Combined interventions for physical activity, sleep, and diet using smartphone apps: A scoping literature review

Accepted Manuscript Title: Combined Interventions for Physical Activity, Sleep, and Diet using Smartphone Apps: A Scoping Literature Review Authors: Atreyi Kankanhalli, Meghna Saxena, Bimlesh Wadhwa PII: DOI: Reference:

S1386-5056(18)30723-8 https://doi.org/10.1016/j.ijmedinf.2018.12.005 IJB 3786

To appear in:

International Journal of Medical Informatics

Received date: Revised date: Accepted date:

27 June 2018 22 November 2018 13 December 2018

Please cite this article as: Kankanhalli A, Saxena M, Wadhwa B, Combined Interventions for Physical Activity, Sleep, and Diet using Smartphone Apps: A Scoping Literature Review, International Journal of Medical Informatics (2018), https://doi.org/10.1016/j.ijmedinf.2018.12.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Combined Interventions for Physical Activity, Sleep, and Diet using Smartphone Apps: A Scoping Literature Review Authors (Family name underlined): 1.

Atreyi Kankanhalli, a. Address: Dept. of Information Systems and Analytics, COM2-04-16, School of Computing, National University of Singapore,

IP T

15 Computing Drive, Singapore 117418. Tel.: +65164865. Email: [email protected] (Atreyi Kankanhalli) b. Degrees: Ph.D. (Information Systems), National University of Singapore; M.S. (Electrical Engineering), RPI, New York; B.Tech. (Electrical Engineering), Indian Institute of Technology, Delhi

Meghna Saxena (Corresponding author),

SC R

2.

a. Address: Dept. of Information Systems and Analytics, COM2-01-07, School of Computing, National University of Singapore, 15 Computing Drive, Singapore 117418. Tel.: +6583009568. Email: [email protected] (Meghna Saxena)

U

b. Degrees: M. Comp. (Pursuing), National University of Singapore; B.Tech. (Computer Science Engineering), SRM University,

Bimlesh Wadhwa,

A

3.

N

India

M

a. Address: Dept. of Computer Science, COM2-02-62, School of Computing, National University of Singapore, 15 Computing Drive, Singapore 117418. Tel.: +65162973. Email: [email protected] (Bimlesh Wadhwa)

TE D

b. Degrees: Ph.D. (Software Metrics), Delhi University, India; M.Tech. (Software Engineering), National University of Singapore

EP

Abstract word count: 347/350

Highlights

A

CC

Text word count: 4000/4000 (excluding Table and References)



Interventions targeting sleep behaviour in combination with activity, diet are rare



Inter-relationships among the 3 dimensions or any 2 are not considered or examined



User profiling and personalization using data from apps is not examined enough

1

Abstract Background: The use of smartphone apps to track and manage physical activity (PA), diet, and sleep is growing rapidly. Many apps aim to change individual behavior on these three key health dimensions (PA, sleep, diet) by using various interventions. Earlier reviews have examined interventions using smartphone apps for one or two of these dimensions. However, there is lack of reviews focusing on

IP T

interventions for all three of these dimensions in combination with each other. This is important since the dimensions are often inter-related, and all are required for a healthy lifestyle.

Objective: The objective of this study is to conduct a review to: (1) map out the research done using

SC R

smartphone app interventions targeting all three or any two of the three dimensions (PA, sleep, and diet), (2) examine if the studies consider the inter-relationships among the dimensions, and (3) identify the personalization methods implemented by the studies.

Methods: A literature search was conducted in electronic databases and libraries related to medical and

U

informatics literature – PubMed, ScienceDirect, PsycINFO (ProQuest, Ovid) – using relevant selected

N

keywords. Article selection and inclusion were done by removing duplicates, analyzing titles and

A

abstracts, and then reviewing the full text of the articles.

M

Results: In the final analysis, 14 articles were selected – 2 articles focusing on PA and sleep, 8 on PA and diet, and 4 that examine or (at least) collect data of all three dimensions (PA, sleep, and diet). No research was found that focused on sleep and diet together. Of the 14 articles, only 4 build user profiles. Further,

TE D

3 of these 4 studies deliver personalized feedback based on the user’s profile, with only 1 study providing automated, personalized recommendations for behavior change. Additionally, 6 of the included studies report all positive outcomes, while for 3 studies the primary outcomes are awaited. The remaining 5

EP

studies do not report significant changes in all outcomes. In all, only 1 study examines the relationship between two (PA and diet) dimensions. No study was found to assess the relationships among the 3

CC

dimensions.

Abbreviations: – BCT: Behavior Change Technique; BMI: Body Mass Index (kg/m2); CG: Control Group; CVH: Cardiovascular Health; DPP:

A

Diabetes Prevention Program; EMA: Ecological Momentary Assessment; FACT-G: Functional Assessment of Cancer Therapy – General; IG: Intervention Group; mDPP: mobile phone-based Diabetes Prevention Program; MVPA: Moderate to Vigorous Physical Activity; NR: Not Reported; PA: Physical Activity; PRISMA: Preferred Reporting of Systematic Reviews and Meta-Analyses; RCT: Randomized Control Trial; SCT: Social Cognitive Theory; WEL: Weight Efficacy Lifestyle Questionnaire

Keywords: scoping review; smartphone intervention; physical activity; sleep; diet; personalization

2

1 Introduction Individuals’ lifestyle choices impact their health and well-being. The main lifestyle dimensions of physical activity (PA), sleep, and diet have been shown to contribute greatly to the cardiovascular health(CVH) and hence mortality of an individual[1][2][3]. Over the last several decades, people’s lifestyles have become sedentary[4], their diets unhealthy[5], and their sleep schedules increasingly

IP T

disturbed[6]. These changes have resulted in an ever-increasing prevalence of lifestyle diseases such as obesity, diabetes, and cardiovascular diseases. The three dimensions i.e., PA, sleep, and diet, significantly

SC R

affect the health of a person, e.g., lack of sleep reduces cognitive functioning[7] while unhealthy eating and lack of physical exercise lead to obesity and other diseases[8]. These dimensions also appear to be

U

inter-related, e.g., insufficient sleep often leads to poor dietary choices and low activity[9] and vice

N

versa[10][11]. The dimensions could be correlated (positive or negative) and the inter-relationships

A

could be linear or non-linear. Clinicians and researchers should ideally consider such inter-relationships

M

among the three dimensions while designing interventions targeting the health of an individual. Further, the relationships among the three dimensions are not static and change across the lifespan,

TE D

during specific physiological or physical states (e.g., pregnancy[12], diabetes[13]) or due to external factors like stress[14] and home environment[15]. They may also be influenced by a person’s

EP

demographics[16] and individual lifestyle choices[17]. Thus, an “N of 1” approach is needed wherein the interventions are personalized (adapted) to the individual, as opposed to the traditional approach of

CC

interventions generalized to the population[18][19]. Recent advancements in technology make collection of user’s PA, sleep, and diet data possible through

A

widely available smartphone apps, sometimes with accompanying devices (e.g., wearable sensors). These apps have become popular due to their ambient data gathering and analysis[20][21][22]. For consumers looking to improve their health, these apps facilitate self-monitoring[23] of their activities like step counts, food logs[24] and sleep[25], and offer a way to gain feedback through data-based insights e.g., plots or visualizations of their step counts. Further, as mentioned earlier, interventions need 3

to be personalized to the individual, which could be achieved through user profiles[26]. This could be done by creating a user profile for each individual based on parameters like their demographic information (e.g., age, gender, ethnicity), health status (e.g., suffers from diabetes or at risk of heart disease), preferences, and interests[27]. For example, personalization based on a user’s profile could recommend consumption of fewer (as compared to an active user) calories/day for a person with a

IP T

sedentary lifestyle. Personalized, combined interventions for PA, sleep, and diet that consider the interrelationships among the dimensions can be an important step in guiding health improvement. Research

SC R

suggests that personalized feedback incorporating suitable behavior change techniques (BCT) delivered through smartphones for PA, sleep, and diet can improve health and prevent diseases[28][29][30][31].

U

This makes smartphone apps an appropriate tool for providing personalized interventions targeting the

N

aforementioned three dimensions in combination, which we aim to review.

A

To our knowledge, our review is unique as its primary goal is to examine combined interventions

M

targeting PA, diet, and sleep dimensions through smartphone apps and their personalization methods. Other similar reviews explore the effect of interventions for one[32][33] or two dimensions[34][35]

TE D

only. For example, some reviews examine feedback in diet and PA interventions only without considering sleep[29][36]. Moreover, these studies do not investigate the inter-relationships among the dimensions or the various personalization methods. Motivated by these gaps, we conduct a scoping

EP

review to map the existing literature on smartphone apps delivering combined interventions for PA, diet,

CC

and sleep. A scoping review is chosen as opposed to a systematic review, as the topic has seen limited studies that are heterogeneous in terms of research questions and variables. Thus, a scoping review that

A

maps the body of literature on the topic[37][38] is appropriate, rather than a systematic review that is meant for summing up the best available research on a specific research question[39]. Our review aims to: (1) map out the research done using smartphone app delivered interventions targeting all three or any two of the three dimensions (PA, sleep, and diet), (2) examine if the studies consider the inter-relationships among the dimensions, and (3) identify the personalization methods

4

implemented by the studies. The goal of this paper is to review this existing research and present it in a consolidated manner, to uncover gaps in the work done, and to identify directions for future research.

