of participants had a baseline BP that was elevated or hypertensive, with over half of the sample classified as hypertensive (58.0%). Most of the women had obesity (71.2%); the average BMI was 33.8±7.6 kg/m2. All participants were employed and owned a smartphone for >2 years, and nearly everyone reported using their cell phone often (96.6%), sending text messages often (83.1%), and using apps on their smartphone (98.3%).
      Table 3Sociodemographic and Clinical Baseline Characteristics of Participants Enrolled in the DASH Cloud Feasibility Trial and Associations With Engagement Class (N=59)
      Characteristics or categoryTotal, N (%) or mean ± SDConsistent engagers, n (%) or mean ± SDDisengagers, n (%) or mean ± SDOR (95% CI) or LS mean difference, (95% CI)p-value
      Demographics
      Age, years49.9 ± 11.952.4 ± 11.946.0 ± 11.16.4 (0.2, 12.6)0.04
      Education
       Less than a college degree10 (16.9)3 (8.3)7 (30.4)ref0.04
       College degree or above49 (83.1)33 (91.7)16 (69.6)4.7 (0.9, 31.8)
      Race/ethnicity
       Non-Hispanic White41 (69.5)26 (72.2)15 (65.2)ref0.78
       Non-Hispanic Black10 (16.9)5 (13.9)5 (21.7)0.6 (0.1, 3.0)
       Other8 (13.6)5 (13.9)3 (13.0)1.0 (0.2, 7.1)
      Married/living with partner
       No20 (33.9)8 (22.2)12 (52.2)ref0.02
       Yes39 (66.1)28 (77.8)11 (47.8)3.8 (1.2, 11.9)
      Children living in household
       None33 (58.9)19 (54.3)14 (66.7)ref0.36
       ≥123 (41.1)16 (45.7)7 (33.3)1.7 (0.5, 5.2)
       Insurance
       Medicaid, Medicare, or none11 (18.6)7 (19.4)4 (17.4)ref1.00
       Private insurance48 (81.4)29 (80.6)19 (82.6)1.1 (0.2, 6.1)
      Anthropometric and clinical measurements
      BMI classification
       Healthy (18.5 to <25 kg/m2)7 (11.%)4 (11.1)3 (13.0)ref0.12
       Overweight (25.0 to <30 kg/m2)10 (16.9)9 (25.0)1 (4.3)0.9 (0.1, 6.2)
       Obese (30 kg/m2 or higher)42 (71.2)23 (63.9)19 (82.6)6.0 (0.4, 392.7)
      Blood pressure classification
      Participants (n=2) did not have a blood pressure measurement because of monitor malfunction (n=1) and poor fitting cuff (n=1).
       Normal (SBP <120 mmHg and DBP <120 mmHg)18 (32)11 (31.4)7 (31.8)ref0.62
       Elevated (120 ≤ SBP < 130 mmHg and DBP <80 mmHg)6 (11)5 (14.3)1 (4.5)3.0 (0.3, 171.6)
       Hypertensive (SBP ≥130 mmHg or DBP ≥80 mmHg)33 (58)19 (54.3)14 (63.6)0.9 (0.2, 3.2)
      Health-related behaviors
      Dietary intake, DASH score2.3 ± 1.32.3 ± 1.22.2 ± 1.40.1 (−0.7, 0.8)0.85
      Currently taking blood pressure medication
       No30 (50.8)17 (47.2)13 (56.5)ref0.49
       Yes29 (49.2)19 (52.8)10 (43.5)1.5 (0.5, 4.2)
      Medication adherence
      Medication adherence was compared among participants who reported currently taking blood pressure medication only. One participant did not complete the medication adherence questionnaire. DASH, Dietary Approaches to Stop Hypertension; DBP, diastolic blood pressure; SBP, systolic blood pressure.
       Nonadherent8 (29)3 (16.7)5 (50.0)ref0.09
       Adherent20 (71)15 (83.3)5 (50.0)4.7 (0.6, 42.3)
      Meets physical activity recommendations
       No30 (51.7)15 (42.9)15 (65.2)ref0.10
       Yes28 (48.3)20 (57.1)8 (34.8)2.5 (0.8, 7.4)
      Text messaging frequency
       Often49 (83.1)27 (75.0)22 (95.7)ref0.07
       Sometimes/rarely/never10 (16.9)9 (25.0)1 (4.3)7.1 (0.9, 335.7)
      Depressive symptoms
       Less than mild37 (62.7)24 (66.7)13 (56.5)ref0.43
       Mild or greater22 (37.3)12 (33.3)10 (43.5)0.7 (0.2, 1.9)
       High30 (50.8)19 (52.8)11 (47.8)1.2 (0.4, 3.5)
      a Participants (n=2) did not have a blood pressure measurement because of monitor malfunction (n=1) and poor fitting cuff (n=1).
      b Medication adherence was compared among participants who reported currently taking blood pressure medication only. One participant did not complete the medication adherence questionnaire.DASH, Dietary Approaches to Stop Hypertension; DBP, diastolic blood pressure; SBP, systolic blood pressure.
