Handheld Electronic Technology for Weight Loss in Overweight/Obese ...

Curr Obes Rep (2014) 3:307–315 DOI 10.1007/s13679-014-0112-0

HEALTH SERVICES AND PROGRAMS (SFL KIRK, SECTION EDITOR)

Handheld Electronic Technology for Weight Loss in Overweight/Obese Adults Michelle C. Carter & V. J. Burley & J. E. Cade

Published online: 15 June 2014 # Springer Science+Business Media New York 2014

Abstract Handheld electronic devices could offer a convenient and scalable platform with which to deliver a weight loss intervention. This paper aims to summarise the evidence provided by randomised trials of such interventions. There is heterogeneity among trials in terms of the components of the intervention package, the theoretical framework, the comparison groups and the duration of follow-up. While in the short term (5 %) was found at six months in all three intervention arms (paper diary vs. PDA vs. PDA+feedback). At the second follow up point at 24 months, weight recidivism was evident and only the ‘PDA+feedback’ group was found to have a small but statistically significant mean percentage weight loss from baseline to follow-up (−2.3 %, 95 % CI: −4.3 % to −0.4 %) [22••]. There was no statistically significant difference in the percentage weight change between the three groups at 24 months (p=0.33). Of the reported trials to date using handheld electronic devices for weight loss, this trial has the largest sample size and longest follow-up so it is an important indicator of results expected from long-term use of such a device. In a smaller sample (n=69), Spring et al. (2013) [13] found a statistically significant greater weight loss in the intervention arm (six months of PDA monitoring and fortnightly coaching calls following a six month biweekly group programme) compared to control (the same initial biweekly group programme but no PDA self-monitoring after six months). Participants were found to have lost an average 8.6 lb (3.9 kg), 95 % CI (4.9, 12.2 lb) more in the intervention group than control across four time points (3, 6, 9 and 12 months).

Curr Obes Rep (2014) 3:307–315

It is worth noting that the study populations for these two trials were different with Spring et al., (2013) [13] testing their intervention on a mostly male (85.5 %) sample of veterans and Burke et al., (2012) [22••] trialling with a mostly female (84.8 %) and younger population. Both of these trials used dietary self-monitoring by electronic device in addition to fairly intensive human contact in the form of existing standard behavioural group weight loss treatments and both showed reasonable weight loss by six months in the intervention arms. However Burke et al., (2012) [22••] showed that weight loss achieved was not different in the PDA arm from dietary selfmonitoring on paper and, after 24 months follow up, weight loss was no longer clinically significant in the PDA groups.

Trials of text Message Interventions Two trials have investigated daily text messages delivered by ‘text pager’ alongside financial incentives and have found different weight loss results. The financial incentives were ‘deposit contract’ whereby participants gave their own money which was returned and doubled if they met their weight loss goal or a ‘lottery’ group where participants were entered into a daily cash lottery if they had met their weight loss goal. The earlier of these two trials [14] (n=57) reported a statistically significantly greater weight loss in the intervention groups compared to the control at 16 weeks (−6.3, SD± 4.6 kg mean weight loss in the deposit contract+text message intervention arm vs. −5.9, SD±5.7 kg mean weight loss in the lottery+text message intervention arm vs. −1.8, SD±4.1 kg mean weight loss in the control arm of monthly weigh-ins). However, the later trial by John et al., (2011) [15] (n=66) found a statistically significantly greater weight loss in the intervention groups compared to the control (consultation with a dietitian and monthly weigh-ins) at 32 weeks but not 4 weeks later at 36 week follow up (mean weight loss in the intervention group=−0.5 kg and −0.31 kg in the control group, no confidence intervals or standard deviation reported, p=0.76). There is little detail reported about the design of the text messages other than that they provided feedback on progress towards weight loss goals. The fact that the messages run alongside financial incentives make it difficult to tease apart the extent of the impact of the text message part of this intervention. Three studies have used mobile phones to deliver a weight loss intervention [16, 17, 23•]. The most recent of these conducted by Shapiro et al. (2012) [23•] trialled a daily text message intervention called ‘Text4diet’ which was reported to deliver 4 messages a day (which encourage the selfmonitoring of weight and step counts along with providing tips, motivational statements and questions relating to behaviour change strategies). This is described as a modified version of the daily text message intervention first trialled by Patrick

