Appetite 95 (2015) 138e151

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Research review

Technology-based interventions in the treatment of overweight and obesity: A systematic review Lieke C.H. Raaijmakers, Sjaak Pouwels, Kim A. Berghuis, Simon W. Nienhuijs* Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 April 2015 Received in revised form 23 June 2015 Accepted 7 July 2015 Available online 10 July 2015

The prevalence of obesity increases worldwide. The use of technology-based interventions can be beneficial in weight loss interventions. This review aims to provide insight in the effectiveness of technology-based interventions on weight loss and quality of life for patients suffering overweight or obesity compared to standard care. Pubmed, PsycInfo, Web of Science, ScienceDirect, CINAHL and Embase were searched from the earliest date (of each database) up to February 2015. Interventions needed to be aimed at reducing or maintaining weight loss in persons with a body mass index (BMI)
25 kg/m2 and have a technology aspect. Cochrane Collaboration's tool for assessing risk of bias was used for rating the methodological quality. Twenty-seven trials met inclusion criteria. Thirteen studies showed significant effects on weight loss compared to controls. Most interventions used a web-based approach (42%). Interventions were screened for five technical key components: self-monitoring, counsellor feedback and communication, group support, use of a structured program and use of an individually tailored program. All interventions that used a combination of all five or four components showed significant decreases in weight compared to controls. No significant results for quality of life were found. Outcomes on program adherence were reported in six studies. No significant results were found between weight loss and program adherence. Evidence is lacking about the optimal use of technology in weight loss interventions. However, when the optimal combination of technological components is found, technology-based interventions may be a valid tool for weight loss. Furthermore, more outcomes on quality of life and information about the effect of technology-based intervention after bariatric surgery are needed. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Obesity Technology-based interventions eHealth

1. Introduction The worldwide prevalence of obesity has more than doubled between 1980 and 2014. Globally, 39% of adults were overweight and 13% of all adults were obese in 2014 (WHO, 2015). Overweight and obese people have an increased risk for diseases such as coronary heart disease, type 2 diabetes, hypertension or dyslipidaemia (CDC, 2015). Technological development makes home- and marketproduction more sedentary, which causes people to be less physically active in general, and food prices are lower through agricultural innovation. This has led to weight gain an reduced exercise behaviour in the general population (Lakdawalla & Philipson, 2009). Although advances in new technology have had some

* Corresponding author. Department of Surgery, Catharina Hospital, Michelangelolaan 2, P.O. Box 1350, 5602 ZA Eindhoven, The Netherlands. E-mail address: [email protected] (S.W. Nienhuijs). http://dx.doi.org/10.1016/j.appet.2015.07.008 0195-6663/© 2015 Elsevier Ltd. All rights reserved.

negative effects on health, new technology also has the potential to improve health (Thomas & Bond, 2014). Eysenbach (2001) describes eHealth as ‘an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies’. As the technical capacity of the Internet grows, it becomes better able to offer a feasible medium for health behaviour interventions and research (Atkinson & Gold, 2002). Approximately 4.5% of all searches on the web might be health-related (Eysenbach & Kohler, 2004). The use of eHealth or technology-based interventions has several potential advantages. For example, it gives the opportunity to tailor information to the specific needs of individuals. Furthermore, it improves the capability of combining a variety of media to address the particular purposes of the intervention and it increases the possibility for users to remain anonymous and receive support from peers or experts about sensitive health issues (Atkinson & Gold, 2002).

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Technology-based clinical trials for overweight or obesity have demonstrated beneficial effects on weight loss. Multiple reviews are done on the effects of eHealth or technology-based interventions on weight loss (Bacigalupo et al., 2013; Thomas & Bond, 2014; Tufano & Karras, 2005; Wieland et al., 2012). However, these are limited to certain types of technology-based interventions, such as the use of computers or mobile phones. Furthermore, they only focus on weight change. Research shows that weight loss can have a positive effect on Quality of Life (Kolotkin, Meter, & Williams, 2001). In addition, program adherence can mediate the effects of the intervention on weight loss (Boutelle & Kirschenbaum, 1998). Thomas and Bond (2014) state that the use of electronic tools may encourage superior adherence compared to, for example, paper diaries. Currently, both health insurance companies and patients are interested in the use of modern eHealth, but gaps in information about eHealth and its working mechanism need to be clarified. Furthermore, health insurance companies need to have this knowledge to be able to give compensation for this type of care. This review aims to provide insight in the available evidence regarding technology-based interventions for overweight or obese adults and their effects on weight change, adherence and quality of life. 2. Methods A systematic literature search was conducted. The patient population of interest consisted of patients with overweight, obesity or morbid obesity. The interventions studied were technology-based compared with standard care. Outcome measures were weight loss, Quality of Life and program adherence. 2.1. Search strategy and data sources Pubmed, PsycInfo, Web of Science, ScienceDirect, CINAHL and Embase were searched from the earliest date (of each database) up to February 2015. No grey literature was searched. The search string used for the literature search, used a combination of the following keywords (or Mesh headings) and was modified for each database: (Telemedicine OR eHealth OR Technology-based) AND (Overweight OR Obesity) AND (Weight loss OR Quality of Life OR adherence). Full search strategy for each database can be found in Appendix 1. Authors LR and SP separately screened and selected studies based on title and abstract. After primary selection, authors (LR and SP) reviewed the full text of each study and determined suitability for inclusion, according to established inclusion criteria discussed below. For additional eligible studies, cross-references were screened. Disagreements were solved by discussion with each other or with the senior author (SN) if necessary. 2.2. Inclusion criteria Studies were eligible and considered to be of acceptable quality if 1) study participants were 18 years or older; 2) study participants were overweight, obese or morbidly obese, defined respectively as having a Body Mass Index (BMI) of >25 kg/m2,
30 kg/m2 and
40 kg/m2 (WHO, 2015); 3) interventions had a technology aspect; 4) interventions aimed at reducing weight or maintaining weight loss; 5) data on weight change were provided. The technology aspect is defined as the use of Internet-based weight management tools, social media, apps for smartphones, telephone and/or smartphone/mobile phone use in general or active video games. The comparison group consisted of standard care, usual care, or wait-list control. Standard or usual care consisted mostly of a lifestyle intervention and/or counselling without technology aspects. The selected primary outcome was weight loss. Selected secondary

