Using virtual representations in mHealth application interventions for health-related behaviour change: A systematic review

Figures & data

Figure 1. PRISMA Flow Diagram showing the pathway of study selection

Table 1. Study characteristics, key findings, and quality rating

(Reference) Authors	Design	Country	Participant Characteristics	Outcomes	Assessment time points	Summary of Key Findings	Quality Rating	Quality rating (comment)
Ossolinski et al., (2017) (Ossolinski et al., 2017)	RCT (no control) comparing two groups using the Future Me app which portrays the effect of lifestyle on future personal appearance using input calorie & exercise information to predict future BMI. The two groups were: current & future self-image of self immediately (early intervention group) or after 8 weeks (delayed intervention)	Australia	N = 145 Aged >18 years; BMI > 25 kg/m^2; wanting to lose weight	Primary outcome: Weight loss over 16 weeks Secondary outcomes: Change in weight loss and waist circumference over 24 weeks	Baseline (0 weeks) and post-intervention (24 weeks)	Delayed intervention with personalised avatar future images at 8 weeks compared to beginning of intervention promoted greater weight loss At 24 weeks there was a significant change in weight overall, and a difference in rate of change between groups Men were found to lose more weight than women	Low	Missing data predicted via linear regression; high dropout rate despite small sample size; missing data predicted by not using ‘real’ data
Fuchs et al., (2019) (Fuchs et al., 2019)	Single armed cross-sectional testing the effectiveness of the mobile food record checklists app which included a future-self avatar based on a participant’s diet to reduce sodium intake. There were no comparator or control groups.	Switzerland	N= 28 (17 males, 11 females) Mean age=1985.6, (SD=9.59)	Primary outcome: Motivation, risk awareness, associated to positive outcomes of change, self-efficacy in engaging in a healthier dietary behaviour Secondary outcome: Comparing pre and post measures for motivation, risk awareness, associate to positive outcomes of change, and dietary behaviours	Baseline (day 0) and post-intervention (day 8)	The avatar improved risk awareness, low-salt outcome expectations and intentions toward a balanced, low-salt diet	Low	Does not report all data (demographics and non-significant results); high dropout rate despite small sample size
Dworkin et al., (2019) (Dworkin et al., 2019)	Single armed mixed methods study to evaluate the efficacy of the My Personal Health Guide app, which included a conversational agent to improve adherence to antiretroviral therapy in young African American men who have sex with men who are HIV positive. There were no comparator or control groups.	USA	N= 43 (HIV positive males) Aged 18–34 years (median = 29, no reported variance)	Primary outcome: ART adherence at baseline versus 3 months of exposure to app Secondary outcome: Acceptability	Baseline (0 weeks) and post-intervention (21 weeks)	Pill count adherence, knowledge of viral load, and CD4 count (immune system health) significantly improved Acceptability of the app was high All outcomes were measured via the app, and self-reported by the participant except for acceptability which was collected via check in calls	Moderate	Small sample size but low dropout rate; results only reported p values
Wonggom et al., (2020) (Wonggom et al., 2020)	Non-blinded (both participants and researchers) RCT comparing effectiveness of intervention group which received a combination of the usual care and an avatar app to control group which received usual care in promoting self-care behaviours and heart failure knowledge.	Australia	N= 36 (males = 20; females = 7) Mean age =65.5; (SD= 11.3)	Primary outcome: Heart failure knowledge Secondary outcomes: Heart failure self-care behaviours, HF-related readmission, and satisfaction	Baseline (day 0) follow-ups (30- and 90-days post intervention)	At 90 days, the intervention group participants had a higher increase in knowledge score, self-care behaviours and responses to symptoms compared with the control group High satisfaction with app	Moderate	Sample was not blinded which could have biased the results; more female than male participants
Mohan et al., (2020) (Mohan et al., 2020)	Mixed-methods six-week cross-sectional study comparing the effectiveness of the Nutriwalking app in promoting exercise with sedentary participants. There were no comparator or control groups. All participants interacted with an empathic agent, a non-empathic control agent, or a text-based control system.	USA	N= 21 diabetics (mean age 51 ± 8 years) No gender reported	Primary outcome: Weekly exercise and lifestyle behaviours Secondary outcome: Adaptability, suitability, and usability of the coach and app	Baseline (week 0) and post intervention (week 6)	Participants increased their weekly exercise volume in response to the empathetic coach’s goal recommendations Participants who are very optimistic are less likely to be successful at recommended exercises Highly rated usability, adaptability, and suitability	Low	More of a focus on design, small sample size, limited reporting of results which makes it difficult to infer if there was truly an affect

