This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Information technology can help individuals to change their health behaviors. This is due to its potential for dynamic and unbiased information processing enabling users to monitor their own progress and be informed about risks and opportunities specific to evolving contexts and motivations. However, in many behavior change interventions, information technology is underused by treating it as a passive medium focused on efficient transmission of information and a positive user experience.
To conduct an interdisciplinary literature review to determine the extent to which the active technological capabilities of dynamic and adaptive information processing are being applied in behavior change interventions and to identify their role in these interventions.
We defined key categories of active technology such as semantic information processing, pattern recognition, and adaptation. We conducted the literature search using keywords derived from the categories and included studies that indicated a significant role for an active technology in health-related behavior change. In the data extraction, we looked specifically for the following technology roles: (1) dynamic adaptive tailoring of messages depending on context, (2) interactive education, (3) support for client self-monitoring of behavior change progress, and (4) novel ways in which interventions are grounded in behavior change theories using active technology.
The search returned 228 potentially relevant articles, of which 41 satisfied the inclusion criteria. We found that significant research was focused on dialog systems, embodied conversational agents, and activity recognition. The most covered health topic was physical activity. The majority of the studies were early-stage research. Only 6 were randomized controlled trials, of which 4 were positive for behavior change and 5 were positive for acceptability. Empathy and relational behavior were significant research themes in dialog systems for behavior change, with many pilot studies showing a preference for those features. We found few studies that focused on interactive education (3 studies) and self-monitoring (2 studies). Some recent research is emerging in dynamic tailoring (15 studies) and theoretically grounded ontologies for automated semantic processing (4 studies).
The potential capabilities and risks of active assistance technologies are not being fully explored in most current behavior change research. Designers of health behavior interventions need to consider the relevant informatics methods and algorithms more fully. There is also a need to analyze the possibilities that can result from interaction between different technology components. This requires deep interdisciplinary collaboration, for example, between health psychology, computer science, health informatics, cognitive science, and educational methodology.
Prevention, early intervention, and self-care are priorities for most health care systems around the world. Policy makers cannot, however, address these priorities solely through conventional clinical means. This is because citizens must make sustained health behavior changes, which are largely beyond the reach of the clinic [
Information technology has the potential to support behavior change [
Furthermore, many interventional studies do not identify which
In this paper we aim to review research on the
We define active assistance technology as any technology involving
In this way, the concept of active assistance draws attention to the distinction between semantic and nonsemantic information processing during an interactive session. This is important, because semantic processing entails a degree of delegation of health decision making to an automated system, which can free up human specialists. It also has more serious consequences if incorrect.
Furthermore, active assistance takes place in an environment in which citizens and experts participate actively in the behavior change intervention (combining push and pull). In other words, the role of the technology is not merely to deliver a fully expert-led intervention where users follow instructions with minimal understanding. Instead, the technology helps users to reflect and learn about the obstacles to successful behavior change. A desirable feature is that users should feel they have ownership or control of the intervention [
A concept related to active assistance is persuasive technology, which is “designed to change attitudes or behaviors of users through persuasion and social influence” [
In the context of these requirements, the following are key examples of active assistance technology that can support behavior change. The technologies may be used together or independently.
An example of this is that an interactive system might use knowledge-based reasoning about the user’s health and circumstances to determine how its responses should be tailored further to the particular individual. Since this process is dynamic, there is more potential for delivery of messages that are tailored to the user’s current environment and state of motivation than would be the case for static tailoring. Similarly, health education can involve answering specific health-related questions, on demand, using inference about what-if scenarios that the user wants to know about (nor necessarily personalized). Examples might include mobile or Web interfaces with dynamic personalization, intelligent reminding, natural-language dialog, or health-related games with an automated player.
Automated sensing can overcome the limits of self-reported online diaries, which depend on memory. Recognition of patterns in online interactions, physical activities, or physiological states can provide useful self-monitoring information for users who are attempting to change their behavior, provided that it is displayed in a user-friendly way. This goes beyond the capability of automated reasoning because the system can acquire data and recognize events automatically without manual data input.
Adaptation occurs in response to emerging patterns and contexts. This goes beyond automated reasoning and automated data collection because the system adapts its methods and decisions according to patterns that it has recognized. For example, an interactive system might learn to predict a user’s state of motivation based on his or her responses to prompts (without any additional sensing).
In each case, the algorithms need to be informed by health-related knowledge, either explicitly as a formal representation or implicitly in the form of assumptions built into their design.