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outcomes were Quality of Life and program adherence. Study designs included all needed to have a control group without technological aspect, such as randomised controlled trials, cohort studies, cluster randomized controlled trials and quasiexperiments. 2.3. Methodological quality of included studies For rating of the methodological quality, the Cochrane Collaboration's tool for assessing risk of bias was used (Higgins et al., 2011). This tool assesses the risk of bias on 6 domains: random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting or other bias. Quality of methodology was scored by a ‘þ’ for a low risk for bias, ‘?’ for an unclear risk of bias, and ‘e‘ for a high risk for bias. Two authors (LR and SP) separately assessed the methodological quality of the included studies. To determine the level of agreement between authors LR and SP, a Cohen's kappa score was calculated. For research purposes, the Cohen's kappa should be at least 0.70 (Wood, 2007). 2.4. Data extraction From the studies that met the inclusion criteria, detailed information such as study and intervention characteristics were extracted individually by two authors (LR en SP), as well as outcome data. To review the characteristics of the studies, the following information was extracted: the country where the study was conducted, the size and description of the study population, the intervention group, the comparison group, outcome measures and study design. Reviewers were blinded for journal and authorship. When the data in the studies could not be presented in a consistent format and systematic reporting of comparable outcome variables was lacking, a meta-analysis was not conducted and only a systematic review will be undertaken. Effect sizes of the weight changes between the intervention with technological aspect and control were obtained by dividing the change scores by the standard deviations (SD) of the control group. Effect sizes are reported in SD units of change. Changes between intervention and control group were considered to be trivial (21 years old and had one or more cardiovascular risk factors

Australia

159 adult men with a BMI of 25e40

Burke et al. (2012)

USA

210 participants 59 year with a BMI 27e43

Cadmus-Bertram et al. (2013)

USA

50 participant BMI >27.5, age 45e70, access to highspeed internet, and have an increased risk for breast cancer.

a

RCT

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Table 2 (continued ) First author

Chambliss et al. (2011)

Country

USA

Population (N)

120 overweight men and women with BMI 25e35

Intervention

Comparison

Outcomes measures

Study design

- Wait-list control

- Primary outcome: weight change - Secondary outcomes: waist circumference, blood pressure, treatment goals, program satisfaction

RCT

- wait- list control group

- Primary outcome: BMI - Secondary outcomes: waist circumference, dietary intake, blood values, metabolic equivalents

RCT

- Control group: a retrospective analysis of previously collected longitudinal data from patients in previous LEAP groups

- Primary outcomes: body weight - Secondary outcomes: waist circumference, BMI, quality of life, anxiety and depression

Con-trolled cohort study

- Intervention: all subjects of intervention group were supplied with an electronic blood pressure monitor. Data transmission through Automated Call Centre.

- Control: regular, hospitalbased, obesity treatment program on an outpatient basis consisted of diet and physical activity guidelines.

RCT

Internet condition: 6-month manualized comprehensive behavioural weight loss program that met weekly online via a synchronous chat group Hybrid condition: substituted an inperson meeting for an online chat once a month. - Colorado weigh weight loss maintenance program: Participants attended class every week for 24 weeks and continued to track their calories, fat grams, and activity using paper logs. - Colorado Weigh High-Tech: Participants interacted on-line with the registered dietician, ‘Healthy Coach’ every other week for 24 weeks. Individuals used computer software, to record their dietary intake an activity and complete a ‘Health Report’ every other week. - Healthy4Baby: Participants set personal goals and receive supportive and monitoring text-messages. Skills training and self-monitoring of all strategies were done through text messaging and Facebook.

InPerson condition: 6-month manualized comprehensive behavioural weight loss program that met weekly in face-to-face groups

- Outcomes: body weight, BMI, systolic blood pressure, diastolic blood pressure, plasma glucose, serum triglycerides, serum HDL-cholesterol and total serum cholesterol, quality of life Primary outcome: body weight Secondary outcome: dietary intake, physical activity, attendance at group sessions, perceived support.

-

-

Collins et al. (2012) b

Australia

309 participants aged 18e60 years, BMI 25e40, not participating in other weight-loss programs.

-

34 patients with BMI >30 or BMI >28 with a comorbidity, aged >18 years, no parallel participation in another weight management group. 122 patients BMI >25, age >18, were able to operate regular phones and electronic microdevices.

-

Donaldson et al. (2014)

UK

Goulis et al. (2004)

USA

Harvey-Berino et al. (2010)

USA

481 healthy overweight adults. BMI 25e50 and access to a computer with an Internet connection.

Haugen et al. (2007)

USA

87 subjects were men and women >18 years of age recruited from individuals who completed a 24week, commercial, behavioural weight loss program.