      We did not observe the study arm to be a significant predictor of the engagement group, with 52.8% of the control group and 47.2% of the intervention group classified as consistent engagers (p=0.49). Thus, we present results from the whole sample. Figure 1 shows the overall engagement trajectory of the full sample. Average engagement over the course of the study was 63.4% (SD=42.3%) or 4.4 days per week, with weekly averages ranging from 42.4% to 91.3% or from 3.0 to 6.4 days per week. The trajectory of the weekly averages is characterized by a steady decline over the course of the study.
      Figure 1
      Figure 1Empirical summary plot of weekly engagement with dietary tracking using a smartphone application over 3 months in the full DASH Cloud sample (mean±1 SE).
      DASH, Dietary Approaches to Stop Hypertension; SE, standard error.
      The 2 latent classes of engagement identified in the 2-class model were consistent engagers (n=36) and disengagers (n=23). Figure 2 shows the overall engagement trajectory by engagement group. The consistent engagers group had a higher average weekly engagement (range=68.3%, 97.6%) and a slower linear decline over time (β= −5.9; p<0.0001). The disengagers group experienced more variability in average weekly engagement (range=0.0%, 81.4%) and a much more rapid decline in engagement (β= −31.7; p<0.0001) that curved over time (β=1.2; p=0.005).
      Figure 2
      Figure 2Empirical summary plots of weekly engagement with dietary tracking using a smartphone application over 3 months by engagement subgroup (mean±1 SE).
      SE, standard error.
      The results of our bivariate analysis of participant characteristics by engagement groups are presented in Table 3. We observed a statistically significant difference in age between the latent classes: consistent engagers were on average older than disengagers (mean±SD: 52.4±11.9 vs 46.0±11.1; least square mean difference: 6.4 [0.2, 12.6]; p=0.04). Consistent engagers were also more educated, with 91.7% having a college degree or above compared with 69.6% of the disengagers (OR=4.7; 95% CI=0.9, 31.8; p=0.04). Being married or living with a partner was also more common among consistent engagers (77.8% vs 47.8% of disengagers; OR=3.8; 95% CI=1.2, 11.9; p=0.02). No other comparisons were statistically significant.
      Figure 3 shows that consistent engagers had a greater improvement in their DASH adherence scores from baseline to 3 months with less within-group variability (mean±SD: 1.0±1.1 vs 0.3±1.8). However, the difference in change between groups was not statistically significant (estimated mean difference=0.6; 95% CI= −0.2, 1.4; p=0.17).
      Figure 3
      Figure 3Box plot of empirical mean change in DASH adherence score between baseline and 3 months by engagement group.
      DASH, Dietary Approaches to Stop Hypertension.

      DISCUSSION

      For this study, we used objective app data and latent class growth modeling to identify 2 different trajectories of engagement with a dietary tracking app among a sample of women with high blood pressure. Women were classified as consistent engagers or disengagers. Many studies note the decrease in the use of smartphone apps over time, highlighting the importance and yet significant challenge of building sustained user engagement.
      • Eysenbach G.
      The law of attrition.
      • Agarwal P
      • Mukerji G
      • Desveaux L
      • et al.
      Mobile app for improved self-management of Type 2 diabetes: multicenter pragmatic randomized controlled trial.
      • Zhao J
      • Freeman B
      • Li M.
      Can mobile phone apps influence people's health behavior change? An evidence review.
      However, few studies highlight different patterns of engagement across time when participating in digital dietary change interventions.
      • Yang Q
      • Hatch D
      • Crowley MJ
      • et al.
      Digital phenotyping self-monitoring behaviors for individuals with type 2 diabetes mellitus: observational study using latent class growth analysis.
      ,
      • Lee J
      • Turchioe MR
      • Creber RM
      • Biviano A
      • Hickey K
      • Bakken S.
      Phenotypes of engagement with mobile health technology for heart rhythm monitoring.
      Instead, many categorize participants by level of overall engagement.
      • Delaney T
      • McLaughlin M
      • Hall A
      • et al.
      Associations between digital health intervention engagement and dietary intake: a systematic review.
      Understanding the patterns of engagement is imperative, for without engagement there is limited exposure to the intervention leading to the potential of no intervention effects.
      • Kelders SM
      • Kok RN
      • Ossebaard HC
      • Van Gemert-Pijnen JE.
      Persuasive system design does matter: a systematic review of adherence to web-based interventions.
      In addition, sustained engagement can facilitate habit formation,
      • Yuan S
      • Ma W
      • Kanthawala S
      • Peng W.
      Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model.
      another factor conducive to behavior change.
      • Gardner B
      • Rebar AL.
      Habit formation and behavior change.
      Although the primary outcome of this study was not to assess differences in DASH score by engagement, we did see greater improvement in DASH scores in the engagers than in the disengagers, although not statistically significant.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      This shows the importance of future, fully powered studies to assess how patterns of engagement are associated with dietary outcomes.
      An interesting finding was the lack of difference in engagement between the intervention and control groups. Although the study design does not allow for further inspection as to why the intervention group, who received tailored feedback on the basis of dietary self-monitoring, did not engage more consistently than the control group, who did not receive any feedback, we can speculate on the basis of responses to the satisfaction survey, reported elsewhere.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      Only 55% of intervention participants felt that the feedback was helpful, and less than half felt that the DASH score reflected their actual dietary pattern.