Some previous literature reviews have addressed topics related to active assistance.
Webb et al [
Fry and Neff [
Lustria et al [
Bickmore and Giorgino [
For the purpose of building personalized health models, Fernandez-Luque et al [
There have been no comprehensive reviews of active assistance technologies in health-related behavior change.
We conducted the literature review in accordance with the guidelines of the PRISMA statement [
We used the following strategy: <technology-related keywords> AND <health psychology-related keywords> AND health AND “behavior change”.
Technology- and psychology-related keywords were combinations of the following: [automated OR technology OR Internet OR “mobile phone” OR intelligent OR computer-based OR interactive OR agent-based OR adaptive OR “context-aware” OR “machine learning” OR “pattern recognition” OR robotic OR “virtual reality” OR semantic OR “knowledge-based” OR “decision support” OR ontology OR dialog OR “natural language”] AND [assistance OR intervention OR personalization OR persuasion OR adherence OR compliance OR motivation OR affective OR emotion OR reminder OR prompt].
We conducted some preliminary searches on a wide range of databases including CINAHL, EMBASE, Inspec, ISI Web of Science, PsycINFO, and ScienceDirect. However, we found that Google Scholar had a wide enough coverage to allow it be used instead of these databases. This is consistent with recent empirical studies such as those of Howland et al [
We used the definition of active assistance above. The studies should have included at least one of the three active assistance technologies listed and have been intended for interactive use by clients or patients attempting to change their behavior. Studies may have described a new technology or design to be used in behavior change interventions, or they may have reported an evaluation of an actual intervention using the technology. Qualitative studies are necessary to evaluate usability and acceptability of active technology. Similarly, prototypes and works in progress help to provide an overview of the current research concepts and their maturity.
We excluded the following kinds of study: (1) interventions merely delivered using the Internet, CD-ROM, or other medium, where the technology only facilitates transmission of information from expert to user, (2) feedback, in which there is no automated processing of personal health-related information—for example, receiving emails from a human counselor would be excluded, (3) tailored-offline interventions, in which the computer processing is used by health professionals to tailor the intervention before or after the user interacts with the technology—this is the case where the semantic content processing is not part of the interactivity with the client (although a health professional may have interactive access), (4) simple data collection or preprogrammed reminding without any pattern recognition or inference (eg, pedometers); interventions where the semantic content of reminder messages were configured by the client were also excluded for this reason—for example, the planning tool of Soureti et al [
We divided studies into the following categories: (1) quantitative and qualitative evaluations of interventions using active technology, (2) pilot studies of new technology, and (3) prototypes or designs that are being developed or tested.
For each study, we asked the following questions. What kind of active technology was used? Was it effective? What was the role of the technology in the intervention? Was it theoretically grounded?
We looked for one or more of the following types of automated content processing, based on capabilities of the active assistance technologies outlined above: (1) automated data collection with pattern recognition, (2) context-sensitive alerts, reminders, and recommendations, (3) knowledge-based reasoning or inference (semantic representation, ontology, decision support, decision algorithm, and automated planning), (4) dialog systems with natural-language processing, (5) simulation or game with an intelligent agent, and (6) online adaptation to build user models and personalization (adaptive websites or interfaces, and user profiling).
In addition to our predefined categories, we identified new technology themes and author keywords describing the technology.
For those studies with evaluations of effectiveness, we asked the following questions. First, what was being evaluated? This could be acceptability or usability (self-reported positive or negative attitude); treatment adherence or technology engagement (observed); self-reported behavior change; or objectively measured behavior change (eg, step counts). Second, what method of evaluation was used (eg, randomized controlled trial [RCT] or qualitative study)? Third, were findings summarized, to give an indication of the maturity of the technology, and any advantages or new problems that it introduces?
It is important to understand the role of the active technology in the intervention.
We used the following three functions (defined above): (1) dynamic tailoring, (2) interactive education to support participations in their own care and disease prevention, and (3) support for self-monitoring in a way that overcomes biases of self-report.
In particular, we looked for an association of an active technology type with a purpose. For example, pattern recognition and context awareness may be used to support dynamic tailoring. Similarly, for unbiased self-monitoring, the technology needs to provide automated data collection, pattern recognition, and representation of the results in a visual format that can be easily understood.
We included here any behavior change theories mentioned by the authors as having a role in the technology design. In addition, we asked whether the study proposed any novel ways of connecting active technology with behavior change theories, and whether the active technology allows new possibilities that would not be available with static technology.