N=57 (BMI 30–40 kg/m2) (5 %)

N = 125 (26–36 kg/m2) (77 %)

N = 65 (BMI >25−39.9 kg/m2) (80 %) N=66 (BMI 30–40 kg/m2) (17 %)

Volpp et al. (2008) [14]

Haapala et al. (2009) [16]

Patrick et al. (2009) [17]

N=96 (25–45 kg/m2) (75 %) N=170 25–39.9 kg/m2 (65 %) N=69 >25 and ≤40 kg/m2 14.5 %

Turner-McGrievy & Tate, (2011) [18•]

Spring et al. (2013) (13)

Shapiro et al. (2012) [23•]

N=210 (27−43 kg/m2) (85 %)

Acharya et al. (2011) [20] Burke et al. (2011) [21] Burke et al. (2012) [22••]

John et al. (2011) [15]

Study population (BMI, kg/m2) (% female)

Author (year)

Yes

Phone – 2.1 kg Control−0.4 kg

RCT. Podcast only vs. Podcast + smartphone app “FatSecret” + Twitter Both groups given twice RCT. Text messages vs. control (monthly e-newsletters) RCT. Both arms received biweekly group sessions and PDA self monitoring

Yes

Phone – 4.5 kg ±5 (5.4 % ±5.8) Control – 1.1 kg±5.8 (1.3 %, ±6.5)

12 months, 32 %

No

Yes

Intervention; −1.7 kg ± 5.4 Control; −1.0 kg ±4.3 Intervention; −2.9 kg; 95 % CI, 0.5 to 6.2 kg. Control; −0.02 kg;

12 months (24 %)

12 months, 22 % (at 12 month f/u)

No

No at 6 and 24 months.

Paper diary – 5.3 %±5.9 % at 6 months, 1.95 % (95 % CI: −3.9, 0.01) at 24 months. PDA− 5.5 %±7 % at 6 months, −1.4 % (95 % CI; −3.4, 0.6) at 24 months PDA with feedback – 7.3 %±6.6 % at 6 months, −2.3 % (95 % CI; −4.3, −0.4) at 24 months. Podcast only −2.6 %±3.8 Podcast + mobile −2.7 %±5.6 24 months (f/up at 6 months), 9 % at 6 months, 14 % at 24 months.

6 months (10 %)

Yes

Deposit contract 1 (with maintenance period) −4.4 kg ± 6.2 kg. Deposit contract 2–3.4 kg ±5.8 kg. Control −0.5 ±6.3 kg

32 weeks, 11 %

4 months, 20 %

Yes

Deposit contract + Texts −6.3 kg ± 4.6 Lottery + text messages −5.9 kg ±5.7. Control −1.8 kg ±4.1 kg.

16 weeks 9 %

Wt change in intervention compared to control statistically significant (p=≤0.05)?

3 armed RCT. Text messages sent by “text pager” to 2 financial incentive groups (own deposit vs. lottery) but not to control (1 Dietitian session) RCT. Mobile phone app “Weight Balance” generated text messages (linked to website) vs. control (no intervention) RCT. Text messages vs. control (monthly phone calls and print materials). 3 armed RCT. Text messages sent by “text pager” to 2 financial incentive groups (own deposit vs. own deposit + maintenance phase) but not to control (1 Dietitian session + monthly “weigh-ins”) RCT. All participants received weekly group sessions. Paper diary vs. PDA (DietMatePro) vs. PDA + daily feedback.

Mean weight loss post−intervention (kg/% body wt ± SD or 95 % CI )

Duration, Attrition (% lost at follow up)

Design and intervention

Table 1 Randomised trials of handheld technology for weight loss in overweight/obese adults

Curr Obes Rep (2014) 3:307–315 309

Curr Obes Rep (2014) 3:307–315

Smartphone: −4.6 kg (95 % CI −6.2 to −3.0); Diary:–2.9 kg (95 % CI −4.7 to −1.1); Web–1.3 kg (95 % CI −2.7 to 0.1) 6 months (38 %)

95 % CI, −2.1 to 2.1 kg).

