Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change

Abstract

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals’ behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.

Keywords

behavioral intervention technology integrated behavior change model intervention design intervention mapping physical activity sedentary behavior

Increasing population levels of physical activity (PA) can be achieved through multiple levels of interventions (e.g., policy, community) but ultimately requires individual-level behavior change. One approach that offers scalable solutions for the delivery of interventions to aid individual-level behavior change is the smartphone. Ownership of smartphones increased from 35% in 2011 to 64% in 2014 among adults in the United States (Pew Research Center, 2015). Furthermore, U.S. adults spend an average of 34 hours per month using mobile applications (apps) and mobile web browsers, compared with 27 hours a month online with computers (The Nielsen Company, 2014).

Given their inbuilt sensors (e.g., accelerometers, GPS), smartphones provide the ability to collect objective and ecologically valid data of individuals’ real-world behavior (Kaplan & Stone, 2013). These sensing capabilities can be leveraged to passively capture an individual’s movement behavior. Despite differences in device accuracy, existing evidence suggests comparable estimates of PA as measured by Android smartphones or research-grade accelerometers in free-living conditions (Hekler et al., 2015) and that smartphone apps can accurately measure step counts (Case, Burwick, Volpp, & Patel, 2015). Moreover, using smartphone apps, interventions can be delivered using the same device.

Information technology (IT)–based interventions to promote PA are now widely available and have been shown to increase PA with variable success (Broekhuizen, Kroeze, van Poppel, Oenema, & Brug, 2012; Norman et al., 2007). However, they are not widely used, use is discontinued by individuals over time, and their positive effects last for limited periods. Typically, the number of logins to IT-delivered interventions rapidly declines after enrolment for most participants (Laing et al., 2014) and throughout the intervention (Duncan et al., 2014). Most current IT-based interventions rely on automated delivery of predefined/scheduled a priori content, which is static and does not adapt dynamically as the individual changes behavior. Often, interventions tailor to initial differences between individuals and primarily capture data via infrequent surveys or assessments (e.g., face-to-face). Yet behavior varies not only between individuals but also within an individual over time (Riley et al., 2011). Requiring engagement from the participant to either self-report PA or wear an additional device like an accelerometer increases the burden on the individual.

While survey data show that fitness and nutrition apps are the most common health apps used, among smartphone health apps users, approximately half stop using such apps and indicate high data entry burden and loss of interest as the main reasons for doing so (Krebs & Duncan, 2015). For instance, engagement with the MyFitnessPal app (a highly rated and downloaded app on app stores; Gray, 2015) during a weight loss trial with primary care patients decreased over time, despite participants reporting satisfaction with the app (79%). Reasons for discontinuing use included it was tedious (84%) to enter data (Laing et al., 2014) and was not perceived as easy to use (24%).

Notably, it has been shown that dynamically tailored interventions have superior efficacy over time compared with those that base their tailoring on single or infrequent assessments (e.g., baseline; Adams et al., 2013; Krebs, Prochaska, & Rossi, 2010). Smartphones allow an opportunity to capture intensive longitudinal data (i.e., continuous, not episodic) in unobtrusive fashion (i.e., reducing respondent burden). The ability to accurately collect information on exposure and to detect dynamic behavior change indicates an ideal opportunity to develop “just-in-time” interventions, delivering content based on people’s behavior, as well as capturing their response to the intervention (Spruijt-Metz, Hekler, et al., 2015; Spruijt-Metz, Wen, et al., 2015).

The literature on designing and describing the process of developing PA promotion interventions is extensive, but few resources exist that integrate both conceptual (i.e., theory, evidence) and technological frameworks to describe the process of developing an mHealth program for promoting PA behavior (Crutzen, 2014; Mohr, Schueller, Montague, Burns, & Rashidi, 2014). This article describes the steps undertaken to develop a dynamic, adaptive, mHealth pilot-intervention using intervention mapping (IM) and the behavioral intervention technology (BIT) frameworks, with the aim of influencing both PA and SB duration in healthy but insufficiently active adults.

Method

Interventions to change behavior are complex since they involve multiple components. These include the behavior change techniques (BCTs; i.e., a replicable component of an intervention) and the procedures for delivery (i.e., who delivers, when, for how long, and mode). Typically, interventions are insufficiently described and many are developed without following a systematic approach (Kok et al., 2016). A behavior change intervention should specify details of both its active content (i.e., BCTs, “the what”) and its mode of delivery (i.e., “the how”). In the present study, we first used the IM framework to develop a theory- and evidence-based program by identifying the behavioral determinants and/or facilitators from the literature and selecting intervention methods and BCTs thought to influence such determinants (Bartholomew, Parcel, & Kok, 1998). Second, the behavioral intervention technology framework was used to specify the procedures for delivery (e.g., library of behavior change content, rules, workflow; Mohr et al., 2014).

Conceptual Framework for Intervention Development: Intervention Mapping—“The What and Conceptual How”

While there is no consensus (Prestwich et al., 2014), it is acknowledged that behavior change interventions should be grounded in theory, as they are more likely to be effective when aiming to influence determinants and/or facilitators/barriers of behavior (Baranowski, Anderson, & Carmack, 1998; Michie, Johnston, Francis, Hardeman, & Eccles, 2008; Webb, Joseph, Yardley, & Michie, 2010). The intervention mapping framework provides a systematic approach to understand the influences on the target behavior and to use theory in the selection of components to design interventions.

When choosing an appropriate theory, we considered traditional social-cognitive models (social cognitive theory [SCT]; Bandura, 1986); however, these may not be fit-for-purpose for the development of a more adaptive and interactive mHealth intervention (Riley et al., 2011). Limitations of PA behavior theories and the so-called intention–behavior gap identified in many PA intervention studies (Rhodes & de Bruijn, 2013) have led to the development of models that integrate multiple theories and predictors of behavior in an attempt to explain psychological processes that influence PA behavior. One of such is the integrated behavior change model (IBCM; Hagger & Chatzisarantis, 2014), which extends beyond deliberative/explicit intentional (i.e., theory of planned behavior; Ajzen, 1991) and motivational (i.e., self-determination theory; Ryan & Deci, 2000) processes by taking into account volitional processes (i.e., action planning; Gollwitzer & Sheeran, 2006) and the nonconscious/implicit processes of behavior (i.e., impulses; Strack & Deutsch, 2004).

The IBCM was chosen to guide the selection of theoretical constructs to influence PA. However, intervention designers are encouraged to consider all available evidence and interpret how to adequately integrate it, ensuring ample attention is paid to understanding the causes of behavior before intervention design (Moore & Evans, 2017). Each construct will have distinct intervention and psychological correlates, and the applied utility of IBCM is not established. Since (a) some of the richness (i.e., in terms of construct comprehensiveness) of theories being consolidated may be lost in translation to integrative models (Teixeira, 2016) and (b) intervention mapping permits integrating theories in a more flexible way than afforded by integrative models, constructs from other theories were also considered, such as SCT’s self-regulatory or reflective capability.

Taxonomies have been developed specifying the content of behavior change interventions in terms of BCTs (Abraham & Michie, 2008; Michie et al., 2011), and have been used in meta-regressions, linking BCTs to intervention effectiveness (Dombrowski et al., 2012; Michie, Abraham, Whittington, McAteer, & Gupta, 2009). The hierarchical classification is now available and incorporates 93 BCTs (Michie et al., 2013). This taxonomy is a comprehensive hierarchically structured set of BCTs that may be used to design interventions and specify intervention content in detail. Conversely, the intervention mapping framework has its own taxonomy, which describes behavior change methods that intervention designers can select from according to circumstances. These behavior change methods are general techniques or processes that have been shown to be able to change one or more determinants of behavior (e.g., self-efficacy) and presumably affect behavior (Kok et al., 2016).