Following a review of title and abstracts, the search identified 228 potentially relevant articles. Of these, 41 satisfied the inclusion criteria after a full-text review.
In
For example, a study with objective measures over the long term, but not showing a significant effect, would be summarized as 5: +/–. We used the same summarized notation if some measures were positive and others negative or insignificant. Details are in the findings column.
Technology themes, study types, and main findings.
Reference and |
Health topic / study population | Technology themes | Type of study | Main findings | Evidence |
Ananthanarayan & Siek 2010 [ |
Obesity / children | Wearable computing, “6th sense;” actionable feedback. | Design of prototype to support children’s motivation for exercise and for self-monitoring. | Not an empirical study. | Not applicable |
Arteaga et al 2009 [ |
Obesity / teenagers | Motivational agent (mobile phone games). | Design of prototype to motivate exercise based on personality type. | Not an empirical study. | Not applicable |
Bickmore & Picard 2005 [ |
Physical activity / healthy adults | ECAb: relational agent. | RCTc (n = 101; 30 days). Measures: acceptability (self-report) + PAd (pedometer). Groups: relational agent, nonrelational agent and control. | Positive acceptance; increased PA during intervention but reduced PA after follow-up. Relational agent more liked. Dialog repetitiveness annoying. | 4: +/– |
Bickmore et al 2005 [ |
PA / older adults | ECA: relational agent. | RCT (n = 21; 2 months), to test acceptability (usage history) + PA (pedometer) + loneliness (self-report). Groups: relational vs control (usual care). | Positive acceptance and significant increase in PA during intervention. No significant decrease in loneliness. | 4: +/– |
Bickmore & Sidner 2006 [ |
General behavior change / adults | Making dialog more robust by linking with ontologies for behavior change theories (TTMe, MIf). | Prototype. | Not an empirical study. | Not applicable |
Bickmore et al 2009 [ |
Physical activity / adults (male students) | Context awareness of mobile PA monitor + ECA (relational agent). | Pilot study (n = 8): test whether agent context awareness promotes social bonding (acceptance). Effectiveness: does it promote walking? | Some positive acceptance but less actual walking in context-aware condition. | 1: +/– |
Bickmore et al 2009 [ |
Compliance / low health-literacy patients (hospital discharge) | ECA: virtual nurse with relational behaviors and empathy. | Self-report usability tests: 2 tests: nonpatients (n = 30) + patients (n = 19) with 47% low literacy. Both groups tested with relational vs nonrelational agent. | Both tests: relational preferred. Overall ECA acceptance. ECA allows more time and sense of control than human face-to-face communication. | 1: + |
Bickmore et al 2010 [ |
Medication adherence, PA / schizophrenia patients | ECA: simple concrete communication. Authors counter ethical criticism of ECA for mental health. | Pilot evaluation (n = 20; 31 days) to test acceptability (self-report) + adherence + PA (no control). | Positive acceptance. Adherence + PA high. ECA provides simplified conversation, less confusing than human face-to-face. | 1: + |
Bickmore et al 2011 [ |
2 domains: exercise and diet / adults | Semantic ontology for behaviors and theories (TTM, MI); semantic models of user, data, and intervention. | Qualitative study (n = 8) on acceptability of ECA health counselor based on reusable ontology. | Positive acceptance, but limited evaluation. | 1: + |
Bickmore et al 2010 [ |
Physical activity / adults | ECA: promoting long-term use; avoid repetitive dialog. Introduce variability + storytelling. | 2 RCTs: 1. Variability (n = 24, 100 days); variable vs nonvariable; 2. Story(n = 26, 30 days): first-person story vs third-person story. Measures: usage + step count + self-reported satisfaction. | 1. Variability: more system usage, but less exercise. 2. Story: first person had more usage than third person, but less exercise. Self-reported satisfaction high for test conditions. | 4: +/– |
Bieber et al 2009 [ |
Physical activity / adults | Mobile phone as sensor for activities and calorie estimate. | Prototype. | Not an empirical study. | Not applicable |
Buttussi & Chittaro 2008 [ |
Physical activity / adults | ECA; context-aware sensing; user model. | Prototype. | Initial qualitative evaluation positive (n = 12). | 1: + |
Consolvo et al 2008 [ |
Physical activity / adults | Graphic display with “garden” metaphor; mobile sensing device with inference; interactive app (edit or add to journal). | RCT: 3-month field experiment (n = 28): full system (10) vs no mobile sensing device (9) vs no display (9). Measures: (1) sensed activities + self-report; (2) qualitative analysis on user experience. | System with display led to more exercise than without display. User experience positive: more self-awareness, which motivated exercise. | 4: + |
De Rosis et al 2006 [ |