N=128 ≥27 kg/m2 Carter et al. (2013) [19•]

for 6 months. For next 6 months intervention group received PDA and biweekly coaching calls. All received monthly group sessions months 7–12. 3 armed Pilot RCT. Smartphone app “My Meal Mate” vs. online food diary vs. paper food diary.

Study population (BMI, kg/m2) (% female) Author (year)

Table 1 (continued)

Design and intervention

Duration, Attrition (% lost at follow up)

Mean weight loss post−intervention (kg/% body wt ± SD or 95 % CI )

Wt change in intervention compared to control statistically significant (p=≤0.05)?

Yes between app and online. No between app and paper diary.

310

et al. (2009) [17]. Haapala et al. (2009) [16] trialled a mobile phone programme called ‘Weight Balance’ which automatically generated text messages advising participants to reduce food intake and increase physical activity. Rather than being an exclusively mobile phone intervention ‘Weight Balance’ was also linked to a website where users could keep a food diary. The trials of text message interventions differ in terms of message frequency, content, theoretical framework and overall intervention components. The earliest of the text messaging trials showed promising results [16]. In a sample of 125 participants, after 1 year, mean weight loss in the text message intervention arm was −4.5 kg (±5.0) compared to a mean −1.1 kg (±5.8) loss in the ‘no intervention’ control arm (p=0.006) (16). However, the two subsequent text messaging trials have shown less impressive weight loss results. Patrick et al. (2009) [17] investigated a text message intervention for 16 weeks (n=65) and found a modest mean weight loss of −2.1 kg (95 % CI; −3, −1) in the group receiving daily text messages compared to −0.4 kg (95 % CI; −1, 1) in the control group (p=0.03 for between group difference). In a subsequent study, a larger (n=170) 12 month trial using a modified version of the text messaging intervention (with extra content such as step counts via pedometer and a larger library of text messages) showed no statistically significant difference in mean weight loss between the intervention and control group at follow up (p=0.39) [23•]. The mean weight loss in the intervention group was −1.7 kg (±5.4) at 12 months and −1.0 kg (±4.3) in the control group. It is worth noting that the earlier trial by Haapala et al. (2009) [16] was not exclusively text messaging and had other intervention components including dietary self-monitoring.

Trials of Smartphone Applications The rapid progression of technology has seen PDA devices now largely superseded by smartphones. A smartphone with its enhanced computational abilities provides an exciting opportunity to combine the self-monitoring capabilities of PDA devices with text messages. Two of the trials in table 1 have investigated different smartphone apps for weight loss, one of these trials showing a promising weight loss result and the other less so. Turner-McGrievy and Tate (2011) [18•] randomised 96 overweight and obese participants to either a ‘podcast only’ arm or an ‘enhanced’ group which received podcasts, Twitter and a smartphone app called ‘FatSecret’ (for diet and physical activity self-monitoring). At six months, the ‘podcast only’ group lost a modest mean −2.5 % (±3.8) body weight compared to a mean −2.7 % (±5.6) in the ‘podcast+ app+Twitter group’. The difference in change in body weight between group was not statistically significant (p=0.98). In a more recent 6 month trial, Carter et al. (2013) [19•] piloted a researcher designed smartphone app “My Meal Mate” and

Curr Obes Rep (2014) 3:307–315

found a weight loss of −4.6 kg (95 % CI −6.2 to −3.0) by 6 months. These two trials differ, in that the former trials an existing commercially available app (in addition to other intervention components) and the latter trials a researcher designed app which is delivered in a minimal contact way with no nutritional advice or on-going support or feedback from the researchers.