Using the intervention mapping framework, we selected theoretical methods to target theoretical constructs (e.g., perceived social norm, intention, and competence) from the IBCM and mapped the theoretical constructs to specific BCTs in order to define intervention content. In conjunction with consulting the taxonomy of behavior change methods (Kok et al., 2016), we undertook a scoping review of the literature of BCTs and their effects on determinants of behavior (Dombrowski et al., 2012; Michie et al., 2009; Olander et al., 2013; Webb et al., 2010; Williams & French, 2011). Caution is warranted as interventions typically include combinations of BCTs (i.e., individual BCTs have not been widely tested, some BCTs are more commonly clustered than others). Nevertheless, the outlined BCTs have been identified from previous work and behavior change methods were selected from the IM taxonomy, which includes methods that have been mapped to specific determinants that they can affect (Kok et al., 2016).

The IM framework comprises six fundamental steps that guide the design, implementation, and evaluation of an intervention (Bartholomew et al., 1998). For the purpose of this study, we focused exclusively on the steps that guide the design of the intervention—Steps 1 to 3. The first step—needs assessment of the population—included a scoping review to identify the determinants of behavior and what needs to change. The second step—specification of the goal and change objectives—created a matrix of change objectives by defining the behavioral outcomes and how determinants can be affected. The third step—theory-based methods and practical strategies—linked the objectives to the determinants of behavior and identified the intervention methods and respective practical applications (Bartholomew et al., 1998; Kok et al., 2016). Importantly, IM acknowledges that parameters for a theoretical method’s effectiveness need to be met and practical applications will deem the method less effective or ineffective when not. Moreover, previous research highlighted the importance of selecting appropriate theoretical methods according to the characteristics of the target population and goals, and not to assume that methods or BCTs will be uniformly effective across conditions (Olander et al., 2013; Peters, de Bruin, & Crutzen, 2013). Step 4—production of program components—results in the design and production of the intervention materials. This is where IM was integrated with BIT in order to translate the BCTs into the “user-facing” app features.

Technological Framework for the mHealth Intervention: Behavioral Intervention Technology—“The Technical How”

BITs—behavioral intervention technologies—employ tools such as smartphones to support individuals in behavior change. This framework aims to aid the translation of the intervention components into technological features by bringing together expertise from behavioral science and developers (Mohr et al., 2014). BITs include both clinical and usage aims; clinical aims—the “why”—reflect the desired changes in the determinants of behavior and the behavior itself, while usage aims refer to engaging the user with the BIT during the intervention period. Intervention aims are realized by BCTs—the “conceptual how”—such as self-monitoring, goal setting, or review of goals. Each BCT is operationalized via specific intervention components or BIT elements—the “what”—such as user interfaces, reminders, or push notifications. A workflow (i.e., a set of rules) determines when and under which conditions each element (intervention component) is delivered to individuals over time—the “when” (Mohr et al., 2014).

Importantly, usage aims relate to clinical aims, as the usability of the technology will influence the individual’s motivation to engage with the intervention. Therefore, the operationalization of BCTs into BIT elements the individual interacts with should take into account characteristics—the “technical how”—that increase the likelihood of relevance to the individual, such as media employed (e.g., text, video), aesthetics, and personalization (Mohr et al., 2014).

The technological implementation of the framework—BIT-Tech—includes four components: (1) profiler, which defines the individual and transmits data to the intervention planner; (2) intervention planner, which chooses the relevant intervention elements and respective characteristics; (3) intervention repository, which may be a database where all the intervention elements are stored; and (4) user interface, which delivers the intervention elements. The content/elements delivered during the intervention is specified in the workflow and depends on the data captured (Mohr et al., 2014).

Results

Using the IM and BIT frameworks we describe the development process of the mHealth activity profile intervention in a systematic way.

Intervention Mapping Step 1: Needs Assessment

A needs assessment was fulfilled via a scoping literature review, which demonstrated that both low levels of PA and high SB increase individuals’ risk of cardiovascular disease (Maddison et al., 2016). Specifically, different activity profiles were associated with different degrees of cardiovascular disease risk, with the highest risk observed among those with low levels of PA and high SB. These findings illustrated the need for interventions that target both daily PA and SB together.

Current guidelines prescribe fixed PA goals that may be beyond an individual’s existing behavior and capacity (i.e., they may not attempt it or fail and get frustrated, leading to nonresponding). Individuals with low levels of PA and high SB typically meet displeasure on initial attempts to be more active and are unlikely to sustain efforts for benefits to occur (Ekkekakis, Parfitt, & Petruzzello, 2011). Moreover, current PA guidelines do not incorporate light intensity PA (LPA; e.g., standing/breaking up sitting time; Hamilton, Healy, Dunstan, Zderic, & Owen, 2008) because limited evidence is available on its benefits (Manini et al., 2015; Smith, Ekelund, & Hamer, 2015; Sparling, Howard, Dunstan, & Owen, 2015). However, individuals may be more receptive to replace SB with standing or LPA (Smith et al., 2015), which are activities typically more easily incorporated into daily life, such as walking (Ogilvie et al., 2007) and cycling (Yang, Sahlqvist, McMinn, Griffin, & Ogilvie, 2010).

Input for the IM process took into account individual and interpersonal determinants and showed that PA behavior is predicted by high levels of self-efficacy, intention, beliefs, motivation (i.e., self-realization via autonomy, competence, and relatedness), planning, social support, and cultural norms (Bauman et al., 2012). The literature also highlighted BCTs that can be used to influence these determinants. Table 1 lists examples of the literature on determinants and successful/effective strategies/BCTs.

Table 1.

Intervention Mapping Step 1: Needs Assessment Based on Literature Review: Examples.

Topic	Summary of findings	Reference
Psychosocial determinants of PA initiation and maintenance	Initiation: intention, goal setting, action planning, SE, self-monitoring	van Stralen, De Vries, Mudde, Bolman, and Lechner (2009)
Psychosocial determinants of PA initiation and maintenance	Maintenance: physical and psychological outcome realizations	van Stralen, De Vries, Mudde, Bolman, and Lechner (2009)
Self-regulation (nonclinical population)	Planning and other self-regulation techniques affected increases in PA over SE and outcome expectations	Rhodes and Pfaeffli (2010)
Self-efficacy (sedentary population)	Barrier SE mediated PA throughout intervention; relationship between task SE and PA became weaker during the intervention	Blanchard et al. (2007)
Message/intervention content delivery frequency	Too many messages and repetition may lead to tedium, increased boredom, and become burdensome. One/day, delivered in the morning.	Dennison, Morrison, Conway, and Yardley (2013); Shaw, Bosworth, Hess, et al. (2013); Shaw, Bosworth, Silva, et al. (2013)
Behavior change techniques	Summary of findings	Reference
BCTs in interventions promoting walking and cycling	Most interventions with statistically significant changes in walking and cycling included BCTs self-monitoring and intention formation	Bird et al. (2013)
BCTs linked to PA and SE (nonclinical population)	BCTs associated with increased SE and PA: action planning, provide instruction, reinforcing effort toward behavior	Williams and French (2011)
	BCTs associated with increased PA: provide information on consequences of behavior, action planning, reinforcing effort or progress toward behavior, provide instruction, facilitate social comparison, time management
	BCTs associated with increased SE: action planning, reinforcing effort/progress toward behavior, provide instructions
	BCTs associated with decreased SE and PA: relapse prevention and setting graded tasks
	BCTs associated with decreased PA: set graded tasks, use of follow-up prompts, relapse prevention
	BCTs associated with decreased SE: set graded tasks, prompt self-monitoring, prompt practice, plan social support, relapse prevention
BCTs linked to PA and SE (obese population)	BCTs associated with increased SE and PA: prompt self-monitoring and plan social support	Olander et al. (2013)
	BCTs associated with largest increases in PA: prompts/cues, prompt practice, prompt rewards contingent on progress
	BCTs associated with increased SE: action planning, time management, prompt self-monitoring and plan social support
	BCTs associated with decreased SE: set graded tasks and prompting generalization of a target behavior
Promoting PA using BCT salience of consequences to stress the harms of inactivity	Emphasizing the PA benefits has not worked well to increase PA for a lot of individuals. With smoking, the public health did not emphasize the benefits of nonsmoking, but stressed the harms of smoking. Perhaps emphasizing the harms of inactivity and not merely the benefits of exercise helps.	Wen and Wu (2012)

Note. PA = physical activity; SE = self-efficacy; BCT = behavior change technique.