      • Steinberg DM
      • Kay MC
      • Svetkey LP
      • et al.
      Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
      This, compounded by technical issues often found in digital interventions,
      • Whitelaw S
      • Pellegrini DM
      • Mamas MA
      • Cowie M
      • Van Spall HGC.
      Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review.
      could have impacted intervention engagement, particularly for those who felt that their dietary behaviors were not concordant with the feedback they were receiving. There are other forms of engagement, such as cognitive and emotional engagement, which were not measured in the feasibility trial that could also account for the lack of differences observed between the intervention and control groups.
      • Lalmas M
      • O'Brien H
      • Yom-Tov E
      Measuring user engagement.
      ,
      • O'Brien HL
      • Toms EG.
      What is user engagement? A conceptual framework for defining user engagement with technology.
      Uncovering who is more likely to disengage can help to design interventions aimed at supporting those who need it the most. In our study, we found that participants who were older, had more education, and were married or living with a partner were more likely to be consistent engagers. Previous studies have shown inconsistent results regarding engagement on the basis of sociodemographic factors.
      • Mitchell UA
      • Chebli PG
      • Ruggiero L
      • Muramatsu N.
      The digital divide in health-related technology use: the significance of race/ethnicity.
      ,
      • Garcia-Ortiz L
      • Recio-Rodriguez JI
      • Agudo-Conde C
      • et al.
      Long-term effectiveness of a smartphone app for improving healthy lifestyles in general population in primary care: randomized controlled trial (Evident II study).
      • Reiners F
      • Sturm J
      • Bouw LJW
      • Wouters EJM.
      Sociodemographic factors influencing the use of ehealth in people with chronic diseases.
      • Shah LM
      • Ding J
      • Spaulding EM
      • et al.
      Sociodemographic characteristics predicting digital health intervention use after acute myocardial infarction.
      Some report that younger age is associated with sustained engagement, and others report older age.
      • Goyal S
      • Morita PP
      • Picton P
      • Seto E
      • Zbib A
      • Cafazzo JA.
      Uptake of a consumer-focused mhealth application for the assessment and prevention of heart disease: the <30 days study.
      ,
      • Edney S
      • Ryan JC
      • Olds T
      • et al.
      User engagement and attrition in an app-based physical activity intervention: secondary analysis of a randomized controlled trial.
      Although young people may be more willing and able to use mobile devices,
      • Anglada-Martínez H
      • Rovira-Illamola M
      • Martin-Conde M
      • Sotoca-Momblona JM
      • Codina-Jané C
      Mhealth intervention to improve medication management in chronically ill patients: analysis of the recruitment process.
      many studies show that if older adults have access to a mobile device and are comfortable with how to use it, then they are more likely to stay engaged.
      • Goyal S
      • Morita PP
      • Picton P
      • Seto E
      • Zbib A
      • Cafazzo JA.
      Uptake of a consumer-focused mhealth application for the assessment and prevention of heart disease: the <30 days study.
      In our study, older participants may have been more motivated to control their hypertension because of the increased likelihood of experiencing negative age and hypertension-related health effects. Given that interventions targeting cardiovascular disease generally focus on older adults, it is important to consider patient population characteristics during intervention design to ensure that technologies incorporate a user-friendly interface that is optimized for older adults. In addition to age, in our study, women with higher levels of education were more likely to consistently engage with dietary tracking. Others have identified education as a significant predictor of engagement.
      • Perski O
      • Blandford A
      • West R
      • Michie S.
      Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis.
      ,
      • Reiners F
      • Sturm J
      • Bouw LJW
      • Wouters EJM.
      Sociodemographic factors influencing the use of ehealth in people with chronic diseases.
      In our study, this may reflect skills and confidence with using Nutritionix and social norms related to the perceived value of dietary tracking.
      • Régnier F
      • Chauvel L.
      Digital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factors.
      Although it is important to uncover variations in engagement by sociodemographic characteristics, what these differences highlight is the importance of assessing the needs and skills of the target population to ensure a high-quality digital intervention that is accessible to all.
      • Nguyen A
      • Mosadeghi S
      • Almario CV.
      Persistent digital divide in access to and use of the Internet as a resource for health information: results from a California population-based study.
      Ensuring that apps are usable, meaning that they are not too difficult to understand and use effectively, for various levels of health and digital literacy is imperative. Incorporating demonstrations could improve uptake and continued engagement among a variety of populations.
      • Choi NG
      • DiNitto DM.
      The digital divide among low-income homebound older adults: Internet use patterns, ehealth literacy, and attitudes toward computer/Internet use. Original paper.
      We also found that women who were married or living with a partner were more likely to engage. We can hypothesize that this may be due to the presence of social support. Others have also identified marital status as a significant predictor of engagement.
      • Goyal S
      • Morita PP
      • Picton P
      • Seto E
      • Zbib A
      • Cafazzo JA.
      Uptake of a consumer-focused mhealth application for the assessment and prevention of heart disease: the <30 days study.