Difficulties in Pooling the Evidence to date Drawing together the results of the trials to date is challenging given variations in study duration and design with different combinations of components of the interventions trialled (i.e. self-monitoring of diet, weight, and physical activity, feedback and motivational messages) tested across different types of device and with differing components of the entire weight loss intervention as a package. There are also substantial differences within the same category of intervention (i.e. text messages might be delivered daily or × 4 a day) and while some trials use portable handheld technology in addition to face to face behavioural programmes [13, 22••], some trials offer little to no ongoing human contact [19•]. Many of these trials have small sample sizes with only four out of nine of the trials described in Table 1 including more than 100 participants. As is often the case with weight loss studies, most of the trials included a predominantly female sample (77 % − 100 %), which reduces the generalisability of the results (the exception being the trial by Spring et al., 2013 [13] which uniquely has a predominantly older male sample). Most of the electronic interventions have been investigated as part of a wider intervention package (i.e. in addition to group sessions, tele-coaching or other ICT such as Twitter or web support) rather than independently which makes it difficult to determine the effect the devices might have as a stand− alone weight loss intervention. Given that existing diet tracking smartphone apps available to download from iTunes and Google Play are likely to be used by the general public in an unsupported way, this is important to investigate. There is a gap in knowledge about what kind of additional support (if any is necessary) will optimise the impact of the electronic intervention and the best combination of approaches (if a combination is necessary). The comparison groups used in the trials were mixed with only one study reporting to use a strict ‘no intervention’ control group [16]. Choosing an appropriate control is a contentious ethical decision in a group of overweight/obese participants who have expressed a desire to lose weight. It is also difficult in healthcare systems where there is a lack of consistency within the delivery of ‘usual care’ for overweight and obese adults. There is also a danger of resentful demoralisation in a ‘no intervention’ control arm of a trial where the intervention is viewed as desirable. The trials also

311

ranged considerably in their rate of attrition (0 %−43 %). Attrition is a serious difficulty in weight loss trials generally due to its potential to bias results [24]. To put this attrition figure into context, a systematic review of long term weight loss trials in obese adults, reported losses to follow up in the range of 30−60 % [25]. A review focussing specifically on web−based interventions for weight loss found the range of attrition to be slightly lower using an online medium (0-52 % attrition) [26]. As a result, the way in which missing data is handled in these types of trials is very important. Bias can arise if participants drop out of the trial because they find the intervention unacceptable or because only those that have lost weight return for follow up giving a false impression of the effectiveness of the intervention. None of the trials conducted to date include any cost effectiveness analysis which is essential in this area given that in theory a handheld intervention could allow for wide dissemination to a large audience in a minimal contact fashion so could potentially be cost effective.

Issues in Dietary Self-monitoring Although delivered on different platforms, the key behaviour change strategy at the heart of several of the interventions detailed in table 1 is dietary self-monitoring. While few of the trials report adherence to dietary self-monitoring, those that do highlight a similar trend of reduced engagement over time. For example, in the trial of the smartphone app ‘My Meal Mate’ [19•] although frequency of intervention use was statistically significantly higher in the smartphone group (as compared to an online and paper food diary), it was found that by 6 months only 7/43 participants had used the intervention daily. Similarly, the trial by Burke et al. (2012) [22••] showed a drop-off in use of the PDA device for dietary selfmonitoring over time. This is important for future research to consider given that a systematic review has found frequency of dietary self-monitoring to be linked to weight loss success [21]. It is worth noting however, that the phenomenon of lack of engagement with ICT based interventions over time is not unique to weight loss and is considered to be a general challenge for trials of e-health interventions [27]. In fact, Eysenbach (2005) has called for a ‘science of attrition’ to study this phenomenon and observes that in trials of e-health interventions (as opposed to drug trials) participants are not monitored as closely and the intervention is often not mandatory for health so may be more readily abandoned [27]. In addition, while there is evidence that the frequency of dietary self-monitoring is associated with weight loss [21] it is not fully understood whether the self-monitoring of diet also needs to be accurate in order to be effective. In a validation study (n=50) comparing accuracy of dietary recording on a smartphone app as compared to 24 hour telephone dietary recalls, agreement at the individual level was found to be fairly