Herring et al. (2014)

US

18 ethnic minority, socioeconomically disadvantaged mothers aged >18 year, early pregnancy cell phone ownership

-

weight management, reference material to aid in self-assessment of fruit/vegetable servings, and information about how to use the SparkPeople website to self-monitor diet and physical activity. Computerized self-monitoring with basic tailored feedback: selfmonitoring and calorie goal setting with software program. Weekly tailored reports from health educator. Computerized self-monitoring with enhance behaviourally tailored feedback: additional: step counters, monthly e-mail newsletters, brief monthly telephone consultations Basic: participants were provided with free access to the basic Webbased program that was commercially available at that time and did not change Enhanced: who were provided with free access to an enhanced version of the web-based program that was provided in a closed test environment. LEAP Beep: Instruction session, pedometer, reminder text messages to text the practitioner their data

RCT

- No program

Primary outcome: body weight Secondary outcome: Program satisfaction: convenience and satisfaction

Quasiexperiment

- Standard care. One visit over the entire first postpartum year with their physicians.

- Primary outcome: change in body weight (kg) at 14 weeks from baseline. - Secondary outcomes: changes in weightrelated dietary and

RCT

(continued on next page)

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Table 2 (continued ) First author

Country

Population (N)

Intervention

Comparison

with unlimited text messaging, and member of Facebook.

- wait- list control group

Hutchesson et al. (2014) b

Australia

268 adults BMI 25 e40

Jeffery et al. (2003) c

USA

1801 overweight participants >18 year BMI >27

Johnston et al. (2012)

USA

54 overweight people age >18, BMI >25, have access to an Internet-connected computer

Lin et al. (2014)

China

123 adults with a BMI
, age 30e50 and current us of a mobile phone

- Intervention group: three group sessions, five coaching calls and a daily text message prompting participants to follow predetermined lifestyle goals.

- Control group: brief advice session

Luley et al. (2011)

Germany

70 diabetic patients with a BMI >25

- Control group: continued with the conventional lowfat diet and standard care according to recommendations

Mehring et al. (2013)

Germany

186 patients with a BMI >25

Morgan et al. (2011) a

Australia

136 overweight men (BMI 25e37) aged 18e60 years

- Intervention group: 4 meetings with an interval of 1 week. The patients visited the clinic every 4 weeks for blood sampling. The telemedical equipment consisted of weighing scales, an accelerometer, and a Homebox which received the data from the scales and the accelerometers via Bluetooth and sent them by a telephone link to a server. - Web-based coaching program which offers interactive buttons, video clips and learning progress quizzes to examine the learning success. At the end of each week participants are asked to give feedback via the internet concerning their condition and level of motivation and whether or not they did their weekly task. Accompanied telephone counselling and monitoring in general practice. - SHED-IT program involved one faceto-face information session on weight loss, a weight loss program booklet, use of the free study website plus 3 months of online support

Napolitano et al. (2013)

USA

- Basic: participants were provided with free access to the basic Webbased program that was commercially available at that time and did not change - Enhanced: who were provided with free access to an enhanced version of the web-based program that was provided in a closed test environment. - Phone intervention. 10 lessons and materials for homework assignments were sent in the mail at the beginning of the program. Phone calls were scheduled between to provide guidance. - Mail intervention used 10 written lessons. Interactions were completed by mail. - Club One Island: interactive weight loss community

- Facebook: Private facebook group: handouts and podcasts available.

Outcomes measures physical activity targets self-reported via questionnaires and relationship between adherence to the intervention with weight loss among intervention participants. - Primary outcome: BMI - Secondary outcomes: waist circumference, dietary intake, blood values, metabolic equivalents

Study design

RCT

Usual care

Primary outcome: body weight Secondary outcomes: self-report measures of behaviour

RCT

- face-to-face comparison

- Primary outcome: BMI - Secondary outcomes: Health, sleep, physical activity, selfefficacy, fruit and vegetables, breakfast, weight efficacy. - Primary outcome: weight change - Secondary outcomes: waist circumference, percent body fat, systolic/diastolic blood pressure - Primary outcome: weight loss - Secondary outcomes: metabolic and cardiovascular risk markers and antidiabetic drug usage

RCT

RCT

RCT

- Usual care

- Primary outcome: weight loss in kg - Secondary outcomes: difference in waist circumference, BMI, eating behaviour and physical activity.

Clusterrandomized trial

- control group: one face-toface information session and a weight loss program booklet.

- Primary outcome: change in body weight - Secondary outcome: BMI, waist circumference, blood pressure - Primary outcome: BMI

RCT

- Waiting list control

RCT

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Table 2 (continued ) First author

Country

Norman et al. (2013)

Population (N)

Intervention

52 students aged 18 e29 years, BMI 25 e50

- Facebook Plus text messaging and personalized feedback: additional theoretically-driven intervention targets: goals setting, self-monitoring, and social support communicated via text messaging. Identified a nonstudy affiliated ‘buddy’ who received an online assent directly from the participant to agree to be an identified support and they received a digital scale, pedometer, Calorie King book, measuring utensils. They received daily text messages, personalized feedback via weekly summary reports, and selection of a ‘buddy’. - mDIET: Intervention group received two to five automatically scheduled, tailored, and sometimes interactive text-messages a day on primarily diet and some physical activity weight management topics. - Phone intervention. 10 lessons and materials for homework assignments were sent in the mail at the beginning of the program. Phone calls were scheduled between to provide guidance. - Mail intervention used 10 written lessons. Interactions were completed by mail. - 10 Session telephone-based weight loss counselling intervention compared to a self-directed weightloss program - 20 session telephone-based weight loss counselling intervention compared to a self-directed weightloss program - Group-based behavioural weight loss education group: 14 GWL sessions - Armband alone group (SWA): Feedback regarding energy balance was received as participants uploaded their armband to the website and recorded daily energy intake and body weight to the Weight Management Solutions web account. - Combined GWL and SWA group - an interactive technology-based intervention in which participants were encouraged to regularly log on to an interactive Web site - a personal-contact intervention in which participants had monthly individual contact with an interventionist.