Intervention Mapping Step 2: Specification of Goals and Change Objectives

The program goals were derived from the needs assessment. The overall goal of the intervention is to decrease health risks by promoting a healthier activity profile (reducing SB and/or increasing PA). The intervention aims to (1) promote breaks in SB among those who are active but are also sedentary and (2) to promote both increases in PA and decreases in SB among those who are insufficiently active and also sedentary.

The integrated behavior change model, which combines constructs from the theory of planned behavior and self-determination theory, was used to specify the performance objectives. Five performance objectives—the behavioral outcomes intended to occur on the target population—were specified. A change objective is a definition of what is needed to change on the determinant of behavior to achieve the performance objective. To specify the change objectives, the behavior determinants of a healthier activity profile that are amenable to change were identified based on the literature (Step 1). The performance objectives and the hypothesized changeable determinants were linked in the matrix of change objectives (Table 2) in order to specify the change objectives.

Table 2.

Matrix Linking the Performance Objectives to the Determinants of Behavior and Behavior Change. Examples of Specification of the Change Objectives for Individuals With Health-Detrimental Activity Profiles.

Performance objectives	Determinants
Performance objectives	TPB Attitude (beliefs)	TPB Subjective norm	TPB Perceived behavioral control (SE)	TPB Intentions	Planning	SDT Autonomy	SDT Competence	SDT Relatedness	Knowledge/awareness
Wishes to be active and reduce SB	Feels positive about a healthier activity profile	Describes significant others as supporters of a healthier activity profile	Feels confident and shows required skills to increase PA and break sitting time			Expresses why becoming more physically active is important according to own values	Identifies activities that are personally enjoyable and trust can perform	Asks for support (when needed); describes others as supporters	Lists own benefits of having a healthier activity profile; becomes aware of benefits of a healthier activity profile
Monitors own PA and SB levels	Feels positively about self-monitoring	Asks for support; feels confident to handle negative social influence	Expresses confidence in ability to self-monitor			Explores own PA and SB levels; understands the rationale for monitoring	Feels confident and explores monitoring options	Feels others accept own monitoring	Knows the PA guidelines; knows how own PA/SB relates to guidelines
Makes specific plans to have a more active activity profile	Feels positively about making plans	Expresses confidence in planning independently of social circumstances	Expresses confidence in ability to implement changes; describes when, where, and how to increase PA/break sitting			Explores available choices and aligns with aspirations/values	Makes plans that suit personal preferences; feels capable of doing plans	Feels others support own plans; feels supported on own initiatives	Knows planning is crucial
Identifies solutions for their barriers to increase PA/reduce SB	Recognizes that challenges occur; has positive feelings toward overcoming challenges	Shares and discusses challenges toward a more active activity profile	Feels confident about overcoming barriers			Explores potential sources of pressure and solutions for change	Explores and finds solutions to overcome barriers that suit own preferences	Feels others appreciate how difficult it can be; discusses issues with others	Knows the reasons preventing them to have a more active activity profile; learns about solutions to overcome barriers

Note. TPB = theory of planned behavior; SE = self-efficacy; SDT = self-determination theory; PA = physical activity; SB = sedentary behavior.

Intervention Mapping Step 3: Theory Informed Methods and Practical Applications

Theoretical methods/BCTs that either likely or previously were shown to be effective at influencing the determinants of behavior were chosen for each of the determinants targeted in the intervention (Michie et al., 2005; Michie et al., 2008). For example, the theoretical method/BCT “instruction on how to perform the behavior” can be applied to influence the determinants of behavior self-efficacy and knowledge.

Next, the selection of promising theoretical methods/BCTs and practical applications was informed by reviews of the literature, other e- and mHealth PA interventions (Abraham & Michie, 2008; Adams et al., 2013; Direito, Carraca, Rawstorn, Whittaker, & Maddison, 2017; Duncan et al., 2014; Fjeldsoe, Miller, & Marshall, 2010; King et al., 2013; Michie et al., 2008; Michie et al., 2009; Michie et al., 2013; Morrison, Yardley, Powell, & Michie, 2012), and a content analysis of existing PA apps (Direito et al., 2014). The BCTs were linked to their behavior determinants and respective practical applications. For example, practical applications of “instruction on how to perform the behavior” could include information on how to incorporate activity into one’s daily routine provided via printed materials, via a “how to section” feature in an app that the user needs to go to, or via a message that pops-up on the user’s smartphone and is not dependent of user initiation. Further examples are provided in Table 3.

Table 3.

Determinants, Theoretical Methods, Examples of Operationalization or Practical Applications, and Parameters for Effectiveness to Promote a Healthier Activity Profile.