2 Materials and Methods 2.1 Search Strategy

IP T

A systematic search of published research was done to find recent research about interventions delivered through smartphone apps for PA, sleep, and diet. The search was conducted electronically

SC R

during March–April 2018 in the following digital databases: PubMed, ScienceDirect, PsycINFO (ProQuest, Ovid). The databases were chosen to incorporate domains related to informatics and medical aspects. This review was restricted to articles published between 2015-2018 since smartphone apps for

U

health became widely prevalent in this period.

N

Selecting articles related to the topic and determining frequently used words helped to identify our

A

search terms. Similar reviews also served as sources for our search terms i.e., “mobile intervention”[40]

M

[41] and “adaptive feedback”[36]. The full set of keywords was ((food OR diet) AND (physical activity OR

TE D

exercise) AND Sleep AND (mobile intervention OR smartphone app) AND (personalized recommendation OR adaptive feedback)). With these keywords, each string was built using AND and OR operators. Combinations of food AND physical activity, food AND sleep, sleep AND physical activity and

EP

all three dimensions were searched for relevant results. The search string was input into the databases

CC

and the results were restricted to articles published after January 1, 2015 till the last date of search i.e., April 17, 2018.

A

2.2 Selection Criteria

An article was selected if it met the following inclusion criteria: (1) It targeted two or all three dimensions (PA, sleep, diet); e.g. PA and sleep, PA and diet, PA and sleep and diet; (2) Smartphone apps were used for the intervention; (3) It presented a study of interventions in the form of recommendations and/or feedback, where recommendations are suggestions for behavior change (e.g., suggestion on eating more 5

fiber), while feedback is in response to user’s performance (e.g., plot of user’s PA against the recommended number of daily steps); (4) It was in English; (5) It was peer-reviewed and published in a conference or journal in the designated time period; and (6) It was available in full-text. Articles were excluded if they did not meet the inclusion criteria, if they presented research on non-humans (mice, beetles), were reviews, were only present as an abstract, or presented as article in press[42]. Articles

IP T

were not excluded if they did or did not use accompanying devices. 2.3 Selection Process

SC R

Search results were exported to reference managing software Mendeley Desktop (Version 1.17.13). Duplicates across databases were identified and excluded using the same software. Each publication’s title, publication year, and abstract were assessed, and inclusion/exclusion criteria were applied. If the

U

inclusion criteria could not be evaluated through the abstract, then we studied the full text to select the

A

N

relevant papers. Regarding the roles, one author searched all the databases based on the search queries,

M

which were discussed and agreed upon by all authors. Then the other two authors also assessed the articles to see if they met the criteria for inclusion or exclusion. Articles were included in or excluded

2.4 Data Extraction

TE D

from our review when all authors agreed upon it.

EP

All authors concurred over the format in which data was to be extracted from the full text of the selected articles. Data was then extracted and transcribed by one author and verified by the other 2 authors.

CC

Specifically, data was extracted to describe the dimensions, study design, duration, sample (age, gender, clinical diagnosis, etc.) and its context (country, setting, etc.), app details, accompanying device (if

A

present), aim, intervention, personalization details, behavioral theory, user profile details, PA, sleep, diet, and other primary outcomes, outcome measures and finally results (which included inter-relationships among dimensions, if studied). Personalization was categorized as automated (generated automatically by the app without human involvement), semi-automated (e.g., selected from a pre-defined list of generic suggestions by a human), or manual (provided by a health coach/professional either remotely or in6

person). As mentioned earlier, personalization was assessed by means of feedback on user performance or recommendations for behavior change. There was considerable heterogeneity in the primary and secondary outcomes of the studies e.g., body weight, glucose levels, and saturated fat intakes. We limited our summary of results to primary outcomes and outcomes that included changes in PA, sleep, and diet. Because of the diversity of outcomes across

IP T

studies, a meta-analysis was not possible. Rather, data were synthesized narratively rather than quantitatively using the constant comparison method that involves data extraction, comparison, and

SC R

conclusion-drawing[43].

3 Results

U

3.1 Selection and Inclusion of Studies

N

Initially 1323 articles were found from the databases and 2 articles (“Other sources”) were found from

A

reference lists of full-text articles (see the PRISMA Flow Diagram in Figure 1). In total 957 articles (21

M

PUBMED, 543 ScienceDirect, 393 PsychINFO) remained after removing 368 duplicates. After this, 56 articles were found to be lists of abstracts (no full-text available) and were excluded. Next 901 titles and

TE D

abstracts were scanned for inclusion, of which 473 articles were identified as irrelevant (off-topic: not related to PA or sleep or diet). Further, 336 abstracts were excluded using the criteria: review papers,

EP

only one dimension (PA or sleep or diet), no feedback/recommendation, or no smartphone app used. After this, 92 remaining articles’ full texts were retrieved for further consideration. Of these, 78 articles

CC

were excluded (did not include smartphone app, discussed only about framework/clustering technology used or, explored only one dimension). Finally, 14 articles were included in the review. For one of the

A

studies, the design[44] and results[45] are in separate articles published in 2015 and 2018 respectively, so they are represented together in our paper as[44,45].

7

IP T SC R U

Figure 1 PRISMA Flow Diagram

N

3.2 Characteristics of Included Studies

A

Of the 14 studies, 10 were randomized control trials (RCTs)[44–53][54], 2 were cohort studies[55][56],

M

and 2 were prospective interventions[57][58] i.e., quasi-experimental trials with no control group. Of

TE D

the 10 RCTs, 2 studies included subjects from a specific setting (hospital[49], summer camp[53]) -here termed as clustered RCTs, 1 study examined the feasibility of mobile-delivered intervention[51], 1 study[52] followed a single-case experiment paradigm called multiple-baseline design[59] and 1

EP

study[54] was an RCT with a cross-over design where each participant served as his/her own matched

CC

control. In the multiple-baseline design[59], subjects were initially exposed to the control condition, followed by the experiment condition; however, the duration of the control condition varied for different

A

users. Of the 14 studies, 2 involved PA and sleep, 8 involved PA and diet, and 4 involved all three dimensions (PA, sleep, and diet). The data extracted from the studies is summarized in Table 1.

8

I N U SC R

Table 1 Summary of Included Studies

Author, Year

PA+ Sleep

Duncan, 2016[46]

Design, Duration, Sample Design: RCTa Duration: 9 weeks Sample: N=64 (IGb: 32, CGc: 32) Adults BMId: 18.5–35

A

App: Researchspecific app for both groups: “Balanced” Device: Wrist-worn activity tracker – “FitBit Charge HR” for IG, no device for CG Both groups provided with “Geneactiv” accelerometer at visit 2,4,6,8 to record data for assessment

Aim: Compare the efficacy of device-entered and user-entered self-monitoring methods to improve PAg, sleep quality Intervention: Both groups receive intervention, only self-monitoring method varies: provision of health info, goal-setting, self-monitoring, and feedback; CG: user-entry (recall and manually enter info on app); IG: Device-entry (automatic) Personalization: Automated feedback via app: user stats progress graphs compared to goals, app changes color of home screen to represent PA, sleep behavior based on recorded user data (green: good, orange: okay, red: poor) Behavioral Theory: SCTh, Self-regulatory theory

App: Researchspecific app made available commercially: “MyHeartCounts” for Apple iPhone/iPod Device: None

Aim: Assess feasibility of obtaining PA, sleep measures from apps and gain insights into activity patterns associated with life satisfaction and self-reported disease Intervention: Reminders - to complete surveys in app, log on app Personalization: Automated feedback via app: plots of user’s heart-age relative to ideal CVHi status, plot of user’s PA, sleep statistics relative to other users of similar demographics Behavioral Theory: NR

ED

PT

Design: Feasibility (Cohort) Duration: 7 days Sample: N=20345 Adults 82% male Age: 36, 27 – 50 years USA

CC E

McConnell , 2017[55]

Aim, Intervention, Personalization, Behavioral Theory

User Profile/ Personalization Parameters User profile: NR Parameters: PA, sleep

M

Agee: NRf, 18-55 years Australia

PA+ Sleep

App, Accompanying Device (if any)

A

Category

User profile: Not personalized, but over groups; Users grouped in 10 clusters (Major clusters – “active”, “weekend warriors”, “drivers”, “inactive”) based on proportion of time spent in PA statesj; Users in 6 clusters based on number of times users changed PA state from active to inactive and vice versa Parameters: PA

Outcomes, Measures

Results

Assessments at 0,3,6,9 weeks: PA: daily minutes of PA using phone sensor, activity tracker, surveys Sleep: duration, efficiency (ratio of duration and time between sleep onset and offset), variability (std. dev. in sleep onset and offset) measured using accelerometer, surveys on sleep quality, sleep timing PA: minutes of daily MVPAk, steps measured using phone sensors, survey, 6minute walk test on last day Sleep: duration selfreported on app, sleep quality by surveys CVH Status: risk of stroke, myocardial infarction calculated according to guidelines, heart-age based on lipid values and age Life Satisfaction rating: self-reported on a scale of 1-10; Disease: survey