USA

78 overweight and moderately obese men and women aged 25e55 years old

USA

1801 overweight participants

Sherwood et al. (2010)

USA

63 participants aged >18 and BMI 30e39

Shuger et al. (2011)

USA

197 men and women aged 18 e64 years who were underactive, overweight or obese, and had access to the internet.

Svetkey et al. (2008)

USA

Tanaka et al. (2010)

Japan

1032 participants with a BMI between 25 and 45, taking medication for hypertension, dyslipidemia, or both, have access to a telephone and to the Internet. 51 participants aged 20e65 years with a BMI>24.

Sherwood et al. (2006)

c

Comparison

- KTP: non-face-to-face commercial program incl. booklet on behavioural weight control, self-assessment of daily behaviours, target behaviour setting, and self-monitoring of daily body weight and targeted behaviours. The process was assisted twice by computer-tailored advises based on the responses to the questionnaire.

Outcomes measures

Study design

- Secondary outcome: Physical activity behaviour, goal setting and planning, physical activity selfefficacy, weight selfefficacy, adapted social support for diet and exercise, engagement/compliance, consumer satisfaction

- Outcomes: weight status, EBI scores, and fruit and vegetable intake over the course of the study - Primary outcome: changes in body weight - Secondary outcome: cost-effectiveness analyses

RCT

weight-loss

- Primary outcome: changes in body weight form baseline to 6 months - Weight and weightrelated behaviour change

RCT

- Standard Care: participants received a self-directed weight loss manual based on two evidence-based programs

- Primary outcomes were body weight and waist circumference. - Secondary outcomes were BMI and percent body fat

RCT

- a self-directed comparison condition in which participants received minimal intervention

- Primary outcome: change in weight

RCT

- control group: read the KT booklet and tried to reduce weight by themselves.

Primary outcome: changes in body weight Secondary outcomes: biological indices and behavioural changes

RCT

- The usual care comparison group received in the mail on to two pages of print materials each month for four months. - usual care

- self-directed program

RCT

Abbreviations: BMI ¼ Body Mass Index in kg/m2; RCT ¼ Randomized controlled trial; PDA ¼ Personal Digital Assistant. a Blomfield et al. and Morgan et al. both used the SHED-IT intervention. b Collins et al. and Hutchesson et al. both used the The Biggest Loser Club intervention. c Jeffery et al. and Sherwood et al. both used the Weigh-to-be intervention.

fewer interventions (Cadmus-Bertram et al., 2013; Chambliss et al., 2011; Collins et al., 2012; Donaldson et al., 2014; Harvey-Berino et al., 2010; Herring et al., 2014; Hutchesson et al., 2014; Johnston

et al., 2012; Lin et al., 2014; Napolitano et al., 2013). The use of a structured program was seen in eleven interventions (Allen et al., 2013; Appel et al., 2011; Blomfield et al., 2014; Burke et al., 2012;

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Table 3 Intervention specifics including technological components, length of the intervention, follow-up, outcomes and results. Study

Intervention

Follow-up Technological Length of intervention components used

Outcomes

Results (significance)

Effect size

Agras et al. (1990)

a. Computer therapy with one introductory session b. Computer therapy with one introductory session and four follow-up group sessions c. Behaviour therapy

SM

12 weeks

12 weeks, 6 months and 12 months

Weight Program adherence

‘a’ vs. ‘c’ ¼ 0.9 ‘b’ vs. ‘c’ ¼ 0.7

Allen et al. (2013)

a. Intensive counselling b. Intensive counselling þ smartphone c. Less intensive counselling þ smartphone d. Smartphone

SM, CFC, SP

6 months

6 months

Weight change Program adherence

Appel et al. (2011)

a. Remote support only b. In-Person support c. Control group

SM, CFC, SP

24 months

6 and 24 months

Weight loss

Blomfield et al. (2014) þ Morgan et al. (2011) Burke et al. (2012)

a. SHED-IT Online b. SHED-IT Resources c. Wait-list control

SM, ITP, SP

3 months

3, 6 and 12 months

Weight loss

a. Paper diary b. PDA c. PDA þ Feedback

SM, CFC, SP

24 months

6, 12, 18 and 24 months

Weight change Adherence to self-monitoring

12 weeks

12 weeks

Weight change

a. 76.6 ± 0.3 b. 76.8 ± 1.9 c. 77.5 ± 1.0 (non-significant) a. 29% b. 70% c. 29% a. 2.5 ± 4.1 b. 5.4 ± 4.0 c. 3.3 ± 5.9 d. 1.8 ± 3.7 (non-significant) a. 58% b. 72% c. 66% d. e a. 4.6 ± 0.7 b. 5.1 ± 0.8 c. 0.8 ± 0.6 ‘a’ vs. ‘c’ ¼ p < 0.001 ‘b’ vs. ‘c’ ¼ p < 0.001 a. 5.2 b. 4.2 c. 3.1 (non-significance) a. 1.77 ± 7.23 b. 1.18 ± 8.78 c. 2.17 ± 7.04 (non-significant) a. 8% b. 19% c. 20% a. 3.3 ± 4.0 b. 0.9 ± 3.4 p < 0.001