Determinants	Theoretical methods/BCTs	Examples of operationalization/practical applications	Parameters for effectiveness
TPB—Attitude (beliefs)	Information about health consequences	Present the likelihood of CVD when individual is insufficiently active; promote the salient advantages of PA, diminish disadvantages	Requires presentation of sufficient information and time (e.g., text vs. infographic); requires assessment of current beliefs before deciding which to intervene on
	Salience of consequences	Emphasize the consequences of inactivity	Requires positive intention to avoid the risk behavior (inactivity)
	Information about social and environmental consequences	Present the decrease of CO₂ emissions if walking/cycling or taking public transport to commute	Requires positive intention; promoting participation in existing social movements may be an effective way to promote health behavior (Robinson, 2010)
	Monitoring of emotional consequences	Agree that the individual will record how they feel after an “activity streak” (i.e., meeting goals several days)	Requires an activity streak
	Pros and cons	Promote decisional balance; advise to list and compare the arguments about pros and cons of a healthier activity profile	Requires assessment of current pros and cons before deciding which to intervene on
	Credible source	Present visual communications given by a high-status professional to emphasize the importance of being active	Requires credible source of information
	Framing/reframing	Suggest that the individual might think of the suggestions as ↓SB rather than ↑PA	Requires high self-efficacy; gain frames typically perceived as more acceptable
TPB—Subjective norm	Social support	Provide information about local groups that offer support for PA behavior (e.g., walking groups); ask to take a friend with them when being active	Requires an available network and that positive support is available
	Provide information about others’ approval	Provide information on what other individuals think about the person’s behavior	Requires positive expectation
	Prompt identification of self as role model	Inform the individual that if he/she is active, that may be a good example for their significant others; prompt assessment of how own behavior may affect social context	Requires high self-efficacy and raising awareness about serving as a role model to others
	Credible source	Present visual communications given by a high-status professional to emphasize the importance of being active	Requires credible source of information
TPB—Perceived behavioral control (self-efficacy)	Persuasion about capability	Advise that a healthier activity profile can be harder at start but is achievable	Requires credible source
TPB—Perceived behavioral control (self-efficacy)	Mental rehearsal of successful performance	Advise to imagine walking in the park and enjoying the activity	Requires multiple scenarios to be presented; most effective when individuals generate their own
	Focus on past success	List the previous occasions on which the individual successfully increased their PA/reduced their SB	Requires past successes; past mastery experiences
	Self-talk	Prompt the person to tell themselves (aloud or silently) that a walk will be energizing	Requires a sense of awareness
	Goal setting	Agree on a daily time spent in PA/SB goal (based on percentile scheduling) with the individual	Requires commitment to the goal; daily updated goals based on percentile scheduling (Adams et al., 2013; Galbicka, 1994); goals should be challenging but achievable
	Problem solving	Prompt the individual to identify barriers preventing them from having a more active activity profile and present ways/possible solutions which could help overcome them; promotion of facilitating factors and diminish barriers	Requires appropriate motivation and skills; requires identification of high-risk situations
	Action planning	Prompt planning the performance of PA/break prolonged SB (context, frequency, duration); link context cues to desired behaviors	Requires preexisting positive intention
	Review behavior goals	Examine how well an individual’s performance corresponds to agreed long term behavior goals and consider modifying future behavioral goals accordingly (e.g., by increasing or decreasing PA target or changing type of behavior targeted (SB, LPA, MPA, VPA)	Requirements as for goal setting; requires commitment to the goal
	Feedback on behavior	Provide personal feedback; provide positive feedback on approximation to goals; inform the individual of how much time they spent in PA and SB each day	Requires individual feedback; requires that feedback adapts over time as behavior changes
	Self-monitoring of behavior	Encourage monitoring of own behavior; ask the individual to check daily whether they have reached their goals for PA/SB	Requires availability of data and monitoring of both behaviors (i.e., SB/PA); data must be interpreted/used
	Social support	Give information about “Auckland Walkers” or a “Meet-Up,” self-help group that offer support for the PA behavior	Requires availability of positive support in the environment; combines support for behavior with caring for individual
	Instruction on how to perform the behavior	Advise the individual how to ↑PA ↓SB	Requires communication/language that suits learner’s
	Demonstration of the behavior	Demonstrate how to break up SB at work or home	Requires instruction and enactment with individual feedback; requires splitting of behavior/sub-behaviors’ demonstration
	Prompts/cues	Prompt or cue the individual to break up SB (e.g., stand up, walk for a while, go to the bathroom) or how to increase PA	More effective when individuals choose and provide own cues
	Habit formation	Prompt the individual to leave the remote control away so that you have to stand up to switch the TV channel	Requires existing positive intention and a sense of awareness
	Habit reversal	Encouraging behaviors that can substitute undesired behaviors; ask the individual to walk up stairs where they previously always took the lift	Requires availability of alternative behaviors
	Generalization of a target behavior	Advise the individual to repeat “breaking up sedentary time” performed at work when at home	Requires individual to have appropriate motivation and skills
	Graded tasks	Ask the individual to ↑PA ↓SB according to the percentile schedule (graded tasks)	Requires that the target behavior can be reduced to sub-behaviors; daily updated goals based on percentile scheduling (Adams et al., 2013; Galbicka, 1994); goals should be challenging but achievable
	Social reward	Congratulate the individual for each day they try to reach their PA/SB goal	Requires tailored rewards and that the reward is seen as a consequence of the behavior (i.e., only if there was effort)
	Self-reward	Encourage individual to reward self with material (e.g., new clothes) or other valued objects if and only if they made an effort and/or progress in being active	Requires reward to be seen as consequence of the behavior
	Restructuring the physical environment	Advise to keep a few copy paper carton boxes to stack and build a standing desk; move the chair away from the desk	Requires real modifications in the environment/context; requires identification of barriers and facilitators
	Avoidance/reducing exposure to cues for the behavior	Suggest the individual to change daily/weekly routines (e.g., stand during phone calls, walking meetings, etc.)	Requires availability of substitute behaviors
TPB—Intentions	Information about health consequences; salience of consequences; information about social and environmental consequences; goal setting; action planning; social support; instruction on how to perform the behavior; graded tasks; credible source	Provide positive feedback on intention; ask the individual to ↑PA ↓SB according to the percentile schedule (Adams et al., 2013; Galbicka, 1994); prompt planning the performance of PA/breaking up SB (context, frequency, duration); advise the individual how to ↑PA ↓SB; present communication given by a high-status professional to emphasize the importance of being active	Requirements as per above
Planning	Goal setting; action planning; problem solving; review behavior goals; discrepancy between current behavior and goal; feedback on behavior; self-monitoring of behavior; social support; instruction on how to perform a behavior; prompts/cues; behavior substitution; habit formation; habit reversal	Prompt formation of “if-then” plan; prompt planning the performance of the behavior: “when I’m in situation x, then I will do y . . .”; point out the difference between goal and actual behavior in a tracking progress graph; ask to check whether goals were reached	Requirements as per aboveDiscrepancy between current behavior and goal/raising awareness requires increase in problem-solving skills and self-efficacyBehavior substitution requires availability of (healthier) alternative behaviors (e.g., standing instead of sitting while on phone)
SDT—Autonomy	Goal setting; problem solving; self-monitoring; action planning; feedback on behavior; information about health consequences; information about social and environmental consequences; information about emotional consequences; demonstration of the behavior; prompts/cues; behavior substitution; generalization of a target behavior; social reward	Explain why ↑PA ↓SB is important/beneficial; provide instruction using an autonomy-supportive language (i.e., using noncontrolling, informational language); provide choice (e.g., a varied list of ways to ↑PA ↓SB, in different contexts, to maintain interest); framing of messages adopting a questioning approach (rather than “should,” “must”); congratulate efforts; prompt exploration of values/aspirations	Requirements as per above
			Requires freedom of choice (goal choice, strategy choice, task involvement, promotion of task interest)
			Requires encouraging self-reflection, offering meaningful choices, and provision of a rationale for the suggested changes; avoid use of controlling communication (e.g., “should,” “must”); support initiative and experimentation for change
SDT—Competence	Goal setting; problem solving; self-monitoring; action planning; review behavior goals; feedback on behavior; instruction on how to perform a behavior; demonstration of the behavior; behavior substitution; graded tasks; monitoring of emotional consequences; social reward	Tailor goals to individual’s current ability; acknowledge sustained efforts and self-improvement; graph personal progress; promote mastery/task-related progress toward personal (proximal) goals rather than physical activity guidelines (distal); provide positive feedback	Requirements as per above
			Requires optimal challenge, and positive feedback
			Competence-supportive environments require provision of activities so that mastery is the dominant experience, gradually increasing difficulty
SDT—Relatedness	Social support (practical, emotional); identification of self as a role model	Prompt to work cooperatively in ↑PA ↓SB with a significant other/coworker; encourage to offer encouragement and feedback to each other; encourage use of their social support network (e.g., walking group)	Relatedness-supportive environments are especially important when individuals face challenges (e.g., goals not achieved, operationalize after x days without reaching goals)
Knowledge/awareness	Instruction on how to perform the behavior; information about health consequences; salience of consequences; information about social and environmental consequences; demonstration of the behavior; prompts/cues; behavior substitution; habit formation; habit reversal; self-monitoring; discrepancy between current behavior and goal; credible source; pros and cons; avoidance/reducing exposure to cues for the behavior	Provide information about PA and SB recommendation; provide information about ↑PA ↓SB benefits; provide personal feedback about PA and SB levels; encourage monitoring of own behavior; provide information about ↑PA ↓SB possibilities	Requirements as per above

Note. BCTs = behavior change techniques; TPB = theory of planned behavior; CVD = cardiovascular disease; PA = physical activity; SB = sedentary behavior; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity; SE = self-efficacy; SDT = self-determination theory.