Results awaited

People with lower time spent on PA but with frequent PA state changes had CVH status equivalent to people with overall high time spent on PA Time spent on PA positively correlated with Life Satisfaction PA negatively correlated with self-reported disease – Inactive cluster had more users with self-reported disease; People active majorly on weekends (Weekend warrior) were correlated with presence of self-reported disease Early bedtime was positively correlated with Life Satisfaction

9

I PA+ Diet

Hales, 2016 [48]

Design: RCT Duration: 3 months Sample: N=51 (IG: 26, CG: 25) Overweight adults 82% female BMI:34.7 ± 6.0 Age: 46.2 ± 12.4 years USA

App: Three researchspecific mobile apps: “GoalGetter”, “BeHealthy”, “TrendSetter” No app for CG Device: Digital scale

N U SC R

Design: RCT Duration: 2 years Sample: N=404 (IG: 202, CG: 202) Overweight or obese college students 70% Female BMI: 29±2.8 Age: 22.7±3.6, 1835 years USA

Aim: Assess efficacy of intervention to reduce weight Intervention: Intention formation, Goal setting, Self-monitoring, encouraged to share on social networks (Social-support), Feedback and Goal review Personalization: Manual feedback (Feedback example: “You can do strength training at home! Do some squats tonight) by health coach through one or more modesl - subjects asked to use at least one of these modes minimum 5 times/week Automated, real-time, location-based text prompts for self-monitoring; subject asked to set individualized PA, diet goals on app and share it on social networks CG: only basic health info provided through website (different from IG) and quarterly newsletters Behavioral Theory: BCTms from Abraham and Michie’s taxonomy of 26 BCTs Aim: Compare efficacy of IG and CG apps to reduce weight Intervention: 2 emails/week of theory-based podcasts (PA, diet info for weight loss), Selfmonitoring (timed in-app notifications to log diet/PA/weight), Social support (Newsfeed: view/rate other users’ activities), Personalization: Semi-automated Feedback: Active users prompted to encourage (select message from automated list of options) other inactive users; Incentives (Stars by other users for completing goals, points for selfmonitoring) CG: Self-monitor PA, diet, weight, periodic email reminders to track weight, no in-app prompts Behavioral Theory: SCT

User profile: NR Parameters: PA, diet

Weight: objectively measured using digital scale (loss significant at 5%) BMI PA: step counts using phone sensors Diet: daily calories consumed selfreported on app

Short-term improvements in all outcomes – significant difference from CG at 6months and 12 months No significant difference in groups after 2 years No difference in outcomes of participants with different levels of engagement (defined as the sum of a participant’s recorded interactions on all modes)

User profile: NR Parameters: PA, weight

Weight: lost by 12 weeks – self-report on app PA: calorie spent due to minutes of intentional PA – phone sensor,self report on app Diet: total calories, food and beverage – self report on app

IG had significantly greater weight loss than CG No significant difference in calorie expenditure due to PA No significant difference in calorie consumption More usage of research-specific app than commercial app

A

Godino, 2016 [47]

IG app: Researchspecific “Social Pounds Off Digitally” (Social POD) CG app: Commercial "Calorie Counter by Fat Secret" Device: None

PT

CC E A

ED

M

PA+ Diet

10

I Design: Clusteredn RCT; Duration: 6month intervention Sample: N=54 (IG: 36, CG: 18) Women- elevated breast cancer risk BMI: 31.9±3.5 Age: 59.5± 5.6, 47-69 years; USA

App: Commercial web and mobile app: “MyFitnessPal” No app for CG Device: “FitBit One” clip-on tracker Actigraph accelerometer for assessment

PA+ Diet

Linde, 2015 [50]

Design: RCT Duration: 2 years (12month intervention + 12month followup) Sample: N=339 (IG1: 114, IG2:109, CG:116) Adults 65% female BMI:33.0±3.6 Age:46.5±10.2, 18-64 years USA

App: Commercial app “LoseIt!” on Apple iPod Touch No app for CG Device: WiFi body scale

N U SC R

Hartman, 2016 [49]

Aim: Assess efficacy of a weight-loss intervention based on PA, diet self-monitoring and phone counselling Intervention: Health info manual at baseline, phone calls Personalization: Manual feedback – 12 personalized phone calls (30min) over 6months Automated personalized daily calorie goal based on current and goal weight CG: 2 general info calls (15 min) over 6months Weight goal: lose 10% bodyweight Diet goal: increase fiber, fruits, veg and decrease fats, refined grains PA goal: increase to 150min MVPA/week Behavioral Theory: SCT (Self-Regulation) Aim: Assess efficacy of a weight-loss intervention based on weight tracking Intervention: Goal Setting, Meal plans, skills trainingo; Diet: Restrict fat Intake to 20 -30% of daily caloric intake; PA: Increase till 250 min MVPA/week; Health info booklets; Intervention provided to all groups (including control), only weight tracking frequency varies Personalization: Manual feedback: 32 Guided face to face sessions by health coach on topics like healthy eating, BCT IG1: Daily weight tracking IG2: Weekly weight tracking CG: No weight tracking, no app – paper diary for self-monitoring PA, diet Behavioral Theory: SCT

User profile: NR Parameters: Weight

Weight: self-report on app BMI PA: minutes of MVPA/week – device, self-report on app Diet: caloric intake, nutritional summary (calculated of food item chosen from food database on app) self-reported on app

IG had significantly more weight loss than CG IG significantly improved time spent in MVPA/week but did not differ significantly from CG Weight loss positively influenced by increase in MVPA Diet not analysed

User profile: NR Parameters: Weight

Weight: WiFi body scale PA: MVPA/ week phone sensors, selfreport on app, survey Diet: caloric-intake self-report (food diary - item chosen from food database on app), surveys Assessments at baseline, 6, 12, 18 and 24 months

Awaited

A

CC E

PT

ED

M

A

PA+ Diet

11

I Fukuoka, 2015 [51]

Design: RCT: Feasibility Duration: 2week run-in, 5-month intervention Sample: N=61 (IG: 30, CG: 31) Overweight adults with no diabetes and at high risk for diabetes 77% female BMI: 33.3±6.0 Age: 55.2±9.2 years USA

App: Research specific mobile app “mDPPt Trial” No app for CG Device: Omron pedometer

PT

CC E A

N U SC R

PA+ Diet

App: Commercial web and mobile app “LoseIt!” (Healthcare-provider version) Device: Bluetooth scale

Aim: Assess efficacy of intervention using healthcare-provider version of app to reduce weight Intervention: Mastery, Social support, Selfefficacy, Goal Setting; Diet: Restrict carbohydrate intake to less than 70g/day, increase fiber intake to 30g/day; PA: more than 190 min MVPA/week; Weight: 1-2 pounds/week Personalization: Manual feedback: guided sessions by teamq of health coaches, motivational responses by team to patient’s input on app via app notification/email/phone call Automated feedback (via app): carbohydrate intake, PA graphs; Automated time-based prompts to log PA, diet Behavioral Theory: SCT, Theory of planned behavior Aim: Examine the feasibility and efficacy of a diabetes prevention intervention combined with a mobile app and pedometer to reduce weight Intervention: Pre-programmed daily messages, videos, quizzes on app with “reply-to” feature; Goal setting: PA: individualized short, long term goals (increase step goal by 20% each week to 12000 steps/day); weight: lose 10% of body weight Personalization: Manual feedback: 6 Guided face-to-face sessions by health coach, personalized responses to PA, diet, weight data recorded on app Automated feedback: In-app reminders to log PA, diet, weight; Device displayed generic messages encouraging MVPA CG: no app, no intervention, device only displayed step counts without automated goals Behavioral Theory: NR

User Profile: NR Parameters: PA, diet

Weight change: selfreport on app, Bluetooth scale for weight tracking BMI PA: minutes of MVPA –phone sensors, self-report on app Diet: macronutrient (carbohydrate, fiber, fat, protein) consumption/week self-report on app Quality of life: survey (FACT-Gr), WELs

Significant weight lost by end of intervention WEL score improved significantly PA increased significantly at 1 week but no significant differences at end of intervention No significant differences in macronutrient intake from baseline to end of intervention Reduced carbohydrate intake: patients reported to be less hungry due to the changes in protein, fiber, and fat intake

User Profile: Average step count of user in first 2 weeks, Demographics Parameters: PA, diet, weight

Weight loss - selfreport on app BMI PA: steps – device, MVPA - self-report on app Diet: caloric intake (saturated fat intake, sweetened beverage intake) – self report

Significant (greater than 5%) decrease in weight and BMI for IG Significant increase in moderate PA for IG Decrease in saturated fat intake for IG

A

Design: Prospective Interventionp Duration: 4 weeks Sample: N=50 Overweight women Early stage cancer survivors endometrial/ breast BMI: 34.9±8.7 Age:58.4±10.3, 50-70 years USA

M

McCarroll, 2015 [57]

ED

PA+ Diet

12

I N U SC R

Rabbi, 2015 [52]

Design: RCT: multiple baselineu Duration: 14 weeks (3week baseline + 3week control + 8week intervention) Sample: N=16 Adults 56.3% female Age: NR, 18 – 60 years USA

App: Research specific app “MyBehavior” Device: None

PA+ Diet

Seto, 2016[56]