      Social support, such as from a spouse or partner, has been shown to foster healthy eating habits and can provide positive encouragement, accountability, and support for overcoming the challenges associated with monitoring one's behavior.
      • Shah LM
      • Ding J
      • Spaulding EM
      • et al.
      Sociodemographic characteristics predicting digital health intervention use after acute myocardial infarction.
      ,
      • Song L
      • Tatum K
      • Greene G
      • Chen RC.
      Ehealth literacy and partner involvement in treatment decision making for men with newly diagnosed localized prostate cancer.
      ,
      • Burke NJ
      • Joseph G
      • Pasick RJ
      • Barker JC.
      Theorizing social context: rethinking behavioral theory.
      Research shows that family members, such as partners, are an important source of support when using technology.
      • Selwyn N
      • Gorard S
      • Furlong J
      • Madden L.
      Older adults’ use of information and communications technology in everyday life.
      In fact, older adults with partners who are comfortable using technology are more likely to engage with it themselves.
      • König R
      • Seifert A
      • Doh M.
      Internet use among older Europeans: an analysis based on SHARE data.
      ,
      • Kamin ST
      • Beyer A
      • Lang FR.
      Social support is associated with technology use in old age.
      Although research shows that social support is important for engagement with digital technologies, the specific mechanisms for how social support positively impacts engagement in older adults warrants further investigation.
      Although our study is not unique in identifying a pattern of engagement that wanes,
      • Leslie E
      • Marshall AL
      • Owen N
      • Bauman A.
      Engagement and retention of participants in a physical activity website.
      it does point to the importance of early and often assessment of engagement as a way to support those who may become uninterested or feel that the content is not beneficial to them. Our results highlight the importance of not only tailoring interventions and the design of technologies to a population's unique needs but also accounting for the variation in engagement that occurs over time. For example, some users may perceive the app as challenging to use and thus disengage. Others may have low engagement in dietary tracking because of low motivation for behavior change.
      • Standage M
      • Sebire SJ
      • Loney T.
      Does exercise motivation predict engagement in objectively assessed bouts of moderate-intensity exercise? A self-determination theory perspective.
      ,
      • Johnson F
      • Wardle J.
      The association between weight loss and engagement with a web-based food and exercise diary in a commercial weight loss programme: a retrospective analysis.
      However, given that baseline engagement was high, the steady decline in engagement across the study is likely because of decreasing motivation for dietary tracking rather than a low level of motivation at the study start, as other studies have demonstrated.
      • Brusniak K
      • Arndt HM
      • Feisst M
      • et al.
      Challenges in acceptance and compliance in digital health assessments during pregnancy: prospective cohort study. Original paper.
      Identifying disengagers early and providing active communication and tailored support to distinguish between challenges may increase engagement and thus intervention exposure. This allows for allocation of additional resources to only certain participants. For people who are highly engaged, minimum support can be provided to save resources because they will be more likely to stay engaged over time. Thus, different retention approaches may be needed for certain populations to maintain engagement with digital health tools in support of behavior change.
      • Michie S
      • Yardley L
      • West R
      • Patrick K
      • Greaves F
      Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop.

      Limitations

      A limitation of our study is the fact that we did not measure participants’ personality traits, such as self-efficacy and motivation for dietary change, because these could be important confounding variables.
      • Aulbach MB
      • Knittle K
      • van Beurden SB
      • Haukkala A
      • Lawrence NS.
      App-based food Go/No-Go training: user engagement and dietary intake in an opportunistic observational study.
      Motivation has been found to be associated with engagement across many studies as has self-efficacy and previous technical experience.
      • Verstuyf J
      • Patrick H
      • Vansteenkiste M
      • Teixeira PJ.
      Motivational dynamics of eating regulation: a self-determination theory perspective.
      Previous technical experience can influence engagement because users are likely to engage more if their expectations match the goal of the intervention.
      • Bentley CL
      • Powell L
      • Potter S
      • et al.
      The use of a smartphone app and an activity tracker to promote physical activity in the management of chronic obstructive pulmonary disease: randomized controlled feasibility study.
      We did not directly measure expectations, but we did capture previous technology use, which did not have an impact on engagement patterns. A limitation of dietary studies is that the accuracy of the data collected is subject to potential recall and response biases.
      • Subar AF
      • Kirkpatrick SI
      • Mittl B
      • et al.
      The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.
      ,
      • Subar AF
      • Freedman LS
      • Tooze JA
      • et al.
      Addressing current criticism regarding the value of self-report dietary data.
      Other limitations include the small sample size limiting our ability to identify additional patterns of engagement and power to show an effect. In addition, the results may not be widely generalizable because this study included a sample of women who were predominately White and educated.

      CONCLUSIONS

      Evidence suggests that higher levels of engagement with self-monitoring lead to better outcomes.
      • Bennett GG
      • Steinberg D
      • Askew S
      • et al.
      Effectiveness of an app and provider counseling for obesity treatment in primary care.
      ,
      • Steinberg DM
      • Levine EL
      • Askew S
      • Foley P
      • Bennett GG.
      Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study.