The translation of theoretical methods/BCTs into practical applications requires that the theoretical conditions are met or else effectiveness will be undermined (Bartholomew et al., 1998). For example, the BCT “demonstration of the behavior” (i.e., “provide an observable sample of the performance of the behavior . . . for the person to aspire to or imitate”) is posited to increase self-efficacy; however, this is unlikely to occur if the conditions that must be satisfied in its practical application are unmet (e.g., when the recipient of such BCT does not identify with the role model, it is unlikely the behavior will be reinforced). In such instances, practical applications deem the theoretical methods/BCTs as less effective or even counter effective.

The intervention content aimed to encourage increases in PA duration by promoting daily life activities, such as transport to/from work, household chores/running errands, and PA at work (e.g., walking to a coworker’s desk instead of calling/emailing). Leisure-time PA, such as walking, cycling, or sports, was also promoted. To address the intention–behavior gap, postmotivational BCTs like action planning and problem solving were included to promote the required behavior changes toward a healthier activity profile.

Behavioral Intervention Technology: Translating the Conceptual How Onto the Technical How

The intervention methods and practical applications identified in the previous steps using IM were operationalized in components and materials developed in line with the BIT framework. The intervention had two main goals: (1) clinical aims were to increase PA and/or to reduce SB, while (2) usage aims were to encourage participants to carry their smartphone and engage and sustain engagement with the BIT over time. BCTs were utilized to achieve the desired behaviors. Each BCT was operationalized using different elements (i.e., messages, push notifications, graphs). The sensing and computational power of smartphones to capture real-world behavior data, process, and react to it, was harnessed to display content based on algorithms embedded in the platform (i.e., workflow). Figure 2 presents an example of how integrating both frameworks contributed to the production of the program components. Table 4 presents examples of translation and operationalization of the BCTs into BIT elements (a comprehensive list can be found in the Supplemental Material).

Table 4.

Examples of Translation and Operationalization of BCTs Targeting Behavior Determinants Into BIT Elements.

Determinant	BCT	Operationalization	Element	Workflow
Attitude (beliefs); Subjective norm; Intentions; Knowledge/awareness	Credible source	Present visual communications given by a high-status professional to emphasize the importance of being active	Message with links to videos (e.g., The importance of being active—Look for opportunities: https://www.youtube.com/watch?v=whPuRLil4c0&index=13&list=PLpQXI97WJt3VQdL8NCetKIqVT6qz3EmJi	IF Day = 2, 30
			The importance of being active—Why is it important: https://www.youtube.com/watch?v=aUaInS6HIGo
Perceived behavioral control (SE); Intentions; Planning; Autonomy; Competence	Goal setting; graded tasks	Ask the individual to ↑PA ↓SB according to the percentile schedule	Home menu/dashboard presents goals for each behavior based on a moving window of the last 7 days of data. PA goals based on the 60th percentile of the last 7 days; SB goals based on the 40th percentile (Figure 1A)	Daily
Planning; Knowledge/awareness	Discrepancy between current behavior and goal	Point out that the recorded PA fell short and/or SB exceeded the goals proposed	Message OR Push notification: “Check how you’ve done on the last days [Link to MENU “Tracking Progress”]. Looks like your activity fell short of your goals. Make them stick by checking them regularly or writing them down (Figure 1B)	IF PA < PA goal and SB > SB goal on 5 consecutive days
Perceived behavioral control; SE; Autonomy; Competence; Knowledge/awareness	Feedback on behavior	Inform the individual of how much time they spent in PA and SB each day	Menu “Yesterday’s achievements” displays degree of achievement/non-achievement and smiley face displays feedback accordingly (5 smiley faces, 25% increments of distance from PA and SB goal) (Figure 1C)	Daily based on previous day achievements
Attitude (beliefs); Intentions; Knowledge/awareness	Salience of consequences	Emphasize the consequences of inactivity	Infographics about the health detrimental effects of prolonged sitting and/or inactivity (Figure 1)	IF SB > SB goal on 6 consecutive days
Subjective norm; Planning; Relatedness	Social support	Give information about “Auckland Walkers” or a “Meet-Up,” self-help groups that offer support for the PA behavior; ask the individual to take a friend with them when being active	Message examples: (1) “Here’s a link to some walking meetups [http://walkers.meetup.com/cities/nz/auckland]. You’ll find plenty options to choose from to help you getting out and about”; (2) “Share with your friends/relatives/colleagues what you are doing. They may have ideas to increase activity or ways to breaking up sitting that you haven’t thought of.”	IF Day = 3, 16. IF Activity profile = coach potato, display walking group; if activity profile = Techno-Active display running group
Perceived behavioral control; SE; Autonomy; Competence; Knowledge/awareness	Self-monitoring of behavior; persuasion about capability	Ask the individual to check daily whether they have reached their goals for PA/SB	Push notification or message (Figure 1D)	Daily

Note. BCTs = behavior change techniques; BIT = behavioral intervention technology; PA = physical activity; SB = sedentary behavior.

Figure 1.

Screenshots of the TODAY app. (A) Menu “Today’s goals.” (B) Menu “Track your progress.” (C) Menu “Yesterday’s achievements.” (D) Push notification.

Figure 2.

An example of translation of BCTs onto technical components (i.e., BIT elements).

The intervention content was adaptive, tailoring the intervention to each individual based on continuous measurement of PA/SB. A research version of the “Movn Activity Sit Pedometer” smartphone app (Moving Analytics, 2016), named AOL, was used to track movement behaviors. Firstly, data captured by the AOL app were used to classify individuals into one of three activity profiles (i.e., couch potato, potterer, or techno-active; Maddison et al., 2016). Second, the proof-of-concept app (TODAY—TailOred Daily ActivitY) accessed movement data generated by the AOL movement tracking app through an application programming interface (API) to deliver intervention content matched to each activity profile (see Figure 3).

Figure 3.

Diagram of the data generated and flow of information from data capture (1) to display (6) on the user-facing application.

The user-facing web application for content delivery was developed using Adobe PhoneGap Framework (Adobe, 2016) and PHP scripting language. The application read information from tables stored in a MySQL database hosted on a secure server: (1) individual information (i.e., profiler), such as MVPA and SB to calculate the activity profile and daily goals of the individual, intervention start date; (2) rules/scheduled tasks (i.e., intervention planner), which chooses the relevant intervention elements based on the conditions met; and (3) a library of intervention content/elements (i.e., intervention repository), such as messages, images, and links to be displayed.

On a daily basis, the TODAY app read the activity profile, calculated the day of the intervention for each individual, and selected the appropriate content/elements to be displayed on the user interface if the app was opened. A push notification was sent daily at 10 a.m. aiming to promote user engagement with the app (i.e., usage aims).

Discussion

The intervention mapping and behavioral intervention technology frameworks were used in combination to systematically develop a theory-based mHealth activity profile intervention. Designing interventions using a systematic approach increases the likelihood of effectiveness and additionally contributes to the growing evidence on how the ingredients of interventions and their practical applications affect effectiveness. This article illustrated a systematic method to develop mHealth interventions combining conceptual and technological frameworks and contributes to future enhancements in the development of mHealth-based behavior change programs.

Designing behavior change interventions involves a complex set of decisions and ways to tackle intervention development specific for mHealth delivery mediums are scarce. We exemplified a practical application of the steps involved in a systematic method to design such interventions through the characterization of the behaviors of interest (i.e., main facilitators and barriers of PA/SB), selection of the behavioral constructs (using IBCM as a model of behavior), and application of specific techniques (i.e., intervention methods and BCTs) to bring about change using mHealth technologies (i.e., intervention planner, elements, workflow). Reporting the rationale and providing a comprehensive description of the intervention in a systematic way provides an example of designing mHealth interventions that others may find helpful. By using the intervention mapping framework, we provide a detailed intervention rationale, which will contribute to the interpretation of findings and may facilitate future replication, adaptation, and improvement. The BIT framework supported the communication between developers and behavioral scientists and aided the translation of BCTs into elements, characteristics, and workflow.