312

wide (− 807 to 775 kcal/d) despite overall close agreement (−16 kcal/day) [28]. Wide limits of agreement were also found to be the case in a validation study of the DietMatePro PDA device [29]. This is important, given that accuracy of recording may or may not have a bearing on weight loss success. One mode of action of dietary self-monitoring could be through the accurate tracking of energy intake and adjusting food choices accordingly throughout the day to meet the target. However, it could be the simple act of frequent recording of food and drink alone regardless of accuracy is enough to develop ‘mindful’ rather than ‘mindless’ eating and that by raising awareness in this way the individual is prompted to make different dietary choices. If the mode of action is the former then inaccurate reporting could lead a person to continue to self-monitor intake and believe they are meeting a prescribed energy target but not see any weight loss and this could be a reason for disengaging with an intervention. This may be even more of a problem in an overweight/obese sample given that this population group are known to underreport their dietary intake compared to healthy weight individuals [30]. Given that the mode of action of several of the most popular diet tracking smartphone apps currently available for public download tends to be dietary self-monitoring it is important that research continues to address some of the outstanding questions in this area. For example, 1) Is there a particular duration of dietary self-monitoring which is necessary for weight loss?, 2) Are there certain characteristics of those who are able to self-monitor consistently and as such does this approach have scope to be tailored to the individual?, 3) Can measures be put in place to curtail the drop-off in engagement over time in dietary self-monitoring and 4) Is accuracy of reporting of dietary intake on a handheld electronic device important for weight loss?

Logistical Issues in Trialling a Smartphone Application for Weight loss To date, there is no definitive randomised controlled trial (RCT) testing a smartphone app (of comparable quality to those available to download) as a stand-alone weight loss intervention. Research of this nature is warranted as the devices trialled thus far are now considered out-dated and have largely been superseded by new technology. It is necessary to spend time determining the most effective behaviour change techniques to optimise a smartphone based obesity intervention however there is a difficulty in balancing this need with the imperative to keep up with the progression in technology and the pressing public health demand for effective interventions with which to tackle the obesity epidemic. In the case of existing commercially available diet tracking smartphone apps in particular, there is evidence for large numbers of

Curr Obes Rep (2014) 3:307–315

downloads by the public from sites such as iTunes and Google Play. However, many of these smartphone apps have been shown to be of questionable quality and not driven by behaviour change theories [31]. In response to this, if the researcher decides to develop their own app they are challenged with making the app as engaging as those that are already available to download which will often require ongoing funding and technological input in order to keep it up to date. It is likely that this will require a multidisciplinary approach with expertise in software development, health psychology, nutrition and physical activity. For example, in the case of the app trialled by Carter et al. (2013) [19•] ‘My Meal Mate’ (MMM), the existing diet tracking apps with which the development was originally benchmarked have since developed newer features such as linking into social networking websites, bar code scanning and cross-platform functionality. Another challenge of this approach (for handheld electronic interventions based on dietary self-monitoring) is maintaining an up to date food composition database given that food and beverage manufacturers regularly reformulate products or introduce new products to the market. The food composition database may therefore be large, requiring the search facility to be optimised and include appropriate options for estimating accurate food portion sizes. Several of the most popular freely available diet tracking apps use ‘crowd sourcing’ (the general public are allowed to add entries to the database) to maintain a large food and drink database which can lead to poorer control over the integrity of the nutrient data within the database. The fact that so many diet tracking apps are now available for download also makes a true randomised controlled trial (RCT) of this approach increasingly logistically difficult due to the large potential for contamination in the control arm of the trial.