      However, engagement with dietary tracking can be difficult. The DASH Cloud study sought to leverage a popular digital diet-tracking app to foster dietary self-monitoring and compliance with the DASH dietary pattern in support of BP management. This study aimed to assess the patterns of dietary self-monitoring and characterize those with the least engagement for better allocation of resources in future studies. We found 2 distinct engagement patterns in dietary self-monitoring among women enrolled in DASH Cloud. Age, education, and marital/partner status were predictive of engagement trajectory. Trajectory modeling may provide a robust method to examine differences in and predictors of self-monitoring engagement and assist in the development of strategies to promote consistency.
      This study highlights an important yet underutilized methodologic approach for uncovering engagement patterns and builds on previous research reporting a general decline in engagement with apps over time. Understanding how certain individuals engage with digital technologies is an important step toward designing cost-effective behavior change interventions. Qualitative studies can extend this work to explore why certain individuals are more likely to disengage either immediately or slowly over the course of the intervention. Such work lays the foundation for improved design of digital technologies that engage individuals for a sustained period.

      ACKNOWLEDGMENTS

      All funders of this study had no role in the preparation, review, or approval of the manuscript and the decision to submit the manuscript for publication.
      DMS was supported by the Building Interdisciplinary Research Careers in Women's Health Career Development Award (K12HD043446) from the NIH. Support for MCK was provided by the Duke Center for Research to Advance Healthcare Equity, which is supported by the National Institute on Minority Health and Health Disparities under Award Number U54MD012530 and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number 1KL2TR002554.
      DMS holds equity in Equip Health. EMS serves as a consultant to Corrie Health. No other disclosures were reported.

      CRediT AUTHOR STATEMENT

      Dori M. Steinberg: Conceptualization, Funding acquisition, Supervision, Writing – review and editing. Sandy Askew: Data curation, Formal analysis, Methodology, Software, Visualization, Writing – review and editing. Qing Yang: Methodology, Validation, Writing – review and editing. Hailey N. Miller: Conceptualization, Writing – original draft, Writing – review and editing. Miriam Chisholm: Writing – review and editing. Jacob Christy: Conceptualization, Writing – review and editing. Erin M. Spaulding: Writing – review and editing. Melissa C. Kay: Writing – original draft, Writing – review and editing.

      REFERENCES

        • Lichtenstein AH
        • Appel LJ
        • Vadiveloo M
        • et al.
        2021 Dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association.
        Circulation. 2021; 144: e472-e487https://doi.org/10.1161/CIR.0000000000001031
        • Appel LJ
        • Moore TJ
        • Obarzanek E
        • et al.
        A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group.
        N Engl J Med. 1997; 336: 1117-1124https://doi.org/10.1056/NEJM199704173361601
        • Appel LJ
        • Sacks FM
        • Carey VJ
        • et al.
        Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial.
        JAMA. 2005; 294: 2455-2464https://doi.org/10.1001/jama.294.19.2455
        • Gadgil MD
        • Appel LJ
        • Yeung E
        • Anderson CA
        • Sacks FM
        • Miller 3rd., ER
        The effects of carbohydrate, unsaturated fat, and protein intake on measures of insulin sensitivity: results from the OmniHeart trial.
        Diabetes Care. 2013; 36: 1132-1137https://doi.org/10.2337/dc12-0869
        • Salehi-Abargouei A
        • Maghsoudi Z
        • Shirani F
        • Azadbakht L.
        Effects of Dietary Approaches to Stop Hypertension (DASH)-style diet on fatal or nonfatal cardiovascular diseases–incidence: a systematic review and meta-analysis on observational prospective studies.
        Nutrition. 2013; 29: 611-618https://doi.org/10.1016/j.nut.2012.12.018
        • Fung TT
        • Chiuve SE
        • McCullough ML
        • Rexrode KM
        • Logroscino G
        • Hu FB.
        Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women.
        Arch Intern Med. 2008; 168: 713-720https://doi.org/10.1001/archinte.168.7.713
        • Powell-Wiley TM
        • Miller PE
        • Agyemang P
        • Agurs-Collins T
        • Reedy J.
        Perceived and objective diet quality in US adults: a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES).
        Public Health Nutr. 2014; 17: 2641-2649https://doi.org/10.1017/S1368980014000196
        • Egan BM
        • Li J
        • Sutherland SE
        • Rakotz MK
        • Wozniak GD.
        Hypertension control in the United States 2009 to 2018: factors underlying falling control rates during 2015 to 2018 across age- and race-ethnicity groups.
        Hypertension. 2021; 78: 578-587https://doi.org/10.1161/HYPERTENSIONAHA.120.16418
        • Steinberg DM
        • Kay MC
        • Svetkey LP
        • et al.
        Feasibility of a digital health intervention to improve diet quality among women with high blood pressure: randomized controlled feasibility trial.
        JMIR mHealth uHealth. 2020; 8: e17536https://doi.org/10.2196/17536
        • Michie S
        • Ashford S
        • Sniehotta FF
        • Dombrowski SU
        • Bishop A
        • French DP.
        A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy.