Although content delivered was individually tailored to the activity profile of the individual over time (as assessed by the smartphone, not self-reported), the degree to which content was tailored can and should be further specified by taking into account multiple factors. For example, tailoring to individual characteristics such as age, sex, health literacy, or theoretical constructs, such as self-efficacy or intention, is likely to result in higher personal relevance and contribute to effectiveness (Head, Noar, Iannarino, & Grant Harrington, 2013; Morrison et al., 2012). Importantly, IM calls attention to the existence of parameters for effectiveness of methods and that their translation into practical applications without paying enough attention to such conditions will affect their established effectiveness. Additionally, the practical application of methods will always likely be more effective when taking into account its congruency with aspects such as fit with the target population, culture, or context (Moore & Evans, 2017).

A number of considerations on the nature of this work are warranted. This is not meant to be a panacea for PA/SB interventions using mHealth technologies but instead illustrate a scientific approach to the development of mHealth PA behavior change interventions. Careful interpretation and refinement of the steps illustrated here are warranted in order to make sure that all available evidence is adequately integrated as part of intervention design. A major limitation of the example illustrated was its explorative nature. For example, we did not focus on a specific population, nor did we assess their specific beliefs, intentions, or motivations. We were experimenting the combination of the IM and BIT frameworks to appraise its fitness and usability, particularly in the translation of intervention methods/BCTs onto technological features (i.e., app features). Since interventions and behavior will always be changed in specific populations and contexts, intervention content (e.g., messages) must be specific to the target population, their beliefs, determinants, and context, so that the translation of intervention methods to practical applications are tailored and consequently more likely to be effective.

Given the explorative nature of our work, we selected determinants based on a scoping literature review. A needs assessment step fostering a user-centered approach by conducting interviews or focus groups with the target population is key. Many interventions do not work well because often we fail to identify what needs to change. Therefore, intervention designers should aim to promote a participatory research approach whereby the identification of barriers, facilitators, and desires are taken into account in the development process to ensure engagement and usability (Hingle, Nichter, Medeiros, & Grace, 2013).

Alongside a proper needs assessment (mHealth), intervention designers (and future iterations of this work) should aim to augment the quality of translation of behavior change methods into practical applications. The same theoretical method or BCT can be translated into practical applications in numerous ways depending on contextual factors (e.g., population, setting), and attending to the parameters for effectiveness can improve method sociocultural relevance. Digital technologies can be harnessed to ensure congruency between the methods’ parameters for effectiveness and both the target population and contextual characteristics in order to reach optimal content delivery (Moore & Evans, 2017). For example, theoretical methods to change an individual’s self-efficacy belief to break prolonged sitting at work may include modelling, whereby a video in a work setting could demonstrate employees doing easy/quick exercises using office furniture, or role-models’ testimonies with ways around interrupting sitting, such as how they take phone calls standing or have walking meetings. One of the parameters of effectiveness of the theoretical method modelling is that the recipient must identify with the model (e.g., age, gender, ethnicity). Therefore, translating a practical application of the method modelling harnessing digital technologies could involve showing videos of different role models according to the demographic characteristics of the recipient (e.g., video with male role model showed to male recipients only).

While mHealth interventions including effective methods/BCTs may increase their potential effectiveness in changing behavior, if the operationalization of such methods is not in line with their parameters of effectiveness, they are unlikely to contribute to behavior change. The quality of operationalization of methods/BCTs (in this exploratory study) requires improvement in order to realize their full potential. For example, for action planning to be effective, the recipient must have a preexisting intention to perform a behavior. mHealth technologies could capitalize on a profiler (i.e., what defines the individual, e.g., via sensing or self-reported assessment of intention) and workflow (i.e., a set of rules, e.g., “IF intention > x value, THEN operationalize action planning, ELSE . . .”) to determine when action planning would be presented to individuals. A different example could be the importance of the timing of provision of choices while trying to foster autonomous motivation—it should occur when relevant (e.g., when choices are available and where enactment is possible). Likewise, social support BCTs should be operationalized especially when individuals face challenges (e.g., when goals are frequently not met) in order for relatedness needs to be satisfied and potentially contribute to autonomous motivation.

BCTs do not have an ascribed effectiveness and the way they are operationalized and presented to individuals may have as great or larger impact as the BCT itself (Michie et al., 2013). Moreover, the optimal combination of BCTs for each context (i.e., what works for whom, in what settings) is unknown, as are interactions with each other (i.e., some BCTs may have synergistic effects and amplify each other, while others may undermine each other’s effects). As an example, while intervention content attempted to promote self-reflection and avoid controlling communication to promote autonomy-supportive interactions, operationalization of some BCTs, such as “discrepancy between current behavior and goal,” may be perceived by individuals as judgmental and controlling, and consequently hinder autonomous motivation.

The full capability of mHealth technologies to tailor content as behavior change occurs is yet to be realized. For example, in this explorative study, among intervention tailoring variables related to PA/SB behavior were, as observed in Figure 3, mCOV (a measure of movement) and day. The same figure highlights the potential to include many other tailoring variables, such as individual beliefs, motivation, or self-efficacy. mHealth technologies allow for repetitive longitudinal measurement and intervention content to be adapted based on input from other tailoring variables. Computational models based on dynamical systems in order to account for within-individual fluctuations of behavior (e.g., day, week, season) based on traditional behavior change theories like SCT are being developed (Riley et al., 2016). To date, research on the underlying mechanisms of the effectiveness of mHealth interventions is scarce and process data have mostly been obtained via self-report. By using the BIT framework to specify decisions on the intervention elements, characteristics, and workflow will contribute to a growing body of data on how such decisions relate to effectiveness (Mohr et al., 2014).

Conclusions

The intervention mapping and behavior intervention technology frameworks were used in a complementary manner to aid the intervention development of a theory-evidence–based mHealth intervention. The IM contributed to the identification of the determinants and optimal theoretical methods to promote behavior change, while the BIT contributed to the translation of the theoretical methods into practical applications and respective technical operationalization.

Supplemental Material

HEB742748_suppl_mat – Supplemental material for Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change

Supplemental material, HEB742748_suppl_mat for Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change by Artur Direito, Deirdre Walsh, Moohamad Hinbarji, Rami Albatal, Mark Tooley, Robyn Whittaker and Ralph Maddison in Health Education & Behavior

Footnotes

Acknowledgements

We would like to acknowledge the Movn Analytics and National Institute for Health Innovation IT teams for their support with the API and setup of online study resources.

Authors’ Note

AD and RM designed the intervention. MH, RA, and MT developed the app. AD led the writing of the article. All authors contributed and provided feedback on the manuscript, and all authors have read and approved the final manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: AD is supported by a Foundation for Science and Technology scholarship (FCT-Portugal SFRH/BD/95762/2013). FCT had no role in experimental design, data collection, or manuscript preparation.

Supplemental Material

The Supplemental Material is available with the article online at .

References

Abraham

Michie

(2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27, 379-387.

Adams

M. A.

Sallis

J. F.

Norman

G. J.

Hovell

M. F.

Hekler

E. B.

Perata

(2013). An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS One, 8(12), e82901. doi:10.1371/journal.pone.0082901

Adobe. (2016). Adobe PhoneGap. Retrieved from http://www.webcitation.org/6fdzymMwG

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. doi:10.1016/0749-5978(91)90020-t

Bandura

(1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Baranowski

Anderson

Carmack

(1998). Mediating variable framework in physical activity interventions: How are we doing? How might we do better? American Journal of Preventive Medicine, 15, 266-297. doi:10.1016/S0749-3797(98)00080-4

Bartholomew

L. K.

Parcel

G. S.

Kok

(1998). Intervention mapping: A process for developing theory and evidence-based health education programs. Health Education & Behavior, 25, 545-563. doi:10.1177/109019819802500502

Bauman

A. E.

Reis

R. S.

Sallis

J. F.

Wells

J. C.

Loos

R. J. F.

Martin

B. W.

(2012). Correlates of physical activity: Why are some people physically active and others not? Lancet, 380, 258-271. doi:10.1016/s0140-6736(12)60735-1

Bird

E. L.

Baker

Mutrie

Ogilvie

Sahlqvist

Powell

(2013). Behavior change techniques used to promote walking and cycling: A systematic review. Health Psychology, 32, 829-838. doi:10.1037/a0032078

10.