Diet / adults | ECA: recognize user’s emotional state, social attitude, and TTM stage during dialog; dynamically update user model during dialog. | Prototype: raters label emotional states, TTM stages, and social attitudes in test dialogs (WOZg and corpus). | Labeling of emotions by raters used to guide design of dialog system. | Not an intervention evaluation |
Farzanfar et al 2007 [ |
Treatment adherence, suicide prevention / depressed adults | Telephone agent: monitoring + self-care management. | Preliminary qualitative trial (n = 15), 4 weeks. Modifications made in response. | Dialog was helpful for adherence, but sounded artificial and insensitive, particularly in suicide risk. Users prefer more human-like agent with empathy and understanding of serious issues. (For suicide, hotline preferred). Authors’ conclusion: anthropomorphism is not valid (people do not attribute human qualities to machines—only in metaphor). | 1: +/– |
Hakulinen et al 2008 [ |
PA / adults | Mobile companion; semantic ontology of user environment for PA planning. | Prototype. | Not an empirical study. | Not applicable |
Hartmann et al 2007 [ |
Improve patient questions to physician / adults with asthma | Educational website to suggest questions, encourage patient involvement in care, prevent more serious illness. | Pilot study: (n = 37) record usage experiences. | Positive self-report: (1) improved relations, (2) more active involvement. | 1: + |
Hayes et al 2009 [ |
Medication adherence / older adults | Instrumented pillbox, home sensors. | Pilot study (n = 10): effectiveness of context awareness on adherence. Test phases (same group): no-prompt, time-based, context-aware prompt. | Initial evaluation: positive for context-aware phase. | 1: + |
Jin 2010 [ |
Stress management / college students | Education-entertainment / health belief, self-efficacy; educational interactive test (game) for responses to stress scenarios. Agent gives educational messages. | RCT: (n = 60). Effectiveness of virtual agent on student’s intent/mood. Interactive test with agent (test) vs no agent (control) vs no test (true control). | Positive self-report on enjoyment and educational value for agent condition. Interactive test improves stress management self-efficacy (over true control, without test). | 2: + |
Kaipainen et al 2011 [ |
General health decisions / adults | Health Personal Guidance System: guide user through day-to-day choices in ecosystem. Virtual individual: maintains user profile and context; HealthGuide: planning, context-aware messages. Personal Guidance System Mall: services all in one place. | Prototype. | Not an empirical study. | Not applicable |
Klein et al 2011 [ |
Adherence / diabetic patients | Automated reasoning based on COMBIh model (combines different theories) ensures dynamic tailored messages depending on user’s context and state of mind. | Prototype: computational model of behavior change (mobile + website). | Not an empirical study. | Not applicable |
Konovalov et al 2010 [ |
Mental health promotion / military service personnel | Blog analysis to understand moods and emotions (combat experience): GATE algorithm + ontology. | Design and pilot study for technology: compare algorithm with expert opinion. | Precision of algorithm: 0.9, recall: 0.75; |
Not an intervention evaluation. |
Lee et al 2010 [ |
Health promotion / older adults | Telehealth: action-based behavior model (1) increase user’s awareness of health, (2) set goals, (3) educate user in how to achieve goal, (4) remind, (5) reward + assess. | Design: overcome limits of sensing only; need high-level assessment information with models of persuasion to determine whether behavior changed. | Not an empirical study. | Not applicable |
Levin & Levin 2006 [ |
Pain management / adults | Ecological momentary assessment, detect unexpected errors in dialog. | Feasibility study: evaluate interactive voice response system dialog for health and behavior monitoring. Feasibility study for pain monitoring voice diary (n = 24). 171 dialog sessions. | Accuracy of voice recognition: 98%. Dialog efficiency increased with user experience. | 1: + |
Lisetti & Wagner 2008 [ |
Mental health promotion / adolescent | ECA companions. | Design: ECA companion to act as MI counselor. | Not an empirical study. | Not applicable |
Looije et al 2010 [ |
Adherence / older adults | ECA (robot cat), MI, persuasion. | Pilot study (n = 24): physical ECA (n = 12) vs virtual (n = 12). Each group experienced text, social ECA, and nonsocial ECA. | 90% acceptance. Social ECA preferred over nonsocial ECA; half preferred text interface over social ECA (“conscientious” personality type). Virtual ECA more “empathic” than physical. | 1: + |
Maier et al 2010 [ |
Work-related disorders and alcohol / adults | Semantic Web portal; semantic search. | Prototype. | Not an empirical study. | Not applicable |
Mazzotta et al 2007 [ |