        Psychol Health. 2011; 26: 1479-1498https://doi.org/10.1080/08870446.2010.540664
      1. Xu H, Long H. The effect of smartphone app–based interventions for patients with hypertension: systematic review and meta-analysis. JMIR MHealth UHealth. 2020/10/19;8(10):e21759. https://doi.org/10.2196/21759.

        • Cheikh-Moussa K
        • Mira JJ
        • Orozco-Beltran D.
        Improving engagement among patients with chronic cardiometabolic conditions using mhealth: critical review of reviews.
        JMIR MHealth UHealth. 2020; 8: e15446https://doi.org/10.2196/15446
        • Han M
        • Lee E.
        Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials.
        Healthc Inform Res. 2018; 24: 207-226https://doi.org/10.4258/hir.2018.24.3.207
        • Ferrara G
        • Kim J
        • Lin S
        • Hua J
        • Seto E.
        A focused review of smartphone diet-tracking apps: usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates.
        JMIR MHealth UHealth. 2019; 7: e9232https://doi.org/10.2196/mhealth.9232
        • Yardley L
        • Spring BJ
        • Riper H
        • et al.
        Understanding and promoting effective engagement with digital behavior change interventions.
        Am J Prev Med. 2016; 51: 833-842https://doi.org/10.1016/j.amepre.2016.06.015
        • Cole-Lewis H
        • Ezeanochie N
        • Turgiss J.
        Understanding health behavior technology engagement: pathway to measuring digital behavior change interventions.
        JMIR Form Res. 2019; 3: e14052https://doi.org/10.2196/14052
      2. Nahum-Shani I, Shaw SD, Carpenter SM, Murphy SA, Yoon C. Engagement in digital interventions. Am Psychol. In press. Online March 17, 2022. https://doi.org/10.1037/amp0000983.

        • Short CE
        • DeSmet A
        • Woods C
        • et al.
        Measuring engagement in ehealth and mhealth behavior change interventions: viewpoint of methodologies.
        J Med Internet Res. 2018; 20: e292https://doi.org/10.2196/jmir.9397
        • Perski O
        • Blandford A
        • West R
        • Michie S.
        Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis.
        Transl Behav Med. 2017; 7: 254-267https://doi.org/10.1007/s13142-016-0453-1
        • Michie S
        • Yardley L
        • West R
        • Patrick K
        • Greaves F
        Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop.
        J Med Internet Res. 2017; 19: e232https://doi.org/10.2196/jmir.7126
        • Spaulding EM
        • Marvel FA
        • Piasecki RJ
        • Martin SS
        • Allen JK
        User engagement with smartphone apps and cardiovascular disease risk factor outcomes: systematic review.
        JMIR Cardio. 2021; 5: e18834https://doi.org/10.2196/18834
        • Andruff H
        • Carraro N
        • Thompson A
        • Gaudreau P
        • Louvet B.
        Latent class growth modelling: a tutorial.
        Tutor Quant Methods Psychol. 2009; 5: 11-24https://doi.org/10.20982/tqmp.05.1.p011
        • van der Nest G
        • Lima Passos V
        • Candel MJJM
        • van Breukelen GJP
        An overview of mixture modelling for latent evolutions in longitudinal data: modelling approaches, fit statistics and software.
        Adv Life Course Res. 2020; 43100323https://doi.org/10.1016/j.alcr.2019.100323
        • Mitchell UA
        • Chebli PG
        • Ruggiero L
        • Muramatsu N.
        The digital divide in health-related technology use: the significance of race/ethnicity.
        Gerontologist. 2019; 59: 6-14https://doi.org/10.1093/geront/gny138
        • Régnier F
        • Chauvel L.
        Digital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factors.
        JMIR MHealth UHealth. 2018; 6: e101https://doi.org/10.2196/mhealth.9189
        • Mellen PB
        • Gao SK
        • Vitolins MZ
        • Goff Jr., DC
        Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004.
        Arch Intern Med. 2008; 168: 308-314https://doi.org/10.1001/archinternmed.2007.119
        • Voils CI
        • Maciejewski ML
        • Hoyle RH
        • et al.
        Initial validation of a self-report measure of the extent of and reasons for medication nonadherence.
        Med Care. 2012; 50: 1013-1019
        • Waxman A
        • Health Assembly World
        WHO global strategy on diet, physical activity and health.
        Food Nutr Bull. 2004; 25: 292-302https://doi.org/10.1177/156482650402500310
        • Smith A.
        The smartphone difference.
        Pew Research Center, Washington, DC2015 (Published April 1, 2015. Accessed October 2, 2017)
        • Pew Research Center
        Internet/broadband fact sheet.
        Pew Research Center, Washington, DC2021 (Published April 7, 2021. Accessed June 26, 2022)
        • Kroenke K
        • Strine TW
        • Spitzer RL
        • Williams JB
        • Berry JT
        • Mokdad AH.
        The PHQ-8 as a measure of current depression in the general population.
        J Affect Disord. 2009; 114: 163-173https://doi.org/10.1016/j.jad.2008.06.026
        • Subar AF
        • Kirkpatrick SI
        • Mittl B
        • et al.
        The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.
        J Acad Nutr Diet. 2012; 112: 1134-1137https://doi.org/10.1016/j.jand.2012.04.016
        • Subar AF
        • Thompson FE
        • Kipnis V
        • et al.