12 weeks

12 weeks

Weight change

12 weeks

12 weeks

Weight change

SM, CFC, ITP

12 weeks

12 weeks

Body change Quality of Life change

SM

6 months

6 months

Weight Quality of Life

Cadmus-Bertram et al. (2013)

Chambliss et al. (2011)

SM, ITP, SP, a. Telephone-based training CFC, GS sessions to use web-based self-monitoring tools b. Information-only group SM, ITP, CFC a. Basic: web-based program b. Enhanced: enhanced web-based program þ step-counters C. Wait-list control

SP, SM, GS, Collins et al. (2012) þ a. Basic: web-based program b. Enhanced: enhanced web-based CFC, ITP Hutchesson et al. program (2014) c. Wait-list control

Donaldson et al. (2014)

Goulis et al. (2004)

a. LEAP Beep: pedometer, text messages b. Control group: retrospective analysis of previously collected data from patients in previous LEAP groups (no text messages) a. Standard care b. Standard care þ telemonitoring

Harvey-Berino et al. (2010)

a. Internet b. InPerson c. Hybrid

GS, SM, SP

6 months

6 months

Weight change Program adherence

Haugen et al. (2007)

a. Traditional program b. Telehealth program

SM, CFC, ITP

6 months

6 months

Weight change

‘a’ vs. ‘b’ ¼ 0.7 ‘a’ vs. ‘c’ ¼ 0.2 ‘a’ vs. ‘d’ ¼ 0.2

‘a’ vs. ‘c’ ¼ 6.3

‘a’ vs. ‘b’ ¼ 0.1 ‘a’ vs. ‘c’ ¼ 0.1

‘a’ vs. ‘b’ ¼ 1.2

a. 3.64 ± 3.42 b. 3.26 ± 3.10 c. 0.32 ± 2.31 ‘a’ and ‘b’ vs. ‘c’ ¼ p < 0.05 a. 2.3 ± 3.4 b. 3.1 ± 4.0 c. þ0.5 ± 2.3 ‘a’ and ‘b’ vs. ‘c’ ¼ p < 0.001 ‘b’ vs. ‘a’ ¼ p < 0.001 a. 1.6 b. þ0.7 p ¼ 0.006 a. 6.8 b. þ1 (non-significant)

‘a’ vs. ‘c’ ¼ 1.7 ‘b’ vs. ‘c’ ¼ 1.5

a. 99.6 ± 23.8 b. 89.2 ± 14.7 p ¼ 0.05 a. 70.7 ± 15 b. 72.1 ± 16.3 (non-significant) a. 5.5 ± 5.6 b. 8.0 ± 6.1 c. 6.0 ± 5.5 ‘a’ vs. ‘b’ ¼ p < 0.01 ‘c’ vs. ‘b’ ¼ p < 0.01 a. 76% b. 72% c. 72% (non-significant) a. 0.5 ± 4.3 b. 0.6 ± 2.5

‘a’ vs. ‘b’ ¼ 0.4

‘a’ vs. ‘c’ ¼ 1.2 ‘b’ vs. ‘c’ ¼ 1.6

‘a’ vs. ‘b’ ¼ 0.4 ‘b’ vs. ‘c’ ¼ 0.3

‘b’ vs. ‘c’ ¼ 0.8

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Table 3 (continued ) Study

Intervention

Follow-up Technological Length of intervention components used

c. No program

Outcomes

c. þ1.7 ± 3.0 ‘a’ vs. ‘c’ ¼ p < 0.05 ‘b’ vs. ‘c’ ¼ P < 0.05 14 weeks Weight change a. 3.3 ± 3.6 b. 0.5 ± 2.3 Adherence to the intervention p ¼ 0.03 a. 34.4 text-messages (max. 57) 6, 12, 18 and Changes in a. 0.73 ± 0.22 24 months body weight b. 0.93 ± 0.22 c. 0.59 ± 0.22 (non-significant) 12 weeks Weight change a. 3.9 b. 2.8 (non-significant) 3 and 6 months Weight change a. 1.6 ± 0.3 b. 0.2 ± 0.3 ‘a’ vs. ‘b’ ¼ P < 0.0001

a. technology-based intervention: text-messaging, telephone calls, Facebook, digital scale, pedometer b. Usual care a. Mail b. Phone c. Control

SM, CFC, SP, GC, ITP

14 weeks

SM, CFC, ITP

12 months

Johnston et al. (2012)

a. Virtual-world group b. Face-to-face group

GS, CFC

12 weeks

Lin et al. (2014)

a. Text Messaging-Assisted lifestyle intervention b. Control group (informationsession only) a. Telemonitoring group b. Standard care: low-fat diet

ITP, SM, CFC, GS

6 months

SM, CFC

6 months

6 months

Weight change

Mehring et al. (2013)

a. web-based coaching program b. usual care

SM, ITP, SP, CFC

12 weeks

12 weeks

Weight change

Napolitano et al. (2013)

a. Facebook b. Facebook Plus text messaging and personalized feedback c. Wait-list control

SM, GS, CFC, ITP

8 weeks

4 and 8 weeks

Weight change Program adherence textmessaging

Norman et al. (2013)

a. text messages b. usual care

SM

4 months

4 months

Weight change (lbs.)