Healthy eating / adults | Persuasion agent: tailoring of messages based on inferred personality traits and likely motivations of user. | Prototype of dialog design, based on corpus analysis of persuasive dialogs produced by participants in role-playing scenarios. | Corpus analysis found that persuasion is most often based on nonrational arguments and positive framing. | Not an intervention evaluation |
Munguia Tapia 2008 [ |
Obesity / adults | Sensors and algorithms for activity recognition and energy expenditure estimate. | Prototype. | Activity recognition most accurate if simple examples are given; high variability is difficult (eg, housework). Energy estimate more accurate for simple activities and with multiple body sensors. | Not an intervention evaluation |
Nguyen & Masthoff 2008 [ |
General behavior change / adults | Persuasive dialog, MI. | Acceptability test (n = 41): is MI dialog more persuasive than argumentation? Questionnaire + qualitative analysis in comments. | Self-report positive: persuasiveness, likeability scores higher for MI than for 2 types of argumentation. | 1: + |
Oddsson et al 2009 [ |
Adherence / adults | Robotic assistance for intelligent reminding and companionship. | Design. | Not an empirical study. | Not applicable |
Op den Akker et al 2011 [ |
PA / adults | Software agent for smart phone: use machine learning to develop user model. Tailor messages to user history and current context. | Prototype. | Not an empirical study. | Not applicable |
Rojas-Barahona & Giorgino 2009 [ |
General behavior change / adults | Framework for health dialog. | Design. | Not an empirical study. | Not applicable |
Smith et al 2008 [ |
Healthy lifestyle / adults | ECA; collaborative planning, update planned activities through ongoing dialog. | Prototype with technical evaluation. | Approach is feasible, although dialog error rate is still high. | Not applicable |
Sorbi et al 2007 [ |
Migraine attack prevention / adults | PDAi + coaching. Response behaviors to precursors of migraine. Ecological momentary intervention experience sampling: randomized calls overcome memory bias. Tailored messages depending on current experience. | Pilot study (n = 5): feasibility and user acceptance. | Positive acceptance but too many calls are annoying. Technical problems: data loss due to buildings. | 1: +/– |
Spring et al 2010 [ |
Obesity / adults | PDA: find optimal advice for exercise; goal thermometers; “in the moment” decision support/multiple theories, including self-regulation.Study design. | Study design. | Design of a trial only. | Not applicable |
Tiwari et al 2011 [ |
Adherence / older adults | Robotic assistance, dialog. | Prototype development using grounded theory participatory design. | Emerging themes: usability, empowerment, collaboration, and safety: used as requirements for dialog design. | Not applicable |
Turunen et al 2011 [ |
Health and fitness / adults | Home and mobile health and fitness companion. | Pilot study (n = 20): feasibility of complex dialogues in home and mobile scenarios. | System behaves robustly in realistic experimental scenarios, but word error rates are still high. | 1 + |
Uribe et al 2011 [ |
Adherence general | Reminders based on inferred mental state; user modeling using ontologies. | Prototype. | Not an empirical study. | Not applicable |
van der Putten et al 2011 [ |
PA / older adults | Social robot; health advisor. | Pilot study(n = 6). Video recording of interactions in homes. Iteratively modify setup based on results of previous session. | 3 iterations, variable interactions, and satisfaction reports. Positive for motivation but some frustration over lack of control of dialog and too much time taken up. | 1: +/– |
Watson et al 2012 [ |
PA / overweight adults | ECA: virtual coach | RCT (n = 70; 12 weeks); primary measure: step count; secondary: weight + self-reported satisfaction, self-efficacy, PA recall, and PA stage of change. Groups: virtual coach vs control (no coach: website + pedometer only). | Average step count for intervention group remained constant over 12 weeks while control group dropped. Repeated measures analysis of variance showed significant difference in step count change between intervention and control. No significant difference in secondary measures; acceptance mixed. | 4: +/– |
a <weight of study>: <effect>: weight of study was scored as 5 (randomized controlled trial [RCT] with at least one objective measure, long-term), 4 (RCT with at least one objective measure, short-term), 3 (RCT with self-report only, long-term), 2 (RCT with self-report only, short-term), or 1 (qualitative or pilot study). Effect was scored as + (positive), (negative), or +/– (mixed or insignificant).
b Embodied conversational agent.
c Randomized controlled trial.
d Physical activity.
e Transtheoretical model.
f Motivational interviewing.
g Wizard of Oz study, where humans pretend to be dialog agents to understand the likely responses to an automated agent.
h Computerized behavior intervention.
i Personal digital assistant.