Gaps in Knowledge about Optimum Intervention Components and Weight loss Maintenance The research in the area to date is incredibly heterogeneous and it is difficult to tease apart the sole effects of the handheld device from other aspects of the overall intervention packages and perhaps tenuous to compare results between handheld devices using completely different behaviour change strategies (i.e. diet self-monitoring vs. motivational messages). It is not yet known which behaviour change strategies incorporated into a smartphone app might be the most successful and more research is needed to determine the optimum intervention. It is also worth considering to what degree this kind of technology is best placed to supplement health care professionals. Some of the trials conducted to date also have ethical implications. For example, two trials of text messaging interventions have been used alongside financial incentives. The use of financial incentives to improve health is controversial

Curr Obes Rep (2014) 3:307–315

and concerns have been raised that this practice is coercive, threatens personal decision-making autonomy and can undermine the therapeutic relationship between health care provider and patient [32]. There is also an issue of how often to deliver text messages to participants as there is a potential for annoyance if the participant perceives the messages to be too frequent. Although this review has discussed electronic handheld devices which support initial weight loss there are currently no trials of smartphone apps which have been developed specifically to support weight loss maintenance. In general, while a number of weight loss studies have shown dietary interventions which restrict energy intake to be effective in the short term, it has also been observed that after treatment has stopped, weight recidivism is likely [33]. Different behavioural approaches are required for weight loss maintenance [34] and there is potential to develop and test a theory-driven smartphone app approach to weight loss maintenance.

Considerations of Health Literacy Obesity is a condition where health inequalities are evident and lower socio-economic status and deprivation have been found to be associated with risk of obesity, especially in women [35]. In order to address health inequalities, further research may seek to investigate the use of handheld technology for weight loss in lower socio-economic status groups. This is an important consideration for the development of smartphone apps given that socio-economic group has been shown to be a factor in smartphone ownership. In the most recent statistics from the Office of Communications (OFCOM) in the UK, in 2012, 29 % of all smartphone users were in social group AB (higher or intermediate managerial professions), 32 % in C1 (supervisory or clerical and junior managerial professions), 17 % in C2 (skilled manual professions) and 21 % in DE (semi and unskilled manual professions or non-working) [36]. Most smartphone manufacturers have extended their range to include lower price handsets, and smartphone prices continue to drop so accessibility among lower income groups may increase over time. There is also evidence that smartphone users from lower socio-economic backgrounds install more apps than others [37]. However, price and accessibility are only relevant if the user has the skills to be able to adequately engage with the intervention. General numeracy and literacy skills are likely to be important in terms of engaging with the devices in Table 1. For example, the user would be required to have fundamental literacy and numeracy skills in order to understand the written content of the interventions (i.e. goal setting, text message content and searching a database for foods) and numeracy skills (in order to estimate portion sizes, input body weight, and understand calorie counting). Studies investigating health

313

literacy in the area of nutrition have shown lower literacy and numeracy to be linked to poorer accuracy of portion size estimation [38] and less understanding of nutritional food labelling [39]. Food and activity diaries in particular have been criticised in a health literacy context for being ‘minimally interactive and requiring high-user motivation for use’ [40]. Given that the use of mobile devices for overweight/obese adults is an emerging area there is little evidence to guide how such an intervention might be tailored to populations with lower literacy and numeracy skills (and indeed how effective such tailoring might be). However, going forward it is worth considering the effect of such interventions on health inequalities.

Conclusion This paper has reported on randomised trials which investigate portable electronic handheld devices for weight loss in overweight/obese adults and discussed some of the issues in the field. The weight loss results have been mixed in this emerging area and whilst some relatively small trials show promising results for clinically significant weight loss in the short term (≤6 months), the two largest trials conducted to date have demonstrated a lack of clinically significant weight loss after 1 year with a text message intervention [23•] and after 2 years with a PDA intervention [22••]. However, it could be argued that trials using smartphone applications for weight loss as a newer avenue for future investigation may yield different results to their now outdated and possibly more cumbersome PDA counter parts. There is currently insufficient evidence to draw a firm conclusion on whether electronic handheld devices are effective for weight loss although the results do seem promising in the short term. There is no evidence of their efficacy in maintaining weight loss. More research is warranted into smartphone apps for weight loss as they confer additional advantages over PDA interventions of ubiquity, familiarity and the capacity to monitor diet discretely in social settings. There is a pressing public health need for cost-effective and convenient tools with which to tackle the global obesity epidemic. Whilst a smartphone delivered weight loss intervention may well have the potential to be cost effective there is currently a real lack of evidence in this area. None of the studies in this review include an investigation of cost effectiveness and this is an imperative for future research when considering whether this approach may be amenable to delivery in primary care. There are currently numerous smartphone apps for weight loss available for the public to download but there is evidence that few of these contain theory driven behaviour change strategies [41]. The quality of existing apps is therefore a key concern and further research to characterise and appraise these (and the quality of those developed in the future) is important.