        Comparative validation of the block, Willett, and National Cancer Institute food frequency questionnaires: the eating at America's table study.
        Am J Epidemiol. 2001; 154: 1089-1099https://doi.org/10.1093/aje/154.12.1089
        • Jones BL
        • Nagin DS
        • Roeder K.
        A SAS procedure based on mixture models for estimating developmental trajectories.
        Sociol Methods Res. 2001; 29: 374-393https://doi.org/10.1177/0049124101029003005
        • Nagin DS
        Group-Based Modeling of Development.
        Harvard University Press, Cambridge, MA2005
        • van de Schoot R
        • Sijbrandij M
        • Winter SD
        • Depaoli S
        • Vermunt JK.
        The GRoLTS-checklist: guidelines for reporting on latent trajectory studies.
        Struct Equ Model Multidiscip J. 2017; 24: 451-467https://doi.org/10.1080/10705511.2016.1247646
        • Eysenbach G.
        The law of attrition.
        J Med Internet Res. 2005; 7: e11https://doi.org/10.2196/jmir.7.1.e11
        • Agarwal P
        • Mukerji G
        • Desveaux L
        • et al.
        Mobile app for improved self-management of Type 2 diabetes: multicenter pragmatic randomized controlled trial.
        JMIR MHealth UHealth. 2019; 7: e10321https://doi.org/10.2196/10321
        • Zhao J
        • Freeman B
        • Li M.
        Can mobile phone apps influence people's health behavior change? An evidence review.
        J Med Internet Res. 2016; 18: e287https://doi.org/10.2196/jmir.5692
        • Yang Q
        • Hatch D
        • Crowley MJ
        • et al.
        Digital phenotyping self-monitoring behaviors for individuals with type 2 diabetes mellitus: observational study using latent class growth analysis.
        JMIR MHealth UHealth. 2020; 8: e17730https://doi.org/10.2196/17730
        • Lee J
        • Turchioe MR
        • Creber RM
        • Biviano A
        • Hickey K
        • Bakken S.
        Phenotypes of engagement with mobile health technology for heart rhythm monitoring.
        JAMIA Open. 2021; 4: ooab043https://doi.org/10.1093/jamiaopen/ooab043
        • Delaney T
        • McLaughlin M
        • Hall A
        • et al.
        Associations between digital health intervention engagement and dietary intake: a systematic review.
        Nutrients. 2021; 13: 3281https://doi.org/10.3390/nu13093281
        • Kelders SM
        • Kok RN
        • Ossebaard HC
        • Van Gemert-Pijnen JE.
        Persuasive system design does matter: a systematic review of adherence to web-based interventions.
        J Med Internet Res. 2012; 14: e152https://doi.org/10.2196/jmir.2104
        • Yuan S
        • Ma W
        • Kanthawala S
        • Peng W.
        Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model.
        Telemed J E Health. 2015; 21: 735-741https://doi.org/10.1089/tmj.2014.0148
        • Gardner B
        • Rebar AL.
        Habit formation and behavior change.
        Psychology. Oxford University Press, Oxford, United Kingdom2019
        • Whitelaw S
        • Pellegrini DM
        • Mamas MA
        • Cowie M
        • Van Spall HGC.
        Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review.
        Eur Heart J Digit Health. 2021; 2: 62-74https://doi.org/10.1093/ehjdh/ztab005
        • Lalmas M
        • O'Brien H
        • Yom-Tov E
        Measuring user engagement.
        Synth Lect Inf Concepts Retrieval Serv. 2014; 6: 1-132
        • O'Brien HL
        • Toms EG.
        What is user engagement? A conceptual framework for defining user engagement with technology.
        J Am Soc Inf Sci Technol. 2008; 59: 938-955https://doi.org/10.1002/asi.20801
        • Garcia-Ortiz L
        • Recio-Rodriguez JI
        • Agudo-Conde C
        • et al.
        Long-term effectiveness of a smartphone app for improving healthy lifestyles in general population in primary care: randomized controlled trial (Evident II study).
        JMIR MHealth UHealth. 2018; 6: e107https://doi.org/10.2196/mhealth.9218
        • Reiners F
        • Sturm J
        • Bouw LJW
        • Wouters EJM.
        Sociodemographic factors influencing the use of ehealth in people with chronic diseases.
        Int J Environ Res Public Health. 2019; 16: 645https://doi.org/10.3390/ijerph16040645
        • Shah LM
        • Ding J
        • Spaulding EM
        • et al.
        Sociodemographic characteristics predicting digital health intervention use after acute myocardial infarction.
        J Cardiovasc Transl Res. 2021; 14: 951-961https://doi.org/10.1007/s12265-021-10098-9
        • Goyal S
        • Morita PP
        • Picton P
        • Seto E
        • Zbib A
        • Cafazzo JA.
        Uptake of a consumer-focused mhealth application for the assessment and prevention of heart disease: the <30 days study.
        JMIR MHealth UHealth. 2016; 4: e32https://doi.org/10.2196/mhealth.4730
        • Edney S
        • Ryan JC
        • Olds T
        • et al.