Sherwood et al. (2010)

a. 10 sessions telephone-based weight loss counselling intervention b. 20 session telephone-based weight loss counselling intervention c. self-directed weight-loss program a. Group-based behavioural weight loss education groups (GWL) b. Armband alone group (SWA) c. Combined GWL and SWA group d. Standard care

CFC, SM, ITP

6 months

6 months

Weight change

SM, CFC, ITP

9 months

4 and 9 months Weight

a. interactive technology-based intervention b. personal-contact intervention c. self-directed comparison condition a. KTP: computerized program b. Control-group

SM, CFC

30 months

30 months

Weight change

SM, ITP

7 months

1, 3 and 7 months

Weight change

Herring et al. (2014)

Jeffery et al. (2003) þ Sherwood et al. (2006)

Luley et al. (2011)

Shuger et al. (2011)

Svetkey et al. (2008)

Tanaka et al. (2010)

Results (significance)

a. 11.8 ± 7.6 b. 0.3 ± 2 0.9 P ¼ 0.000 a. 4.2 ± 4.3 b. 1.7 ± 4.1 p < 0.001 a. 0.63 ± 2.4 b. 2.4 ± 2.5 c. 0.24 ± 2.6 ‘b’ vs. ‘a’ and ‘c’ ¼ p < 0.05 b. 68.5% a. 5.09 ± 7.90 b. 1.39 ± 5.90 (non-significant) a. 3.2 ± 1.1 b. 4.9 ± 1.1 c. 2.3 ± 1.1 (non-significance)

Effect size

‘a’ vs. ‘b’ ¼ 1.7

‘a’ vs. ‘c’ ¼ 0.6 ‘b’ vs. ‘c’ ¼ 1.5

‘a’ vs. ‘b’ ¼ 6.0

‘a’ vs. ‘b’ ¼ 4.0

‘a’ vs. ‘b’ ¼ 0.6

‘a’ vs. ‘c’ ¼ 0.2 ‘b’ vs. ‘c’ ¼ 0.8

‘a’ vs. ‘b’ ¼ 0.6

‘a’ vs. ‘c’ ¼ 0.8 ‘b’ vs. ‘c’ ¼ 2.4

a. 99.98 ± 3.0 b. 97.60 ± 2.99 c.93.73 ± 2.99 d.101.32 ± 3.05 ‘a’ vs. ‘d’ ¼ p ¼ 0.05 ‘b’ vs. ‘d’ ¼ p ¼ 0.0002 ‘c’ vs. ‘d’ ¼ p < 0.0001 a. 3.3 ± 0.4 b. 4.2 ± 0.4 c. 2.9 ± 0.4 (non-significance)

‘b’ vs. ‘d’ ¼ 1.2 ‘c’ vs. ‘d’ ¼ 2.5

a. 2.4 ± 3.2 b. 1.6 ± 2.8 (non-significant)

‘a’ vs. ‘b’ ¼ 0.3

‘a’ vs. ‘b’ ¼ 2.3 ‘a’ vs. ‘c’ ¼ 1.0

Abbreviations: SM ¼ self-monitoring; CFC ¼ counsellor feedback and communication; GS ¼ group support; SP ¼ structured program; ITP ¼ individually tailored program.

Cadmus-Bertram et al., 2013; Collins et al., 2012; Harvey-Berino et al., 2010; Herring et al., 2014; Hutchesson et al., 2014; Mehring et al., 2013; Morgan et al., 2011). Most interventions in these structured programs were based on Bandura's Social Cognitive Theory (Allen et al., 2013; Bandura, 1986; Blomfield et al., 2014; Burke et al., 2012; Cadmus-Bertram et al., 2013; Collins et al., 2012; Morgan et al., 2011). In seventeen interventions the program was tailored to the participants (Blomfield et al., 2014; CadmusBertram et al., 2013; Chambliss et al., 2011; Collins et al., 2012; Donaldson et al., 2014; Haugen et al., 2007; Herring et al., 2014; Hutchesson et al., 2014; Jeffery et al., 2003; Lin et al., 2014; Mehring

et al., 2013; Morgan et al., 2011; Napolitano et al., 2013; Sherwood et al., 2006, 2010; Shuger et al., 2011; Tanaka et al., 2010). 3.3.3. Effects on weight change, quality of life and program adherence In Table 3, changes in weight, quality of life and program adherence are reported for each study, including statistical significance and effect sizes. If an article had several measurement time points, only the first pre-intervention measurement point and the last post-intervention measurement point were taken into account. Weight change was assessed in all articles. Six interventions