Blanchard

C. M.

Fortier

Sweet

O’Sullivan

Hogg

Reid

R. D.

Sigal

R. J.

(2007). Explaining physical activity levels from a self-efficacy perspective: The physical activity counseling trial. Annals of Behavioral Medicine, 34, 323-328. doi:10.1080/08836610701677857

11.

Broekhuizen

Kroeze

van Poppel

M. N.

Oenema

Brug

(2012). A systematic review of randomized controlled trials on the effectiveness of computer-tailored physical activity and dietary behavior promotion programs: An update. Annals of Behavioral Medicine, 44, 259-286. doi:10.1007/s12160-012-9384-3

12.

Case

M. A.

Burwick

H. A.

Volpp

K. G.

Patel

M. S.

(2015). Accuracy of smartphone applications and wearable devices for tracking physical activity data. Journal of the American Medical Association, 313, 625-626. doi:10.1001/jama.2014.17841

13.

Crutzen

(2014). The behavioral intervention technology model and intervention mapping: The best of both worlds. Journal of Medical Internet Research, 16(8), e188. doi:10.2196/jmir.3620

14.

Dennison

Morrison

Conway

Yardley

(2013). Opportunities and challenges for smartphone applications in supporting health behavior change: Qualitative study. Journal of Medical Internet Research, 15(4), e86. doi:10.2196/jmir.2583

15.

Direito

Carraca

Rawstorn

Whittaker

Maddison

(2017). mHealth technologies to influence physical activity and sedentary behaviors: Behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Annals of Behavioral Medicine, 51, 226-239. doi:10.1007/s12160-016-9846-0

16.

Direito

Dale

L. P.

Shields

Dobson

Whittaker

Maddison

(2014). Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health, 14, 646. doi:10.1186/1471-2458-14-646

17.

Dombrowski

S. U.

Sniehotta

F. F.

Avenell

Johnston

MacLennan

Araujo-Soares

(2012). Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: A systematic review. Health Psychology Review, 6(1), 7-32. doi:10.1080/17437199.2010.513298

18.

Duncan

Vandelanotte

Kolt

G. S.

Rosenkranz

R. R.

Caperchione

C. M.

George

E. S.

. . . Mummery

W. K.

(2014). Effectiveness of a web- and mobile phone-based intervention to promote physical activity and healthy eating in middle-aged males: Randomized controlled trial of the ManUp study. Journal of Medical Internet Research, 16(6), e136. doi:10.2196/jmir.3107

19.

Ekkekakis

Parfitt

Petruzzello

S. J.

(2011). The pleasure and displeasure people feel when they exercise at different intensities: Decennial update and progress towards a tripartite rationale for exercise intensity prescription. Sports Medicine, 41, 641-671. doi:10.2165/11590680-000000000-00000

20.

Fjeldsoe

B. S.

Miller

Y. D.

Marshall

A. L.

(2010). MobileMums: A randomized controlled trial of an SMS-based physical activity intervention. Annals of Behavioral Medicine, 39, 101-111. doi:10.1007/s12160-010-9170-z

21.

Galbicka

(1994). Shaping in the 21st century: Moving percentile schedules into applied settings. Journal of Applied Behavior Analysis, 27, 739-760. doi:10.1901/jaba.1994.27-739.

22.

Gollwitzer

P. M.

Sheeran

(2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69-119. doi:10.1016/S0065-2601(06)38002-1

23.

Gray

(2015). The 2015 state of the U.S. health & fitness apps economy: Analyzing the state of mobile app quality in the health & fitness and medical industries. Retrieved from http://go.applause.com/rs/539-CKP-074/images/ARC-The-State-of-the-US-Health-%26-Fitness-Apps-Economy-2015.pdf

24.

Hagger

M. S.

Chatzisarantis

N. L.

(2014). An integrated behavior change model for physical activity. Exercise and Sport Sciences Reviews, 42(2), 62-69. doi:10.1249/JES.0000000000000008

25.

Hamilton

M. T.

Healy

G. N.

Dunstan

D. W.

Zderic

T. W.

Owen

(2008). Too little exercise and too much sitting: Inactivity physiology and the need for new recommendations on sedentary behavior. Current Cardiovascular Risk Reports, 2, 292-298. doi:10.1007/s12170-008-0054-8

26.

Head

K. J.

Noar

S. M.

Iannarino

N. T.

Grant Harrington

(2013). Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Social Science & Medicine, 97, 41-48. doi:10.1016/j.socscimed.2013.08.003

27.

Hekler

E. B.

Buman

M. P.

Grieco

Rosenberger

Winter

S. J.

Haskell

King

A. C.

(2015). Validation of physical activity tracking via android smartphones compared to actigraph accelerometer: Laboratory-based and free-living validation studies. JMIR Mhealth Uhealth, 3(2), e36. doi:10.2196/mhealth.3505

28.

Hingle

Nichter

Medeiros

Grace

(2013). Texting for health: The use of participatory methods to develop healthy lifestyle messages for teens. Journal of Nutrition Education and Behavior, 45(1), 12-19.

29.

Kaplan

R. M.

Stone

A. A.

(2013). Bringing the laboratory and clinic to the community: mobile technologies for health promotion and disease prevention. Annual Review Psychology, 64, 471-498. doi:10.1146/annurev-psych-113011-143736

30.

King

A. C.

Hekler

E. B.

Grieco

L. A.

Winter

S. J.

Sheats

J. L.

Buman

M. P.

. . . Cirimele

(2013). Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS One, 8(4), e62613. doi:10.1371/journal.pone.0062613

31.

Kok

Gottlieb

N. H.

Peters

G. J.

Mullen

P. D.

Parcel

G. S.

Ruiter

R. A.

. . . Bartholomew

L. K.

(2016). A taxonomy of behaviour change methods: An intervention mapping approach. Health Psychology Review, 10, 297-312. doi:10.1080/17437199.2015.1077155

32.

Krebs

Duncan

D. T.

(2015). Health app use among us mobile phone owners: A national survey. JMIR Mhealth Uhealth, 3(4), e101. doi:10.2196/mhealth.4924

33.

Krebs

Prochaska

J. O.

Rossi

J. S.

(2010). A meta-analysis of computer-tailored interventions for health behavior change. Preventive Medicine, 51, 214-221. doi:10.1016/j.ypmed.2010.06.004

34.

Laing

B. Y.

Mangione

C. M.

Tseng

C. H.

Leng

Vaisberg

Mahida

. . . Bell

D. S.

(2014). Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial. Annals Internal Medicine, 161(10 Suppl.), S5-S12. doi:10.7326/M13-3005

35.

Maddison

Jiang

Foley

Scragg

Direito

Olds

(2016). The association between the activity profile and cardiovascular risk. Journal of Science & Medicine in Sport, 19, 605-610. doi:10.1016/j.jsams.2015.08.001

36.

Manini

T. M.

Carr

L. J.

King

A. C.

Marshall

Robinson

T. N.

Rejeski

W. J.

(2015). Interventions to reduce sedentary behavior. Medicine and Science in Sports and Exercise, 47, 1306-1310. doi:10.1249/MSS.0000000000000519

37.