314

Curr Obes Rep (2014) 3:307–315

Acknowledgments J. E Cade receives grant support from National Prevention Research Initiative (NPRI) to develop and validate a smartphone weight loss app; we are exploring the potential for use of our smartphone weight loss app in the UK National Health Service. Compliance with Ethics Guidelines Conflict of Interest Michelle C. Carter, V. J Burley, and J. E Cade declare that they have no conflict of interest. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

References Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance 1. 2.

3.

4.

5.

6.

7.

8.

9.

10. 11.

12.

13.

Krishna S, Boren SA, Ballas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15:231–40. Prabhakaren L, Chee WY, Chua KC, et al. The use of text messaging to improve asthma control: a pilot study using the mobile phone short messaging service (SMS). J Telemed Telecare. 2010;16:286– 90. Horvath T, Azman H, Kennedy GE, et al. Mobile phone text messaging for promoting adherence to antiretroviral therapy in patients with HIV infection. Cochrane Database Syst Rev. 2010;3, CD009756. Whittaker R, Borland R, Bullen C. et al.:Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev 2009., 14:11, CD006611. Hurling R, Catt M, Boni MD, et al. Using internet and mobile phone technology to deliver an automated physical activity program: randomized controlled trial. J Med Internet Res. 2007;9:e7. Spring B, Schenider K, McFadden H, et al. Multiple behavior changes in diet and activity: a randomized controlled trial using mobile technology. Arch Intern Med. 2012;172:789–96. Sherwood NE, Morton N, Jeffery RW, et al. Consumer preferences in format and type of community-based weight control programs. Am J Health Promot. 1998;13:12–8. Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted therapy for obesity: a new frontier for behavior therapy. J Consult Clin Psychol. 1985;53:698–703. Agras WS, Taylor CB, Feldman DE, et al. Developing computerassisted therapy for the treatment of obesity. Behav Ther. 1990;21: 99–109. Taylor CB, Agras WS, Losch M, et al. Improving the effectiveness of computer-assisted weight loss. Behav Ther. 1991;22:229–36. Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted behavior therapy for obesity: An empirical model for examining behavioral correlates of treatment outcome. Comput Hum Behav. 1992;8:239–48. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011;111:92– 102. Spring B, Duncan JM, Janke E, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern Med. 2013;173:105–11.

14.