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showed significant effects when looking at the technological-based intervention versus ‘no care’ (Appel et al., 2011; Chambliss et al., 2011; Collins et al., 2012; Haugen et al., 2007; Hutchesson et al., 2014; Lin et al., 2014; Napolitano et al., 2013). When compared to usual care, seven interventions showed significant results (Cadmus-Bertram et al., 2013; Donaldson et al., 2014; Goulis et al., 2004; Herring et al., 2014; Luley et al., 2011; Mehring et al., 2013; Shuger et al., 2011). Of these statistically significant weight change improvements, 85% were moderate to large (i.e., effect sizes
0.5). Other interventions showed no significant results on weight change. The study from Harvey-Berino et al. (Harvey-Berino et al., 2010) even shows that in-person therapy leads to significantly higher weight loss than an internet-only intervention. All interventions that used a combination of all five or four components showed significant decreases in weight (Cadmus-Bertram et al., 2013; Collins et al., 2012; Herring et al., 2014; Hutchesson et al., 2014; Lin et al., 2014; Mehring et al., 2013; Napolitano et al., 2013). Of the fifteen interventions using an individually tailored program, eleven showed significant changes in weight (73%). Counsellor feedback and communication was used in nineteen interventions, in these, twelve interventions showed significant results (63%). Seventy percent of the web-based interventions showed significant changes in weight. Most of the phone-based interventions showed significant results in weight change. Only the text-message based intervention of Lin et al. (2014) did found significant results for weight change. No significant results for quality of life were found (Donaldson et al., 2014; Goulis et al., 2004). However, only two studies reported outcomes on quality of life. Outcomes on program adherence were reported in six studies (Agras et al., 1990; Allen et al., 2013; Burke et al., 2012; HarveyBerino et al., 2010; Herring et al., 2014; Napolitano et al., 2013). The number of texts sent or number of calls completed, frequency of computer use assessed the adherence of patients to the program. No significant results were reported or found between weight loss and program adherence. However, interventions with a technological component do show higher adherence rates compared to control groups. Furthermore, in most cases, weight loss is higher in groups with higher adherence rates, although not significant (Agras et al., 1990; Allen et al., 2013; Burke et al., 2012). 4. Discussion This systematic review was conducted to study the effectiveness of technology-based interventions on weight loss, quality of life and program adherence for patients being overweight or obesity compared to standard care. To give a more adequate overview of this research field, cohort/observational studies were also included. Results of this review demonstrate that compared to no care or usual care, half of the technology-based interventions (13/24, 54%) help patients to significantly lose weight and that they can possibly be from additional value in promoting a healthy lifestyle. Effect sizes show that of the studies that show statistically relevant weight change outcomes, 85% shows an effect which is meaningful within this research context. However, the interventions varied considerably. Study populations varied in size and comorbidity. Interventions varied in length, but all were technology-based. Previous systematic reviews showed similar results. Bacigalupo et al. (2013) demonstrated that mobile technology interventions show significant weight loss in the short-term and moderate effects for the medium-term. However, this study is an early review which included studies up to 2011. In the last four years a lot of development in the field of technology has taken place. The current review gives an contemporary overview. Furthermore, our study results are in line with other systematic reviews of technologic

interventions in other chronic diseases, such as Diabetes Mellitus (El-Gayar, Timsina, Nawar, & Eid, 2013; Toma, Athanasiou, Harling, Darzi, & Ashrafian, 2014), Chronic Obstructive Pulmonary Disease (Cruz, Brooks, & Marques, 2014; Fitzner, Heckinger, Tulas, Specker, & McKoy, 2014) and Heart Failure (Hameed, Sauermann, & Schreier, 2014) and address the importance of investigating technologic interventions in optimizing healthcare, adherence and patient outcomes in various chronic diseases. This review contributes to the assumption that technology can add or replace essential components of weight loss interventions. Different studies demonstrate that self-monitoring dietary intake; physical activity and body weight is the centrepiece of weight loss intervention programs (Burke, Wang, & Sevick, 2011; Shay, Seibert, Watts, Sbrocco, & Pagliara, 2009; Tate, Jackvony, & Wing, 2006; Wadden et al., 2005). Self-monitoring is associated with greater weight loss (Tate et al., 2006; Tate, Wing, & Winett, 2001; Womble et al., 2004). Using the Internet or digital scales could for example, replace the paper-monitoring diary. In the study of Wharton, Johnston, Cunningham, and Sterner (2014) participants using an app for self-monitoring in weight management more consistently entered complete days of dietary data compared with the paperand-pencil group. This is important for both increased commitment to behaviour change interventions as well as for health outcomes and weight management. Counsellor feedback and motivation has a positive impact on weight loss as well. Research shows that the more counselling is provided, the more weight is lost (Perri et al., 2014). However, intensive counselling results in higher costs. By providing counselling by e-mail, these costs can be drastically decreased (van Wier et al., 2012). Furthermore, technology-based interventions can be easily implemented in daily practice and also in the daily life of patients (Franc et al., 2011). Another effective component in weight loss interventions is social support (Greaves et al., 2011; Jeffery et al., 1984). Support from another person in a weight loss intervention not only helps to improve weight loss, it also reduces time spent on weight counselling. Technology can help by developing, for example, online forums or chat rooms or the usage of social media (TurnerMcGrievy & Tate, 2013). On the other hand, also incorrect health information can circulate in chat-rooms or on social media. Therefore, supervision of these social media outlets by medical professionals is mandatory in our opinion. Furthermore, it is of great importance that the interventions used, are based on essential theories and therapies, such as the cognitive behavioural therapy. Intervention Mapping states that theory-informed methods and practical strategies have to be used when developing an intervention to effect changes in health behaviour. An intervention method is a defined process by which theory postulates and empirical research provides evidence for how change may occur in behaviour of individuals (Bartholomew, Parcel, Kok, Gottlieb, & Fernandez, 2011). Structured weight loss programs often show more significant differences in weight loss (Grilo, Masheb, Wilson, Gueorguieva, & White, 2011; Leahey et al., 2014). However, this review shows that among the 9 interventions that used a structured program, only 4 showed significant results (Cadmus-Bertram et al., 2013; Collins et al., 2012; Herring et al., 2014; Mehring et al., 2013). The non-significance of these studies could also be explained by short length of intervention or small populations (Allen et al., 2013; Blomfield et al., 2014; CadmusBertram et al., 2013; Morgan et al., 2011). More attention should be paid to choosing the right methods and strategies to achieve the desirable behaviour and lifestyle change. Nonetheless, it is shown effective that tailored materials lead to significantly higher weight loss compared to none tailored materials (Kreuter, Bull, Clark, & Oswald, 1999; Kreuter & Wray, 2003).