Michie

Abraham

Whittington

McAteer

Gupta

(2009). Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychology, 28, 690-701. doi:10.1037/a0016136

38.

Michie

Ashford

Sniehotta

F. F.

Dombrowski

S. U.

Bishop

French

D. P.

(2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychology & Health, 26, 1479-1498. doi:10.1080/08870446.2010.540664

39.

Michie

Johnston

Abraham

Lawton

Parker

Walker

(2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. Quality & Safety in Health Care, 14(1), 26-33. doi:10.1136/qshc.2004.011155

40.

Michie

Johnston

Francis

Hardeman

Eccles

(2008). From theory to intervention: Mapping theoretically derived behavioural determinants to behaviour change techniques. Applied Psychology—An International Review (Psychologie Appliquee—Revue Internationale), 57, 660-680. doi:10.1111/j.1464-0597.2008.00341.x

41.

Michie

Richardson

Johnston

Abraham

Francis

Hardeman

. . . Wood

C. E.

(2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46, 81-95. doi:10.1007/s12160-013-9486-6

42.

Mohr

D. C.

Schueller

S. M.

Montague

Burns

M. N.

Rashidi

(2014). The behavioral intervention technology model: An integrated conceptual and technological framework for eHealth and mHealth interventions. Journal of Medical Internet Research, 16(6), e146. doi:10.2196/jmir.3077

43.

Moore

G. F.

Evans

R. E.

(2017). What theory, for whom and in which context? Reflections on the application of theory in the development and evaluation of complex population health interventions. SSM—Population Health, 3, 132-135. doi:10.1016/j.ssmph.2016.12.005

44.

Morrison

L. G.

Yardley

Powell

Michie

(2012). What design features are used in effective e-health interventions? A review using techniques from critical interpretive synthesis. Telemedicine and e-Health, 18, 137-144. doi:10.1089/tmj.2011.0062

45.

Moving Analytics. (2016). Movn activity sit pedometer. Retrieved from http://www.webcitation.org/6fdyiQ9Xa

46.

The Nielsen Company. (2014). The digital consumer. Retrieved from https://www.nielsen.com/content/dam/corporate/us/en/reports-downloads/2014%20Reports/the-digital-consumer-report-feb-2014.pdf

47.

Norman

G. J.

Zabinski

M. F.

Adams

M. A.

Rosenberg

D. E.

Yaroch

A. L.

Atienza

A. A.

(2007). A review of eHealth interventions for physical activity and dietary behavior change. American Journal of Preventive Medicine, 33, 336-345. doi:10.1016/j.amepre.2007.05.007

48.

Ogilvie

Foster

C. E.

Rothnie

Cavill

Hamilton

Fitzsimons

C. F.

(2007). Interventions to promote walking: Systematic review. British Medical Journal, 334(7605), 1204. doi:10.1136/bmj.39198.722720.BE

49.

Olander

E. K.

Fletcher

Williams

Atkinson

Turner

French

D. P.

(2013). What are the most effective techniques in changing obese individuals’ physical activity self-efficacy and behaviour: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 29. doi:10.1186/1479-5868-10-29

50.

Peters

G. J.

de Bruin

Crutzen

(2013). Everything should be as simple as possible, but no simpler: Towards a protocol for accumulating evidence regarding the active content of health behaviour change interventions. Health Psychology Review, 9(1), 1-14. doi:10.1080/17437199.2013.848409

51.

Pew Research Center. (2015). The smartphone difference. Retrieved from http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

52.

Prestwich

Sniehotta

F. F.

Whittington

Dombrowski

S. U.

Rogers

Michie

(2014). Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Health Psychology, 33, 465-474. doi:10.1037/a0032853

53.

Rhodes

R. E.

de Bruijn

G. J.

(2013). How big is the physical activity intention-behaviour gap? A meta-analysis using the action control framework. British Journal of Health Psychology, 18, 296-309. doi:10.1111/bjhp.12032

54.

Rhodes

R. E.

Pfaeffli

L. A.

(2010). Mediators of physical activity behaviour change among adult non-clinical populations: A review update. International Journal of Behavioral Nutrition and Physical Activity, 7, 37. doi:10.1186/1479-5868-7-37

55.

Riley

W. T.

Martin

C. A.

Rivera

D. E.

Hekler

E. B.

Adams

M. A.

Buman

M. P.

. . . King

A. C.

(2016). Development of a dynamic computational model of social cognitive theory. Translational Behavioral Medicine, 6, 483-495. doi:10.1007/s13142-015-0356-6

56.

Riley

W. T.

Rivera

D. E.

Atienza

A. A.

Nilsen

Allison

S. M.

Mermelstein

(2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Translational Behavioral Medicine, 1(1), 53-71. doi:10.1007/s13142-011-0021-7

57.

Robinson

T. N.

(2010). Save the world, prevent obesity: Piggybacking on existing social and ideological movements. Obesity (Silver Spring), 18(Suppl. 1), S17-S22. doi:10.1038/oby.2009.427

58.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68-78.

59.

Shaw

R. J.

Bosworth

H. B.

Hess

J. C.

Silva

S. G.

Lipkus

I. M.

Davis

L. L.

Johnson

C. M.

(2013). Development of a theoretically driven mHealth text messaging application for sustaining recent weight loss. JMIR Mhealth Uhealth, 1(1), e5. doi:10.2196/mhealth.2343

60.

Shaw

R. J.

Bosworth

H. B.

Silva

S. S.

Lipkus

I. M.

Davis

L. L.

Sha

R. S.

Johnson

C. M.

(2013). Mobile health messages help sustain recent weight loss. American Journal of Medicine, 126, 1002-1009. doi:10.1016/j.amjmed.2013.07.001

61.

Smith

Ekelund

Hamer

(2015). The potential yield of non-exercise physical activity energy expenditure in public health. Sports Medicine, 45, 449-452. doi:10.1007/s40279-015-0310-2

62.

Sparling

P. B.

Howard

B. J.

Dunstan

D. W.

Owen

(2015). Recommendations for physical activity in older adults. British Medical Journal, 350, h100. doi:10.1136/bmj.h100

63.

Spruijt-Metz

Hekler

Saranummi

Intille

Korhonen

Nilsen

. . . Pavel

(2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational Behavioral Medicine, 5, 335-346. doi:10.1007/s13142-015-0324-1

64.

Spruijt-Metz

Wen

C. K.

O’Reilly

Lee

Emken

B. A.

. . . Narayanan

(2015). Innovations in the use of interactive technology to support weight management. Current Obesity Reports, 4, 510-519. doi:10.1007/s13679-015-0183-6

65.

Strack

Deutsch

(2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220-247. doi:10.1207/s15327957pspr0803_1

66.

Teixeira

P. J.

(2016). Health behavior change: A field just picking up speed. A comment on Ogden (2016). Health Psychology Review, 10, 269-273. doi:10.1080/17437199.2016.1183507

67.

van Stralen

M. M.

De Vries

Mudde

A. N.

Bolman

Lechner

(2009). Determinants of initiation and maintenance of physical activity among older adults: A literature review. Health Psychology Review, 3, 147-207. doi:10.1080/17437190903229462

68.

Webb

T. L.

Joseph

Yardley

Michie

(2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4. doi:10.2196/jmir.1376

69.

Wen

C. P.

(2012). Stressing harms of physical inactivity to promote exercise. Lancet, 380, 192-193.

70.

Williams

S. L.

French

D. P.

(2011). What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same? Health Education Research, 26, 308-322. doi:10.1093/her/cyr005

71.

Yang

Sahlqvist

McMinn

Griffin

S. J.

Ogilvie

(2010). Interventions to promote cycling: Systematic review. British Medical Journal, 341, c5293. doi:10.1136/bmj.c5293

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