Design: Cohort Duration: 2week: 2week PA, 3day/week diet Sample: N=12 University students BMI: 17 – 30.5 Age:26.4±3.06, 18 – 31 years China

App: Research specific app “CalFit Chi and Dong” Device: None

Design: Prospective Intervention Duration: 6 month - ongoing Sample: N=200 Employees of companies Age: NR France

App: Researchspecific software, “WittyFit” – multiple platforms (website, app) Device: None

Aim: Assess efficacy of app “MyBehavior” delivered personalized recommendations to improve PA, diet behavior Intervention: Self-monitoring; Self-efficacy Personalization: Automated feedback: 10 inapp personalized suggestions/day for PA, diet to increase PA and maximize calorie loss; Example: “Take short walks in office”, “Example of a good meal”; Daily 5 question inapp survey; Control: generic prescriptive recommendations generated from a pool of 42 suggestions for healthy living, such as “walk for 30 minutes” and “eat fish for dinner” Behavioral Theory: SCT (Self-efficacy, Low effort), Fogg behavior model

A

CC E

PT

ED

M

A

PA+ Diet

PA+ Diet+ Sleep

Dutheil, 2017 [58]

Aim: To assess efficacy of mobile app, and compare prediction of portion size by individual based user profile vs group profiles Intervention: Baseline training on standard portion sizes, provided with a written protocol with detailed instructions and a handout with common food items with standard portion sizes as a reminder; In-app reminders to log outcomes; 6 question EMAv surveys for PA, diet (5/day); Personalization: Dieticians review meal and code portion size, meal groups Behavioral Theory: NR

Aim: Demonstrate that effective use of “WittyFit” will increase well-being and improve health-related behaviors Intervention: Info sessions, self-monitoring, goal-setting, e-coaching; Automated prompts for surveys at 15 days (short surveys), and at 6months (long surveys); Virtual rewards by software, actual rewards by company Personalization: Manual feedback: human (by manager), campaigns based on employee data e.g.: quit smoking initiative in workplace Automated feedback: Visualization about wellbeing status, PA, diet, sleep behaviors,

User Profile Created by clustering user’s previously recorded PA and diet; Locations clustered to suggest possible activity; Food items clustered on ingredients, food intake frequency and calorie content to suggest healthier preferred options Parameters: PA, diet, location User Profile Predict meal portion size using 4 factors: Routine – Time (Lunch, breakfast, dinner), Energy spent in last 3 hours – PA, Emotional Mood, Environment – availability of food outlets around user’s location Parameters: Time, PA, emotion, location User profile: Individualized using demographic data from company records; PA, diet, sleep, stress Parameters: Demographics, PA, diet, sleep, stress

PA: calorie loss – phone sensor Diet: caloric intake – automated (crowd sourced food photos)

Significant increase in PA Significant decrease in caloric intake; Users followed personalized suggestions (intervention) more than generic suggestions (control) Users followed more MyBehavior suggestions where there was no barrier such as bad weather

PA: calorie loss using phone sensors Diet, portion size: and nutrient information from voice-annotated video of each meal recorded by user Time, location data: phone sensors Emotion: EMA questions on happiness, stress, tiredness, and sadness - self-report on app

Models based on individual user profile predict meal portion size of user better than group model Meal portion size was successfully predicted by amount of PA performed (energy spent) in last 3 hours by the user Food Environment was the best predictor of meal portion size followed by Routine (Time) and PA performed

PA: time spent in PA self-reported, surveys Diet: nutrition derived from surveys, 24h recalls, self-report Sleep: quantity by duration hours using self-reports, quality using surveys Stress: mood, recognition at work

Awaited

13

I Design: Clustered RCT-2 Duration: 10week, weekend clubs Sample: N= 51 Children via mother/father Design: Clustered RCT-3 Duration: 12week WhatsApp group of mothers Sample: N=18 mothers of 7 boys, 11 girls Design: RCT Duration: 9 months (12-week intervention+28 week follow-up) Sample: N=212 (IG1: 84, IG2: 84, CG: 44) Adults 76.4% Female BMI: 34.3±8.8 PA: <150min MVPA/week, >120min sedentary

App: Commercial app “Instagram” accounts given to parents to take photos of food

PT

CC E Pellegrini, 2015 [44]; Spring, 2018[45]

A

PA+ Diet+ Sleep

N U SC R

App: NR Device: Smartphone camera actigraphy sensorAxivity AX3 (All RCTs)

User profile: NR Parameters: Weight, PA, diet, sleep

A

Design: Clustered RCT-1 Duration: 2-week health camp Sample: N=227 (IG: 108, CG: 119); Children (students) Age: NR,9-12 years Qatar (All RCTs)

Work atmosphere: surveys

M

Fernandez -Luque, 2017 [53]

ED

PA+ Diet+ Sleep

personalized e-learning, personal motivating messages Behavioral Theory: Health behavior theory Aim: Feasibility of capturing quantified-self data of overweight children using social media, wearables and mobiles (All RCTs) Intervention: Health camp: in-person sessions – 2 weeks Personalization: Lifestyle coaching, physical and social activities, diet/nutrition counselling; Photos of food trays (breakfast, lunch) used to create diet plans, calorie consumption estimates Behavioral Theory: 360° Quantified Self (360QS) methodology (All RCTs) Intervention: Weekend clubs: in person Personalization: 4-hour sessions every weekend – encourage healthy PA, diet, sleep behavior

App: Commercial app “WhatsApp”

Intervention: Summer “WhatsApp” group: counseling mothers to watch children’s food habits Personalization: short messages, daily reminders, weekly health tips, answers to questions by dietician via app

App: 3 researchspecific apps, “Simultaneous Make Better Choices-2” app (IG1), “Sequential Make Better Choices-2” app (IG2), “Stress Control” app (CG) Device: None

Aim: Assess efficacy of sequential vs simultaneous PA, diet intervention in improving PA, diet behaviors Intervention: Self-monitoring; Goal setting: 1) ≤90 min/day of sedentary leisure screen time 2) ≥5 servings of fruits and vegetables, and 3) ≥150 min/week of MVPA Personalization: Manual feedback: info sessions, 23 phone calls based on PA, diet data recorded on app Automated feedback: Graphs of user’s performance relative to goals IG1: PA, Diet (simultaneous) IG2: First diet, then PA after 6weeks (sequential)

User profile: NR Parameters: PA, diet

BMI Diet: proportion of food eaten (fruits, vegetables, meat, dairy, grains and desserts) derived from before and after meal photos of food trays taken by researchers PA, sleep: time spent through actigraphy sensor (All RCTs) BMI Diet: number of “Instagram” photos (taken by parents)

Breakfast is important correlated with BMI PA and sleep change not assessed

BMI Diet: number of “WhatsApp” photos (taken by mothers), response to educational information

No significant results on children

Composite diet and activity improvement scorew PA: time in MVPA using phone sensors, self-report on app Sedentary leisure screen-time: derived from app self-report Diet: fruit, vegetable and saturated fat intake derived from food item selfreported on app Sleep: duration

Both IGs had better diet and activity score than CG at 3,6 and 9 months IG2 had significantly greater composite diet and activity score improvement than IG1 at 6 months, however, no differences were evident at 3 and 9 months IGs significantly increased fruits and vegetables consumption, increased MVPA/day, decreased sedentary leisure time and decreased saturated fat intake by 9 months

Wearing the sensor and Instagram activity (uploading food photos) was positively correlated with healthier BMI PA, sleep influence on BMI not significant

14

I App: Research specific website, app Device: PA sensor (designed for study)[60]

Aim: examine the effects of Activity Promotion System (APS) on promoting PA Intervention: Self-monitoring, Goal setting, Social support: check peer group’s activities, Reminders, counseling, Personalization: Manual feedback: human health coach (at least 1/week) Automated feedback: app visualization (sleep hours, efficacy, diet and PA); Individualized reminders – “Line” app/email IG1: intervention first 3months, then usual care for next 3 months IG2: Usual care for first 3 months, then intervention for next 3 months Behavioral Theory: NR

User profile: NR Parameters: PA, diet

A

Design: RCT: Cross-overx Duration: 6 months Sample: N: 53 (IG1: 26, IG2: 27) Overweight Adults 58.5% female BMI: 28.75 Waist circumference: males ≥ 90cm, females ≥ 80cm; Age: 33.2±9.6, 18 – 56 years Taiwan

M

Yang, 2017 [54]

CG: Relaxation exercises (stress control, sleep), no self-monitoring of PA, diet; Goals: ≥7.5 hours sleep/day and 30% reduction in stress; Behavioral Theory: Mastery, synergy hypothesis (Goal systems theory)

hours, quality survey Stress scale (0 – 10) self-report on app, surveys Assessments at 3, 6 and 9 months PA steps, time spent in MVPA, sedentary leisure time, walking distance: phone sensor, self-report on app Diet calories consumed: selfreport on app Sleep hours, duration: phone sensor;

Sleep, stress changes not reported

Significant difference in daily steps, amount of MVPA for users at end of intervention Objectively measured (phone sensor) sedentary leisure time was significantly more than self-reported sedentary time Diet change not assessed Sleep change not assessed