Active technology role and theoretical grounding.
Reference and |
Active technology type | Dynamic tailoring | Interactive education | Self-monitoring | Theoretical grounding |
Ananthanarayan & Siek 2010 [ |
Inference; pattern recognition | Not specified | Yes, but details not given | Yes; provide awareness of physical activity | General awareness only; no specific theory mentioned |
Arteaga et al 2009 [ |
Dialog; pattern recognition | Not specified. Static tailoring only | No | Not specified | Big 5 personality theory; technology acceptance model; theory of planned behavior, theory of meaning behavior |
Bickmore & Picard 2005 [ |
Dialog | Not specified | No; passive educational content only | Very basic, pedometer steps only | Relational agents |
Bickmore et al 2005 [ |
Dialog | Not specified | No; passive educational content only | Progress charts only | Relational agents |
Bickmore & Sidner 2006 [ |
Inference; dialog | Not specified, but possible | No | Progress charts only | TTMa, MIb: link with agent reasoning and ontology |
Bickmore et al 2009 [ |
Pattern recognition | Not specified in detail | No | No | Relational agents |
Bickmore et al 2009 [ |
Dialog | Not specified in detail, only mentioned as a property of dialog systems in general | Yes, support low health-literacy patients | No | Relational agents |
Bickmore et al 2010 [ |
Dialog | Not specified in detail | No | Not considered usable by schizophrenia patients | Relational agents |
Bickmore et al 2011 [ |
Inference; dialog; user models | Not specified; fixed tailoring only mentioned | No, but mentioned in a generic way | No | TTM, MI encoded in ontology for agent reasoning and user model |
Bickmore et al 2010 [ |
Dialog | Not specified | No | Charts only | Relational agents |
Bieber et al 2009 [ |
Physical activity recognition, mobile phones | Not specified | No | No | Not mentioned |
Buttussi & Chittaro 2008 [ |
Pattern recognition; adaptation; user model | Yes, due to context awareness | No | Not mentioned, but possible | Not mentioned |
Consolvo et al 2008 [ |
Activity recognition; inference | Not specified | No | Yes; visual display | No |
De Rosis et al 2006 [ |
Dialog; user modeling, adaptation | Yes, due to adaptation | No | No | TTM |
Farzanfar et al 2007 [ |
Dialog; pillbox sensors + adherence data analysis—linked to dialog system | Not specified, although possible | Yes, telephone instructions but limited interactivity | No | Self-efficacy theory, MI |
Hakulinen et al 2008 [ |
Dialog; automated planning; knowledge-based inference | Not specified, although possible | No | No | Not mentioned |
Hartmann et al 2007 [ |
Inference: evidence-based decision rules | Not specified in detail, but possible | Yes, but limited | No | No |
Hayes et al 2009 [ |
Context-aware reminders; activity recognition; rule-based inference | Yes, decision to prompt based on recognized activity pattern | No | Not mentioned, but possible to include | Not mentioned |
Jin 2010 [ |
Virtual agent in game | Not specified | Yes, education-entertainment | No | Health belief model, self-efficacy |
Kaipainen et al 2011 [ |
Context awareness, pattern recognition, inference, planning, user modeling | Yes, messages tailored to changing context of user | Not a main focus | Not mentioned, but possible to include | Hybrid approach including self-efficacy and social influence |
Klein et al 2011 [ |
Knowledge-based reasoning; user models | Yes, automated reasoning based on COMBIc model ensures dynamic tailored messages depending on user’s context and state of mind | No | No | COMBI model includes aspects of TTM, health belief model, social cognitive theory, self-regulation theories, attitude formation theory, and relapse prevention model; interaction based on MI |
Konovalov et al 2010 [ |
Pattern recognition; inference | No, but could be used in an intervention with dynamic tailoring | No | No | No |
Lee et al 2010 [ |
Pattern recognition; user modeling (profiling), including mental states | Not specified in detail, but planned | Not specified, but planned | Not specified, but planned | Action-based behavior model: (1) increase user’s awareness of health; (2) set goals; (3) educate user in how to achieve goal; (4) remind; (5) reward + assess |