Volpp KG, John LK, Troxel AB. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300:2631–7. 15. John LK, Loewenstein G, Troxel AB, et al. Financial incentives for extended weight loss: a randomized, controlled trial. J Gen Intern Med. 2011;26:621–6. 16. Haapala I, Barengo NC, Biggs S, et al. Weight loss by mobile phone: a 1-year effectiveness study. Public Health Nutr. 2009;12: 2382. 17. Patrick K, Raab F, Adams AM, et al. A text message–based intervention for weight loss: randomized controlled trial. J Med Internet Res. 2009;11:e1. 18.• Turner-McGrievy G, Tate D. Tweets, apps, and pods: results of the 6-month mobile pounds off digitally (Mobile POD) randomized weight-loss intervention among adults. J Med Internet Res. 2012;13:e120. Randomised trial which uses a smartphone app alongside a Twitter and podcasting intervention. One of the first trials to use a commerically available app in a weight loss intervention. The intervention arm which used the smartphone app did not produce a statistically signifcantly greater weight loss than the intervention arm without. 19.• Carter MC, Burley VJ, Nykjaer C, et al. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res. 2013;15:e32. Randomised pilot trial which uses a researcher developed smartphone app as a weight loss intervention. The trial found the app to be an acceptable and feasible intervention and showed promising weight loss results. 20. Acharya SD, Elci OU, Sereika SM, et al. Using a personal digital assistant for self-monitoring influences diet quality in comparison to a standard paper record among overweight/obese adults. J Am Diet Assoc. 2011;111:583–8. 21. Burke LE, Conroy MB, Sereika SM, et al. The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial. Obesity. 2011;19:338–44. 22.•• Burke LE, Styn MA, Sereika SM, et al. Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med. 2012;43:20–6. The SMART trial is a three armed RCT comparing weight loss using a PDA, PDA with feedback and paper diary. It is important given the length of it’s follow up which at 24 months is the longest trial of it’s type to date. The trial found that over the longer term there was no statistically significant difference in weight loss between different types of dietary self-monitoring. 23.• Shapiro JR, Koro T, Doran N, et al. Text4Diet: a randomized controlled study using text messaging for weight loss behaviors. Prev Med. 2012;55:412–7. Long term randomised trial of a text message intervention for weight loss. Weight loss not found to be statistically significantly greater in the intervention arm at 1 year. 24. Ware JH. Interpreting incomplete data in studies of diet and weight loss. N Engl J Med. 2003;348:2136–7. 25. Douketis JD, Macie C, Thabane L, et al. Systematic review of long-term weight loss studies in obese adults: clinical significance and applicability to clinical practice. Int J Obes. 2005;29:1153–67. 26. Neve M, Morgan PJ, Jones PR, et al. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with metaanalysis. Obes Rev. 2009;11:306–21. 27. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7:e11. 28. Carter MC, Burley VJ, Nykjaer C, et al. 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr. 2012;3:1–8. 29. Beasley JM, Riley WT, Davis A, et al. Evaluation of a PDA-based Dietary Assessment and Intervention Program: A Randomized Controlled Trial. J Am Coll Nutr. 2008;27:280–6.

Curr Obes Rep (2014) 3:307–315 30.

Rennie KL, Coward A, Jebb SA. Estimating under-reporting of energy intake in dietary surveys using an individualised method. Br J Nutr. 2007;97:1169–76. 31. Azar KM, Lesser LI, Laing BY, et al. Mobile Applications for Weight Management: Theory-Based Content Analysis. Am J Prev Med. 2013;45:583–9. 32. Shaw J. Is it acceptable for people to be paid to adhere to medication? No BMJ. 2007;335:233. 33. Hill JO, Wing RR. The challenge of weight loss maintenance: successful losers. In: Akabas S, Lederman SA, Moore BJ, editors. Textbook of obesity: biological, psychological and cultrual influences. Malaysia: Wiley-Blackwell; 2012. p. 354–67. 34. Butryn ML, Webb V, Wadden TA. Behavioural treatment of obesity. Pyschiatr Clin North Am. 2011;34(4):841–59. 35. National Obesity Observatory: Trends in obesity prevalence. Available at http://www.noo.org.uk/NOO_about_obesity/trends. Accessed Feb 2013.

315 36.

37.

38.

39.

40.

OFCOM. 2012. The Communications Market (July). Available at http://stakeholders.ofcom.org.uk/binaries/research/cmr/cmr12/ CMR_UK_2012.pdf. Accessed Feb 2013 Rahmati A,Tossell C, Shepard C, et al.:Explorinng iPhone usage: The influence of socioeconomic differences on smartphone adoption, usage and usability. Proceedings of the 14th international conference on human-computer interaction with mobile devices and services 2012. pp.11-20. Huizinga MM, Carlisle AJ, Cavanaugh KL, et al. Literacy, numeracy, and portion-size estimation skills. Am J Prev Med. 2009;36: 324–8. Rothman RL, Housam R, Weiss H, et al. Patient understanding of food labels: the role of literacy and numeracy. Am J Prev Med. 2006;31:391–8. Zarcadoolas C, Sealy Y, Levy J, et al. Health literacy at work to address overweight and obesity in adults: The development of the obesity action kit. J Commun Healthcare. 2011;4:88–101.