PT

ED

PA+ Diet+ Sleep

N U SC R

behavior/week Age: 40.8±11.9, 18-65 years USA

RCT: Randomized Control Trial IG: Intervention Group c CG: Control Group d BMI: Body Mass Index (kg/m 2) e Age: Represented as mean ± standard deviation, range f NR: Not Reported g PA: Physical Activity h SCT: Social Cognitive Theory i CVH: Cardiovascular Health j PA states: “stationary”, “driving”, “walking”, “cycling”, and “running” during weekdays as well as weekends k MVPA: Moderate to Vigorous Physical Activity l Modes: “Facebook”, any of the 3 apps, text messaging, emails, website with blog posts, technology-mediated communication with a health coach m BCT: Behavior Change Technique n Clustered RCT: Groups: Schools, Hospitals, Health Camps o Weight Loss protocol University of Minnesota p Prospective Intervention: Quasi-experimental Trial (No Control) q Team: gynecologic oncologist, resident physician, research coordinator, registered dietician nutritionist, and a certified clinical exercise specialist r FACT-G: Functional Assessment of Cancer Therapy - General s WEL: Weight Efficacy Lifestyle Questionnaire t mDPP: mobile phone-based Diabetes Prevention Program u Multiple Baseline – Single Case Experiment design v EMA: Ecological Momentary Assessment w Changes in fruit and vegetable intakes, saturated fat intake, sedentary leisure screen-time, MVPA weighted equally to calculate the aggregate score x Cross-over study design: each participant serves as his/her own control a

A

CC E

b

15

Only 2 out of 14 studies targeted PA and sleep[46][55] (see Table 1). These studies’ durations range from 7 days[55] to 9 weeks[46] and sample sizes range from 64[46] to 20345[55]. Of these, one study mentions participant’s eligibility for inclusion as BMI range 18.5 to 35.0, reporting sedentary lifestyle, and insufficient sleep for more than 14 days/month[46], while the other study does not specify participant inclusion/exclusion criteria[55]. Further, one study uses a research-specific app along with

IP T

a “FitBit Charge HR” device[46], while the other study uses a research-specific app made available commercially for Apple iPhone/iPod[55]. Both studies use devices for tracking PA, while sleep is self-

SC R

reported on the app. The first study[46] only details the design and data collection as the results are awaited. The other study reports positive correlation of time in PA and self-reported CVH status[55]. They[55] further report that people who change PA states (sitting, running, walking) frequently have

U

better life satisfaction and CVH status than people who are active mainly on weekends (“weekend

N

warriors”) even if weekend warriors spend overall more time performing PA.

A

The majority of the included studies, 8 to be precise, belonged to PA and diet category[47–52][56][57].

M

The studies’ durations range from 2 weeks[56] to 2 years[47][50] and sample sizes range from 12[56]

TE D

to 404[47]. A majority (6) of these studies target specific populations such as breast cancer survivors[57], elevated breast cancer risk subjects[49], overweight adults[48][51], and university students[47][56]; the remaining 2 studies do not report any such focus[52][50]. In total, 4 studies design

EP

research-specific apps for providing intervention[48][51][52][56]; 3 studies use a commercial app[49][50][57] while 1 study uses both research-specific and commercial apps for the intervention[47].

CC

Further, 2 of the studies employ an accompanying device[49][51], while 6 studies use phone sensors to

A

track PA[47][48][50][52][56][57]. Of the 8 studies, 2 studies[52][56] replace manual diet logging by using voice-annotated meal videos[56] or crowdsourced food photos[52]. All studies provide information booklets and training at baseline and 3 of the 8 studies involve guided sessions from a team of health professionals[51][50][57]. Of the 8 studies, 3 report positive results of the intervention[51][52][56], 1 study records improvements for the intervention group for 6–12 months but not at 24 months[47], 3 studies report improvements in only one of the measured outcomes with no 16

significant differences in other outcomes[49][48][57]; while 1 study offers only preliminary analysis with results awaited[50]. We did not find any study targeting intervention on diet and sleep together using smartphone apps. Of the 14 included studies, 4 studies [44,45][53][54][58] target all 3 dimensions (PA, sleep, diet). Study durations range from 2 weeks[53] to 9 months[44,45] and sample sizes range from 53[54] to 227[53].

IP T

Of these, 2 studies deal with specific groups (employees in workplace[58], children at health camp[53]), 1 study targets overweight adults[54] and 1 study samples adults with sedentary lifestyles and

SC R

insufficient fruits, vegetables consumption[44,45]. Further, 3 of the studies develop research-specific apps for providing intervention[54][58] [44,45], while 1 study uses commercial apps[53]. In this category, 2 studies use accompanying devices to track PA and sleep[53][54] with 1 of them designing

U

their own device[54]; 1 study uses smartphone sensors to track PA, while diet and sleep are self-reported

N

on app[44,45]; and 1 study only uses self-reported PA, sleep, and diet[58]. Of the 4 studies, 1 study

A

reports preliminary analysis only[58], 1 study describes improvement in PA and diet outcomes for

M

intervention groups, while examining sleep only for the control group[44,45], 1 study reports positive

TE D

influence of intervention on improving PA and does not assess sleep and diet outcomes[54], and 1 study reports mixed results for BMI[53].

3.3 Inter-relationships among Dimensions

EP

Neither of the 2 studies in the PA and sleep category examine the inter-relationship of sleep and PA

CC

dimensions. Of these 2 studies, 1 study explores the relationship between life-satisfaction and sleep quality as well as between life satisfaction and PA, and reports positive correlation between early

A

bedtime or PA and higher life-satisfaction rating[55]. Similarly, although 8 of the 14 included studies target diet and PA, only 1 study examines the relationship between the two dimensions[56]. The study finds that a person’s meal portion size is positively correlated with PA performed before mealtime. The rest of the studies aim for increased weight loss,

17

diabetes prevention, or adherence to routines on PA and diet separately without considering or examining the relationships between PA and diet. Of the 4 studies that target all three dimensions, 1 study proposes to examine the effects of sleep on PA and diet and vice versa in their design[44] but does not report such results when published[45]. The remaining 3 studies do not examine the inter-relationships among the dimensions.

IP T

3.4 Personalization in Included studies

Both studies[46][55] in the PA and sleep category deliver automated feedback through the app to users.

SC R

Of these, 1 study delivers automated feedback by representing user’s self-reported PA, sleep data as plots and charts on the app using a traffic light analogy for visualization (red–poor, yellow–okay, greengood)[46]. The other study uses a commercial app for PA and health data tracking including periodic

U

surveys on diet, well-being, risk perception, work-related and leisure-time PA, sleep, and CVH[55]. It

A

N

provides surveys on the app to assess risks to user’s health based on those surveys. These risks are then

M

shown as feedback to users in the form of graphs and visualizations comparing them with other users of similar demographics[55].

TE D

Of the 8 studies in the PA and diet category, 7 studies offer personalized recommendations to intervention participants, with 4 studies delivering manual feedback/recommendations through app notifications, phone calls, emails or messages[49][47][50][57]; 1 study providing both manual and

EP

automated feedback/recommendations[51]; 1 study providing automated recommendations by

CC

learning user preferences using machine learning techniques and behavioral theories[52]; and 1 study offering personalized feedback in the form of social support by asking other participants to give feedback

A

(from a list of predefined options) or ratings on the user’s activities and achieved goals via the app’s newsfeed[48]. The remaining 1 of the 8 studies provides automated feedback (visualizations) without recommendations[56] and does not assess the intervention effect, instead focusing on predicting meal portion size. Of the 8 studies, 3 studies build user profiles to predict user behavior. Of these, 2 studies provide automated recommendations, with 1 study recommending weekly step goals[51], and the other

18

recommending diet and PA based on user’s previous entries on the app[52]. The remaining study estimates user’s meal portion sizes based on PA performed, routine, mood and food environment[56]. Further, 1 of these 3 studies incorporates suggestions from users e.g., their acceptance or rejection of automated recommendations, to build the user profile[52]. All 4 studies in the PA, sleep, and diet category involve coaching sessions, of which 3

IP T

studies[53][54][44,45] provide manual feedback/recommendations using app notifications, emails, messages, or phone calls, and 1 study[58] provides automated recommendations based on the user

SC R

profile created. Of the 4 studies, 3 studies also provide automated feedback by means of visualizations of activity and sleep patterns[54][58][44,45]. Only 1 of the 4 studies[58] builds a user profile through user surveys supplemented by information from health manuals and health behavior theory[61] to

U

provide automated recommendations. The user profiling process also allows for incorporation of user

N

suggestions and prioritization of feedback.

A

In all, 5 of the 14 included studies provide manual feedback/recommendation[49][47][50][53][57], 5

M

studies provide automated feedback[46][55][56] as well as recommendations[52][58], 3 studies

TE D

provide both automated and manual feedback/recommendations[44,45][51][54], and 1 study provides semi-automated feedback[48].