Levin & Levin 2006 [ |
Voice recognition; semantic representation; dialog | Not specified, but personalization of dialog possible | No | No | No |
Lisetti & Wagner 2008 [ |
Dialog system considered | Not specified, but possible | No | No | MI |
Looije et al 2010 [ |
Dialog | Not specified, but possible | No | No | MI |
Maier et al 2010 [ |
Text mining; ontologies; machine learning; semantic search | Yes, personalized search results based on user profile built automatically | Yes, information portal | No | MI |
Mazzotta et al 2007 [ |
Dialog, user model | Yes, tailoring of persuasion messages based on inferred personality traits and likely motivations of user | No | No | Persuasion theories, argumentation |
Munguia Tapia 2008 [ |
Activity recognition; energy estimate | No, but possible in an intervention | No | No, but possible in an intervention | No |
Nguyen & Masthoff 2008 [ |
Dialog | Not specified | No | No | MI-based dialog design |
Oddsson et al 2009 [ |
Intelligent reminding | Yes, part of robotic companion | No | Not mentioned, but possible to include | No |
Op den Akker et al 2011 [ |
Pattern recognition, machine learning, context awareness, user modeling | Yes, messages are tailored based on user model and context | No | Not mentioned | No |
Rojas-Barahona & Giorgino 2009 [ |
Dialog; adaptation | Yes, dialog can be adapted according to patient answers | No | No | No |
Smith et al 2008 [ |
Dialog control; inference; automated planning | Yes, update planned activities through ongoing dialog | No | No | No |
Sorbi et al 2007 [ |
Adaptation, automated personalized feedback | Yes, tailored messages depending on current experience | No | No | No |
Spring et al 2010 (Make Better Choices–MBC) [ |
Decision support; coaching algorithms. (PDAd) | Not specified, but possible | No | Yes, PDA allows this but not described in detail | No, although some theories mentioned |
Tiwari et al 2011 [ |
Robot, dialog | Not specified in detail, but dynamic adaptation is a required feature in the design | No | No | No |
Turunen et al 2011 [ |
Dialog; inference; automated panning | Yes, adaptive dialog, collaborative planning | No | No, but possible to include | No |
Uribe et al 2011 [ |
Knowledge-based inference | Yes, reminders based on inferred mental state | No | Yes, implied in the design but not described in detail | TTM incorporated in ontology |
van der Putten et al 2011 [ |
Robot, dialog | Not mentioned | No | No | Not mentioned |
Watson et al 2012 [ |
Dialog | Yes, dialog utterances tailored according to user progress with system | Not specified in detail | Not specified in detail | Relational agents |
a Transtheoretical model.
b Motivational interviewing.
c Computerized behavior intervention.
d Personal digital assistant.
From
Ecological momentary assessment [
Most studies (18) were prototypes or design concepts. A total of 17 were feasibility or usability studies. Only 6 were RCTs measuring effectiveness for behavior change [
The study of Bickmore et al [
The study of Consolvo et al [
Jin [
In qualitative pilot studies (17 studies), agents with empathy and social behavior tended to be preferred over nonsocial agents. In particular, Farzanfar et al [
A total of 15 studies emphasized dynamic tailoring. Of these, 10 were prototypes, 1 was an RCT [
Three studies [
Maier et al [
Two studies on physical activity were concerned with accurate self-monitoring and visualization. Consolvo et al [
Ananthanarayan and Siek [
Behavior change models were used in 14 studies. Motivational interviewing [
An important novel development in theoretically grounded active assistance is the incorporation of behavior change theories into the ontologies used in knowledge-based reasoning and dialog design (5 studies). The prototype in Bickmore and Sidner’s study [
The results show that significant research has been focused on dialog systems, ECAs, and activity recognition. There was also some work on ecological momentary intervention and intelligent context-aware prompting. The most covered health topic is physical activity. Most studies were still at an early stage, either prototype work in progress or pilot studies. Only 6 were RCTs, of which 4 were positive for behavior change and 5 were positive for acceptability.