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Kreuter, Farrell, Olevitch, and Brennan (2013) define tailoring as ‘any combination of information or change strategies intended to reach one specific person, based on characteristics that are unique to that person, related to the outcome of interest, and have been derived from an individual assessment’. Computer-tailored feedback is proven to be as effective as human e-mail counselling (Tate et al., 2006). Since computer tailoring is less costly, patient friendly and time consuming, it is seen as a good alternative. This study shows that the majority (69%) of the interventions that used individually tailored programs showed significant results on various outcome measures such as weight loss. It is seen that a combination of these above mentioned components lead to effective interventions. All interventions that used a combination of the methods self-monitoring, counsellor feedback and communication, group support, a structured program and an individually tailored program showed significant better results. Results are equal on short- and long-term effect. Using a technology-based model using all 5 components has several advantages. For example, it is convenient and adherence rates might increase. Interventions with a technological component tend to show higher adherence rates (Thomas & Bond, 2014). However, no significant results were found between technological interventions compared with standard care, but weight loss did increase when higher adherence rates (to technological interventions) were seen. Finally, technology-based interventions could give people a higher sense of control. Portable devices give opportunities for continuous and discrete self-monitoring. 4.1. Limitations Weight loss in associated with an increase in quality of life for obese patients (Kolotkin et al., 2001). However, quality of life is not well reported in the included studies. More research is needed to gain more information about quality of life related to weight loss after using a technology-based intervention. Participants in the included interventions varied in comorbidity, which can give bias in interpreting effects of technology-based interventions. Nonetheless, some studies focused on disadvantaged populations, whereas others on homogenous countries. This increases generalisability of the results. Another limitation is the possible ‘digital division’ due to socioeconomic status (SES) and age. Those with a lower SES or higher age may have difficulties interacting with technology-based interventions. Furthermore, with this kind of studies publication bias might be an issue because when technology does not work or is not understood by the patient group, this can give biased results and may not be published. Also the comparison group, mostly called standard care, is often not adequately described, which makes it difficult to interpret the results of the technology-based intervention. Improvement of technology can be seen as a limitation or a positive effect in these studies. It does not necessarily mean that if an intervention is not technologically up to date, that the intervention does not work. For example, SMS-services are dated, but can still be effective to remind patients that they have a follow-up meeting at the hospital. 4.2. Directions for future research Technology-based interventions can be valuable in the treatment of overweight or obesity. The area of eHealth is rapidly growing and improving. Therefore, new technology becomes available to implement and use in the eHealth environment, which also embodies various wireless devices (for example weighing scales and blood pressure meters). The most effective method to lose weight in the long-term is bariatric surgery (Picot et al., 2009).

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It is of importance to support these patients after weight loss surgery. The bariatric surgery patient differs from the lifestyle intervention patients. Bariatric surgery leads to, for example, losing a lot of weight in a short period of time (Martins et al., 2011). This has an important impact on the patient and needs careful accompaniment. It can therefore be assumed that the bariatric patient needs another approach compared to the lifestyle intervention patient. 5. Conclusion Results show that evidence is lacking about the optimal use of technology in weight loss interventions. From this review, the following can be concluded: (1) technology-based interventions are a valid tool for weight loss, when the optimal combination of technological components is found; (2) technological-based interventions seem to lead to higher program adherence; and (3) more outcomes on quality of life and information about the effects of technology-based interventions after bariatric surgery are needed. Author contribution Study Design and data collection: LR, SP, KB, SN. Manuscript Creation: LR, SP, KB, SN. Manuscript Revision: LR, SP, KB, SN. Conflict of interest None. Financial disclosure statement L. Raaijmakers has nothing to disclose. S. Pouwels has nothing to disclose. K. Berghuis has nothing to disclose. S. Nienhuijs has nothing to disclose. Appendix 1. Search strategy - Pubmed: “Telemedicine”[Mesh]) AND “Obesity”[Mesh]) AND “Weight Loss”[Mesh]) AND “Quality of Life”[Mesh] OR (((“Telemedicine”[Mesh]) AND “Obesity”[Mesh]) AND “Weight Loss”[Mesh]) - PsycInfo: TX (telemedicine OR eHealth OR technology based interventions) AND TX obesity AND TX weight loss AND TX quality of life OR overweight AND ((technology based interventions OR eHealth OR telemedicine)) AND Weight loss - Web of Science: TOPIC: (Technology based interventions OR eHealth OR telemedicine) AND TOPIC: (obesity) AND TOPIC: (quality of life) AND TOPIC: (weight loss) (- Reviews) OR TOPIC: (Technology based interventions OR eHealth OR telemedicine) AND TOPIC: (overweight) AND TOPIC: (weight loss) - Sciencedirect: Telemedicine OR eHealth OR technology based interventions) AND obesity AND weight loss AND quality of life AND LIMIT-TO(contenttype, “1,2”,“Journal”) AND LIMITTO(topics, “weight loss”) OR (Telemedicine OR eHealth OR technology based interventions) AND overweight AND weight loss AND LIMIT-TO(topics, “weight loss”) - Medline: (Telemedicine OR eHealth OR technology based interventions) AND weight loss AND quality of life AND obesity [limit to five stars] OR (Telemedicine OR eHealth OR technology based interventions) AND overweight AND weight loss [limit to five stars]

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