EP

4 Discussion

CC

4.1 Combined Interventions in Research Both the PA and sleep studies[46][55] are constrained by duration (7 days/ 9 weeks). Further, both these

A

studies give feedback in the form of visualizations and do not provide recommendations for behavior change. In the PA and diet category, 5 studies suffer from sample size constraints -number of subjects around 50 or less[48][49][57][52][56]. Further, 2 studies are of short duration– spanning 2 weeks[56] and 4 weeks[57]. Of the 8 studies in this category, 2 studies target breast cancer survivors or women at increased risk of breast, endometrial cancer[49][57]. These studies’ could suffer from bias due to increased motivation of participants that are in a high-risk group. Further, 1 of the studies uses a 19

convenience sample of 12 university students for comparing individual profiling with group/clustering, and hence the results could be limited [56]. The majority of the studies using smartphone-based interventions focus on PA and diet interventions for weight loss, while studies including sleep are fewer (6/14). In fact, we found no relevant study that targeted diet and sleep behaviors together. Thus, a key finding of our review is that sleep behavior has

IP T

rarely been targeted in combination with other dimensions. In all, 2 studies measure sleep as a secondary outcome[54] and/or relaxation measure[44,45], while 3 studies do not measure change in sleep

SC R

outcomes[53][54][55]. Only 1 study[58] provides feedback related to sleep behavior. This may be because sleep measurement through app/devices is still not mature. Further, 3 out of 6 studies assess

U

sleep through self-assessment of sleep quantity and quality i.e., users need to recall and manually enter

N

sleep time and rate the quality on a scale on the app[58][55][44,45]. The other 3 studies[53][54][46] use

A

actigraphy/ accelerometer sensors to track the user’s movement overnight to determine sleep quantity

M

and quality (through variability of sleep duration[62][63]) objectively. Indeed, smartphones/devices with such sensors can help measure sleep objectively. However, such objective measures should be

TE D

combined with subjective measures of user’s perception of his/her sleep quality [64] and state of restfulness for comprehensive sleep assessment, as in [46]. Only 4 of 14 studies[49][50][57][53] use commercially available apps for providing intervention, the

EP

remaining studies design apps specifically for their research. This suggests that existing commercial apps

CC

are less suitable for providing research intervention. While studies use smartphone or accompanying devices to track PA and sleep, diet is usually tracked through self-reports (logging on app). Only 3 of 12

A

studies that involve diet tracking allow food photos/videos as an alternative reporting method[52][56][53], which could be encouraged to reduce users’ food logging effort.

20

4.2 Inter-relationships among Dimensions None of the included studies consider the inter-relationships among the dimensions (PA, sleep, diet) in the design of their intervention. Moreover, they do not aim to examine the inter-relationships even for the intervention outcomes. For example, does sleep behavior also improve for study participants who show improved PA and diet outcomes? Even studies targeting only two of the three dimensions (PA and

IP T

diet, PA and sleep) do not examine the inter-relationships. Only 1 study considers the relationship

SC R

between PA and diet by estimating participant’s meal portion size based on his/her PA levels[56]. 4.3 Personalization Methods in Included Studies

Most studies in our review (8/14) offer personalization through phone calls or in-person sessions

U

[44,45][49][53][51][54][47][50][57]. Only 5 studies create user profiles, of which 1 study clusters users

N

into groups[55] and the rest implement individualized user profiling[51][52][56][58]. Moreover, the

A

study that clusters users into groups does not provide feedback to them[55]. Even out of the 4 studies

M

that build individualized user profiles, 1 study does not provide feedback to users based on their

TE D

profile[56]. In sum, only 3 of the 14 studies deliver personalized feedback/recommendations based on user’s profile[51][52][58]. As mentioned earlier, 2 of these 3 studies report positive outcomes[51][52], while 1 study’s results are awaited[58]. This suggests that further research examining feedback based

EP

on user profile is warranted. Clinicians and researchers designing apps for providing multi-dimensional interventions should consider building user profiles for personalized feedback to improve health

CC

outcomes.

A

4.4 Strengths and Limitations A major strength of our review derives from its focus on smartphone apps as the key delivery mode in interventions for PA, sleep, and diet. We focused only on interventions using smartphone apps and accompanying devices (if present), rather than looking broadly across all delivery methods (such as web apps, face-to-face). We do so because smartphone apps work as tools for delivering interventions to the

21

majority of the population and personalization can be readily implemented using various techniques. Our review is limited by the inclusion of a few studies that do not report their results. 4.5 Recommendations for Future Work Before this article, no review had examined studies of smartphone apps delivering combined

IP T

interventions targeting PA, diet and sleep and identified the personalization methods they implemented. The importance of a holistic measurement of these dimensions raises a number of areas that should be

SC R

considered for future research. Given what we know about the 3 dimensions and their link to better health, these dimensions should be measured objectively for accuracy (supplemented by subjective measures). The inter-relationships among these dimensions should be considered while designing such

U

interventions and the subsequent effect on individuals’ health outcomes should be examined.

N

Although user data regarding these dimensions is readily captured nowadays, research has not yet

A

adequately leveraged such data for designing behavior change interventions. Models for automated

M

feedback based on individualized user profiling are rare as per our review, and the relationships among

TE D

the dimensions are not considered. With the proliferation of artificial intelligence and machine learning techniques, it is imperative that the focus of interventions through smartphone apps moves from clustering of users into groups to individual-based user profiles. It is important to build user profiles for

EP

both personalized (hence more useful) feedback to users and for researchers to gain a better

A

CC

understanding of the dimensions and their effect on individuals’ health.

22

Summary Points What was already known on the topic? 

Smartphone apps support the user in tracking their physical activity, sleep, and diet and are an important mode to provide personalized intervention for behavior change in these dimensions. Physical activity, sleep, and diet behaviors of an individual are inter-related.

IP T



What this study added to our knowledge:

Most combined interventions delivered through smartphones focus on physical activity and

SC R



diet.

Interventions targeting sleep and diet together are not seen in the literature.



Interventions for sleep and physical activity are rare in prior research.



Interventions using smartphone apps that consider the inter-relationships among physical

N

U



Individualized user profiling and personalization are not examined sufficiently in the literature.

M



A

activity, sleep, and diet, or any two of them are rare in prior research.

TE D

There is much scope for building models using machine-learning techniques based on user

EP

profiles derived from data from smartphone apps and accompanying devices.

Conflicts of Interest

Author Statements

A

CC

None Declared.

The preparation of this manuscript was supported in part by Grant: MOE R-253-000-129-114 and the National University of Singapore. The supporting sources had no involvement in study design, collection, analyses, and interpretation or writing the article. Authors’ contributions

23

IP T

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article and revising it critically for important intellectual content; and (c) approval of the final version. Dr. Kankanhalli performed the major analyses and was responsible for error checking and revision of the manuscript. Saxena was responsible for initial organization and creation of the manuscript. She performed the literature search, data extraction and transcription from articles. Dr. Kankanhalli and Dr. Wadhwa evaluated and structured analyses and scientific findings of the articles and aided in overall manuscript structuring. All authors reviewed and contributed to the preparation of the final manuscript.

References

G. Hulsegge, M. Looman, H.A. Smit, M.L. Daviglus, Y.T. van der Schouw, W.M.M. Verschuren, Lifestyle Changes in

SC R

[1]

Young Adulthood and Middle Age and Risk of Cardiovascular Disease and All-Cause Mortality: The Doetinchem Cohort Study., J. Am. Heart Assoc. 5 (2016). doi:10.1161/JAHA.115.002432.

M.-P. St-Onge, M.A. Grandner, D. Brown, M.B. Conroy, G. Jean-Louis, M. Coons, D.L. Bhatt, Sleep Duration and Quality:

U

[2]

N

Impact on Lifestyle Behaviors and Cardiometabolic Health: A Scientific Statement From the American Heart

H. Arem, M. SC, A. Patel, et al, Leisure time physical activity and mortality: A detailed pooled analysis of the dose-

M

[3]

A

Association, Circulation. 134 (2016) e367 LP-e386. http://circ.ahajournals.org/content/134/18/e367.abstract.

response relationship, JAMA Intern. Med. 175 (2015) 959–967.

[4]

TE D

http://dx.doi.org/10.1001/jamainternmed.2015.0533.

U. Ekelund, J. Steene-Johannessen, W.J. Brown, M.W. Fagerland, N. Owen, K.E. Powell, A. Bauman, I.-M. Lee, Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A

EP

harmonised meta-analysis of data from more than 1 million men and women, Lancet. 388 (2016) 1302–1310. doi:https://doi.org/10.1016/S0140-6736(16)30370-1. F. Imamura, R. Micha, S. Khatibzadeh, S. Fahimi, P. Shi, J. Powles, D. Mozaffarian, on behalf of the G.B. of D.N. and

CC

[5]

C.D.E.G. (NutriCoDE), Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic

[6]

A

assessment, Lancet. Glob. Heal. 3 (2015) e132–e142. doi:10.1016/S2214-109X(14)70381-X. K.M. Keyes, J. Maslowsky, A. Hamilton, J. Schulenberg, The Great Sleep Recession: Changes in Sleep Duration Among US Adolescents, 1991–2012, Pediatrics. 135 (2015) 460 LP-468. http://pediatrics.aappublications.org/content/135/3/460.abstract. [7]

U.M.H. Klumpers, D.J. Veltman, M.-J. van Tol, R.W. Kloet, R. Boellaard, A.A. Lammertsma, W.J.G. Hoogendijk, Neurophysiological Effects of Sleep Deprivation in Healthy Adults, a Pilot Study, PLoS One. 10 (2015) e0116906.