        User engagement and attrition in an app-based physical activity intervention: secondary analysis of a randomized controlled trial.
        J Med Internet Res. 2019; 21: e14645https://doi.org/10.2196/14645
        • Anglada-Martínez H
        • Rovira-Illamola M
        • Martin-Conde M
        • Sotoca-Momblona JM
        • Codina-Jané C
        Mhealth intervention to improve medication management in chronically ill patients: analysis of the recruitment process.
        Postgrad Med. 2016; 128: 427-431https://doi.org/10.1080/00325481.2016.1170580
        • Nguyen A
        • Mosadeghi S
        • Almario CV.
        Persistent digital divide in access to and use of the Internet as a resource for health information: results from a California population-based study.
        Int J Med Inform. 2017; 103: 49-54https://doi.org/10.1016/j.ijmedinf.2017.04.008
        • Choi NG
        • DiNitto DM.
        The digital divide among low-income homebound older adults: Internet use patterns, ehealth literacy, and attitudes toward computer/Internet use. Original paper.
        J Med Internet Res. 2013; 15: e93https://doi.org/10.2196/jmir.2645
        • Song L
        • Tatum K
        • Greene G
        • Chen RC.
        Ehealth literacy and partner involvement in treatment decision making for men with newly diagnosed localized prostate cancer.
        Oncol Nurs Forum. 2017; 44: 225-233https://doi.org/10.1011/17.ONF.225-233
        • Burke NJ
        • Joseph G
        • Pasick RJ
        • Barker JC.
        Theorizing social context: rethinking behavioral theory.
        Health Educ Behav. 2009; 36 (suppl): 55S-70Shttps://doi.org/10.1177/1090198109335338
        • Selwyn N
        • Gorard S
        • Furlong J
        • Madden L.
        Older adults’ use of information and communications technology in everyday life.
        Ageing Soc. 2003; 23: 561-582https://doi.org/10.1017/S0144686X03001302
        • König R
        • Seifert A
        • Doh M.
        Internet use among older Europeans: an analysis based on SHARE data.
        Univers Access Inf Soc. 2018; 17: 621-633https://doi.org/10.1007/s10209-018-0609-5
        • Kamin ST
        • Beyer A
        • Lang FR.
        Social support is associated with technology use in old age.
        Z Gerontol Geriatr. 2020; 53: 256-262https://doi.org/10.1007/s00391-019-01529-z
        • Leslie E
        • Marshall AL
        • Owen N
        • Bauman A.
        Engagement and retention of participants in a physical activity website.
        Prev Med. 2005; 40: 54-59https://doi.org/10.1016/j.ypmed.2004.05.002
        • Standage M
        • Sebire SJ
        • Loney T.
        Does exercise motivation predict engagement in objectively assessed bouts of moderate-intensity exercise? A self-determination theory perspective.
        J Sport Exerc Psychol. 2008; 30: 337-352https://doi.org/10.1123/jsep.30.4.337
        • Johnson F
        • Wardle J.
        The association between weight loss and engagement with a web-based food and exercise diary in a commercial weight loss programme: a retrospective analysis.
        Int J Behav Nutr Phys Act. 2011; 8: 83https://doi.org/10.1186/1479-5868-8-83
        • Brusniak K
        • Arndt HM
        • Feisst M
        • et al.
        Challenges in acceptance and compliance in digital health assessments during pregnancy: prospective cohort study. Original paper.
        JMIR mHealth uHealth. 2020; 8: e17377https://doi.org/10.2196/17377
        • Aulbach MB
        • Knittle K
        • van Beurden SB
        • Haukkala A
        • Lawrence NS.
        App-based food Go/No-Go training: user engagement and dietary intake in an opportunistic observational study.
        Appetite. 2021; 165105315https://doi.org/10.1016/j.appet.2021.105315
        • Verstuyf J
        • Patrick H
        • Vansteenkiste M
        • Teixeira PJ.
        Motivational dynamics of eating regulation: a self-determination theory perspective.
        Int J Behav Nutr Phys Act. 2012; 9: 21https://doi.org/10.1186/1479-5868-9-21
        • Bentley CL
        • Powell L
        • Potter S
        • et al.
        The use of a smartphone app and an activity tracker to promote physical activity in the management of chronic obstructive pulmonary disease: randomized controlled feasibility study.
        JMIR mHealth uHealth. 2020; 8: e16203https://doi.org/10.2196/16203
        • Subar AF
        • Freedman LS
        • Tooze JA
        • et al.
        Addressing current criticism regarding the value of self-report dietary data.
        J Nutr. 2015; 145: 2639-2645https://doi.org/10.3945/jn.115.219634
        • Bennett GG
        • Steinberg D
        • Askew S
        • et al.
        Effectiveness of an app and provider counseling for obesity treatment in primary care.
        Am J Prev Med. 2018; 55: 777-786https://doi.org/10.1016/j.amepre.2018.07.005
        • Steinberg DM
        • Levine EL
        • Askew S
        • Foley P
        • Bennett GG.
        Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study.
        J Med Internet Res. 2013; 15: e244https://doi.org/10.2196/jmir.2844