Table 2. Summary outcomes, statistical measures, and reported findings

Reference (authors)	Outcome(s)	Statistic	Results
			Baseline	Follow-up	p value
Ossolinski et al., (2017) (Ossolinski et al., 2017)	Weight change (baseline to post-intervention) between groups	% Reduction	-0.77%	-0.49%	0.018*
	Weight change (delayed-image vs early-image) between groups	% Reduction estimated using intention to treat (ITT)	-0.50%	-0.30%	0.007*
			Pre/ Post Avatar		p value
Fuchs et al., (2019) (Fuchs et al., 2019)	Self-reported sugar intake	Z-value	0.542		.58
	Self-reported salt intake	Z-value	2.204		0.027*
	Self-reported fruit and vegetable intake	Z-value	-0.586		.56
	Self-reported alcohol intake	Z-value	-0.259		.80
	Self-reported balanced diet	Z-value	-1.512		.13
	Observed sugar intake	Z-value	0.091		.30
	Observed salt intake	t-value	-1.062		.93
	Observed fruit and vegetable intake	Z-value	0.00		1
	Observed alcohol intake	Z–value	-0.914		.36
	Intention to pursue balanced diet	Z-value	1.95		0.051
	Intention to pursue low-sodium diet	t-value	2.748		0.007**
	Combined intention	t-value	2.925		0.011*
	Outcome expectancy unbalanced diets	Z-value	.262		.79
	Outcome expectancy sodium intake	Z-value	2.289		0.022*
	Self-efficacy	Z-value	-.36		.71
	Risk awareness	t-value	5.299		0.000**
			Delayed-image Group	Early Image Group	p value
Dworkin et al., (2019) (Dworkin et al., 2019)	Adherence, pill count, the number (%) of participants who are >80% adherent	N (%)	16 (61.5)	23 (88.5)	Non-significant
	Adherence, the number (%) of participants who are >90% adherent	N (%)	22 (68.8)	26 (81.3)	p <0.05*
			Intervention Group	Usual Care Group	F value
Wonggom et al., (2020) (Wonggom et al., 2020)	Knowledge, measured using the Dutch Heart Failure Knowledge Scale (DHFKS) in intervention and usual care groups	F-statistic (ANOVA), % increase (from baseline)	15.3%	3.5%	3.756
	Self-care behaviours, measured using, Self-care of Heart Failure Index (SCHFI; Version 6.2) in intervention and usual care groups	% Increase (from baseline)	11.9%	-4.3%
	Healthcare use: HF-caused readmission including both emergency and elective admissions to hospital and unplanned visits to general physician units in intervention and usual care groups	HF-related general practitioner presentation, N	1	0
		Emergency department presentation, N	1	0
		Hospital readmission, N	1	0
			Post Avatar		p value
Mohan et al., (2020) (Mohan et al., 2020)	Change in Regular aerobic exercise from baseline, measured in MET-mins/week	Mean (SD)	392.25 (24.830)		p < 0.001
*significant at 5% level **significant at 1% level T: paired samples t-test for normal distributions; W: Wilcoxon signed-ranks test; N: number of participants; SD: standard deviation

Table 3. Intervention characteristics

(Reference) Authors	Outcome	mHealth app and representation	Virtual Representation Manipulation	Behaviour change techniques (Michie et al., 2013)	Theory	Mechanisms of Action
Ossolinski et al., (2017) (Ossolinski et al., 2017)	Weight loss	Future Me app, using computer-generated future self-image avatars	Self-resembling avatar	Self-monitoring (recording weight); Credible source (provision of leaflet containing online resources); Comparative imaginings of future outcomes (provision of future self images); Identification of self as role model (future self image with a lower BMI)	Transtheoretical Model (Prochaska et al., 1994)	Self-image; Reinforcement; Motivation
Fuchs et al., (2019) (Fuchs et al., 2019)	Diet	Using the food record checklist app (mFRCL), an avatar-based system.	Customisable avatar	Self-monitoring (logging food consumption via app); Feedback on outcome(s) of behaviour (the avatar provides emotional feedback, in addition to messages presented on the app); Demonstration of behaviour (avatar demonstrates desired behaviours by eating fruits and vegetables); Prompts/cues (app sends reminders to log food); Comparative imaginings of future outcomes (avatar presented as looking healthy or unhealthy because of the logged foods; Identification of self as role model (customisable avatar to represent participant); Credible source (information from app provided by Swiss Society for Nutrition)	Health Action Process Approach (Schwarzer & Lusczynska, 2008)	Behavioural regulation; Self-image; Needs; Feedback processes; Reinforcement; Motivation
Dworkin et al., (2019) (Dworkin et al., 2019)	Treatment Adherence	Using an embodied conversational agent on the My Personal Health Guide app.	Customisable agent	Self-monitoring (logging pill count, viral load, and CD4 count in calendar on app); Social support (agent providing supportive feedback in response to participant answers); Information about antecedents (app provides advice relating to medication, side effects, and dosage for current treatment); Prompts/cues (reminder message alerts); Credible source (motivational statements containing educational information); Verbal persuasion about capability (agent has verbal responses and greetings)	Information, Motivation, and Behavioural Skills Model of Adherence (Fisher et al., 2006) Technology Acceptance Model (Holden & Karsh, 2010)	Behavioural cueing; Motivation; Knowledge; Memory, attention, and decision processes
Wonggom et al., (2020) (Wonggom et al., 2020)	Knowledge	An avatar-based (using a nurse as a representation) on an education app	Customisable avatar	Instruction on how to perform a behaviour (information about self-care behaviours); Information about health consequences (information about heart failure and self-care behaviours to prevent this); Credible source (information provided in app is based on National Heart Foundation of Australia’s booklet, Living Well with Heart Failure); Identification of self as role model (customisable avatar to represent participant)	Self-efficacy Theory (Bandura, 2011)	Feedback processes; Knowledge; Beliefs about consequences
Mohan et al., (2020) (Mohan et al., 2020)	Exercise	NutriWalking app and tailored coaching agent	Stranger-resembling agent	Goal-setting (participants asked to set exercise goals in app); Review behaviour goal(s) (app reviews exercise behaviours and provides new goal according to results); Feedback on behaviour (app provides feedback messages and advice to achieve goal); Self-monitoring (participants log exercise in app); Graded task (participants set exercise tasks in app)	Goal-setting Theory (Locke & Latham, 1994)	Needs; Feedback processes; Behavioural cueing; Goals; Behavioural regulation

Ossolinski, G., Jiwa, M., McManus, A., & Parsons, R. (2017). Do images of a personalised future body shape help with weight loss? A randomised controlled study. Trials, 18(1), 1–7. doi:10.1186/s13063-017-1907-6

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Data availability statement

The data that support the findings of this study are available from the corresponding author, Lauren Taylor (LT), upon reasonable request.