24

doi:10.1371/journal.pone.0116906. [8]

K.H. Wagner, H. Brath, A global view on the development of non communicable diseases, Prev. Med. (Baltim). (2012). doi:10.1016/j.ypmed.2011.11.012.

[9]

J.-P. Chaput, Sleep patterns, diet quality and energy balance., Physiol. Behav. 134 (2014) 86–91. doi:http://dx.doi.org/10.1016/j.physbeh.2013.09.006.

[10]

M.P. St-Onge, A. Roberts, A. Shechter, A.R. Choudhury, Fiber and saturated fat are associated with sleep arousals and

[11]

IP T

slow wave sleep, J. Clin. Sleep Med. (2016). doi:10.5664/jcsm.5384.

B.A. Dolezal, E. V Neufeld, D.M. Boland, J.L. Martin, C.B. Cooper, Interrelationship between Sleep and Exercise : A

[12]

SC R

Systematic Review, 2017 (2017).

L. Hayes, C. Mcparlin, T.I. Kinnunen, L. Poston, S.C. Robson, R. Bell, Change in level of physical activity during pregnancy in obese women: findings from the UPBEAT pilot trial, BMC Pregnancy Childbirth. 15 (2015) 52.

[13]

U

doi:10.1186/s12884-015-0479-2.

S. Cassidy, J.Y. Chau, M. Catt, A. Bauman, M.I. Trenell, Cross-sectional study of diet, physical activity, television

N

viewing and sleep duration in 233 110 adults from the UK Biobank; the behavioural phenotype of cardiovascular

A. Norman, A. Berlin, E. Sundblom, L.S.S. Elinder, G.A.I.-O. http://orcid. org/Berli. Nyberg Anita; ORCID:

M

[14]

A

disease and type 2 diabetes, BMJ Open. 6 (2016). http://bmjopen.bmj.com/content/6/3/e010038.abstract.

http://orcid.org/0000-0001-9402-3315, Å. Norman, A. Berlin, E. Sundblom, L.S.S. Elinder, G. Nyberg, Stuck in a

TE D

vicious circle of stress. Parental concerns and barriers to changing children’s dietary and physical activity habits., Appetite. 87 (2015) 137–142. doi:http://dx.doi.org/10.1016/j.appet.2014.12.208. [15]

B.M. Appelhans, S.L. Fitzpatrick, H. Li, V. Cail, M.E. Waring, K.L. Schneider, M.C. Whited, A.M. Busch, S.L. Pagoto, The

EP

home environment and childhood obesity in low-income households: indirect effects via sleep duration and screen time, BMC Public Health. 14 (2014) 1160. doi:10.1186/1471-2458-14-1160. J.J. McClain, D.S. Lewin, A.D. Laposky, L. Kahle, D. Berrigan, Associations between physical activity, sedentary time,

CC

[16]

sleep duration and daytime sleepiness in US adults, Prev. Med. (Baltim). 66 (2014) 68–73.

A

doi:10.1016/J.YPMED.2014.06.003.

[17]

S. Masood, C. Cappelli, Y. Li, H. Tanenbaum, C.P. Chou, D. Spruijt-Metz, P.H. Palmer, C. Anderson Johnson, B. Xie, Cigarette smoking is associated with unhealthy patterns of food consumption, physical activity, sleep impairment, and alcohol drinking in Chinese male adults, Int. J. Public Health. (2015). doi:10.1007/s00038-015-0730-7.

[18]

E.O. Lillie, B. Patay, J. Diamant, B. Issell, E.J. Topol, N.J. Schork, The n-of-1 clinical trial: The ultimate strategy for individualizing medicine?, Per. Med. (2011). doi:10.2217/pme.11.7.

25

[19]

S. Yoon, J.E. Schwartz, M.M. Burg, I.M. Kronish, C. Alcantara, J. Julian, F. Parsons, K.W. Davidson, K.M. Diaz, Using Behavioral Analytics to Increase Exercise: A Randomized N-of-1 Study, Am. J. Prev. Med. (2018). doi:10.1016/j.amepre.2017.12.011.

[20]

Ciara O’Brien, Checking the health of the fitness-tracking wearables market, The Irish Times. (2017). https://www.irishtimes.com/business/technology/checking-the-health-of-the-fitness-tracking-wearables-market1.2931560 (accessed May 25, 2018). T. Page, A Forecast of the Adoption of Wearable Technology, Int. J. Technol. Diffus. 6 (2015) 12–29.

IP T

[21]

doi:10.4018/IJTD.2015040102.

P. Krebs, D.T. Duncan, Health App Use Among US Mobile Phone Owners: A National Survey, JMIR MHealth UHealth. 3

SC R

[22]

(2015) e101. doi:10.2196/mhealth.4924. [23]

G.M. Turner-McGrievy, M.W. Beets, J.B. Moore, A.T. Kaczynski, D.J. Barr-Anderson, D.F. Tate, Comparison of

U

traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program, J. Am. Med. Informatics Assoc. 20 (2013) 513–518.

A.G. Arens-Volland, L. Spassova, T. Bohn, Promising approaches of computer-supported dietary assessment and

A

[24]

N

http://dx.doi.org/10.1136/amiajnl-2012-001510.

M

management—Current research status and available applications, Int. J. Med. Inform. 84 (2015) 997–1008. doi:https://doi.org/10.1016/j.ijmedinf.2015.08.006.

A. Gruwez, W. Libert, L. Ameye, M. Bruyneel, Reliability of commercially available sleep and activity trackers with

TE D

[25]

manual switch-to-sleep mode activation in free-living healthy individuals, Int. J. Med. Inform. 102 (2017) 87–92. doi:https://doi.org/10.1016/j.ijmedinf.2017.03.008. L.M. Collins, S.A. Murphy, K.L. Bierman, A conceptual framework for adaptive preventive interventions, Prev. Sci.

EP

[26]

(2004). doi:10.1023/B:PREV.0000037641.26017.00. C. LeRouge, J. Ma, S. Sneha, K. Tolle, User profiles and personas in the design and development of consumer health

CC

[27]

technologies, Int. J. Med. Inform. (2013). doi:10.1016/j.ijmedinf.2011.03.006. T. Harries, P. Eslambolchilar, R. Rettie, C. Stride, S. Walton, H.C. Van Woerden, Effectiveness of a smartphone app in

A

[28]

increasing physical activity amongst male adults: A randomised controlled trial, BMC Public Health. 16 (2016) 1–11. doi:10.1186/s12889-016-3593-9.

[29]

S. Schoeppe, S. Alley, W. Van Lippevelde, N.A. Bray, S.L. Williams, M.J. Duncan, C. Vandelanotte, Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review, Int. J. Behav. Nutr. Phys. Act. 13 (2016) 127. doi:10.1186/s12966-016-0454-y.

26

[30]

A.M. Muller, S. Alley, S. Schoeppe, C.A.I.-O. http://orcid. org/Mulle. Vandelanotte Andre Matthias; ORCID: http://orcid.org/0000-0001-5770-6723, The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review., Int. J. Behav. Nutr. Phys. Act. 13 (2016). doi:http://dx.doi.org/10.1186/s12966-016-0434-2.

[31]

F.P. Cappuccio, D. Cooper, L. D’Elia, P. Strazzullo, M.A. Miller, Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies., Eur. Heart J. 32 (2011) 1484–1492.

[32]

IP T

doi:10.1093/eurheartj/ehr007.

J. Bort-Roig, N.D. Gilson, A. Puig-Ribera, R.S. Contreras, S.G. Trost, Measuring and influencing physical activity with

[33]

D.S. Grigsby-Toussaint, J.C. Shin, D.M. Reeves, A. Beattie, E. Auguste, G. Jean-Louis, Sleep apps and behavioral constructs: A content analysis, Prev. Med. Reports. 6 (2017) 126–129.

U

doi:https://doi.org/10.1016/j.pmedr.2017.02.018. [34]

SC R

smartphone technology: A systematic review, Sport. Med. (2014). doi:10.1007/s40279-014-0142-5.

G.B. Samdal, G.E. Eide, T. Barth, G. Williams, E. Meland, Effective behaviour change techniques for physical activity

N

and healthy eating in overweight and obese adults; systematic review and meta-regression analyses., Int. J. Behav.

S. Hermsen, J. Frost, R.J. Renes, P. Kerkhof, Using feedback through digital technology to disrupt and change habitual

M

[35]

A

Nutr. Phys. Act. 14 (2017). doi:http://dx.doi.org/10.1186/s12966-017-0494-y.

behavior: A critical review of current literature, Comput. Human Behav. 57 (2016) 61–74.

[36]

TE D

doi:https://doi.org/10.1016/j.chb.2015.12.023. S.M. Schembre, Y. Liao, M.C. Robertson, G.F. Dunton, J. Kerr, M.E. Haffey, T. Burnett, K. Basen-Engquist, R.S. Hicklen, Just-in-Time Feedback in Diet and Physical Activity Interventions: Systematic Review and Practical Design

[37]

EP

Framework, J. Med. Internet Res. 20 (2018) e106. doi:10.2196/jmir.8701. H. Arksey, L. O’Malley, Methodological Framework of Scoping Studies, Int. J. Soc. Res. Methodol. Theory Pract. 8

[38]

CC

(2005) 2013. doi:https://doi.org/10.1080/1364557032000119616. M.T. Pham, A. Rajić, J.D. Greig, J.M. Sargeant, A. Papadopoulos, S.A. Mcewen, A scoping review of scoping reviews:

A

Advancing the approach and enhancing the consistency, Res. Synth. Methods. (2014). doi:10.1002/jrsm.1123.