The studies on dialog and ECA systems showed that empathy and relational behavior are significant research themes in behavior change, with many pilot studies showing preference for those features. The effect on actual behavior also tended to be positive. Too much interaction, however, might interrupt and inhibit positive health behaviors. So there is a need for careful consideration of the frequency and duration of interactive sessions in context.
Ecological momentary intervention is an opportunity for generating models from captured user experiences in the user’s own language (eg, from social networking sites) and for integrating these models with expert knowledge. Such models can include the mental states and emotions of the user, particularly if they are used in conjunction with theoretically grounded ontologies [
We found relatively few studies explicitly focusing on the functions of active technology that we selected above: dynamic tailoring, interactive education, and self-monitoring. Although some interventions may have included these functions implicitly, it seems that many studies did not recognize the role of a particular technology in enabling or improving these aspects.
Behavior change research needs to be informed by a deep understanding of algorithms and techniques that can support interventions. For this purpose, interdisciplinary collaboration with computer science and cognitive science is needed. In particular, behavior change technology has some parallels with educational technology. In educational systems, an intelligent tutor builds a model of the learner based on his or her performance and responses to questions (eg, what concepts does this person find difficult?) [
Making users aware of the models can draw their attention to emotions and environmental circumstances (ecology) that are associated with negative behavior outcomes. Similarly, opening up models and giving users more control may enable users to spot any serious misunderstanding by an agent or dialog system, thus avoiding the problem of users blindly following incorrect instructions. In some educational systems [
Most studies on active assistance technologies in behavior change are based on natural-language dialog and ECA. We did not find many alternatives to these approaches that could be used if natural language or the ECA format is not suitable or preferable. For example, users might interact with adaptive interfaces where the users’ actions are interpreted semantically as if they were dialog responses. Many of the core principles, such as model-based reasoning, activity recognition, and context-aware reminders, can be effective with different forms of interface.
Studies on ECA and dialog systems are mostly focused on relational behavior and enjoyment of usage. If ECA systems are to be deployed in areas such as mental health and low health literacy [
Most studies in behavior change were focused on one or two technologies (eg, dialog and activity recognition) without specifying how the components can interact to infer further information. For example, coordination between activity recognition and content analysis of online diary entries might determine the circumstances in which relapses tend to occur, and tailor messages accordingly. Similarly, reliable automated decision making requires an interactive system to be connected with diverse specialist knowledge sources that can be requested on demand. More research is needed on how the components of an active assistance system are coordinated together and how they may be connected with the Semantic Web and other health informatics resources (eg, risk modeling).
Articles not indexed in Google Scholar or PubMed would have been missed—most scholarly publications, however, are captured by Google Scholar. The review required the mention of “health” and “behavior change” in the articles. We did not include gray literature such as white papers and unpublished reports. We selected the date range (2005–2012) to focus on recent developments, but this may also have excluded innovative earlier work.
The review required specific mention of a key technology. There may be some interventions that use active technologies, but the studies did not mention this. Similarly, some studies mentioning only general intelligent technology were excluded from the full-text review because they could not be categorized. This may be a limitation because included studies need to involve significant interdisciplinary communication between technology specialists and health specialists. On the other hand, it may be a strength, as such communication is important for understanding a particular technology in context.
Since we limited the search to behavior change, it is also possible that many of the technologies are being applied in other areas of health informatics. For example, we found some prototypes early in the date range (2006–2007) but found no subsequent study relating to behavior change. In these cases, citation searching sometimes revealed further development of the techniques and algorithms, but no application in the health domain.
The potential of active technologies for dynamic and unbiased information processing is not being fully exploited in current health behavior change research. Most research has focused on specialist areas, such as dialog and ECA systems, and has been largely restricted to the study of persuasive dialog in respect of relational behavior and motivation of behavior change.
In addition to the potential benefits of active technologies, there is a need for a thorough understanding of the potential risks. Expected benefits such as that of dynamic tailoring of the content and presentation of information can be measured using established evaluation methods (eg, [
To exploit the full potential of active assistance technology, health behavior change researchers need a deep understanding of how the different components of information systems might change the intervention—its safety, effectiveness, efficiency, and acceptability. This requires more collaboration between disciplines such as health psychology, computer science, cognitive science, health informatics, medical sociology, and public health and health promotion.
embodied conversational agent
randomized controlled trial
This work was funded by the Northwest Institute for BioHealth Informatics (NIBHI), UK and by EPSRC grant "e-Health+: Citizen-driven Information for Healthcare and Wellbeing" EP/G002134/1.
None declared.