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

https://doi.org/10.1016/j.ijmedinf.2018.12.005Get rights and content

Highlights

  • 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.

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 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 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 informatics literature – PubMed, ScienceDirect, PsycINFO (ProQuest, Ovid) – using relevant selected keywords. Article selection and inclusion were done by removing duplicates, analyzing titles and abstracts, and then reviewing the full text of the articles.

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, 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 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 dimensions.

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 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 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 inter-related, e.g., insufficient sleep often leads to poor dietary choices and low activity [9] and vice versa [10,11]. The dimensions could be correlated (positive or negative) and the inter-relationships could be linear or non-linear. Clinicians and researchers should ideally consider such inter-relationships 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, 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 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 interventions generalized to the population [18,19].

Recent advancements in technology make collection of user’s PA, sleep, and diet data possible through 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 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 sedentary lifestyle. Personalized, combined interventions for PA, sleep, and diet that consider the inter-relationships among the dimensions can be an important step in guiding health improvement. Research 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]]. This makes smartphone apps an appropriate tool for providing personalized interventions targeting the aforementioned three dimensions in combination, which we aim to review.

To our knowledge, our review is unique as its primary goal is to examine combined interventions 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] 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 review to map the existing literature on smartphone apps delivering combined interventions for PA, diet, 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 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 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.

Section snippets

Search strategy

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 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 health

Selection and inclusion of studies

Initially 1323 articles were found from the databases and 2 articles (“Other sources”) were found from reference lists of full-text articles (see the PRISMA Flow Diagram in Fig. 1). In total 957 articles (21 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 abstracts were scanned for inclusion, of which 473 articles were identified as

Combined interventions in research

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

Conflicts of interest

None declared.

Author statements

The preparation of this manuscript was supported in part by Grant: MOER-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

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

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