[39]

K.S. Khan, R. Kunz, J. Kleijnen, G. Antes, Five steps to conducting a systematic review, J. R. Soc. Med. (2003). doi:10.1258/jrsm.96.3.118.

[40]

R. McCarroll, H. Eyles, C. Ni Mhurchu, Effectiveness of mobile health (mHealth) interventions for promoting healthy eating in adults: A systematic review, Prev. Med. (Baltim). 105 (2017) 156–168. doi:https://doi.org/10.1016/j.ypmed.2017.08.022.

27

[41]

S. Schoeppe, S. Alley, W. Van Lippevelde, N.A. Bray, S.L. Williams, M.J. Duncan, C. Vandelanotte, Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review, Int. J. Behav. Nutr. Phys. Act. 13 (2016) 127. doi:10.1186/s12966-016-0454-y.

[42]

F. Mata, M. Torres-Ruiz, R. Zagal, G. Guzman, M. Moreno-Ibarra, R. Quintero, A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning, Telemat. Informatics. (2016). doi:10.1016/j.tele.2017.04.005. H.G. Ridder, M.B. Miles, A. Michael Huberman, J. Saldaña, Qualitative data analysis. A methods sourcebook, Zeitschrift

IP T

[43]

Fur Pers. (2014). doi:10.1177/239700221402800402.

C.A. Pellegrini, J. Steglitz, W. Johnston, J. Warnick, T. Adams, H.G. McFadden, J. Siddique, D. Hedeker, B. Spring, Design

SC R

[44]

and protocol of a randomized multiple behavior change trial: Make Better Choices 2 (MBC2), Contemp. Clin. Trials. 41 (2015) 85–92. doi:10.1016/j.cct.2015.01.009.

B. Spring, C. Pellegrini, H.G. McFadden, A.F. Pfammatter, T.K. Stump, J. Siddique, A.C. King, D. Hedeker,

U

[45]

Multicomponent mHealth Intervention for Large, Sustained Change in Multiple Diet and Activity Risk Behaviors: The

M.J. Duncan, C. Vandelanotte, S.G. Trost, A.L. Rebar, N. Rogers, N.W. Burton, B. Murawski, A. Rayward, S. Fenton, W.J.

A

[46]

N

Make Better Choices 2 Randomized Controlled Trial., J. Med. Internet Res. 20 (2018) e10528. doi:10.2196/10528.

M

Brown, Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multibehaviour intervention to improve physical activity, sitting and sleep in adults., BMC Public Health. 16 (2016) 670.

[47]

TE D

doi:10.1186/s12889-016-3256-x.

J.G. Godino, G. Merchant, G.J. Norman, M.C. Donohue, S.J. Marshall, J.H. Fowler, K.J. Calfas, J.S. Huang, C.L. Rock, W.G. Griswold, A. Gupta, F. Raab, B.J. Fogg, T.N. Robinson, K. Patrick, K.P. Administrative, Using social and mobile tools for

EP

weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial, Lancet Diabetes Endocrinol. 4 (2016) 747–755. doi:https://doi.org/10.1016/S2213-8587(16)30105-X. S. Hales, G.M. Turner-McGrievy, S. Wilcox, A. Fahim, R.E. Davis, M. Huhns, H. Valafar, Social networks for improving

CC

[48]

healthy weight loss behaviors for overweight and obese adults: A randomized clinical trial of the social pounds off

A

digitally (Social POD) mobile app, Int. J. Med. Inform. 94 (2016) 81–90. doi:10.1016/j.ijmedinf.2016.07.003.

[49]

S.J. Hartman, S.H. Nelson, L.A. Cadmus-Bertram, R.E. Patterson, B.A. Parker, J.P. Pierce, Technology- and Phone-Based Weight Loss Intervention: Pilot RCT in Women at Elevated Breast Cancer Risk, Am. J. Prev. Med. 51 (2016) 714–721. doi:https://doi.org/10.1016/j.amepre.2016.06.024.

[50]

J.A. Linde, R.W. Jeffery, S.J. Crow, K.L. Brelje, C.R. Pacanowski, K.L. Gavin, D.J. Smolenski, The Tracking Study: Description of a randomized controlled trial of variations on weight tracking frequency in a behavioral weight loss

28

program, Contemp. Clin. Trials. 40 (2015) 199–211. doi:10.1016/j.cct.2014.12.007. [51]

Y. Fukuoka, C.L. Gay, K.L. Joiner, E. Vittinghoff, A Novel Diabetes Prevention Intervention Using a Mobile App: A Randomized Controlled Trial With Overweight Adults at Risk, Am. J. Prev. Med. 49 (2015) 223–237. doi:https://doi.org/10.1016/j.amepre.2015.01.003.

[52]

M. Rabbi, M.H. Aung, M. Zhang, T. Choudhury, MyBehavior: Automatic Personalized Health Feedback from User Behaviors and Preferences Using Smartphones, Ubicomp ’15, Sept. 7–11, 2015, Osaka, Japan. (2015) 707–718.

[53]

IP T

doi:10.1145/2750858.2805840.

L. Fernandez-Luque, M. Singh, F. Ofli, Y.A. Mejova, I. Weber, M. Aupetit, S.K. Jreige, A. Elmagarmid, J. Srivastava, M.

SC R

Ahmedna, Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar, BMC Med. Inform. Decis. Mak. 17 (2017). doi:http://dx.doi.org/10.1186/s12911-017-0432-6. [54]

Y.-P. Yang, C.-J. Wang, J.-J. Wang, C.-W. Lin, Y.-T.C. Yang, J. Wang, Y.K. Yang, Y.-C. Yang, The Effects of an Activity

U

Promotion System on active living in overweight subjects with metabolic abnormalities, Obes. Res. Clin. Pract. 11 (2017) 718–727. doi:https://doi.org/10.1016/j.orcp.2017.06.002.

M. V. McConnell, A. Shcherbina, A. Pavlovic, J.R. Homburger, R.L. Goldfeder, D. Waggot, M.K. Cho, M.E. Rosenberger,

N

[55]

A

W.L. Haskell, J. Myers, M.A. Champagne, E. Mignot, M. Landray, L. Tarassenko, R.A. Harrington, A.C. Yeung, E.A.

M

Ashley, Feasibility of obtaining measures of lifestyle from a smartphone app: The MyHeart Counts cardiovascular health study, JAMA Cardiol. 2 (2017) 67–76. doi:10.1001/jamacardio.2016.4395. E. Seto, J. Hua, L. Wu, V. Shia, S. Eom, M. Wang, Y. Li, Models of Individual Dietary Behavior Based on Smartphone

TE D

[56]

Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment, PLoS One. 11 (2016). doi:http://dx.doi.org/10.1371/journal.pone.0153085. M.L. McCarroll, S. Armbruster, R.J. Pohle-Krauza, A.M. Lyzen, S. Min, D.W. Nash, G.D. Roulette, S.J. Andrews, V.E. von

EP

[57]

Gruenigen, Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors

CC

using an interactive mobile application, Gynecol. Oncol. 137 (2015) 508–515. doi:https://doi.org/10.1016/j.ygyno.2014.12.025. F. Dutheil, M. Duclos, G. Naughton, S. Dewavrin, T. Cornet, P. Huguet, J.-C. Chatard, B. Pereira, WittyFit-Live Your

A

[58]

Work Differently: Study Protocol for a Workplace-Delivered Health Promotion., JMIR Res. Protoc. 6 (2017) e58. doi:10.2196/resprot.6267.

[59]

J. Dallery, R.N. Cassidy, B.R. Raiff, Single-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions, J. Med. Internet Res. 15 (2013) e22. doi:10.2196/jmir.2227.

[60]

C.W. Lin, Y.T.C. Yang, J.S. Wang, Y.C. Yang, A wearable sensor module with a neural-network-based activity

29

classification algorithm for daily energy expenditure estimation, IEEE Trans. Inf. Technol. Biomed. (2012). doi:10.1109/TITB.2012.2206602. [61]

S.M. Noar, R.S. Zimmerman, Health Behavior Theory and cumulative knowledge regarding health behaviors: are we moving in the right direction?, Health Educ. Res. 20 (2005) 275–290. http://dx.doi.org/10.1093/her/cyg113.

[62]

M. Marino, Y. Li, M.N. Rueschman, J.W. Winkelman, J.M. Ellenbogen, J.M. Solet, H. Dulin, L.F. Berkman, O.M. Buxton, Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography, Sleep.

[63]

IP T

(2013). doi:10.5665/sleep.3142.

S. Lemola, T. Ledermann, E.M. Friedman, Variability of Sleep Duration Is Related to Subjective Sleep Quality and

[64]

SC R

Subjective Well-Being: An Actigraphy Study, PLoS One. (2013). doi:10.1371/journal.pone.0071292.

D. Buysse, C. Reynolds, T. Monk, S. Berman, The Pittsburgh Sleep Quality Index: a new instrument for psychiatric

A

CC

EP

TE D

M

A

N

U

practice and research, Psychiatry Res. (1989).

30